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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. Here, we hypothesized 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 found that attention enhanced 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. Population analysis revealed that neuronal responses converged to a low dimensional subspace for natural but not for synthetic images. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the dominant low dimensional subspace, suggesting an alignment between the attentional and natural stimulus variance. The alignment was pronounced for late evoked responses but not for early transient responses of V1 neurons, supporting the notion that top-down feedback was required. We argue that attention and perception share top-down pathways, which mediate hierarchical interactions optimized for natural vision.
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.
Owing to their morphological complexity and dense network connections, neurons modify their proteomes locally, using mRNAs and ribosomes present in the neuropil (tissue enriched for dendrites and axons). Although ribosome biogenesis largely takes place in the nucleus and perinuclear region, neuronal ribosomal protein (RP) mRNAs have been frequently detected remotely, in dendrites and axons. Here, using imaging and ribosome profiling, we directly detected the RP mRNAs and their translation in the neuropil. Combining brief metabolic labeling with mass spectrometry, we found that a group of RPs quickly associated with translating ribosomes in the cytoplasm and that this incorporation is independent of canonical ribosome biogenesis. Moreover, the incorporation probability of some RPs was regulated by location (neurites vs. cell bodies) and changes in the cellular environment (in response to oxidative stress). Our results suggest new mechanisms for the local activation, repair and/or specialization of the translational machinery within neuronal processes, potentially allowing remote neuronal synapses a rapid solution to the relatively slow and energy-demanding requirement of nuclear ribosome biogenesis.
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.
Anticipating future events is a key computational task for neuronal networks. Experimental evidence suggests that reliable temporal sequences in neural activity play a functional role in the association and anticipation of events in time. However, how neurons can differentiate and anticipate multiple spike sequences remains largely unknown. We implement a learning rule based on predictive processing, where neurons exclusively fire for the initial, unpredictable inputs in a spiking sequence, leading to an efficient representation with reduced post-synaptic firing. Combining this mechanism with inhibitory feedback leads to sparse firing in the network, enabling neurons to selectively anticipate different sequences in the input. We demonstrate that intermediate levels of inhibition are optimal to decorrelate neuronal activity and to enable the prediction of future inputs. Notably, each sequence is independently encoded in the sparse, anticipatory firing of the network. Overall, our results demonstrate that the interplay of self-supervised predictive learning rules and inhibitory feedback enables fast and efficient classification of different input sequences.
We explore the potential of optically-pumped magnetometers (OPMs) to infer the laminar origins of neural activity non-invasively. OPM sensors can be positioned closer to the scalp than conventional cryogenic MEG sensors, opening an avenue to higher spatial resolution when combined with high-precision forward modelling. By simulating the forward model projection of single dipole sources onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts at moderate to high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and number of measurement axes. Surprisingly, moving sensors closer to the scalp is less advantageous than anticipated - and even detrimental at high SNRs. Biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis pose further challenges. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.
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.
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.
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.
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.