scholarly journals A Gaussian process model of human electrocorticographic data

2017 ◽  
Author(s):  
Lucy L. W. Owen ◽  
Tudor A. Muntianu ◽  
Andrew C. Heusser ◽  
Patrick Daly ◽  
Katherine Scangos ◽  
...  
Keyword(s):  
Neural Activity ◽  
Process Model ◽  
Neural Dynamics ◽  
Low Density ◽  
Spatial Correlations ◽  
Spatiotemporal Resolution ◽  
Ongoing Activity ◽  
Simplifying Assumptions ◽  
Intracranial Recordings ◽  
And Task

AbstractWe present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual’s brain: given recordings from a limited set of locations in that individual’s brain, along with the observed spatial correlations learned from other people’s recordings, how much can be inferred about ongoing activity at other locations throughout that individual’s brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

scholarly journals A Gaussian Process Model of Human Electrocorticographic Data

2020 ◽  
Vol 30 (10) ◽  
pp. 5333-5345 ◽  
Author(s):  
Lucy L W Owen ◽  
Tudor A Muntianu ◽  
Andrew C Heusser ◽  
Patrick M Daly ◽  
Katherine W Scangos ◽  
...  
Keyword(s):  
Neural Activity ◽  
Process Model ◽  
Neural Dynamics ◽  
Low Density ◽  
Spatial Correlations ◽  
Spatiotemporal Resolution ◽  
Ongoing Activity ◽  
Simplifying Assumptions ◽  
Intracranial Recordings ◽  
And Task

Abstract We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual’s brain: given recordings from a limited set of locations in that individual’s brain, along with the observed spatial correlations learned from other people’s recordings, how much can be inferred about ongoing activity at other locations throughout that individual’s brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.

scholarly journals Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

2021 ◽  
Vol 15 ◽  
Author(s):  
Katherine Wilson Scangos ◽  
Ankit N. Khambhati ◽  
Patrick M. Daly ◽  
Lucy W. Owen ◽  
Jeremy R. Manning ◽  
...  
Keyword(s):  
Neural Activity ◽  
Functional Organization ◽  
Activity Patterns ◽  
Treatment Resistance ◽  
Beta Activity ◽  
Subcortical Structures ◽  
Spatiotemporal Resolution ◽  
Neural Recordings ◽  
Biological Substrates ◽  
Intracranial Recordings

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.

scholarly journals Can binocular rivalry reveal neural correlates of consciousness?

2014 ◽  
Vol 369 (1641) ◽  
pp. 20130211 ◽  
Author(s):  
Randolph Blake ◽  
Jan Brascamp ◽  
David J. Heeger
Keyword(s):  
Decision Making ◽  
Binocular Rivalry ◽  
Neural Activity ◽  
Neural Correlates ◽  
Causal Link ◽  
Neural Dynamics ◽  
Perceptual Decision Making ◽  
Neural Correlate ◽  
Neural Correlates Of Consciousness ◽  
Neural Correlate Of Consciousness

This essay critically examines the extent to which binocular rivalry can provide important clues about the neural correlates of conscious visual perception. Our ideas are presented within the framework of four questions about the use of rivalry for this purpose: (i) what constitutes an adequate comparison condition for gauging rivalry's impact on awareness, (ii) how can one distinguish abolished awareness from inattention, (iii) when one obtains unequivocal evidence for a causal link between a fluctuating measure of neural activity and fluctuating perceptual states during rivalry, will it generalize to other stimulus conditions and perceptual phenomena and (iv) does such evidence necessarily indicate that this neural activity constitutes a neural correlate of consciousness? While arriving at sceptical answers to these four questions, the essay nonetheless offers some ideas about how a more nuanced utilization of binocular rivalry may still provide fundamental insights about neural dynamics, and glimpses of at least some of the ingredients comprising neural correlates of consciousness, including those involved in perceptual decision-making.

scholarly journals Awake perception is associated with dedicated neuronal assemblies in cerebral cortex

2021 ◽  
Author(s):  
Anton Filipchuk ◽  
Alain Destexhe ◽  
Brice Bathellier
Keyword(s):  
Cerebral Cortex ◽  
Auditory Cortex ◽  
Neural Activity ◽  
Sensory Perception ◽  
Sensory Cortex ◽  
State Change ◽  
Ongoing Activity ◽  
Sensory Inputs ◽  
Neuronal Assemblies ◽  
Sensory Responses

AbstractNeural activity in sensory cortex combines stimulus responses and ongoing activity, but it remains unclear whether they reflect the same underlying dynamics or separate processes. Here we show that during wakefulness, the neuronal assemblies evoked by sounds in the auditory cortex and thalamus are specific to the stimulus and distinct from the assemblies observed in ongoing activity. In contrast, during anesthesia, evoked assemblies are indistinguishable from ongoing assemblies in cortex, while they remain distinct in the thalamus. A strong remapping of sensory responses accompanies this dynamical state change produced by anesthesia. Together, these results show that the awake cortex engages dedicated neuronal assemblies in response to sensory inputs, which we suggest is a network correlate of sensory perception.One-Sentence SummarySensory responses in the awake cortex engage specific neuronal assemblies that disappear under anesthesia.

scholarly journals Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

2020 ◽  
Author(s):  
KW Scangos ◽  
AN Khambhati ◽  
PM Daly ◽  
LW Owen ◽  
JR Manning ◽  
...  
Keyword(s):  
Neural Activity ◽  
Functional Organization ◽  
Activity Patterns ◽  
Computational Approach ◽  
Beta Activity ◽  
Targeted Intervention ◽  
Left Temporal Lobe ◽  
Spatiotemporal Resolution ◽  
Neural Recordings ◽  
Biological Substrates

AbstractQuantitative biological substrates of depression remain elusive. We carried out this study to determine whether application of a novel computational approach to high spatiotemporal resolution direct neural recordings may unlock the functional organization and coordinated activity patterns of depression networks. We identified two subnetworks conserved across the majority of individuals studied. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings identify distributed circuit activity associated with depression, link neural activity with functional connectivity profiles, and inform strategies for personalized targeted intervention.

scholarly journals Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Biao Yang ◽  
Jinmeng Cao ◽  
Tiantong Zhou ◽  
Li Dong ◽  
Ling Zou ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Neural Activity ◽  
Canonical Correlation ◽  
Cognitive Reappraisal ◽  
Fmri Data ◽  
Spatiotemporal Resolution ◽  
Neutral State ◽  
Negative State ◽  
Mode Decomposition

Background. Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function. Method. An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data. Results. The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. Conclusions. The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.

scholarly journals Dual-color volumetric imaging of neural activity of cortical columns

2018 ◽  
Author(s):  
Shuting Han ◽  
Weijian Yang ◽  
Rafael Yuste
Keyword(s):  
Neural Activity ◽  
High Speed ◽  
Neural Circuits ◽  
Emergent Properties ◽  
Spatiotemporal Resolution ◽  
Population Activity ◽  
Volumetric Imaging ◽  
Two Photon ◽  
Dual Color

To capture the emergent properties of neural circuits, high-speed volumetric imaging of neural activity at cellular resolution is desirable. But while conventional two-photon calcium imaging is a powerful tool to study population activity in vivo, it is restrained to two-dimensional planes. Expanding it to 3D while maintaining high spatiotemporal resolution appears necessary. Here, we developed a two-photon microscope with dual-color laser excitation that can image neural activity in a 3D volume. We imaged the neuronal activity of primary visual cortex from awake mice, spanning from L2 to L5 with 10 planes, at a rate of 10 vol/sec, and demonstrated volumetric imaging of L1 long-range PFC projections and L2/3 somatas. Using this method, we map visually-evoked neuronal ensembles in 3D, finding a lack of columnar structure in orientation responses and revealing functional correlations between cortical layers which differ from trial to trial and are missed in sequential imaging. We also reveal functional interactions between presynaptic L1 axons and postsynaptic L2/3 neurons. Volumetric two-photon imaging appears an ideal method for functional connectomics of neural circuits.

scholarly journals Flygenvectors: The spatial and temporal structure of neural activity across the fly brain

2021 ◽  
Author(s):  
Evan S. Schaffer ◽  
Neeli Mishra ◽  
Matthew R. Whiteway ◽  
Wenze Li ◽  
Michelle B. Vancura ◽  
...  
Keyword(s):  
Neural Activity ◽  
Temporal Structure ◽  
Large Fraction ◽  
Residual Activity ◽  
Cell Types ◽  
Global Dynamics ◽  
Spatiotemporal Resolution ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Distributed Components

What are the spatial and temporal scales of brainwide neuronal activity, and how do activities at different scales interact? We used SCAPE microscopy to image a large fraction of the central brain of adult Drosophila melanogaster with high spatiotemporal resolution while flies engaged in a variety of behaviors, including running, grooming and flailing. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons across the brain correlated (or, in some cases, anticorrelated) with running and flailing over timescales that ranged from seconds to almost a minute. Grooming elicited a much weaker global response. Although these behaviors accounted for a large fraction of neural activity, residual activity not directly correlated with behavior was high dimensional. Many dimensions of the residual activity reflect the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate, conferring a useful balance of specificity and flexibility.

scholarly journals Rat anterior cingulate cortex recalls features of remote reward locations after disfavoured reinforcements

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ali Mashhoori ◽  
Saeedeh Hashemnia ◽  
Bruce L McNaughton ◽  
David R Euston ◽  
Aaron J Gruber
Keyword(s):  
Anterior Cingulate Cortex ◽  
Cognitive Processes ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Task Space ◽  
Spatiotemporal Resolution ◽  
Related Information ◽  
Current State ◽  
Multimodal Information ◽  
And Task

The anterior cingulate cortex (ACC) encodes information supporting mnemonic and cognitive processes. We show here that a rat’s position can be decoded with high spatiotemporal resolution from ACC activity. ACC neurons encoded the current state of the animal and task, except for brief excursions that sometimes occurred at target feeders. During excursions, the decoded position became more similar to a remote target feeder than the rat’s physical position. Excursions recruited activation of neurons encoding choice and reward, and the likelihood of excursions at a feeder was inversely correlated with feeder preference. These data suggest that the excursion phenomenon was related to evaluating real or fictive choice outcomes, particularly after disfavoured reinforcements. We propose that the multiplexing of position with choice-related information forms a mental model isomorphic with the task space, which can be mentally navigated via excursions to recall multimodal information about the utility of remote locations.

scholarly journals The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Luca L. Bologna ◽  
Roberto Smiriglia ◽  
Dario Curreri ◽  
Michele Migliore
Keyword(s):  
Neural Activity ◽  
Ad Hoc ◽  
Neural Model ◽  
Data Driven ◽  
Neural Dynamics ◽  
Model Optimization ◽  
Activity Data ◽  
Online Resource ◽  
Public Repositories ◽  
Scientific Questions

The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed data-driven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking. To address this problem, in the framework of the Human Brain Project and the EBRAINS research infrastructure, we have developed and made available to the scientific community the NeuroFeatureExtract, an open-access online resource for the extraction of electrophysiological features from neural activity data. This tool allows to select electrophysiological traces of interest, fetched from public repositories or from users’ own data, and provides ad hoc functionalities to extract relevant features. The output files are properly formatted for further analysis, including data-driven neural model optimization.

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