How small can the epileptogenic region be?

Objective:To present a case that demonstrates that seizures and interictal disturbances can be driven by a small area of functionally abnormal cortex.Methods:Two novel functional MRI network analysis methods were used to supplement conventional seizure and lesion localization methods: (1) regional homogeneity to quantify local connectivity, or synchrony, with a resolution of less than 1 cm3 of cortex; and (2) small-worldness to combine information about whole brain network segregation and integration.Results:After a small corticectomy in the dominant supramarginal gyrus (13 × 7 × 6 mm) limited to the area of abnormal local connectivity, and smaller than the PET and SPECT abnormalities, the patient has been seizure-free for 3 years with no language deficit. Whole brain network characteristics normalized (small-worldness) to that of healthy controls.Conclusions:This case demonstrates that small areas of cortex may be highly epileptogenic, drive intractable epilepsy, and disrupt large-scale networks likely to be involved in core cognitive functions.

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Brain network dynamics are hierarchically organized in time

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1705120114 ◽

2017 ◽

Vol 114 (48) ◽

pp. 12827-12832 ◽

Cited By ~ 192

Author(s):

Diego Vidaurre ◽

Stephen M. Smith ◽

Mark W. Woolrich

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

Brain Network ◽

Specific Measure ◽

Brain Areas ◽

Neuronal Populations ◽

Subject Specific ◽

Large Scale Networks ◽

Network Patterns ◽

The Brain

The brain recruits neuronal populations in a temporally coordinated manner in task and at rest. However, the extent to which large-scale networks exhibit their own organized temporal dynamics is unclear. We use an approach designed to find repeating network patterns in whole-brain resting fMRI data, where networks are defined as graphs of interacting brain areas. We find that the transitions between networks are nonrandom, with certain networks more likely to occur after others. Further, this nonrandom sequencing is itself hierarchically organized, revealing two distinct sets of networks, or metastates, that the brain has a tendency to cycle within. One metastate is associated with sensory and motor regions, and the other involves areas related to higher order cognition. Moreover, we find that the proportion of time that a subject spends in each brain network and metastate is a consistent subject-specific measure, is heritable, and shows a significant relationship with cognitive traits.

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Meditation-induced effects on whole-brain structural and effective connectivity

10.1101/2021.06.10.447903 ◽

2021 ◽

Author(s):

Eleonora De Filippi ◽

Anira Escrichs ◽

Matthieu Gilson ◽

Marti Sanchez-Fibla ◽

Estela Camara ◽

...

Keyword(s):

High Performance ◽

Large Scale ◽

Effective Connectivity ◽

Temporal Structure ◽

Functional Domain ◽

Anatomical Connectivity ◽

Whole Brain ◽

Spatio Temporal ◽

Large Scale Networks ◽

And Function

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Using EC features we reached high performance for the condition-based classification within each group and moderately high accuracies when comparing the two groups in each condition. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

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Functional brain network reconfiguration during learning in a dynamic environment

10.1101/800284 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chang-Hao Kao ◽

Ankit N. Khambhati ◽

Danielle S. Bassett ◽

Matthew R. Nassar ◽

Joseph T. McGuire ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Functional Integration ◽

Dynamic Environment ◽

Brain Network ◽

Uncertain Environments ◽

Whole Brain ◽

Functional Brain Network ◽

Brain Pattern ◽

Brain Functional Connectivity

AbstractWhen learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

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Heterogeneity of EEG resting-state brain networks in absolute pitch

10.1101/2020.05.03.063206 ◽

2020 ◽

Author(s):

Marielle Greber ◽

Carina Klein ◽

Simon Leipold ◽

Silvano Sele ◽

Lutz Jäncke

Keyword(s):

Functional Connectivity ◽

Auditory Cortex ◽

Resting State ◽

Large Scale ◽

Absolute Pitch ◽

Brain Regions ◽

Brain Network ◽

Higher Order ◽

Neural Basis ◽

Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

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Brain network topology and personality traits: a source level magnetoencephalographic study

10.21203/rs.3.rs-947089/v1 ◽

2021 ◽

Author(s):

Emahnuel Troisi Lopez ◽

Valentina Colonnello ◽

Marianna Liparoti ◽

Mauro Castaldi ◽

Paolo Maria Russo ◽

...

Keyword(s):

Individual Differences ◽

Personality Traits ◽

Caudate Nucleus ◽

Large Scale ◽

Brain Network ◽

Neural Communication ◽

Character Inventory ◽

Left Caudate ◽

Source Level ◽

Large Scale Networks

Abstract Personality neuroscience is focusing on the correlation between individual differences and the efficiency of large-scale networks from the perspective of the brain as an interconnected network. A suitable technique to explore this relationship is the magnetoencephalography (MEG), but little are MEG studies aimed at investigating topological properties correlated to personality traits. By using MEG, the present study is aimed at evaluating how individual differences described in Cloninger’s psychobiological model are correlated with specific cerebral structures. Fifty healthy individuals (20 males, 30 females, mean age: 27.4 ± 4.8 years) underwent Temperament and Character Inventory examination and MEG recording during a resting state condition. High harm avoidance scores were associated with a reduced centrality of the left caudate nucleus and this negative correlation was maintained in females when we analyzed gender differences. Our data suggest that the caudate nucleus plays a key role in adaptive behavior and could be a critical node in insular salience network. The clear difference between males and females allows us to suggest that topological organization correlated to personality is highly dependent on gender. Our findings provide new insights to evaluate the mutual influences of topological and functional connectivity in neural communication efficiency and disruption as biomarkers of psychopathological traits.

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Meditation-Induced Effects on Whole-Brain Structural and Effective Connectivity

10.21203/rs.3.rs-637392/v1 ◽

2021 ◽

Author(s):

Eleonora De Filippi ◽

Anira Escrichs ◽

Estela Càmara ◽

César Garrido ◽

Martí Sánchez-Fibla ◽

...

Keyword(s):

Large Scale ◽

Effective Connectivity ◽

Temporal Structure ◽

Functional Domain ◽

Anatomical Connectivity ◽

Whole Brain ◽

Spatio Temporal ◽

Magnetic Resonance Imaging Mri ◽

Large Scale Networks ◽

And Function

Abstract In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in structural and functional connectivities (SC and FC, respectively). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain's structure and function. First, we performed a network-based analysis of anatomical connectivity. Then, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals' spatio-temporal structure, akin to FC with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. The whole-brain SC analysis revealed strengthened anatomical connectivity across large-scale networks for meditators compared to controls. We found that differences in SC were reflected in the functional domain as well. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. Moreover, we showed that the most informative EC links that discriminated between meditators and controls involved the same large-scale networks previously found to have increased anatomical connectivity. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

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Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques

Journal of Stroke ◽

10.5853/jos.2021.02376 ◽

2021 ◽

Vol 23 (3) ◽

pp. 297-311

Author(s):

Jae-Sung Lim ◽

Jae-Joong Lee ◽

Choong-Wan Woo

Keyword(s):

Cognitive Impairment ◽

Large Scale ◽

Imaging Techniques ◽

Brain Network ◽

Clinical Neurology ◽

Post Stroke ◽

Large Scale Networks ◽

High Level ◽

New Treatments ◽

The Brain

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

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Brain network constraints and recurrent neural networks reproduce unique trajectories and state transitions seen over the span of minutes in resting-state fMRI

Network Neuroscience ◽

10.1162/netn_a_00129 ◽

2020 ◽

Vol 4 (2) ◽

pp. 448-466

Author(s):

Amrit Kashyap ◽

Shella Keilholz

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Resting State ◽

Large Scale ◽

Brain Network ◽

Generative Models ◽

Network Activity ◽

Fmri Data ◽

State Transitions ◽

Whole Brain

Large-scale patterns of spontaneous whole-brain activity seen in resting-state functional magnetic resonance imaging (rs-fMRI) are in part believed to arise from neural populations interacting through the structural network (Honey, Kötter, Breakspear, & Sporns, 2007 ). Generative models that simulate this network activity, called brain network models (BNM), are able to reproduce global averaged properties of empirical rs-fMRI activity such as functional connectivity (FC) but perform poorly in reproducing unique trajectories and state transitions that are observed over the span of minutes in whole-brain data (Cabral, Kringelbach, & Deco, 2017 ; Kashyap & Keilholz, 2019 ). The manuscript demonstrates that by using recurrent neural networks, it can fit the BNM in a novel way to the rs-fMRI data and predict large amounts of variance between subsequent measures of rs-fMRI data. Simulated data also contain unique repeating trajectories observed in rs-fMRI, called quasiperiodic patterns (QPP), that span 20 s and complex state transitions observed using k-means analysis on windowed FC matrices (Allen et al., 2012 ; Majeed et al., 2011 ). Our approach is able to estimate the manifold of rs-fMRI dynamics by training on generating subsequent time points, and it can simulate complex resting-state trajectories better than the traditional generative approaches.

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Group and individual level variations between symmetric and asymmetric DLPFC montages for tDCS over large scale brain network nodes

Scientific Reports ◽

10.1038/s41598-020-80279-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ghazaleh Soleimani ◽

Mehrdad Saviz ◽

Marom Bikson ◽

Farzad Towhidkhah ◽

Rayus Kuplicki ◽

...

Keyword(s):

Large Scale ◽

Current Flow ◽

Brain Network ◽

Brain Anatomy ◽

Global Networks ◽

Individual Level ◽

Dorsolateral Prefrontal ◽

Two Factors ◽

Large Scale Networks ◽

Flow Stimulation

AbstractTwo challenges to optimizing transcranial direct current stimulation (tDCS) are selecting between, often similar, electrode montages and accounting for inter-individual differences in response. These two factors are related by how tDCS montage determines current flow through the brain considered across or within individuals. MRI-based computational head models (CHMs) predict how brain anatomy determines electric field (EF) patterns for a given tDCS montage. Because conventional tDCS produces diffuse brain current flow, stimulation outcomes may be understood as modulation of global networks. Therefore, we developed a network-led, rather than region-led, approach. We specifically considered two common “frontal” tDCS montages that nominally target the dorsolateral prefrontal cortex; asymmetric “unilateral” (anode/cathode: F4/Fp1) and symmetric “bilateral” (F4/F3) electrode montages. CHMs of 66 participants were constructed. We showed that cathode location significantly affects EFs in the limbic network. Furthermore, using a finer parcellation of large-scale networks, we found significant differences in some of the main nodes within a network, even if there is no difference at the network level. This study generally demonstrates a methodology for considering the components of large-scale networks in CHMs instead of targeting a single region and specifically provides insight into how symmetric vs asymmetric frontal tDCS may differentially modulate networks across a population.

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Whole-brain estimates of directed connectivity for human connectomics

10.1101/2020.02.20.958124 ◽

2020 ◽

Cited By ~ 2

Author(s):

Stefan Frässle ◽

Zina M. Manjaly ◽

Cao T. Do ◽

Lars Kasper ◽

Klaas P. Pruessmann ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Motor Task ◽

Brain Network ◽

Brain Organization ◽

Whole Brain ◽

Functional Roles ◽

Reciprocal Connections ◽

Theoretical Analyses

ABSTRACTConnectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation.Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics.

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