Characterisation of multimodal network organisation after focal prefrontal lesions in humans

AbstractLesion research in humans and non-human primates classically maps the behavioral effects of focal damage to the directly-injured brain region. However, given the interconnectedness of the brain, it has long been known that such damage can also have distant effects. Modern imaging methods provide new ways to assess those effects. Further, triangulating across these methods in a lesion model may shed light on the biological basis of structural and functional networks in the healthy brain. We characterised network organization assessed with multiple MRI imaging modalities in 13 patients with chronic focal damage affecting either superior or inferior frontal gyrus (SFG, IFG) and 18 demographically-matched healthy Controls. We first defined structural and functional network parameters for the two frontal regions-of-interest in healthy Controls, and then used voxel-based morphology (VBM) and tract-based spatial statistics (TBSS) analyses to investigate structural grey matter (GM) and white matter (WM) differences between patients and Controls. The functional and structural networks defined in healthy participants were then used to constrain interpretation of the whole brain network effects in patients. Finally, we applied dual regression to examine the differences in functional coupling to large-scale resting state networks (RSNs), focusing on the RSNs which most overlapped structurally with the lesion sites. Overall, the results show that lesions are associated with widespread within-network GM loss at sites distal from the lesion, yet leave WM and RSNs relatively preserved. Lesions to either prefrontal region had a very similar impact on structural networks, but SFG lesions had larger impact on RSNs than did IFG lesions. The findings provide evidence for causal contributions of specific prefrontal regions to structural and functional brain networks in humans, relevant to interpreting connectomic findings in studies of healthy people or those with psychiatric illnesses.

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Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

International Journal of Neural Systems ◽

10.1142/s0129065720500513 ◽

2020 ◽

Vol 30 (10) ◽

pp. 2050051

Author(s):

Feng Fang ◽

Thomas Potter ◽

Thinh Nguyen ◽

Yingchun Zhang

Keyword(s):

Emotion Regulation ◽

Large Scale ◽

Brain Networks ◽

Cognitive Reappraisal ◽

Brain Network ◽

Affective Processing ◽

Functional Brain ◽

Affective Stimuli ◽

Functional Brain Network ◽

Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

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Adolescent development of multiscale cortical wiring and functional connectivity in the human connectome

10.1101/2021.08.16.456455 ◽

2021 ◽

Author(s):

Bo-yong Park ◽

Casey Paquola ◽

Richard A.I. Bethlehem ◽

Oualid Benkarim ◽

Bratislav Misic ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Network ◽

Network Development ◽

Network Function ◽

Age Related ◽

Mri Features ◽

Functional Brain Network ◽

Structural Networks ◽

Advanced Model

Adolescence is a time of profound changes in the structural wiring of the brain and maturation of large-scale functional interactions. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14-25 years (n = 199). Core to our work was an advanced model of cortical wiring that incorporates multimodal MRI features of (i) cortico-cortical proximity, (ii) microstructural similarity, and (iii) diffusion tractography. Longitudinal analyses assessing age-related changes in cortical wiring during adolescence identified increases in cortical wiring within attention and default-mode networks, as well as between transmodal and attention, and sensory and limbic networks, indicative of a continued differentiation of cortico-cortical structural networks. Cortical wiring changes were statistically independent from age-related cortical thinning seen in the same subjects. Conversely, resting-state functional MRI analysis in the same subjects indicated an increasing segregation of sensory and transmodal systems during adolescence, with age-related reductions in their functional connectivity alongside with an increase in structural wiring distance. Our findings provide new insights into adolescent brain network development, illustrating how the maturation of structural wiring interacts with the development of macroscale network function.

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Transcutaneous auricular vagus nerve stimulation induces stabilizing modifications in large-scale functional brain networks: towards understanding the effects of taVNS in subjects with epilepsy

Scientific Reports ◽

10.1038/s41598-021-87032-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Randi von Wrede ◽

Thorsten Rings ◽

Sophia Schach ◽

Christoph Helmstaedter ◽

Klaus Lehnertz

Keyword(s):

Vagus Nerve ◽

Nerve Stimulation ◽

Vagus Nerve Stimulation ◽

Large Scale ◽

Brain Networks ◽

Prospective Trial ◽

Brain Network ◽

Short Term ◽

Functional Brain Networks ◽

Network Properties

AbstractTranscutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for subjects with refractory epilepsy. Its exact mechanism of action is not yet fully understood. We developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks and accompanying changes of cognition and behaviour. In this prospective trial, we applied short-term (1 h) taVNS to 14 subjects with epilepsy during a continuous 3-h EEG recording which was embedded in two standardized neuropsychological assessments. From these EEG, we derived evolving epileptic brain networks and tracked important topological, robustness, and stability properties of networks over time. In the majority of investigated subjects, taVNS induced measurable and persisting modifications in network properties that point to a more resilient epileptic brain network without negatively impacting cognition, behaviour, or mood. The stimulation was well tolerated and the usability of the device was rated good. Short-term taVNS has a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale epileptic brain networks. It has no detrimental effects on cognition and behaviour. Translation into clinical practice requires further studies to detail knowledge about the exact mechanisms by which taVNS prevents or inhibits seizures.

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BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

10.1101/106625 ◽

2017 ◽

Author(s):

Mite Mijalkov ◽

Ehsan Kakaei ◽

Joana B. Pereira ◽

Eric Westman ◽

Giovanni Volpe ◽

...

Keyword(s):

Graph Theory ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Large Scale ◽

Brain Connectivity ◽

Resting State Fmri ◽

Brain Network ◽

Local Network ◽

Healthy Controls ◽

The Brain

AbstractThe brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH – BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment.

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Altered structural connectivity networks in dementia with lewy bodies

Brain Imaging and Behavior ◽

10.1007/s11682-020-00444-x ◽

2021 ◽

Author(s):

Nicolas Nicastro ◽

Elijah Mak ◽

Ajenthan Surendranathan ◽

Timothy Rittman ◽

James B. Rowe ◽

...

Keyword(s):

Functional Connectivity ◽

Dementia With Lewy Bodies ◽

Large Scale ◽

Brain Networks ◽

Structural Connectivity ◽

Lewy Bodies ◽

Occipital Lobe ◽

Mri Imaging ◽

Structural Networks ◽

Graph Parameters

AbstractThe impairment of large-scale brain networks has been observed in dementia with Lewy bodies (DLB) using functional connectivity, but the potential for an analogous effect on structural covariance patterns has not been determined. Twenty-four probable DLB subjects (mean age 74.3 ± 6.7 years, 16.7% female) and 23 similarly aged Controls were included. All participants underwent 3T MRI imaging with high-resolution T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence. Graph theoretical analyses were performed using variation in regional cortical thickness to construct a structural association matrix with pairwise Pearson correlations. Global and nodal graph parameters were computed to assess between-group differences and community structure was studied in order to quantify large-scale brain networks in both groups. In comparison to Controls, DLB subjects had decreased global efficiency, clustering, modularity and small-worldness of structural networks (all p < 0.05). Nodal measures showed that DLB subjects also had decreased clustering in bilateral temporal regions and decreased closeness centrality in extensive areas including right middle frontal, left cingulate and bilateral occipital lobe (all false-discovery rate (FDR)-corrected q < 0.05). Whereas four distinct modules could be clearly identified in Controls, DLB showed extensively disorganized modules, including default-mode network and dorsal attentional network. Our results suggest a marked impairment in large-scale brain structural networks in DLB, mirroring functional connectivity networks disruption.

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Decreased integration of EEG source-space networks in disorders of consciousness

10.1101/493395 ◽

2018 ◽

Author(s):

Jennifer Rizkallah ◽

Jitka Annen ◽

Julien Modolo ◽

Olivia Gosseries ◽

Pascal Benquet ◽

...

Keyword(s):

Information Processing ◽

Large Scale ◽

Brain Networks ◽

Brain Network ◽

High Density ◽

Disorders Of Consciousness ◽

Functional Brain ◽

Space Networks ◽

Functional Brain Networks ◽

Processing Network

AbstractIncreasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients’ functional brain networks.Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS- (n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups.Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

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Schizophrenia and abnormal brain network hubs

Dialogues in Clinical Neuroscience ◽

10.31887/dcns.2013.15.3/mrubinov ◽

2013 ◽

Vol 15 (3) ◽

pp. 339-349 ◽

Cited By ~ 27

Keyword(s):

White Matter ◽

Psychiatric Disorder ◽

Strong Evidence ◽

Large Scale ◽

Brain Regions ◽

Brain Network ◽

Functional Brain ◽

Future Studies ◽

Functional Brain Networks ◽

Abnormal Brain

Schizophrenia is a heterogeneous psychiatric disorder of unknown cause or characteristic pathology. Clinical neuroscientists increasingly postulate that schizophrenia is a disorder of brain network organization. In this article we discuss the conceptual framework of this dysconnection hypothesis, describe the predominant methodological paradigm for testing this hypothesis, and review recent evidence for disruption of central/hub brain regions, as a promising example of this hypothesis. We summarize studies of brain hubs in large-scale structural and functional brain networks and find strong evidence for network abnormalities of prefrontal hubs, and moderate evidence for network abnormalities of limbic, temporal, and parietal hubs. Future studies are needed to differentiate network dysfunction from previously observed gray- and white-matter abnormalities of these hubs, and to link endogenous network dysfunction phenotypes with perceptual, behavioral, and cognitive clinical phenotypes of schizophrenia.

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A large-scale brain network mechanism for increased seizure propensity in Alzheimer’s disease

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009252 ◽

2021 ◽

Vol 17 (8) ◽

pp. e1009252

Author(s):

Luke Tait ◽

Marinho A. Lopes ◽

George Stothart ◽

John Baker ◽

Nina Kazanina ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Large Scale ◽

Healthy Aging ◽

Computational Modelling ◽

Brain Network ◽

Healthy Controls ◽

Network Organization ◽

Cortical Tissue

People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

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A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer’s Disease

10.1101/2021.01.19.427236 ◽

2021 ◽

Author(s):

Luke Tait ◽

Marinho A Lopes ◽

George Stothart ◽

John Baker ◽

Nina Kazanina ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Structure ◽

Large Scale ◽

Network Models ◽

Brain Network ◽

Healthy Controls ◽

Cortical Tissue ◽

Whole Brain ◽

Network Mechanism

AbstractPeople with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider increased excitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, both local dynamics and large-scale brain network structure are believed to be crucial for determining seizure likelihood and phenotype. In this study, we combine computational modelling with electrophysiological data to demonstrate a potential large-scale brain network mechanism for increased seizure propensity in people with AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions using an approach previously validated on participants with epilepsy vs controls. As cortical tissue excitability was increased in the simulations, network models of AD simulations were more likely to transition into seizures than simulations from healthy controls. Our results suggest an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. These included cingulate, medial temporal, and orbital regions. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with amyloid/tau pathology and cortical atrophy.Author summaryPeople with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.

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Reappraisal of Interpersonal Criticism in Social Anxiety Disorder: A Brain Network Hierarchy Perspective

Cerebral Cortex ◽

10.1093/cercor/bhy181 ◽

2018 ◽

Vol 29 (7) ◽

pp. 3154-3167 ◽

Cited By ~ 2

Author(s):

Y Jacob ◽

O Shany ◽

P R Goldin ◽

J J Gross ◽

T Hendler

Keyword(s):

Anxiety Disorder ◽

Social Anxiety ◽

Social Anxiety Disorder ◽

Emotion Dysregulation ◽

Large Scale ◽

Negative Emotion ◽

Inferior Frontal Gyrus ◽

Brain Network ◽

Network Organization ◽

Emotion Reactivity

Abstract Emotion regulation is thought to involve communication between and within large-scale brain networks that underlie emotion reactivity and cognitive control. Aberrant network interaction might therefore be a key neural feature of mental disorders that involve emotion dysregulation. Here we tested whether connectivity hierarchies within and between emotion reactivity and cognitive reappraisal networks distinguishes social anxiety disorder (SAD) patients (n = 70) from healthy controls (HC) (n = 25). To investigate network organization, we implemented a graph-theory method called Dependency Network Analysis. Participants underwent fMRI while watching or reappraising video clips involving interpersonal verbal criticism. During reappraisal, the reappraisal network exerted less influence on the reactivity network in SAD participants. Specifically, the influence of the right inferior frontal gyrus on both reappraisal and reactivity networks was significantly reduced in SAD compared with HC, and correlated negatively with negative emotion ratings among SAD participants. Surprisingly, the amygdala exhibited reduced influence on the reappraisal network in SAD relative to HC. Yet, during the watch condition, the left amygdala’s influence on the reactivity network increased with greater social anxiety symptoms among SAD participants. These findings refine our understanding of network organization that contributes to efficient reappraisal or to disturbances in applying this strategy in SAD.

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