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In a dynamic environment, the already limited information that human working memory can maintain needs to be constantly updated to optimally guide behaviour. Indeed, previous studies showed that working memory representations are continuously being transformed during delay periods leading up to a response. This goes hand-in-hand with the removal of task-irrelevant items. However, does such removal also include veridical, original stimuli, as they were prior to transformation? Here we aimed to assess the neural representation of task-relevant transformed representations, compared to the no-longer-relevant veridical representations they originated from. We applied multivariate pattern analysis to electroencephalographic data during maintenance of orientation gratings with and without mental rotation. During maintenance, we perturbed the representational network by means of a visual impulse stimulus, and were thus able to successfully decode veridical as well as imaginary, transformed orientation gratings from impulse-driven activity. On the one hand, the impulse response reflected only task-relevant (cued), but not task-irrelevant (uncued) items, suggesting that the latter were quickly discarded from working memory. By contrast, even though the original cued orientation gratings were also no longer task-relevant after mental rotation, these items continued to be represented next to the rotated ones, in different representational formats. This seemingly inefficient use of scarce working memory capacity was associated with reduced probe response times and may thus serve to increase precision and flexibility in guiding behaviour in dynamic environments.
Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis
(2022)
Quantitative MRI (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (R1), effective transverse relaxation rate (R2∗), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (R2∗ at 3T, R1 at 7T), endothelial cells (R1 and MTsat at 3T), microglia (R1 and MTsat at 3T, R1 at 7T), and oligodendrocytes (R1 at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types.
Difficulty producing intelligible speech is a common and debilitating symptom of Parkinson’s disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. We examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N=59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N=65), we show that articulation impairments in patients with PD are robustly predicted from aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.
Some pitfalls of measuring representational similarity using Representational Similarity Analysis
(2022)
A core challenge in cognitive and brain sciences is to assess whether different biological systems represent the world in a similar manner. Representational Similarity Analysis (RSA) is an innovative approach that addresses this problem by looking for a second-order isomorphisim in neural activation patterns. This innovation makes it easy to compare latent representations across individuals, species and computational models, and accounts for its popularity across disciplines ranging from artificial intelligence to computational neuroscience. Despite these successes, using RSA has led to difficult-to-reconcile and contradictory findings, particularly when comparing primate visual representations with deep neural networks (DNNs): even though DNNs have been shown to learn and behave in vastly different ways to humans, comparisons based on RSA have shown striking similarities in some studies. Here, we demonstrate some pitfalls of using RSA and explain how contradictory findings can arise due to false inferences about representational similarity based on RSA-scores. In a series of studies that capture increasingly plausible training and testing scenarios, we compare neural representations in computational models, primate cortex and human cortex. These studies reveal two problematic phenomena that are ubiquitous in current research: a “mimic effect”, where confounds in stimuli can lead to high RSA-scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on a number of influential findings, such as comparisons made between human visual representations and those of primates and DNNs, we provide recommendations to avoid these pitfalls and sketch a way forward to a more solid science of representation in cognitive systems.
The pitfalls of measuring representational similarity using representational similarity analysis
(2022)
A core challenge in cognitive and brain sciences is to assess whether different biological systems represent the world in a similar manner. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines ranging from artificial intelligence to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here, we demonstrate the pitfalls of using RSA and explain how contradictory findings arise due to false inferences about representational similarity based on RSA-scores. In a series of studies that capture increasingly plausible training and testing scenarios, we compare neural representations in computational models, primate cortex and human cortex. These studies reveal two problematic phenomena that are ubiquitous in current research: a “mimic” effect, where confounds in stimuli can lead to high RSA-scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on a number of influential findings and the inferences drawn by current practitioners in a wide range of disciplines, we provide recommendations to avoid these pitfalls and sketch a way forward to a more solid science of representation in cognitive systems.
The pitfalls of measuring representational similarity using representational similarity analysis
(2022)
A core challenge in neuroscience is to assess whether diverse systems represent the world similarly. Representational Similarity Analysis (RSA) is an innovative approach to address this problem and has become increasingly popular across disciplines from machine learning to computational neuroscience. Despite these successes, RSA regularly uncovers difficult-to-reconcile and contradictory findings. Here we demonstrate the pitfalls of using RSA to infer representational similarity and explain how contradictory findings arise and support false inferences when left unchecked. By comparing neural representations in primate, human and computational models, we reveal two problematic phenomena that are ubiquitous in current research: a “mimic” effect, where confounds in stimuli can lead to high RSA scores between provably dissimilar systems, and a “modulation effect”, where RSA-scores become dependent on stimuli used for testing. Since our results bear on existing findings and inferences, we provide recommendations to avoid these pitfalls and sketch a way forward.
Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might lead to selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modeled this selective entrainment with a Dynamic Causal Model for Cross-Spectral Densities and found that it can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.
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 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.
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.
The ability to extract regularities from the environment is arguably an adaptive characteristic of intelligent systems. In the context of speech, statistical learning is thought to be an important mechanism for language acquisition. By considering individual differences in speech auditory-motor synchronization, an independent component analysis of fMRI data revealed that the neural substrates of statistical word form learning are not fully shared across individuals. While a network of auditory and superior pre/motor regions is universally activated in the process of learning, a fronto-parietal network is instead additionally and selectively engaged by some individuals, boosting their performance. Furthermore, interfering with the use of this network via articulatory suppression (producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on language-related statistical learning and reconciles previous contrasting findings, while highlighting the need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, relative duration and relative distance in time of two sequentially-organized events —standard S, with constant duration, and comparison C, varying trial-by-trial— are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer ISI helps counteracting such serial distortion effect only the constant S is in first position, but not if the unpredictable C is in first position. These results suggest the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. We simulated our behavioral results with a Bayesian model and replicated the finding that participants disproportionately expand first-position dynamic (unpredictable) short events. Our results clarify the mechanics generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.
Research points to neurofunctional differences underlying fluent speech production in stutterers and non-stutterers. There has been considerably less work focusing on the processes that underlie stuttered speech, primarily due to the difficulty of reliably eliciting stuttering in the unnatural contexts associated with neuroimaging experiments. We used magnetoencephalography (MEG) to test the hypothesis that stuttering events result from global motor inhibition–a “freeze” response typically characterized by increased beta power in nodes of the action-stopping network. We leveraged a novel clinical interview to develop participant-specific stimuli in order to elicit a comparable amount of stuttered and fluent trials. Twenty-nine adult stutterers participated. The paradigm included a cue prior to a go signal, which allowed us to isolate processes associated with stuttered and fluent trials prior to speech initiation. During this pre-speech time window, stuttered trials were associated with greater beta power in the right pre-supplementary motor area, a key node in the action-stopping network, compared to fluent trials. Beta power in the right pre-supplementary area was related to a clinical measure of stuttering severity. We also found that anticipated words identified independently by participants were stuttered more often than those generated by the researchers, which were based on the participants’ reported anticipated sounds. This suggests that global motor inhibition results from stuttering anticipation. This study represents the largest comparison of stuttered and fluent speech to date. The findings provide a foundation for clinical trials that test the efficacy of neuromodulation on stuttering. Moreover, our study demonstrates the feasibility of using our approach for eliciting stuttering during MEG and functional magnetic resonance imaging experiments so that the neurobiological bases of stuttered speech can be further elucidated.
When speech is too fast, the tracking of the acoustic signal along the auditory pathway deteriorates, leading to suboptimal speech segmentation and decoding of speech information. Thus, speech comprehension is limited by the temporal constraints of the auditory system. Here we ask whether individual differences in auditory-motor coupling strength in part shape these temporal constraints. In two behavioral experiments, we characterize individual differences in the comprehension of naturalistic speech as function of the individual synchronization between the auditory and motor systems and the preferred frequencies of the systems. Obviously, speech comprehension declined at higher speech rates. Importantly, however, both higher auditory-motor synchronization and higher spontaneous speech motor production rates were predictive of better speech-comprehension performance. Furthermore, performance increased with higher working memory capacity (Digit Span) and higher linguistic, model-based sentence predictability – particularly so at higher speech rates and for individuals with high auditory-motor synchronization. These findings support the notion of an individual preferred auditory– motor regime that allows for optimal speech processing. The data provide evidence for a model that assigns a central role to motor-system-dependent individual flexibility in continuous speech comprehension.
Speech imagery (the ability to generate internally quasi-perceptual experiences of speech) is a fundamental ability linked to cognitive functions such as inner speech, phonological working memory, and predictive processing. Speech imagery is also considered an ideal tool to test theories of overt speech. The study of speech imagery is challenging, primarily because of the absence of overt behavioral output as well as the difficulty in temporally aligning imagery events across trials and individuals. We used magnetoencephalography (MEG) paired with temporal-generalization-based neural decoding and a simple behavioral protocol to determine the processing stages underlying speech imagery. We monitored participants’ lip and jaw micromovements during mental imagery of syllable production using electromyography. Decoding participants’ imagined syllables revealed a sequence of task-elicited representations. Importantly, participants’ micromovements did not discriminate between syllables. The decoded sequence of neuronal patterns maps well onto the predictions of current computational models of overt speech motor control and provides evidence for hypothesized internal and external feedback loops for speech planning and production, respectively. Additionally, the results expose the compressed nature of representations during planning which contrasts with the natural rate at which internal productions unfold. We conjecture that the same sequence underlies the motor-based generation of sensory predictions that modulate speech perception as well as the hypothesized articulatory loop of phonological working memory. The results underscore the potential of speech imagery, based on new experimental approaches and analytical methods, and further pave the way for successful non-invasive brain-computer interfaces.
In meditation practices that involve focused attention to a specific object, novice practitioners often experience moments of distraction (i.e., mind wandering). Previous studies have investigated the neural correlates of mind wandering during meditation practice through Electroencephalography (EEG) using linear metrics (e.g., oscillatory power). However, their results are not fully consistent. Since the brain is known to be a chaotic/nonlinear system, it is possible that linear metrics cannot fully capture complex dynamics present in the EEG signal. In this study, we assess whether nonlinear EEG signatures can be used to characterize mind wandering during breath focus meditation in novice practitioners. For that purpose, we adopted an experience sampling paradigm in which 25 participants were iteratively interrupted during meditation practice to report whether they were focusing on the breath or thinking about something else. We compared the complexity of EEG signals during mind wandering and breath focus states using three different algorithms: Higuchi’s fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Our results showed that EEG complexity was generally reduced during mind wandering relative to breath focus states. We conclude that EEG complexity metrics are appropriate to disentangle mind wandering from breath focus states in novice meditation practitioners, and therefore, they could be used in future EEG neurofeedback protocols to facilitate meditation practice.
Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t–distributed stochastic neighbour embedding (t–SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of ∼30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab.
Neuronal hyperexcitability is a feature of Alzheimer’s disease (AD). Three main mechanisms have been proposed to explain it: i), dendritic degeneration leading to increased input resistance, ii), ion channel changes leading to enhanced intrinsic excitability, and iii), synaptic changes leading to excitation-inhibition (E/I) imbalance. However, the relative contribution of these mechanisms is not fully understood. Therefore, we performed biophysically realistic multi-compartmental modelling of excitability in reconstructed CA1 pyramidal neurons of wild-type and APP/PS1 mice, a well-established animal model of AD. We show that, for synaptic activation, the excitability promoting effects of dendritic degeneration are cancelled out by excitability decreasing effects of synaptic loss. We find an interesting balance of excitability regulation with enhanced degeneration in the basal dendrites of APP/PS1 cells potentially leading to increased excitation by the apical but decreased excitation by the basal Schaffer collateral pathway. Furthermore, our simulations reveal that three additional pathomechanistic scenarios can account for the experimentally observed increase in firing and bursting of CA1 pyramidal neurons in APP/PS1 mice. Scenario 1: increased excitatory burst input; scenario 2: enhanced E/I ratio and scenario 3: alteration of intrinsic ion channels (IAHP down-regulated; INap, INa and ICaT up-regulated) in addition to enhanced E/I ratio. Our work supports the hypothesis that pathological network and ion channel changes are major contributors to neuronal hyperexcitability in AD. Overall, our results are in line with the concept of multi-causality and degeneracy according to which multiple different disruptions are separately sufficient but no single disruption is necessary for neuronal hyperexcitability.
The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve target excitability.
Spatial attention increases both inter-areal synchronization and spike rates across the visual hierarchy. To investigate whether these attentional changes reflect distinct or common mechanisms, we performed simultaneous laminar recordings of identified cell classes in macaque V1 and V4. Enhanced V4 spike rates were expressed by both excitatory neurons and fast-spiking interneurons, and were most prominent and arose earliest in time in superficial layers, consistent with a feedback modulation. By contrast, V1-V4 gamma-synchronization reflected feedforward communication and surprisingly engaged only fast-spiking interneurons in the V4 input layer. In mouse visual cortex, we found a similar motif for optogenetically identified inhibitory-interneuron classes. Population decoding analyses further indicate that feedback-related increases in spikes rates encoded attention more reliably than feedforward-related increases in synchronization. These findings reveal distinct, cell-type-specific feedforward and feedback pathways for the attentional modulation of inter-areal synchronization and spike rates, respectively.