Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Cortical Areas Interact through a Communication Subspace.

ABSTRACT: Most brain functions involve interactions among multiple, distinct areas or nuclei. For instance, visual processing in primates requires the appropriate relaying of signals across many distinct cortical areas. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Here we investigate how trial-to-trial fluctuations of population responses in primary visual cortex (V1) are related to simultaneously recorded population responses in area V2. Using dimensionality reduction methods, we find that V1-V2 interactions occur through a communication subspace: V2 fluctuations are related to a small subset of V1 population activity patterns, distinct from the largest fluctuations shared among neurons within V1. In contrast, interactions between subpopulations within V1 are less selective. We propose that the communication subspace may be a general, population-level mechanism by which activity can be selectively routed across brain areas.

SUBMITTER: Semedo JD

PROVIDER: S-EPMC6449210 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Cortical Areas Interact through a Communication Subspace.

Semedo João D JD Zandvakili Amin A Machens Christian K CK Yu Byron M BM Kohn Adam A

Neuron 20190212 1

Most brain functions involve interactions among multiple, distinct areas or nuclei. For instance, visual processing in primates requires the appropriate relaying of signals across many distinct cortical areas. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Here we investigate how trial-to-trial fluctuations of population responses in primary visual cortex (V1) are related to simultaneously recorded population responses in area V2. ...[more]

PMID: 30770252

Similar Datasets

Subcortical circuits mediate communication between primary sensory cortical areas in mice.

Project description:Integration of information across the senses is critical for perception and is a common property of neurons in the cerebral cortex, where it is thought to arise primarily from corticocortical connections. Much less is known about the role of subcortical circuits in shaping the multisensory properties of cortical neurons. We show that stimulation of the whiskers causes widespread suppression of sound-evoked activity in mouse primary auditory cortex (A1). This suppression depends on the primary somatosensory cortex (S1), and is implemented through a descending circuit that links S1, via the auditory midbrain, with thalamic neurons that project to A1. Furthermore, a direct pathway from S1 has a facilitatory effect on auditory responses in higher-order thalamic nuclei that project to other brain areas. Crossmodal corticofugal projections to the auditory midbrain and thalamus therefore play a pivotal role in integrating multisensory signals and in enabling communication between different sensory cortical areas.

| S-EPMC8225818 | biostudies-literature

Adaptation to visuomotor rotation through interaction between posterior parietal and motor cortical areas.

Project description:Studying how motor adaptation to visuomotor rotation for one reach direction generalizes to other reach directions can provide insight into how visuomotor maps are represented and learned in the brain. Previous psychophysical studies have concluded that postadaptation generalization is restricted to a narrow range of directions around the training direction. A population-coding model that updates the weights between narrow Gaussian-tuned visual units and motor units on each trial reproduced experimental trial-by-trial learning curves for rotation adaptation and the generalization function measured postadaptation. These results suggest that the neurons involved in rotation adaptation have a relatively narrow directional tuning width ( approximately 23 degrees ). Population coding models with units having broader tuning functions (such as cosine tuning in motor cortex and Gaussian sum in the cerebellum) could not reproduce the narrow single-peaked generalization pattern. Visually selective neurons with narrow Gaussian tuning curves have been identified in posterior parietal cortex, making it a possible site of adaptation to visuomotor rotation. We propose that rotation adaptation proceeds through changes in synaptic weights between neurons in posterior parietal cortex and motor cortex driven by a prediction error computed by the cerebellum.

| S-EPMC2777823 | biostudies-other

Dynamic causal communication channels between neocortical areas.

Project description:Processing of sensory information depends on the interactions between hierarchically connected neocortical regions, but it remains unclear how the activity in one area causally influences the activity dynamics in another and how rapidly such interactions change with time. Here, we show that the communication between the primary visual cortex (V1) and high-order visual area LM is context-dependent and surprisingly dynamic over time. By momentarily silencing one area while recording activity in the other, we find that both areas reliably affected changing subpopulations of target neurons within one hundred milliseconds while mice observed a visual stimulus. The influence of LM feedback on V1 responses became even more dynamic when the visual stimuli predicted a reward, causing fast changes in the geometry of V1 population activity and affecting stimulus coding in a context-dependent manner. Therefore, the functional interactions between cortical areas are not static but unfold through rapidly shifting communication subspaces whose dynamics depend on context when processing sensory information.

| S-EPMC9616801 | biostudies-literature

Predictions interact with missing sensory evidence in semantic processing areas.

Project description:Human brain function draws on predictive mechanisms that exploit higher-level context during lower-level perception. These mechanisms are particularly relevant for situations in which sensory information is compromised or incomplete, as for example in natural speech where speech segments may be omitted due to sluggish articulation. Here, we investigate which brain areas support the processing of incomplete words that were predictable from semantic context, compared with incomplete words that were unpredictable. During functional magnetic resonance imaging (fMRI), participants heard sentences that orthogonally varied in predictability (semantically predictable vs. unpredictable) and completeness (complete vs. incomplete, i.e. missing their final consonant cluster). The effects of predictability and completeness interacted in heteromodal semantic processing areas, including left angular gyrus and left precuneus, where activity did not differ between complete and incomplete words when they were predictable. The same regions showed stronger activity for incomplete than for complete words when they were unpredictable. The interaction pattern suggests that for highly predictable words, the speech signal does not need to be complete for neural processing in semantic processing areas. Hum Brain Mapp 37:704-716, 2016. © 2015 Wiley Periodicals, Inc.

| S-EPMC6867522 | biostudies-literature

Streamlined sensory motor communication through cortical reciprocal connectivity in a visually guided eye movement task.

Project description:Cortical computation is distributed across multiple areas of the cortex by networks of reciprocal connectivity. However, how such connectivity contributes to the communication between the connected areas is not clear. In this study, we examine the communication between sensory and motor cortices. We develop an eye movement task in mice and combine it with optogenetic suppression and two-photon calcium imaging techniques. We identify a small region in the secondary motor cortex (MOs) that controls eye movements and reciprocally connects with a rostrolateral part of the higher visual areas (VRL/A/AL). These two regions encode both motor signals and visual information; however, the information flow between the regions depends on the direction of the connectivity: motor information is conveyed preferentially from the MOs to the VRL/A/AL, and sensory information is transferred primarily in the opposite direction. We propose that reciprocal connectivity streamlines information flow, enhancing the computational capacity of a distributed network.

| S-EPMC5780522 | biostudies-literature

Curvature processing in human visual cortical areas.

Project description:Curvature is one of many visual features shown to be important for visual perception. We recently showed that curvilinear features provide sufficient information for categorizing animate vs. inanimate objects, while rectilinear features do not (Zachariou et al., 2018). Results from our fMRI study in rhesus monkeys (Yue et al., 2014) have shed light on some of the neural substrates underlying curvature processing by revealing a network of visual cortical patches with a curvature response preference. However, it is unknown whether a similar network exists in human visual cortex. Thus, the current study was designed to investigate cortical areas with a preference for curvature in the human brain using fMRI at 7T. Consistent with our monkey fMRI results, we found a network of curvature preferring cortical patches-some of which overlapped well-known face-selective areas. Moreover, principal component analysis (PCA) using all visually-responsive voxels indicated that curvilinear features of visual stimuli were associated with specific retinotopic regions in visual cortex. Regions associated with positive curvilinear PC values encompassed the central visual field representation of early visual areas and the lateral surface of temporal cortex, while those associated with negative curvilinear PC values encompassed the peripheral visual field representation of early visual areas and the medial surface of temporal cortex. Thus, we found that broad areas of curvature preference, which encompassed face-selective areas, were bound by central visual field representations. Our results support the hypothesis that curvilinearity preference interacts with central-peripheral processing biases as primary features underlying the organization of temporal cortex topography in the adult human brain.

| S-EPMC7885662 | biostudies-literature

Basal Forebrain Cholinergic-Auditory Cortical Network: Primary Versus Nonprimary Auditory Cortical Areas.

Project description:Acetylcholine (ACh) release in the cortex is critical for learning, memory, attention, and plasticity. Here, we explore the cholinergic and noncholinergic projections from the basal forebrain (BF) to the auditory cortex using classical retrograde and monosynaptic viral tracers deposited in electrophysiologically identified regions of the auditory cortex. Cholinergic input to both primary (A1) and nonprimary auditory cortical (belt) areas originates in a restricted area in the caudal BF within the globus pallidus (GP) and in the dorsal part of the substantia innominata (SId). On the other hand, we found significant differences in the proportions of cholinergic and noncholinergic projection neurons to primary and nonprimary auditory areas. Inputs to A1 projecting cholinergic neurons were restricted to the GP, caudate-putamen, and the medial part of the medial geniculate body, including the posterior intralaminar thalamic group. In addition to these areas, afferents to belt-projecting cholinergic neurons originated from broader areas, including the ventral secondary auditory cortex, insular cortex, secondary somatosensory cortex, and the central amygdaloid nucleus. These findings support a specific BF projection pattern to auditory cortical areas. Additionally, these findings point to potential functional differences in how ACh release may be regulated in the A1 and auditory belt areas.

| S-EPMC6059257 | biostudies-literature

Robust auto-weighted multi-view subspace clustering with common subspace representation matrix.

Project description:In many computer vision and machine learning applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is a powerful technology to find the underlying subspaces and cluster data points correctly. However, traditional subspace clustering methods can only be applied on data from one source, and how to extend these methods and enable the extensions to combine information from various data sources has become a hot area of research. Previous multi-view subspace methods aim to learn multiple subspace representation matrices simultaneously and these learning task for different views are treated equally. After obtaining representation matrices, they stack up the learned representation matrices as the common underlying subspace structure. However, for many problems, the importance of sources and the importance of features in one source both can be varied, which makes the previous approaches ineffective. In this paper, we propose a novel method called Robust Auto-weighted Multi-view Subspace Clustering (RAMSC). In our method, the weight for both the sources and features can be learned automatically via utilizing a novel trick and introducing a sparse norm. More importantly, the objective of our method is a common representation matrix which directly reflects the common underlying subspace structure. A new efficient algorithm is derived to solve the formulated objective with rigorous theoretical proof on its convergency. Extensive experimental results on five benchmark multi-view datasets well demonstrate that the proposed method consistently outperforms the state-of-the-art methods.

| S-EPMC5441581 | biostudies-literature

Normative cerebral cortical thickness for human visual areas.

Project description:Studies of changes in cerebral neocortical thickness often rely on small control samples for comparison with specific populations with abnormal visual systems. We present a normative dataset for FreeSurfer-derived cortical thickness across 25 human visual areas derived from 960 participants in the Human Connectome Project. Cortical thickness varies systematically across visual areas, in broad agreement with canonical visual system hierarchies in the dorsal and ventral pathways. In addition, cortical thickness estimates show consistent within-subject variability and reliability. Importantly, cortical thickness estimates in visual areas are well described by a normal distribution, making them amenable to direct statistical comparison.

| S-EPMC6892250 | biostudies-literature

Functional specialization of mouse higher visual cortical areas.

Project description:The mouse is emerging as an important model for understanding how sensory neocortex extracts cues to guide behavior, yet little is known about how these cues are processed beyond primary cortical areas. Here, we used two-photon calcium imaging in awake mice to compare visual responses in primary visual cortex (V1) and in two downstream target areas, AL and PM. Neighboring V1 neurons had diverse stimulus preferences spanning five octaves in spatial and temporal frequency. By contrast, AL and PM neurons responded best to distinct ranges of stimulus parameters. Most strikingly, AL neurons preferred fast-moving stimuli while PM neurons preferred slow-moving stimuli. By contrast, neurons in V1, AL, and PM demonstrated similar selectivity for stimulus orientation but not for stimulus direction. Based on these findings, we predict that area AL helps guide behaviors involving fast-moving stimuli (e.g., optic flow), while area PM helps guide behaviors involving slow-moving objects.

| S-EPMC3876958 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data