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 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 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 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.

scholarly journals Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Kevin A Bolding ◽  
Shivathmihai Nagappan ◽  
Bao-Xia Han ◽  
Fan Wang ◽  
Kevin M Franks
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Activity Patterns ◽  
Piriform Cortex ◽  
Theoretical Studies ◽  
Pattern Completion ◽  
Attractor Networks ◽  
Attractor Dynamics ◽  
Brain States ◽  
Associative Function

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

scholarly journals Multiple states in ongoing neural activity in the rat visual cortex

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256791
Author(s):  
Daichi Konno ◽  
Shinji Nishimoto ◽  
Takafumi Suzuki ◽  
Yuji Ikegaya ◽  
Nobuyoshi Matsumoto
Keyword(s):  
Visual Cortex ◽  
Neural Activity ◽  
Activity Patterns ◽  
Electrode Array ◽  
Nonlinear Dimensionality Reduction ◽  
Neural Responses ◽  
Spontaneous Neural Activity ◽  
Custom Made ◽  
Visual Cortices ◽  
Freely Behaving

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.

scholarly journals Exploring the role of expectations and stimulus relevance on stimulus-specific neural representations and conscious report

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Erik L Meijs ◽  
Pim Mostert ◽  
Heleen A Slagter ◽  
Floris P de Lange ◽  
Simon van Gaal
Keyword(s):  
Attentional Blink ◽  
Neural Activity ◽  
Subjective Experience ◽  
Activity Patterns ◽  
Neural Mechanisms ◽  
Top Down ◽  
Neural Representations ◽  
Dependent Signal ◽  
Task Irrelevant

Abstract Subjective experience can be influenced by top-down factors, such as expectations and stimulus relevance. Recently, it has been shown that expectations can enhance the likelihood that a stimulus is consciously reported, but the neural mechanisms supporting this enhancement are still unclear. We manipulated stimulus expectations within the attentional blink (AB) paradigm using letters and combined visual psychophysics with magnetoencephalographic (MEG) recordings to investigate whether prior expectations may enhance conscious access by sharpening stimulus-specific neural representations. We further explored how stimulus-specific neural activity patterns are affected by the factors expectation, stimulus relevance and conscious report. First, we show that valid expectations about the identity of an upcoming stimulus increase the likelihood that it is consciously reported. Second, using a series of multivariate decoding analyses, we show that the identity of letters presented in and out of the AB can be reliably decoded from MEG data. Third, we show that early sensory stimulus-specific neural representations are similar for reported and missed target letters in the AB task (active report required) and an oddball task in which the letter was clearly presented but its identity was task-irrelevant. However, later sustained and stable stimulus-specific representations were uniquely observed when target letters were consciously reported (decision-dependent signal). Fourth, we show that global pre-stimulus neural activity biased perceptual decisions for a ‘seen’ response. Fifth and last, no evidence was obtained for the sharpening of sensory representations by top-down expectations. We discuss these findings in light of emerging models of perception and conscious report highlighting the role of expectations and stimulus relevance.

scholarly journals Prestimulus feedback connectivity biases the content of visual experiences

2019 ◽  
Vol 116 (32) ◽  
pp. 16056-16061 ◽  
Author(s):  
Elie Rassi ◽  
Andreas Wutz ◽  
Nadia Müller-Voggel ◽  
Nathan Weisz
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Low Frequency ◽  
Brain Regions ◽  
Stimulus Onset ◽  
Neural Excitability ◽  
Gamma Frequency ◽  
Face Area ◽  
Oscillatory Power ◽  
The Impact

Ongoing fluctuations in neural excitability and in networkwide activity patterns before stimulus onset have been proposed to underlie variability in near-threshold stimulus detection paradigms—that is, whether or not an object is perceived. Here, we investigated the impact of prestimulus neural fluctuations on the content of perception—that is, whether one or another object is perceived. We recorded neural activity with magnetoencephalography (MEG) before and while participants briefly viewed an ambiguous image, the Rubin face/vase illusion, and required them to report their perceived interpretation in each trial. Using multivariate pattern analysis, we showed robust decoding of the perceptual report during the poststimulus period. Applying source localization to the classifier weights suggested early recruitment of primary visual cortex (V1) and ∼160-ms recruitment of the category-sensitive fusiform face area (FFA). These poststimulus effects were accompanied by stronger oscillatory power in the gamma frequency band for face vs. vase reports. In prestimulus intervals, we found no differences in oscillatory power between face vs. vase reports in V1 or in FFA, indicating similar levels of neural excitability. Despite this, we found stronger connectivity between V1 and FFA before face reports for low-frequency oscillations. Specifically, the strength of prestimulus feedback connectivity (i.e., Granger causality) from FFA to V1 predicted not only the category of the upcoming percept but also the strength of poststimulus neural activity associated with the percept. Our work shows that prestimulus network states can help shape future processing in category-sensitive brain regions and in this way bias the content of visual experiences.

Sign in / Sign up

Export Citation Format

Share Document