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 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 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.

Intracranial Recordings Demonstrate Both Cortical and Medial Temporal Lobe Engagement in Visual Search in Humans

2021 ◽  
pp. 1-29
Author(s):  
S. J. Katarina Slama ◽  
Richard Jimenez ◽  
Sujayam Saha ◽  
David King-Stephens ◽  
Kenneth D. Laxer ◽  
...  
Keyword(s):  
Visual Search ◽  
Frontal Cortex ◽  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Association Cortex ◽  
Subcortical Structures ◽  
Spatiotemporal Resolution ◽  
Activity Increase ◽  
Anatomical Space ◽  
Intracranial Recordings

Abstract Visual search is a fundamental human behavior, providing a gateway to understanding other sensory domains as well as the role of search in higher-order cognition. Search has been proposed to include two component processes: inefficient search (search) and efficient search (pop-out). According to extant research, these two processes map onto two separable neural systems located in the frontal and parietal association cortices. In this study, we use intracranial recordings from 23 participants to delineate the neural correlates of search and pop-out with an unprecedented combination of spatiotemporal resolution and coverage across cortical and subcortical structures. First, we demonstrate a role for the medial temporal lobe in visual search, on par with engagement in frontal and parietal association cortex. Second, we show a gradient of increasing engagement over anatomical space from dorsal to ventral lateral frontal cortex. Third, we confirm previous intracranial work demonstrating nearly complete overlap in neural engagement across cortical regions in search and pop-out. We further demonstrate pop-out selectivity, manifesting as activity increase in pop-out as compared to search, in a distributed set of sites including frontal cortex. This result is at odds with the view that pop-out is implemented in low-level visual cortex or parietal cortex alone. Finally, we affirm a central role for the right lateral frontal cortex in search.

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 The rat medial prefrontal cortex exhibits flexible neural activity states during the performance of an odor span task

2018 ◽  
Author(s):  
E De Falco ◽  
L An ◽  
N Sun ◽  
AJ Roebuck ◽  
Q Greba ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Neural Activity ◽  
Activity Patterns ◽  
Span Task ◽  
Neuron Activity ◽  
Neural Recordings ◽  
Odor Span ◽  
Abrupt Shifts

AbstractMedial prefrontal cortex (mPFC) activity is fundamental for working memory (WM), attention, and behavioral inhibition; however, a comprehensive understanding of the neural computations underlying these processes is still forthcoming. Towards this goal, neural recordings were obtained from the mPFC of awake, behaving rats performing an odor span task of WM capacity. Neural populations were observed to encode distinct task epochs and the transitions between epochs were accompanied by abrupt shifts in neural activity patterns. Putative pyramidal neuron activity increased significantly earlier in the delay for sessions where rats achieved higher spans. Furthermore, increased putative interneuron activity was only observed at the termination of the delay thus indicating that local processing in inhibitory networks was a unique feature to initiate foraging. During foraging, changes in neural activity patterns associated with the approach to a novel odor, but not familiar odors, were robust. Collectively, these data suggest that distinct mPFC activity states underlie the delay, foraging, and reward epochs of the odor span task. Transitions between these states enable successful performance in dynamic environments placing strong demands on the substrates of working memory.

scholarly journals Intracranial recordings demonstrate medial temporal lobe engagement in visual search in humans

2020 ◽  
Author(s):  
S. J. Katarina Slama ◽  
Richard Jimenez ◽  
Sujayam Saha ◽  
David King-Stephens ◽  
Kenneth D. Laxer ◽  
...  
Keyword(s):  
Visual Search ◽  
Frontal Cortex ◽  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Association Cortex ◽  
Subcortical Structures ◽  
Spatiotemporal Resolution ◽  
Activity Increase ◽  
Anatomical Space ◽  
Intracranial Recordings

AbstractVisual search is a fundamental human behavior, which has been proposed to include two component processes: inefficient search (Search) and efficient search (Pop-out). According to extant research, these two processes map onto two separable neural systems located in the frontal and parietal association cortices. In the present study, we use intracranial recordings from 23 participants to delineate the neural correlates of Search and Pop-out with an unprecedented combination of spatiotemporal resolution and coverage across cortical and subcortical structures. First, we demonstrate a role for the medial temporal lobe in visual search, on par with engagement in frontal and parietal association cortex. Second, we show a gradient of increasing engagement over anatomical space from dorsal to ventral lateral frontal cortex. Third, we confirm previous work demonstrating nearly complete overlap in neural engagement across cortical regions in Search and Pop-out. We further demonstrate Pop-out selectivity manifesting as activity increase in Pop-out as compared to Search in a distributed set of sites including frontal cortex. This result is at odds with the view that Pop-out is implemented in low-level visual cortex or parietal cortex alone. Finally, we affirm a central role for the right lateral frontal cortex in Search.

scholarly journals Dynamics of neural microstates in the VTA-striatal-prefrontal loop during novelty exploration in the rat

2020 ◽  
Author(s):  
A. Mishra ◽  
N. Marzban ◽  
M. X Cohen ◽  
B. Englitz
Keyword(s):  
Spatial Patterns ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Activity Patterns ◽  
Field Potential ◽  
Brain Regions ◽  
Brain Areas ◽  
Neural Recordings ◽  
Temporal Correlations

AbstractEEG microstates refer to quasi-stable spatial patterns of scalp potentials, and their dynamics have been linked to cognitive and behavioral states. Neural activity at single and multiunit levels also exhibit spatiotemporal coordination, but this spatial scale is difficult to relate to EEG. Here, we translated EEG microstate analysis to triple-area local field potential (LFP) recordings from up to 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions.We performed simultaneous recordings from the prefrontal cortex (PFC), striatum (STR), and ventral tegmental area (VTA) during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring, quasi-stable spatial activity patterns with an average period of stability of ~60-100 ms. The top four maps accounted for 60-80% of the total variance, compared to ~25% for shuffled data. Cross-correlation of the microstate time-series across brain regions revealed rhythmic patterns of microstate activations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, microstate features, and patterns of temporal correlations across microstates, were modulated by behavioural states such as movement and novel object exploration. These results support the existence of a functional mesoscopic organization across multiple brain areas, and open up the opportunity to investigate their relation to EEG microstates, of particular interest to the human research community.Significance StatementThe coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates, i.e. short-lived, recurring spatial patterns of neural activity. We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioural states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

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