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Individual differences in perception are widespread. Considering inter-individual variability, synesthetes experience stable additional sensations; schizophrenia patients suffer perceptual deficits in e.g. perceptual organization (alongside hallucinations and delusions). Is there a unifying principle explaining inter-individual variability in perception? There is good reason to believe perceptual experience results from inferential processes whereby sensory evidence is weighted by prior knowledge about the world. Different perceptual phenotypes may result from different precision weighting of sensory evidence and prior knowledge. We tested this hypothesis by comparing visibility thresholds in a perceptual hysteresis task across medicated schizophrenia patients, synesthetes, and controls. Participants rated the subjective visibility of stimuli embedded in noise while we parametrically manipulated the availability of sensory evidence. Additionally, precise long-term priors in synesthetes were leveraged by presenting either synesthesia-inducing or neutral stimuli. Schizophrenia patients showed increased visibility thresholds, consistent with overreliance on sensory evidence. In contrast, synesthetes exhibited lowered thresholds exclusively for synesthesia-inducing stimuli suggesting high-precision long-term priors. Additionally, in both synesthetes and schizophrenia patients explicit, short-term priors – introduced during the hysteresis experiment – lowered thresholds but did not normalize perception. Our results imply that distinct perceptual phenotypes might result from differences in the precision afforded to prior beliefs and sensory evidence, respectively.
Ribosomes translate the genetic code into proteins. Recent technical advances have facilitated in situ structural analyses of ribosome functional states inside eukaryotic cells and the minimal bacterium Mycoplasma. However, such analyses of Gram-negative bacteria are lacking, despite their ribosomes being major antimicrobial drug targets. Here we compare two E. coli strains, a lab E. coli K-12 and human gut isolate E. coli ED1a, for which tetracycline exhibits bacteriostatic and bactericidal action, respectively. The in situ ribosome structures upon tetracycline treatment show a virtually identical drug binding-site in both strains, yet the distribution of ribosomal complexes clearly differs. While K-12 retains ribosomes in a translation competent state, tRNAs are lost in the vast majority of ED1a ribosomes. A differential response is also reflected in proteome-wide abundance and thermal stability assessment. Our study underlines the need to include molecular analyses and to consider gut bacteria when addressing antibiotic mode of action.
Taraxerol und 3α, 7α, 22α-Trihydroxy-stigmasten-(5) in den Blättern der Haselnuß (Corylus avellana)
(1966)
Aus den Blättern der Haselnuß (Corylus avellana) konnte Taraxerol, β-Sitosterin und 3α,7α,22α-Trihydroxy-stigmasten- (5) isoliert werden. Letzteres war bisher lediglich in den Blättern der Roßkastanie (Aesculus hyppocastanum) nachgewiesen worden. Triterpene mit dem Dammaranskelett waren in den Haselnußblättern nicht auffindbar.
An der Umwandlung von Tritium-markiertem Tropin- (3β-T) zu Pseudotropin- (3α-T) in Hirnhomogenat, unter der synergistischen Wirkung eines Sporenbildners und eines Enterococcen-Stammes, konnte bewiesen werden, daß diese trans-cis-Umlagerung durch Abspaltung und Wiederanlagerung von Wasser erfolgt. Die Abspaltung von Wasser aus 3α-Tropanol zu Tropen- (2) ist reversibel, wie aus dem Einbau von Tritiumwasser in das Tropin hervorgeht.
Unter dem synergistischen Einfluß zweier Bakterien-Stämme, eines aeroben Sporenbildners (Bac. alvei) und eines Enterococcen-Stammes (Diplococcus I) wird Tropin vollständig in Pseudotropin umgewandelt. Der Mechanismus dieser trans-cis-Umlagerung wird diskutiert.
Zur Trennung von Tropan-Alkaloiden und deren Derivate werden geeignete chromatographische Laufmittelsysteme angegeben.
The traditional view on coding in the cortex is that populations of neurons primarily convey stimulus information through the spike count. However, given the speed of sensory processing, it has been hypothesized that sensory encoding may rely on the spike-timing relationships among neurons. Here, we use a recently developed method based on Optimal Transport Theory called SpikeShip to study the encoding of natural movies by high-dimensional ensembles of neurons in visual cortex. SpikeShip is a generic measure of dissimilarity between spike train patterns based on the relative spike-timing relations among all neurons and with computational complexity similar to the spike count. We compared spike-count and spike-timing codes in up to N > 8000 neurons from six visual areas during natural video presentations. Using SpikeShip, we show that temporal spiking sequences convey substantially more information about natural movies than population spike-count vectors when the neural population size is larger than about 200 neurons. Remarkably, encoding through temporal sequences did not show representational drift both within and between blocks. By contrast, population firing rates showed better coding performance when there were few active neurons. Furthermore, the population firing rate showed memory across frames and formed a continuous trajectory across time. In contrast to temporal spiking sequences, population firing rates exhibited substantial drift across repetitions and between blocks. These findings suggest that spike counts and temporal sequences constitute two different coding schemes with distinct information about natural movies.
Human language relies on hierarchically structured syntax to facilitate efficient and robust communication. The correct processing of syntactic information is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. We analyzed a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic operations. We examined whether prosody enhances the cortical encoding of syntactic representations. We decoded left-sided dependencies directly from brain activity and evaluated possible modulations of the decoding by the presence of prosodic boundaries. Our findings demonstrate that prosodic boundary presence improves the representation of left-sided dependencies, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This study gives neurobiological evidence for the boosting of syntactic processing via interaction with prosody.
Neural computations emerge from recurrent neural circuits that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, it is challenging to predict which spiking network connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We established a mapping between the stabilized supralinear network (SSN) and spiking activity which allowed us to pinpoint the location in parameter space where these activity regimes occur. Notably, we found that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we showed that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.
The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that dopamine neuron in vivo activity deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive, depolarizing shift in action potential threshold; and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive, hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error and sensory cue coding in dopamine neurons by tuning action potential threshold dynamics.
Several studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the discrete Fourier transform (DFT) and the least square spectrum (LSS). DFT and LSS can be applied both on the average accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effects) or on the population (random-effects) of simulated participants. Multiple comparisons across frequencies were corrected using False Discovery Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated sensitivity, specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the average accuracy time course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effects approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the Discrete Fourier Transform (DFT) and the Least Square Spectrum (LSS). DFT and LSS can be applied both on the averaged accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effect) or on the population (random-effect) of simulated participants. Multiple comparisons across frequencies were corrected using False-Discovery-Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated Sensitivity, Specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the averaged-accuracy-time-course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved Sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest Specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effect approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding
(2020)
Spatial navigation requires landmark coding from two perspectives, relying on viewpoint-invariant and self-referenced representations. The brain encodes information within each reference frame but their interactions and functional dependency remains unclear. Here we investigate the relationship between neurons in the rat's retrosplenial cortex (RSC) and entorhinal cortex (MEC) that increase firing near boundaries of space. Border cells in RSC specifically encode walls, but not objects, and are sensitive to the animal’s direction to nearby borders. These egocentric representations are generated independent of visual or whisker sensation but are affected by inputs from MEC that contains allocentric spatial cells. Pharmaco- and optogenetic inhibition of MEC led to a disruption of border coding in RSC, but not vice versa, indicating allocentric-to-egocentric transformation. Finally, RSC border cells fire prospective to the animal’s next motion, unlike those in MEC, revealing the MEC-RSC pathway as an extended border coding circuit that implements coordinate transformation to guide navigation behavior.
Borders and edges are salient and behaviourally relevant features for navigating the environment. The brain forms dedicated neural representations of environmental boundaries, which are assumed to serve as a reference for spatial coding. Here we expand this border coding network to include the retrosplenial cortex (RSC) in which we identified neurons that increase their firing near all boundaries of an arena. RSC border cells specifically encode walls, but not objects, and maintain their tuning in the absence of direct sensory detection. Unlike border cells in the medial entorhinal cortex (MEC), RSC border cells are sensitive to the animal’s direction to nearby walls located contralateral to the recorded hemisphere. Pharmacogenetic inactivation of MEC led to a disruption of RSC border coding, but not vice versa, indicating network directionality. Together these data shed light on how information about distance and direction of boundaries is generated in the brain for guiding navigation behaviour.
Brookshire (2022) claims that previous analyses of periodicity in detection performance after a reset event suffer from extreme false-positive rates. Here we show that this conclusion is based on an incorrect implemention of a null-hypothesis of aperiodicity, and that a correct implementation confirms low false-positive rates. Furthermore, we clarify that the previously used method of shuffling-in-time, and thereby shuffling-in-phase, cleanly implements the null hypothesis of no temporal structure after the reset, and thereby of no phase locking to the reset. Moving from a corresponding phase-locking spectrum to an inference on the periodicity of the underlying process can be accomplished by parameterizing the spectrum. This can separate periodic from non-periodic components, and quantify the strength of periodicity.
Cognition requires the dynamic modulation of effective connectivity, i.e., the modulation of the postsynaptic neuronal response to a given input. If postsynaptic neurons are rhythmically active, this might entail rhythmic gain modulation, such that inputs synchronized to phases of high gain benefit from enhanced effective connectivity. We show that visually induced gamma-band activity in awake macaque area V4 rhythmically modulates responses to unpredictable stimulus events. This modulation exceeded a simple additive superposition of a constant response onto ongoing gamma-rhythmic firing, demonstrating the modulation of multiplicative gain. Gamma phases leading to strongest neuronal responses also led to shortest behavioral reaction times, suggesting functional relevance of the effect. Furthermore, we find that constant optogenetic stimulation of anesthetized cat area 21a produces gamma-band activity entailing a similar gain modulation. As the gamma rhythm in area 21a did not spread backward to area 17, this suggests that postsynaptic gamma is sufficient for gain modulation.
Cognition requires the dynamic modulation of effective connectivity, i.e. the modulation of the postsynaptic neuronal response to a given input. If postsynaptic neurons are rhythmically active, this might entail rhythmic gain modulation, such that inputs synchronized to phases of high gain benefit from enhanced effective connectivity. We show that visually induced gamma-band activity in awake macaque area V4 rhythmically modulates responses to unpredictable stimulus events. This modulation exceeded a simple additive superposition of a constant response onto ongoing gamma-rhythmic firing, demonstrating the modulation of multiplicative gain. Gamma phases leading to strongest neuronal responses also led to shortest behavioral reaction times, suggesting functional relevance of the effect. Furthermore, we find that constant optogenetic stimulation of anesthetized cat area 21a produces gamma-band activity entailing a similar gain modulation. As the gamma rhythm in area 21a did not spread backwards to area 17, this suggests that postsynaptic gamma is sufficient for gain modulation.
Synchronization has been implicated in neuronal communication, but causal evidence remains indirect. We use optogenetics to generate depolarizing currents in pyramidal neurons of the cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. The cortex transforms constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increases the strength and frequency of synchronization. Slow, symmetric excitation profiles reveal hysteresis of power and frequency. White-noise input sequences enable causal analysis of network transmission, establishing that the cortical gamma-band resonance preferentially transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncover a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.
Synchronization has been implicated in neuronal communication, but causal evidence remains indirect. We used optogenetics to generate depolarizing currents in pyramidal neurons of cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. Cortex transformed constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increased the strength and frequency of synchronization. Slow, symmetric excitation profiles revealed hysteresis of power and frequency. Crucially, white-noise input sequences enabled causal analysis of network transmission, establishing that cortical resonance selectively transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncovered a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.
The gamma rhythm has been implicated in neuronal communication, but causal evidence remains indirect. We measured spike output of local neuronal networks and emulated their synaptic input through optogenetics. Opsins provide currents through somato-dendritic membranes, similar to synapses, yet under experimental control with high temporal precision. We expressed Channelrhodopsin-2 in excitatory neurons of cat visual cortex and recorded neuronal responses to light with different temporal characteristics. Sine waves of different frequencies entrained neuronal responses with a reliability that peaked for input frequencies in the gamma band. Crucially, we also presented white-noise sequences, because their temporal unpredictability enables analysis of causality. Neuronal spike output was caused specifically by the input’s gamma component. This gamma-specific transfer function is likely an emergent property of in-vivo networks with feedback inhibition. The method described here could reveal the transfer function between the input to any one and the output of any other neuronal group.
Signal transfer of visual stimuli to V4 occurs in gamma-rhythmic, pulsed information packages
(2020)
Summary Selective visual attention allows the brain to focus on behaviorally relevant information while ignoring irrelevant signals. As a possible mechanism, routing by synchronization was proposed: neural populations sending attended signals align their gamma-rhythmic activities with receiving populations, such that spikes from the senders arrive at excitability peaks of the receivers, enhancing signal transfer. Conversely, the non-attended signals arrive unaligned to the receiver’s oscillation, reducing signal transfer. Therefore, visual signals should be transferred through periodically pulsed information packages, resulting in a modulation of the stimulus content within the receiver’s activity by its gamma phase and amplitude. To test this prediction, we quantified gamma phase-specific stimulus content within neural activity from area V4 of macaques performing a visual attention task. For the attended stimulus we find enhanced stimulus content reaching its maximum near excitability peaks, with effect magnitude increasing with oscillation amplitude, establishing a functional link between selective processing and gamma activity.
Afterimages result from a prolonged exposure to still visual stimuli. They are best detectable when viewed against uniform backgrounds and can persist for multiple seconds. Consequently, the dynamics of afterimages appears to be slow by their very nature. To the contrary, we report here that about 50% of an afterimage intensity can be erased rapidly—within less than a second. The prerequisite is that subjects view a rich visual content to erase the afterimage; fast erasure of afterimages does not occur if subjects view a blank screen. Moreover, we find evidence that fast removal of afterimages is a skill learned with practice as our subjects were always more effective in cleaning up afterimages in later parts of the experiment. These results can be explained by a tri-level hierarchy of adaptive mechanisms, as has been proposed by the theory of practopoiesis.
Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.
How much data do we need? Lower bounds of brain activation states to predict human cognitive ability
(2022)
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Despite their low frequency of occurrence, states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture (derived from resting-state fMRI) and to be highly subject-specific. However, it is currently unclear whether such network-defining states of high cofluctuation also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, an eigenvector-based prediction framework, we show that functional connectivity estimates from as few as 20 temporally separated time frames (< 3% of a 10 min resting-state fMRI scan) are significantly predictive of individual differences in intelligence (N = 281, p < .001). In contrast and against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not achieve significant prediction of intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest brain connectivity, temporally distributed information is necessary to extract information about cognitive abilities from functional connectivity time series. This information, however, is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Probing the association between resting state brain network dynamics and psychological resilience
(2021)
Abstract
This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, i.e., the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores, that were however very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.
Author Summary We tested the replicability and generalizability of a previously proposed negative association between dynamic brain network reconfigurations derived from multilayer modularity detection (node flexibility) and psychological resilience. Using multiband resting-state BOLD fMRI data and exploring several parcellation schemes, sliding window approaches, and temporal resolutions of the data, we could not replicate previously reported findings regarding the association between node flexibility and resilience. By extending this work to other measures of brain dynamics (node promiscuity, degree) we observe a rather inconsistent pattern of correlations with resilience, that strongly varies across analysis choices. We conclude that further research is needed to understand the network neuroscience basis of mental health and discuss several reasons that may account for the variability in results.
Probing the association between resting-state brain network dynamics and psychological resilience
(2022)
Abstract
This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.
Author Summary
We tested the replicability and generalizability of a previously proposed negative association between dynamic brain network reconfigurations derived from multilayer modularity detection (node flexibility) and psychological resilience. Using multiband resting-state BOLD fMRI data and exploring several parcellation schemes, sliding window approaches, and temporal resolutions of the data, we could not replicate previously reported findings regarding the association between node flexibility and resilience. By extending this work to other measures of brain dynamics (node promiscuity, degree) we observe a rather inconsistent pattern of correlations with resilience that strongly varies across analysis choices. We conclude that further research is needed to understand the network neuroscience basis of mental health and discuss several reasons that may account for the variability in results.
Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/ phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N=38 human participants) while Experiment 2 assessed behavioral facilitation effects (N=24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context based-facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in pseudowords familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context based-facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.
To characterize the left-ventral occipito-temporal cortex (lvOT) role during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filter out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging. Empirically, using functional magnetic resonance imaging, we demonstrate that quantitative LCM simulations predict lvOT activation across three studies better than alternative models. Besides, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT. In contrast, a dichotomous word/non-word contrast, which is assumed as the LCM’s output, could be localized to upstream frontal brain regions. Finally, we found that training lexical categorization results in more efficient reading. Thus, we propose a ventral-visual-stream processing framework for reading involving word-likeness extraction followed by lexical categorization, before meaning extraction.
To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading—visual, orthographic, phonological, and/or lexical-semantic—contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in “explaining away” predictable stimulus features, but rather in a “sharpening” of brain responses involved in word processing.
To a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading – visual, orthographic, phonological, and/or lexical-semantic – contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in ‘explaining away’ predictable stimulus features, but rather in a ‘sharpening’ of brain responses involved in word processing.
Word familiarity and predictive context facilitate visual word processing, leading to faster recognition times and reduced neuronal responses. Previously, models with and without top-down connections, including lexical-semantic, pre-lexical (e.g., orthographic/phonological), and visual processing levels were successful in accounting for these facilitation effects. Here we systematically assessed context-based facilitation with a repetition priming task and explicitly dissociated pre-lexical and lexical processing levels using a pseudoword (PW) familiarization procedure. Experiment 1 investigated the temporal dynamics of neuronal facilitation effects with magnetoencephalography (MEG; N = 38 human participants), while experiment 2 assessed behavioral facilitation effects (N = 24 human participants). Across all stimulus conditions, MEG demonstrated context-based facilitation across multiple time windows starting at 100 ms, in occipital brain areas. This finding indicates context-based facilitation at an early visual processing level. In both experiments, we furthermore found an interaction of context and lexical familiarity, such that stimuli with associated meaning showed the strongest context-dependent facilitation in brain activation and behavior. Using MEG, this facilitation effect could be localized to the left anterior temporal lobe at around 400 ms, indicating within-level (i.e., exclusively lexical-semantic) facilitation but no top-down effects on earlier processing stages. Increased pre-lexical familiarity (in PWs familiarized utilizing training) did not enhance or reduce context effects significantly. We conclude that context-based facilitation is achieved within visual and lexical processing levels. Finally, by testing alternative hypotheses derived from mechanistic accounts of repetition suppression, we suggest that the facilitatory context effects found here are implemented using a predictive coding mechanism.
Abstract
To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.
Author summary
Visual word recognition is a critical process for reading and relies on the human brain’s left ventral occipito-temporal (lvOT) regions. However, the lvOTs specific function in visual word recognition is not yet clear. We propose that these occipito-temporal brain systems are critical for lexical categorization, i.e., the process of determining whether an orthographic percept is a known word or not, so that further lexical and semantic processing can be restricted to those percepts that are part of our "mental lexicon". We demonstrate that a computational model implementing this process, the lexical categorization model, can explain seemingly contradictory benchmark results from the published literature. We further use functional magnetic resonance imaging to show that the lexical categorization model successfully predicts brain activation in the left ventral occipito-temporal cortex elicited during a word recognition task. It does so better than alternative models proposed so far. Finally, we provide causal evidence supporting this model by empirically demonstrating that training the process of lexical categorization improves reading performance.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations – typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to ‘explain away’ redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, i.e., the ease vs. difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English, German) read isolated words, we encode and decode word vectors taken from the family of prediction-based word2vec algorithms. We find that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
The outstanding speed of language comprehension necessitates a highly efficient implementation of cognitive-linguistic processes. The domain-general theory of Predictive Coding suggests that our brain solves this problem by continuously forming linguistic predictions about expected upcoming input. The neurophysiological implementation of these predictive linguistic processes, however, is not yet understood. Here, we use EEG (human participants, both sexes) to investigate the existence and nature of online-generated, category-level semantic representations during sentence processing. We conducted two experiments in which some nouns – embedded in a predictive spoken sentence context – were unexpectedly delayed by 1 second. Target nouns were either abstract/concrete (Experiment 1) or animate/inanimate (Experiment 2). We hypothesized that if neural prediction error signals following (temporary) omissions carry specific information about the stimulus, the semantic category of the upcoming target word is encoded in brain activity prior to its presentation. Using time-generalized multivariate pattern analysis, we demonstrate significant decoding of word category from silent periods directly preceding the target word, in both experiments. This provides direct evidence for predictive coding during sentence processing, i.e., that information about a word can be encoded in brain activity before it is perceived. While the same semantic contrast could also be decoded from EEG activity elicited by isolated words (Experiment 1), the identified neural patterns did not generalize to pre-stimulus delay period activity in sentences. Our results not only indicate that the brain processes language predictively, but also demonstrate the nature and sentence-specificity of category-level semantic predictions preactivated during sentence comprehension.
Across languages, the speech signal is characterized by a predominant modulation of the amplitude spectrum between about 4.3-5.5Hz, reflecting the production and processing of linguistic information chunks (syllables, words) every ∼200ms. Interestingly, ∼200ms is also the typical duration of eye fixations during reading. Prompted by this observation, we demonstrate that German readers sample written text at ∼5Hz. A subsequent meta-analysis with 142 studies from 14 languages replicates this result, but also shows that sampling frequencies vary across languages between 3.9Hz and 5.2Hz, and that this variation systematically depends on the complexity of the writing systems (character-based vs. alphabetic systems, orthographic transparency). Finally, we demonstrate empirically a positive correlation between speech spectrum and eye-movement sampling in low-skilled readers. Based on this convergent evidence, we propose that during reading, our brain’s linguistic processing systems imprint a preferred processing rate, i.e., the rate of spoken language production and perception, onto the oculomotor system.
Mental imagery provides an essential simulation tool for remembering the past and planning the future, with its strength affecting both cognition and mental health. Research suggests that neural activity spanning prefrontal, parietal, temporal, and visual areas supports the generation of mental images. Exactly how this network controls the strength of visual imagery remains unknown. Here, brain imaging and transcranial magnetic phosphene data show that lower resting activity and excitability levels in early visual cortex (V1-V3) predict stronger sensory imagery. Further, electrically decreasing visual cortex excitability using tDCS increases imagery strength, demonstrating a causative role of visual cortex excitability in controlling visual imagery. Together, these data suggest a neurophysiological mechanism of cortical excitability involved in controlling the strength of mental images.
Mental imagery provides an essential simulation tool for remembering the past and planning the future, with its strength affecting both cognition and mental health. Research suggests that neural activity spanning prefrontal, parietal, temporal, and visual areas supports the generation of mental images. Exactly how this network controls the strength of visual imagery remains unknown. Here, brain imaging and transcranial magnetic phosphene data show that lower resting activity and excitability levels in early visual cortex (V1-V3) predict stronger sensory imagery. Electrically decreasing visual cortex excitability using tDCS increases imagery strength, demonstrating a causative role of visual cortex excitability in controlling visual imagery. These data suggest a neurophysiological mechanism of cortical excitability involved in controlling the strength of mental images.
Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.
Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesize that attentional and contextual top-down signals interact in V1, in a manner that primarily benefits the representation of natural visual stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we show that attention enhances the decodability of stimulus identity from population responses evoked by natural scenes but, critically, not by synthetic stimuli in which higher-order statistical regularities were eliminated. Attentional enhancement of stimulus decodability from population responses occurs in low dimensional spaces, as revealed by principal component analysis, suggesting an alignment between the attentional and the natural stimulus variance. Moreover, natural scenes produce stimulus-specific oscillatory responses in V1, whose power undergoes a global shift from low to high frequencies with attention. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.
Reducing neuronal size results in less cell membrane and therefore lower input conductance. Smaller neurons are thus more excitable as seen in their voltage responses to current injections in the soma. However, the impact of a neuron’s size and shape on its voltage responses to synaptic activation in dendrites is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs and show that these are entirely independent of dendritic length. For a given synaptic density, a neuron’s response depends only on the average dendritic diameter and its intrinsic conductivity. These results remain true for the entire range of possible dendritic morphologies irrespective of any particular arborisation complexity. Also, spiking models result in morphology invariant numbers of action potentials that encode the percentage of active synapses. Interestingly, in contrast to spike rate, spike times do depend on dendrite morphology. In summary, a neuron’s excitability in response to synaptic inputs is not affected by total dendrite length. It rather provides a homeostatic input-output relation that specialised synapse distributions, local non-linearities in the dendrites and synaptic plasticity can modulate. Our work reveals a new fundamental principle of dendritic constancy that has consequences for the overall computation in neural circuits.
Abstract Trial-to-trial variability and spontaneous activity of cortical recordings have been suggested to reflect intrinsic noise. This view is currently challenged by mounting evidence for structure in these phenomena: Trial-to-trial variability decreases following stimulus onset and can be predicted by previous spontaneous activity. This spontaneous activity is similar in magnitude and structure to evoked activity and can predict decisions. Allof the observed neuronal properties described above can be accounted for, at an abstract computational level, by the sampling-hypothesis, according to which response variability reflects stimulus uncertainty. However, a mechanistic explanation at the level of neural circuit dynamics is still missing.
In this study, we demonstrate that all of these phenomena can be accounted for by a noise-free self-organizing recurrent neural network model (SORN). It combines spike-timing dependent plasticity (STDP) and homeostatic mechanisms in a deterministic network of excitatory and inhibitory McCulloch-Pitts neurons. The network self-organizes to spatio-temporally varying input sequences.
We find that the key properties of neural variability mentioned above develop in this model as the network learns to perform sampling-like inference. Importantly, the model shows high trial-to-trial variability although it is fully deterministic. This suggests that the trial-to-trial variability in neural recordings may not reflect intrinsic noise. Rather, it may reflect a deterministic approximation of sampling-like learning and inference. The simplicity of the model suggests that these correlates of the sampling theory are canonical properties of recurrent networks that learn with a combination of STDP and homeostatic plasticity mechanisms.
Author Summary Neural recordings seem very noisy. If the exact same stimulus is shown to an animal multiple times, the neural response will vary. In fact, the activity of a single neuron shows many features of a stochastic process. Furthermore, in the absence of a sensory stimulus, cortical spontaneous activity has a magnitude comparable to the activity observed during stimulus presentation. These findings have led to a widespread belief that neural activity is indeed very noisy. However, recent evidence indicates that individual neurons can operate very reliably and that the spontaneous activity in the brain is highly structured, suggesting that much of the noise may in fact be signal. One hypothesis regarding this putative signal is that it reflects a form of probabilistic inference through sampling. Here we show that the key features of neural variability can be accounted for in a completely deterministic network model through self-organization. As the network learns a model of its sensory inputs, the deterministic dynamics give rise to sampling-like inference. Our findings show that the notorious variability in neural recordings does not need to be seen as evidence for a noisy brain. Instead it may reflect sampling-like inference emerging from a self-organized learning process.
An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. convey information beyond what can be performed by isolated signals. Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in three common awake marmosets and investigated to what extent event-related-potentials (ERP) and broadband (BB) dynamics exhibit redundancy and synergy for auditory prediction error signals. We observed multiple patterns of redundancy and synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between lower and higher areas in the frontal cortex. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.
The prevalence and specificity of local protein synthesis during neuronal synaptic plasticity
(2021)
To supply proteins to their vast volume, neurons localize mRNAs and ribosomes in dendrites and axons. While local protein synthesis is required for synaptic plasticity, the abundance and distribution of ribosomes and nascent proteins near synapses remain elusive. Here, we quantified the occurrence of local translation and visualized the range of synapses supplied by nascent proteins during basal and plastic conditions. We detected dendritic ribosomes and nascent proteins at single-molecule resolution using DNA-PAINT and metabolic labeling. Both ribosomes and nascent proteins positively correlated with synapse density. Ribosomes were detected at ~85% of synapses with ~2 translational sites per synapse; ~50% of the nascent protein was detected near synapses. The amount of locally synthesized protein detected at a synapse correlated with its spontaneous Ca2+ activity. A multifold increase in synaptic nascent protein was evident following both local and global plasticity at respective scales, albeit with substantial heterogeneity between neighboring synapses.
Background: Cognitive dysfunctions represent a core feature of schizophrenia and a predictor for clinical outcomes. One possible mechanism for cognitive impairments could involve an impairment in the experience-dependent modifications of cortical networks.
Methods: To address this issue, we employed magnetoencephalography (MEG) during a visual priming paradigm in a sample of chronic patients with schizophrenia (n = 14), and in a group of healthy controls (n = 14). We obtained MEG-recordings during the presentation of visual stimuli that were presented three times either consecutively or with intervening stimuli. MEG-data were analyzed for event-related fields as well as spectral power in the 1–200 Hz range to examine repetition suppression and repetition enhancement. We defined regions of interest in occipital and thalamic regions and obtained virtual-channel data.
Results: Behavioral priming did not differ between groups. However, patients with schizophrenia showed prominently reduced oscillatory response to novel stimuli in the gamma-frequency band as well as significantly reduced repetition suppression of gamma-band activity and reduced repetition enhancement of beta-band power in occipital cortex to both consecutive repetitions as well as repetitions with intervening stimuli. Moreover, schizophrenia patients were characterized by a significant deficit in suppression of the C1m component in occipital cortex and thalamus as well as of the late positive component (LPC) in occipital cortex.
Conclusions: These data provide novel evidence for impaired repetition suppression in cortical and subcortical circuits in schizophrenia. Although behavioral priming was preserved, patients with schizophrenia showed deficits in repetition suppression as well as repetition enhancement in thalamic and occipital regions, suggesting that experience-dependent modification of neural circuits is impaired in the disorder.
Glia, the helper cells of the brain, are essential in maintaining neural resilience across time and varying challenges: By reacting to changes in neuronal health glia carefully balance repair or disposal of injured neurons. Malfunction of these interactions is implicated in many neurodegenerative diseases. We present a reductionist model that mimics repair-or-dispose decisions to generate a hypothesis for the cause of disease onset. The model assumes four tissue states: healthy and challenged tissue, primed tissue at risk of acute damage propagation, and chronic neurodegeneration. We discuss analogies to progression stages observed in the most common neurodegenerative conditions and to experimental observations of cellular signaling pathways of glia-neuron crosstalk. The model suggests that the onset of neurodegeneration can result as a compromise between two conflicting goals: short-term resilience to stressors versus long-term prevention of tissue damage.
Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.
Non-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP) which depends on the interactions of three precisely-timed spikes and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We derive synaptic changes arising from correlations up to third-order and describe them as the sum of structural motifs which determine how any spike in the network influences a given synaptic connection through possible connectivity paths. This motif expansion framework reveals novel structural motifs under the triplet STDP rule, which support the formation of bidirectional connections and ultimately the spontaneous emergence of global network structure in the form of self-connected groups of neurons, or assemblies. We propose that under triplet STDP assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule common in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of the triplet STDP rule or the synaptic transmission function differentially promote structural motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
Type IV pili are flexible filaments on the surface of bacteria, consisting of a helical assembly of pilin proteins. They are involved in bacterial motility (twitching), surface adhesion, biofilm formation and DNA uptake (natural transformation). Here, we use cryo-electron microscopy and mass spectrometry to show that the bacterium Thermus thermophilus produces two forms of type IV pilus ("wide" and "narrow"), differing in structure and protein composition. Wide pili are composed of the major pilin PilA4, while narrow pili are composed of a so-far uncharacterized pilin which we name PilA5. Functional experiments indicate that PilA4 is required for natural transformation, while PilA5 is important for twitching motility.
In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive.
Rhythmic actions benefit from synchronization with external events. Auditory-paced finger tapping studies indicate the two cerebral hemispheres preferentially control different rhythms. It is unclear whether left-lateralized processing of faster rhythms and right-lateralized processing of slower rhythms bases upon hemispheric timing differences that arise in the motor or sensory system or whether asymmetry results from lateralized sensorimotor interactions. We measured fMRI and MEG during symmetric finger tapping, in which fast tapping was defined as auditory-motor synchronization at 2.5 Hz. Slow tapping corresponded to tapping to every fourth auditory beat (0.625 Hz). We demonstrate that the left auditory cortex preferentially represents the relative fast rhythm in an amplitude modulation of low beta oscillations while the right auditory cortex additionally represents the internally generated slower rhythm. We show coupling of auditory-motor beta oscillations supports building a metric structure. Our findings reveal a strong contribution of sensory cortices to hemispheric specialization in action control.
Spermatogonial stem cells (SSCs) are adult stem cells that are slowly cycling and self-renewing. The pool of SSCs generates very large numbers of male gametes throughout the life of the individual. SSCs can be cultured in vitro for long periods of time, and established SSC lines can be manipulated genetically. Upon transplantation into the testes of infertile mice, long-term cultured mouse SSCs can differentiate into fertile spermatozoa, which can give rise to live offspring. Here, we show that the testicular soma of mice with a conditional knockout (conKO) in the X-linked gene Tsc22d3 supports spermatogenesis and germline transmission from cultured mouse SSCs upon transplantation. Infertile males were produced by crossing homozygous Tsc22d3 floxed females with homozygous ROSA26-Cre males. We obtained 96 live offspring from six long-term cultured SSC lines with the aid of intracytoplasmic sperm injection. We advocate the further optimization of Tsc22d3-conKO males as recipients for testis transplantation of SSC lines.
Previous reports of improved oral reading performance for dyslexic children but not for regular readers when between-letter spacing was enlarged led to the proposal of a dyslexia-specific deficit in visual crowding. However, it is in this context also critical to understand how letter spacing affects visual word recognition and reading in unimpaired readers. Adopting an individual differences approach, the present study, accordingly, examined whether wider letter spacing improves reading performance also for non-impaired adults during silent reading and whether there is an association between letter spacing and crowding sensitivity. We report eye movement data of 24 German students who silently read texts presented either with normal or wider letter spacing. Foveal and parafoveal crowding sensitivity were estimated using two independent tests. Wider spacing reduced first fixation durations, gaze durations, and total fixation time for all participants, with slower readers showing stronger effects. However, wider letter spacing also reduced skipping probabilities and elicited more fixations, especially for faster readers. In terms of words read per minute, wider letter spacing did not provide a benefit, and faster readers in particular were slowed down. Neither foveal nor parafoveal crowding sensitivity correlated with the observed letter-spacing effects. In conclusion, wide letter spacing reduces single word processing time in typically developed readers during silent reading, but affects reading rates negatively since more words must be fixated. We tentatively propose that wider letter spacing reinforces serial letter processing in slower readers, but disrupts parallel processing of letter chunks in faster readers. These effects of letter spacing do not seem to be mediated by individual differences in crowding sensitivity.
Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations
(2019)
The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30–80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context.
The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal–frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder.
Synesthesia is a phenomenon in which additional perceptual experiences are elicited by sensory stimuli or cognitive concepts. Synesthetes possess a unique type of phenomenal experiences not directly triggered by sensory stimulation. Therefore, for better understanding of consciousness it is relevant to identify the mental and physiological processes that subserve synesthetic experience. In the present work we suggest several reasons why synesthesia has merit for research on consciousness. We first review the research on the dynamic and rapidly growing field of the studies of synesthesia. We particularly draw attention to the role of semantics in synesthesia, which is important for establishing synesthetic associations in the brain. We then propose that the interplay between semantics and sensory input in synesthesia can be helpful for the study of the neural correlates of consciousness, especially when making use of ambiguous stimuli for inducing synesthesia. Finally, synesthesia-related alterations of brain networks and functional connectivity can be of merit for the study of consciousness.
Following a brief review of current efforts to identify the neuronal correlates of conscious processing (NCCP) an attempt is made to bridge the gap between the material neuronal processes and the immaterial dimensions of subjective experience. It is argued that this "hard problem" of consciousness research cannot be solved by only considering the neuronal underpinnings of cognition. The proposal is that the hard problem can be treated within a naturalistic framework if one considers not only the biological but also the socio-cultural dimensions of evolution. The argument is based on the following premises: perceptions are the result of a constructivist process that depends on priors. This applies both for perceptions of the outer world and the perception of oneself. Social interactions between agents endowed with the cognitive abilities of humans generated immaterial realities, addressed as social or cultural realities. This novel class of realities assumed the role of priors for the perception of oneself and the embedding world. A natural consequence of these extended perceptions is a dualist classification of observables into material and immaterial phenomena nurturing the concept of ontological substance dualism. It is argued that perceptions shaped by socio-cultural priors lead to the construction of a self-model that has both a material and an immaterial dimension. As priors are implicit and not amenable to conscious recollection the perceived immaterial dimension is experienced as veridical and not derivable from material processes—which is the hallmark of the hard problem. These considerations let the hard problem appear as the result of cognitive constructs that are amenable to naturalistic explanations in an evolutionary framework.
In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.
Information processing performed by any system can be conceptually decomposed into the transfer, storage and modification of information—an idea dating all the way back to the work of Alan Turing. However, formal information theoretic definitions until very recently were only available for information transfer and storage, not for modification. This has changed with the extension of Shannon information theory via the decomposition of the mutual information between inputs to and the output of a process into unique, shared and synergistic contributions from the inputs, called a partial information decomposition (PID). The synergistic contribution in particular has been identified as the basis for a definition of information modification. We here review the requirements for a functional definition of information modification in neuroscience, and apply a recently proposed measure of information modification to investigate the developmental trajectory of information modification in a culture of neurons vitro, using partial information decomposition. We found that modification rose with maturation, but ultimately collapsed when redundant information among neurons took over. This indicates that this particular developing neural system initially developed intricate processing capabilities, but ultimately displayed information processing that was highly similar across neurons, possibly due to a lack of external inputs. We close by pointing out the enormous promise PID and the analysis of information modification hold for the understanding of neural systems
Inhibitory interneurons govern virtually all computations in neocortical circuits and are in turn controlled by neuromodulation. While a detailed understanding of the distinct marker expression, physiology, and neuromodulator responses of different interneuron types exists for rodents and recent studies have highlighted the role of specific interneurons in converting rapid neuromodulatory signals into altered sensory processing during locomotion, attention, and associative learning, it remains little understood whether similar mechanisms exist in human neocortex. Here, we use whole-cell recordings combined with agonist application, transgenic mouse lines, in situ hybridization, and unbiased clustering to directly determine these features in human layer 1 interneurons (L1-INs). Our results indicate pronounced nicotinic recruitment of all L1-INs, whereas only a small subset co-expresses the ionotropic HTR3 receptor. In addition to human specializations, we observe two comparable physiologically and genetically distinct L1-IN types in both species, together indicating conserved rapid neuromodulation of human neocortical circuits through layer 1.
In homeostatic scaling at central synapses, the depth and breadth of cellular mechanisms that detect the offset from the set-point, detect the duration of the offset and implement a cellular response are not well understood. To understand the time-dependent scaling dynamics we treated cultured rat hippocampal cells with either TTX or bicucculline for 2 hr to induce the process of up- or down-scaling, respectively. During the activity manipulation we metabolically labeled newly synthesized proteins using BONCAT. We identified 168 newly synthesized proteins that exhibited significant changes in expression. To obtain a temporal trajectory of the response, we compared the proteins synthesized within 2 hr or 24 hr of the activity manipulation. Surprisingly, there was little overlap in the significantly regulated newly synthesized proteins identified in the early- and integrated late response datasets. There was, however, overlap in the functional categories that are modulated early and late. These data indicate that within protein function groups, different proteomic choices can be made to effect early and late homeostatic responses that detect the duration and polarity of the activity manipulation.
Drebrin (DBN) regulates cytoskeletal functions during neuronal development, and is thought to contribute to structural and functional synaptic changes associated with aging and Alzheimer’s disease. Here we show that DBN coordinates stress signalling with cytoskeletal dynamics, via a mechanism involving kinase ataxia-telangiectasia mutated (ATM). An excess of reactive oxygen species (ROS) stimulates ATM-dependent phosphorylation of DBN at serine-647, which enhances protein stability and accounts for improved stress resilience in dendritic spines. We generated a humanized DBN Caenorhabditis elegans model and show that a phospho-DBN mutant disrupts the protective ATM effect on lifespan under sustained oxidative stress. Our data indicate a master regulatory function of ATM-DBN in integrating cytosolic stress-induced signalling with the dynamics of actin remodelling to provide protection from synapse dysfunction and ROS-triggered reduced lifespan. They further suggest that DBN protein abundance governs actin filament stability to contribute to the consequences of oxidative stress in physiological and pathological conditions.
Startle disease or hereditary hyperekplexia has been shown to result from mutations in the α1‐subunit gene of the inhibitory glycine receptor (GlyR). In hyperekplexia patients, neuromotor symptoms generally become apparent at birth, improve with age, and often disappear in adulthood. Loss‐of‐function mutations of GlyR α or β‐subunits in mice show rather severe neuromotor phenotypes. Here, we generated mutant mice with a transient neuromotor deficiency by introducing a GlyR β transgene into the spastic mouse (spa/spa), a recessive mutant carrying a transposon insertion within the GlyR β‐subunit gene. In spa/spa TG456 mice, one of three strains generated with this construct, which expressed very low levels of GlyR β transgene‐dependent mRNA and protein, the spastic phenotype was found to depend upon the transgene copy number. Notably, mice carrying two copies of the transgene showed an age‐dependent sensitivity to tremor induction, which peaked at ∼ 3–4 weeks postnatally. This closely resembles the development of symptoms in human hyperekplexia patients, where motor coordination significantly improves after adolescence. The spa/spa TG456 line thus may serve as an animal model of human startle disease.
Full reconstruction of large lobula plate tangential cells in Drosophila from a 3D EM dataset
(2018)
With the advent of neurogenetic methods, the neural basis of behavior is presently being analyzed in more and more detail. This is particularly true for visually driven behavior of Drosophila melanogaster where cell-specific driver lines exist that, depending on the combination with appropriate effector genes, allow for targeted recording, silencing and optogenetic stimulation of individual cell-types. Together with detailed connectomic data of large parts of the fly optic lobe, this has recently led to much progress in our understanding of the neural circuits underlying local motion detection. However, how such local information is combined by optic flow sensitive large-field neurons is still incompletely understood. Here, we aim to fill this gap by a dense reconstruction of lobula plate tangential cells of the fly lobula plate. These neurons collect input from many hundreds of local motion-sensing T4/T5 neurons and connect them to descending neurons or central brain areas. We confirm all basic features of HS and VS cells as published previously from light microscopy. In addition, we identified the dorsal and the ventral centrifugal horizontal, dCH and vCH cell, as well as three VSlike cells, including their distinct dendritic and axonal projection area.
A wealth of data has elucidated the mechanisms by which sensory inputs are encoded in the neocortex, but how these processes are regulated by the behavioral relevance of sensory information is less understood. Here, we focus on neocortical layer 1 (L1), a key location for processing of such top-down information. Using Neuron-Derived Neurotrophic Factor (NDNF) as a selective marker of L1 interneurons (INs) and in vivo 2-photon calcium imaging, electrophysiology, viral tracing, optogenetics, and associative memory, we find that L1 NDNF-INs mediate a prolonged form of inhibition in distal pyramidal neuron dendrites that correlates with the strength of the memory trace. Conversely, inhibition from Martinotti cells remains unchanged after conditioning but in turn tightly controls sensory responses in NDNF-INs. These results define a genetically addressable form of dendritic inhibition that is highly experience dependent and indicate that in addition to disinhibition, salient stimuli are encoded at elevated levels of distal dendritic inhibition.
To crack the neural code and read out the information neural spikes convey, it is essential to understand how the information is coded and how much of it is available for decoding. To this end, it is indispensable to derive from first principles a minimal set of spike features containing the complete information content of a neuron. Here we present such a complete set of coding features. We show that temporal pairwise spike correlations fully determine the information conveyed by a single spiking neuron with finite temporal memory and stationary spike statistics. We reveal that interspike interval temporal correlations, which are often neglected, can significantly change the total information. Our findings provide a conceptual link between numerous disparate observations and recommend shifting the focus of future studies from addressing firing rates to addressing pairwise spike correlation functions as the primary determinants of neural information.
We examined alterations in E/I-balance in schizophrenia (ScZ) through measurements of resting-state gamma-band activity in participants meeting clinical high-risk (CHR) criteria (n = 88), 21 first episode (FEP) patients and 34 chronic ScZ-patients. Furthermore, MRS-data were obtained in CHR-participants and matched controls. Magnetoencephalographic (MEG) resting-state activity was examined at source level and MEG-data were correlated with neuropsychological scores and clinical symptoms. CHR-participants were characterized by increased 64–90 Hz power. In contrast, FEP- and ScZ-patients showed aberrant spectral power at both low- and high gamma-band frequencies. MRS-data showed a shift in E/I-balance toward increased excitation in CHR-participants, which correlated with increased occipital gamma-band power. Finally, neuropsychological deficits and clinical symptoms in FEP and ScZ-patients were correlated with reduced gamma band-activity, while elevated psychotic symptoms in the CHR group showed the opposite relationship. The current study suggests that resting-state gamma-band power and altered Glx/GABA ratio indicate changes in E/I-balance parameters across illness stages in ScZ.
The retinal rod pathway, featuring dedicated rod bipolar cells (RBCs) and AII amacrine cells, has been intensely studied in placental mammals. Here, we analyzed the rod pathway in a nocturnal marsupial, the South American opossum Monodelphis domestica to elucidate whether marsupials have a similar rod pathway. The retina was dominated by rods with densities of 338,000–413,000/mm². Immunohistochemistry for the RBC-specific marker protein kinase Cα (PKCα) and the AII cell marker calretinin revealed the presence of both cell types with their typical morphology. This is the first demonstration of RBCs in a marsupial and of the integration of RBCs and AII cells in the rod signaling pathway. Electron microscopy showed invaginating synaptic contacts of the PKCα-immunoreactive bipolar cells with rods; light microscopic co-immunolabeling for the synaptic ribbon marker CtBP2 confirmed dominant rod contacts. The RBC axon terminals were mostly located in the innermost stratum S5 of the inner plexiform layer (IPL), but had additional side branches and synaptic varicosities in strata S3 and S4, with S3-S5 belonging to the presumed functional ON sublayer of the IPL, as shown by immunolabeling for the ON bipolar cell marker Gγ13. Triple-immunolabeling for PKCα, calretinin and CtBP2 demonstrated RBC synapses onto AII cells. These features conform to the pattern seen in placental mammals, indicating a basically similar rod pathway in M. domestica. The density range of RBCs was 9,900–16,600/mm2, that of AII cells was 1,500–3,260/mm2. The numerical convergence (density ratio) of 146–156 rods to 4.7–6.0 RBCs to 1 AII cell is within the broad range found among placental mammals. For comparison, we collected data for the Australian nocturnal dunnart Sminthopsis crassicaudata, and found it to be similar to M. domestica, with rod-contacting PKCα-immunoreactive bipolar cells that had axon terminals also stratifying in IPL strata S3-S5.
In mammalian species, including humans, the hippocampal dentate gyrus (DG) is a primary region of adult neurogenesis. Aberrant adult hippocampal neurogenesis is associated with neurological pathologies. Understanding the cellular mechanisms controlling adult hippocampal neurogenesis is expected to open new therapeutic strategies for mental disorders. Microglia is intimately associated with neural progenitor cells in the hippocampal DG and has been implicated, under varying experimental conditions, in the control of the proliferation, differentiation and survival of neural precursor cells. But the underlying mechanisms remain poorly defined. Using fluorescent in situ hybridization we show that microglia in brain express the ADP-activated P2Y13 receptor under basal conditions and that P2ry13 mRNA is absent from neurons, astrocytes, and neural progenitor cells. Disrupting P2ry13 decreases structural complexity of microglia in the hippocampal subgranular zone (SGZ). But it increases progenitor cell proliferation and new neuron formation. Our data suggest that P2Y13 receptor-activated microglia constitutively attenuate hippocampal neurogenesis. This identifies a signaling pathway whereby microglia, via a nucleotide-mediated mechanism, contribute to the homeostatic control of adult hippocampal neurogenesis. Selective P2Y13R antagonists could boost neurogenesis in pathological conditions associated with impaired hippocampal neurogenesis.
Regulation of protein turnover allows cells to react to their environment and maintain homeostasis. Proteins can show different turnover rates in different tissue, but little is known about protein turnover in different brain cell types. We used dynamic SILAC to determine half-lives of over 5100 proteins in rat primary hippocampal cultures as well as in neuron-enriched and glia-enriched cultures ranging from <1 to >20 days. In contrast to synaptic proteins, membrane proteins were relatively shorter-lived and mitochondrial proteins were longer-lived compared to the population. Half-lives also correlate with protein functions and the dynamics of the complexes they are incorporated in. Proteins in glia possessed shorter half-lives than the same proteins in neurons. The presence of glia sped up or slowed down the turnover of neuronal proteins. Our results demonstrate that both the cell-type of origin as well as the nature of the extracellular environment have potent influences on protein turnover.
Electron transfer in respiratory chains generates the electrochemical potential that serves as energy source for the cell. Prokaryotes can use a wide range of electron donors and acceptors and may have alternative complexes performing the same catalytic reactions as the mitochondrial complexes. This is the case for the alternative complex III (ACIII), a quinol:cytochrome c/HiPIP oxidoreductase. In order to understand the catalytic mechanism of this respiratory enzyme, we determined the structure of ACIII from Rhodothermus marinus at 3.9 Å resolution by single-particle cryo-electron microscopy. ACIII presents a so-far unique structure, for which we establish the arrangement of the cofactors (four iron–sulfur clusters and six c-type hemes) and propose the location of the quinol-binding site and the presence of two putative proton pathways in the membrane. Altogether, this structure provides insights into a mechanism for energy transduction and introduces ACIII as a redox-driven proton pump.
A key hallmark of visual perceptual awareness is robustness to instabilities arising from unnoticeable eye and eyelid movements. In previous human intracranial (iEEG) work (Golan et al., 2016) we found that excitatory broadband high-frequency activity transients, driven by eye blinks, are suppressed in higher-level but not early visual cortex. Here, we utilized the broad anatomical coverage of iEEG recordings in 12 eye-tracked neurosurgical patients to test whether a similar stabilizing mechanism operates following small saccades. We compared saccades (1.3°−3.7°) initiated during inspection of large individual visual objects with similarly-sized external stimulus displacements. Early visual cortex sites responded with positive transients to both conditions. In contrast, in both dorsal and ventral higher-level sites the response to saccades (but not to external displacements) was suppressed. These findings indicate that early visual cortex is highly unstable compared to higher-level visual regions which apparently constitute the main target of stabilizing extra-retinal oculomotor influences.
Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.
The neuronal transcriptome changes dynamically to adapt to stimuli from the extracellular and intracellular environment. In this study, we adapted for the first time a click chemistry technique to label the newly synthesized RNA in cultured hippocampal neurons and intact larval zebrafish brain. Ethynyl uridine (EU) was incorporated into neuronal RNA in a time- and concentration-dependent manner. Newly synthesized RNA granules observed throughout the dendrites were colocalized with mRNA and rRNA markers. In zebrafish larvae, the application of EU to the swim water resulted in uptake and labeling throughout the brain. Using a GABA receptor antagonist, PTZ (pentylenetetrazol), to elevate neuronal activity, we demonstrate that newly transcribed RNA signal increased in specific regions involved in neurogenesis.
Criticality meets learning : criticality signatures in a self-organizing recurrent neural network
(2017)
Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences.
Ocular gene therapy approaches have been developed for a variety of different diseases. In particular, clinical gene therapy trials for RPE65 mutations, X-linked retinoschisis, and choroideremia have been conducted at different centers in recent years, showing that adeno-associated virus (AAV)-mediated gene therapy is safe, but limitations exist as to the therapeutic benefit and long-term duration of the treatment. The technique of vector delivery to retinal cells relies on subretinal injection of the vector solution, causing a transient retinal detachment. Although retinal detachments are known to cause remodeling of retinal neuronal structures as well as significant cell loss, the possible effects of this short-term therapeutic retinal detachment on retinal structure and circuitry have not yet been studied in detail. In this study, retinal morphology and apoptotic status were examined in healthy rat retinas following AAV-mediated gene transfer via subretinal injection with AAV2/5.CMV.d2GFP or sham injection with fluorescein. Outer plexiform layer (OPL) morphology was assessed by immunohistochemical labeling, laser scanning confocal microscopy, and electron microscopy. The number of synaptic contacts in the OPL was quantified after labeling with structural markers. To assess the apoptotic status, inflammatory and pro-apoptotic markers were tested and TUNEL assay for the detection of apoptotic nuclei was performed. Pre- and postsynaptic structures in the OPL, such as synaptic ribbons or horizontal and bipolar cell processes, did not differ in size or shape in injected versus non-injected areas and control retinas. Absolute numbers of synaptic ribbons were not altered. No signs of relevant gliosis were detected. TUNEL labeling of retinal cells did not vary between injected and non-injected areas, and apoptosis-inducing factor was not delocalized to the nucleus in transduced areas. The neuronal circuits in the OPL of healthy rat retinas undergoing AAV-mediated gene transfer were not altered by the temporary retinal detachment caused by subretinal injection, the presence of viral particles, or the expression of green fluorescent protein as a transgene. This observation likely requires further investigations in the dog model for RPE65 deficiency in order to determine the impact of RPE65 transgene expression on diseased retinas in animals and men.
Retinal OFF bipolar cells show distinct connectivity patterns with photoreceptors in the wild-type mouse retina. Some types are cone-specific while others penetrate further through the outer plexiform layer (OPL) to contact rods in addition to cones. To explore dendritic stratification of OFF bipolar cells in the absence of rods, we made use of the ‘cone-full’ Nrl-/- mouse retina in which all photoreceptor precursor cells commit to a cone fate including those which would have become rods in wild-type retinas. The dendritic distribution of OFF bipolar cell types was investigated by confocal and electron microscopic imaging of immunolabeled tissue sections. The cells’ dendrites formed basal contacts with cone terminals and expressed the corresponding glutamate receptor subunits at those sites, indicating putative synapses. All of the four analyzed cell populations showed distinctive patterns of vertical dendritic invasion through the OPL. This disparate behavior of dendritic extension in an environment containing only cone terminals demonstrates type-dependent specificity for dendritic outgrowth in OFF bipolar cells: rod terminals are not required for inducing dendritic extension into distal areas of the OPL.
Thyroid hormone is a crucial regulator of gene expression in the developing and adult retina. Here we sought to map sites of thyroid hormone signaling at the cellular level using the transgenic FINDT3 reporter mouse model in which neurons express β-galactosidase (β-gal) under the control of a hybrid Gal4-TRα receptor when triiodothyronine (T3) and cofactors of thyroid receptor signaling are present. In the adult retina, nearly all neurons of the ganglion cell layer (GCL, ganglion cells and displaced amacrine cells) showed strong β-gal labeling. In the inner nuclear layer (INL), a minority of glycineric and GABAergic amacrine cells showed β-gal labeling, whereas the majority of amacrine cells were unlabeled. At the level of amacrine types, β-gal labeling was found in a large proportion of the glycinergic AII amacrines, but only in a small proportion of the cholinergic/GABAergic ‘starburst’ amacrines. At postnatal day 10, there also was a high density of strongly β-gal-labeled neurons in the GCL, but only few amacrine cells were labeled in the INL. There was no labeling of bipolar cells, horizontal cells and Müller glia cells at both stages. Most surprisingly, the photoreceptor somata in the outer nuclear layer also showed no β-gal label, although thyroid hormone is known to control cone opsin expression. This is the first record of thyroid hormone signaling in the inner retina of an adult mammal. We hypothesize that T3 levels in photoreceptors are below the detection threshold of the reporter system. The topographical distribution of β-gal-positive cells in the GCL follows the overall neuron distribution in that layer, with more T3-signaling cells in the ventral than the dorsal half-retina.
GABARAP belongs to an evolutionary highly conserved gene family that has a fundamental role in autophagy. There is ample evidence for a crosstalk between autophagy and apoptosis as well as the immune response. However, the molecular details for these interactions are not fully characterized. Here, we report that the ablation of murine GABARAP, a member of the Atg8/LC3 family that is central to autophagosome formation, suppresses the incidence of tumor formation mediated by the carcinogen DMBA and results in an enhancement of the immune response through increased secretion of IL-1β, IL-6, IL-2 and IFN-γ from stimulated macrophages and lymphocytes. In contrast, TGF-β1 was significantly reduced in the serum of these knockout mice. Further, DMBA treatment of these GABARAP knockout mice reduced the cellularity of the spleen and the growth of mammary glands through the induction of apoptosis. Gene expression profiling of mammary glands revealed significantly elevated levels of Xaf1, an apoptotic inducer and tumor-suppressor gene, in knockout mice. Furthermore, DMBA treatment triggered the upregulation of pro-apoptotic (Bid, Apaf1, Bax), cell death (Tnfrsf10b, Ripk1) and cell cycle inhibitor (Cdkn1a, Cdkn2c) genes in the mammary glands. Finally, tumor growth of B16 melanoma cells after subcutaneous inoculation was inhibited in GABARAP-deficient mice. Together, these data provide strong evidence for the involvement of GABARAP in tumorigenesis in vivo by delaying cell death and its associated immune-related response.
Neural oscillations at low- and high-frequency ranges are a fundamental feature of large-scale networks. Recent evidence has indicated that schizophrenia is associated with abnormal amplitude and synchrony of oscillatory activity, in particular, at high (beta/gamma) frequencies. These abnormalities are observed during task-related and spontaneous neuronal activity which may be important for understanding the pathophysiology of the syndrome. In this paper, we shall review the current evidence for impaired beta/gamma-band oscillations and their involvement in cognitive functions and certain symptoms of the disorder. In the first part, we will provide an update on neural oscillations during normal brain functions and discuss underlying mechanisms. This will be followed by a review of studies that have examined high-frequency oscillatory activity in schizophrenia and discuss evidence that relates abnormalities of oscillatory activity to disturbed excitatory/inhibitory (E/I) balance. Finally, we shall identify critical issues for future research in this area.
During CNS development and adult neurogenesis, immature neurons travel from the germinal zones towards their final destination using cellular substrates for their migration. Classically, radial glia and neuronal axons have been shown to act as physical scaffolds to support neuroblast locomotion in processes known as gliophilic and neurophilic migration, respectively (Hatten, 1999; Marin and Rubenstein, 2003; Rakic, 2003). In adulthood, long distance neuronal migration occurs in a glial-independent manner since radial glia cells differentiate into astrocytes after birth. A series of studies highlight a novel mode of neuronal migration that uses blood vessels as scaffolds, the so-called vasophilic migration. This migration mode allows neuroblast navigation in physiological and also pathological conditions, such as neuronal precursor migration after ischemic stroke or cerebral invasion of glioma tumor cells. Here we review the current knowledge about how vessels pave the path for migrating neurons and how trophic factors derived by glio-vascular structures guide neuronal migration both during physiological as well as pathological processes
Purpose: In secondary progressive Multiple Sclerosis (SPMS), global neurodegeneration as a driver of disability gains importance in comparison to focal inflammatory processes. However, clinical MRI does not visualize changes of tissue composition outside MS lesions. This quantitative MRI (qMRI) study investigated cortical and deep gray matter (GM) proton density (PD) values and T1 relaxation times to explore their potential to assess neuronal damage and its relationship to clinical disability in SPMS.
Materials and Methods: 11 SPMS patients underwent quantitative T1 and PD mapping. Parameter values across the cerebral cortex and deep GM structures were compared with 11 healthy controls, and correlation with disability was investigated for regions exhibiting significant group differences.
Results: PD was increased in the whole GM, cerebral cortex, thalamus, putamen and pallidum. PD correlated with disability in the whole GM, cerebral cortex, putamen and pallidum. T1 relaxation time was prolonged and correlated with disability in the whole GM and cerebral cortex.
Conclusion: Our study suggests that the qMRI parameters GM PD (which likely indicates replacement of neural tissue with water) and cortical T1 (which reflects cortical damage including and beyond increased water content) are promising qMRI candidates for the assessment of disease status, and are related to disability in SPMS.
Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.
Purpose: Quantitative T2'-mapping detects regional changes of the relation of oxygenated and deoxygenated hemoglobin (Hb) by using their different magnetic properties in gradient echo imaging and might therefore be a surrogate marker of increased oxygen extraction fraction (OEF) in cerebral hypoperfusion. Since elevations of cerebral blood volume (CBV) with consecutive accumulation of Hb might also increase the fraction of deoxygenated Hb and, through this, decrease the T2’-values in these patients we evaluated the relationship between T2’-values and CBV in patients with unilateral high-grade large-artery stenosis.
Materials and Methods Data from 16 patients (13 male, 3 female; mean age 53 years) with unilateral symptomatic or asymptomatic high-grade internal carotid artery (ICA) or middle cerebral artery (MCA) stenosis/occlusion were analyzed. MRI included perfusion-weighted imaging and high-resolution T2’-mapping. Representative relative (r)CBV-values were analyzed in areas of decreased T2’ with different degrees of perfusion delay and compared to corresponding contralateral areas.
Results: No significant elevations in cerebral rCBV were detected within areas with significantly decreased T2’-values. In contrast, rCBV was significantly decreased (p<0.05) in regions with severe perfusion delay and decreased T2’. Furthermore, no significant correlation between T2’- and rCBV-values was found. Conclusions rCBV is not significantly increased in areas of decreased T2’ and in areas of restricted perfusion in patients with unilateral high-grade stenosis. Therefore, T2’ should only be influenced by changes of oxygen metabolism, regarding our patient collective especially by an increase of the OEF. T2’-mapping is suitable to detect altered oxygen consumption in chronic cerebrovascular disease.
Abstract: Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition.
Author Summary: The capacity to transmit information between connected parts of a neuronal network is fundamental to its function. The organization of network connections (the topology of the network) is therefore expected to play an important role in determining network transmission. Since modular topology characterizes many brain circuits on multiple scales, investigating the role of modularity in activity gating is clearly desirable. By engineering such modular networks in vitro, we were able to perform such an investigation. Under these experimental conditions, we can independently control the degree of modularity, as well as inhibition in the network. We show that a combination of these two properties is highly beneficial from a communication perspective. Namely, it equips connected modules and large modular networks with the capacity to gate and temporally coordinate activity between the different parts of the network.
Background: Prions and amyloid-β (Aβ) oligomers trigger neurodegeneration by hijacking a poorly understood cellular signal mediated by the prion protein (PrP) at the plasma membrane. In early zebrafish embryos, PrP-1-dependent signals control cell-cell adhesion via a tyrosine phosphorylation-dependent mechanism.
Results: Here we report that the Src family kinases (SFKs) Fyn and Yes act downstream of PrP-1 to prevent the endocytosis and degradation of E-cadherin/β-catenin adhesion complexes in vivo. Accordingly, knockdown of PrP-1 or Fyn/Yes cause similar zebrafish gastrulation phenotypes, whereas Fyn/Yes expression rescues the PrP-1 knockdown phenotype. We also show that zebrafish and mouse PrPs positively regulate the activity of Src kinases and that these have an unexpected positive effect on E-cadherin-mediated cell adhesion. Interestingly, while PrP knockdown impairs β-catenin adhesive function, PrP overexpression enhances it, thereby antagonizing its nuclear, wnt-related signaling activity and disturbing embryonic dorsoventral specification. The ability of mouse PrP to influence these events in zebrafish embryos requires its neuroprotective, polybasic N-terminus but not its neurotoxicity-associated central region. Remarkably, human Aβ oligomers up-regulate the PrP-1/SFK/E-cadherin/β-catenin pathway in zebrafish embryonic cells, mimicking a PrP gain-of-function scenario.
Conclusions: Our gain- and loss-of-function experiments in zebrafish suggest that PrP and SFKs enhance the cell surface stability of embryonic adherens junctions via the same complex mechanism through which they over-activate neuroreceptors that trigger synaptic damage. The profound impact of this pathway on early zebrafish development makes these embryos an ideal model to study the cellular and molecular events affected by neurotoxic PrP mutations and ligands in vivo. In particular, our finding that human Aβ oligomers activate the zebrafish PrP/SFK/E-cadherin pathway opens the possibility of using fish embryos to rapidly screen for novel therapeutic targets and compounds against prion- and Alzheimer's-related neurodegeneration. Altogether, our data illustrate PrP-dependent signals relevant to embryonic development, neuronal physiology and neurological disease.
Attractive growth cone turning requires Igf2bp1-dependent local translation of β-actin mRNA in response to external cues in vitro. While in vivo studies have shown that Igf2bp1 is required for cell migration and axon terminal branching, a requirement for Igf2bp1 function during axon outgrowth has not been demonstrated. Using a timelapse assay in the zebrafish retinotectal system, we demonstrate that the β-actin 3’UTR is sufficient to target local translation of the photoconvertible fluorescent protein Kaede in growth cones of pathfinding retinal ganglion cells (RGCs) in vivo. Igf2bp1 knockdown reduced RGC axonal outgrowth and tectal coverage and retinal cell survival. RGC-specific expression of a phosphomimetic Igf2bp1 reduced the density of axonal projections in the optic tract while sparing RGCs, demonstrating for the first time that Igf2bp1 is required during axon outgrowth in vivo. Therefore, regulation of local translation mediated by Igf2bp proteins may be required at all stages of axon development.
Stimulation of a principal whisker yields sparse action potential (AP) spiking in layer 2/3 (L2/3) pyramidal neurons in a cortical column of rat barrel cortex. The low AP rates in pyramidal neurons could be explained by activation of interneurons in L2/3 providing inhibition onto L2/3 pyramidal neurons. L2/3 interneurons classified as local inhibitors based on their axonal projection in the same column were reported to receive strong excitatory input from spiny neurons in L4, which are also the main source of the excitatory input to L2/3 pyramidal neurons. Here, we investigated the remaining synaptic connection in this intracolumnar microcircuit. We found strong and reliable inhibitory synaptic transmission between intracolumnar L2/3 local-inhibitor-to-L2/3 pyramidal neuron pairs [inhibitory postsynaptic potential (IPSP) amplitude -0.88 ± 0.67 mV]. On average, 6.2 ± 2 synaptic contacts were made by L2/3 local inhibitors onto L2/3 pyramidal neurons at 107 ± 64 µm path distance from the pyramidal neuron soma, thus overlapping with the distribution of synaptic contacts from L4 spiny neurons onto L2/3 pyramidal neurons (67 ± 34 µm). Finally, using compartmental simulations, we determined the synaptic conductance per synaptic contact to be 0.77 ± 0.4 nS. We conclude that the synaptic circuit from L4 to L2/3 can provide efficient shunting inhibition that is temporally and spatially aligned with the excitatory input from L4 to L2/3.
DNA methylation reader MECP2 : cell type- and differentiation stage-specific protein distribution
(2014)
Background: Methyl-CpG binding protein 2 (MECP2) is a protein that specifically binds methylated DNA, thus regulating transcription and chromatin organization. Mutations in the gene have been identified as the principal cause of Rett syndrome, a severe neurological disorder. Although the role of MECP2 has been extensively studied in nervous tissues, still very little is known about its function and cell type specific distribution in other tissues.
Results: Using immunostaining on tissue cryosections, we characterized the distribution of MECP2 in 60 cell types of 16 mouse neuronal and non-neuronal tissues. We show that MECP2 is expressed at a very high level in all retinal neurons except rod photoreceptors. The onset of its expression during retina development coincides with massive synapse formation. In contrast to astroglia, retinal microglial cells lack MECP2, similar to microglia in the brain, cerebellum, and spinal cord. MECP2 is also present in almost all non-neural cell types, with the exception of intestinal epithelial cells, erythropoietic cells, and hair matrix keratinocytes. Our study demonstrates the role of MECP2 as a marker of the differentiated state in all studied cells other than oocytes and spermatogenic cells. MECP2-deficient male (Mecp2−/y) mice show no apparent defects in the morphology and development of the retina. The nuclear architecture of retinal neurons is also unaffected as the degree of chromocenter fusion and the distribution of major histone modifications do not differ between Mecp2−/y and Mecp2wt mice. Surprisingly, the absence of MECP2 is not compensated by other methyl-CpG binding proteins. On the contrary, their mRNA levels were downregulated in Mecp2−/y mice.
Conclusions: MECP2 is almost universally expressed in all studied cell types with few exceptions, including microglia. MECP2 deficiency does not change the nuclear architecture and epigenetic landscape of retinal cells despite the missing compensatory expression of other methyl-CpG binding proteins. Furthermore, retinal development and morphology are also preserved in Mecp2-null mice. Our study reveals the significance of MECP2 function in cell differentiation and sets the basis for future investigations in this direction.
Correlative microscopy incorporates the specificity of fluorescent protein labeling into high-resolution electron micrographs. Several approaches exist for correlative microscopy, most of which have used the green fluorescent protein (GFP) as the label for light microscopy. Here we use chemical tagging and synthetic fluorophores instead, in order to achieve protein-specific labeling, and to perform multicolor imaging. We show that synthetic fluorophores preserve their post-embedding fluorescence in the presence of uranyl acetate. Post-embedding fluorescence is of such quality that the specimen can be prepared with identical protocols for scanning electron microscopy (SEM) and transmission electron microscopy (TEM); this is particularly valuable when singular or otherwise difficult samples are examined. We show that synthetic fluorophores give bright, well-resolved signals in super-resolution light microscopy, enabling us to superimpose light microscopic images with a precision of up to 25 nm in the x-y plane on electron micrographs. To exemplify the preservation quality of our new method we visualize the molecular arrangement of cadherins in adherens junctions of mouse epithelial cells.
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these necessary observations, available estimators typically assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble of realizations is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that is suitable for the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method for transfer entropy estimation. We test the performance and robustness of our implementation on data from numerical simulations of stochastic processes. We also demonstrate the applicability of the ensemble method to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscience data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.
The neurophysiological changes associated with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) include an increase in low frequency activity, as measured with electroencephalography or magnetoencephalography (MEG). A relevant property of spectral measures is the alpha peak, which corresponds to the dominant alpha rhythm. Here we studied the spatial distribution of MEG resting state alpha peak frequency and amplitude values in a sample of 27 MCI patients and 24 age-matched healthy controls. Power spectra were reconstructed in source space with linearly constrained minimum variance beamformer. Then, 88 Regions of Interest (ROIs) were defined and an alpha peak per ROI and subject was identified. Statistical analyses were performed at every ROI, accounting for age, sex and educational level. Peak frequency was significantly decreased (p < 0.05) in MCIs in many posterior ROIs. The average peak frequency over all ROIs was 9.68 ± 0.71 Hz for controls and 9.05 ± 0.90 Hz for MCIs and the average normalized amplitude was (2.57 ± 0.59)·10(-2) for controls and (2.70 ± 0.49)·10(-2) for MCIs. Age and gender were also found to play a role in the alpha peak, since its frequency was higher in females than in males in posterior ROIs and correlated negatively with age in frontal ROIs. Furthermore, we examined the dependence of peak parameters with hippocampal volume, which is a commonly used marker of early structural AD-related damage. Peak frequency was positively correlated with hippocampal volume in many posterior ROIs. Overall, these findings indicate a pathological alpha slowing in MCI.
Evidence from anatomical and functional imaging studies have highlighted major modifications of cortical circuits during adolescence. These include reductions of gray matter (GM), increases in the myelination of cortico-cortical connections and changes in the architecture of large-scale cortical networks. It is currently unclear, however, how the ongoing developmental processes impact upon the folding of the cerebral cortex and how changes in gyrification relate to maturation of GM/WM-volume, thickness and surface area. In the current study, we acquired high-resolution (3 Tesla) magnetic resonance imaging (MRI) data from 79 healthy subjects (34 males and 45 females) between the ages of 12 and 23 years and performed whole brain analysis of cortical folding patterns with the gyrification index (GI). In addition to GI-values, we obtained estimates of cortical thickness, surface area, GM and white matter (WM) volume which permitted correlations with changes in gyrification. Our data show pronounced and widespread reductions in GI-values during adolescence in several cortical regions which include precentral, temporal and frontal areas. Decreases in gyrification overlap only partially with changes in the thickness, volume and surface of GM and were characterized overall by a linear developmental trajectory. Our data suggest that the observed reductions in GI-values represent an additional, important modification of the cerebral cortex during late brain maturation which may be related to cognitive development.
Role of N-cadherin cis and trans interfaces in the dynamics of adherens junctions in living cells
(2013)
Cadherins, Ca2+-dependent adhesion molecules, are crucial for cell-cell junctions and remodeling. Cadherins form inter-junctional lattices by the formation of both cis and trans dimers. Here, we directly visualize and quantify the spatiotemporal dynamics of wild-type and dimer mutant N-cadherin interactions using time-lapse imaging of junction assembly, disassembly and a FRET reporter to assess Ca2+-dependent interactions. A trans dimer mutant (W2A) and a cis mutant (V81D/V174D) exhibited an increased Ca2+-sensitivity for the disassembly of trans dimers compared to the WT, while another mutant (R14E) was insensitive to Ca2+-chelation. Time-lapse imaging of junction assembly and disassembly, monitored in 2D and 3D (using cellular spheroids), revealed kinetic differences in the different mutants as well as different behaviors in the 2D and 3D environment. Taken together, these data provide new insights into the role that the cis and trans dimers play in the dynamic interactions of cadherins.