Topological Re-Organisation of the Brain Connectivity During Olfactory Adaptation - an EEG Functional Connectome Study

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

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The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa088 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Camille Fauchon ◽

David Meunier ◽

Isabelle Faillenot ◽

Florence B Pomares ◽

Hélène Bastuji ◽

...

Keyword(s):

Information Integration ◽

Functional Organization ◽

Brain Connectivity ◽

Brain Networks ◽

Modular Organization ◽

Noxious Heat ◽

Intracranial Eeg ◽

Brain Connectome ◽

Posterior Parietal ◽

The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

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The Brain Connectivity Basis of Semantic Dementia: A Selective Review

CNS Neuroscience & Therapeutics ◽

10.1111/cns.12449 ◽

2015 ◽

Vol 21 (10) ◽

pp. 784-792 ◽

Cited By ~ 7

Author(s):

Qing Yang ◽

Qi-Hao Guo ◽

Yan-Chao Bi

Keyword(s):

Brain Connectivity ◽

Semantic Dementia ◽

Selective Review ◽

The Brain

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Category representations in the brain are both discretely localized and widely distributed

Journal of Neurophysiology ◽

10.1152/jn.00912.2017 ◽

2018 ◽

Vol 119 (6) ◽

pp. 2256-2264 ◽

Cited By ~ 3

Author(s):

Zarrar Shehzad ◽

Gregory McCarthy

Keyword(s):

Functional Mri ◽

Brain Connectivity ◽

Strong Support ◽

Brain Regions ◽

Distributed Representations ◽

Domain Specific ◽

Category Information ◽

Shared Information ◽

Unique Domain ◽

The Brain

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

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Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges

International Journal of Neural Systems ◽

10.1142/s0129065715500069 ◽

2015 ◽

Vol 25 (05) ◽

pp. 1550006 ◽

Cited By ~ 23

Author(s):

Dimitris Kugiumtzis ◽

Vasilios K. Kimiskidis

Keyword(s):

Transcranial Magnetic Stimulation ◽

Network Structure ◽

Magnetic Stimulation ◽

Brain Connectivity ◽

Effective Connectivity ◽

Epileptic Focus ◽

Sham Stimulation ◽

Focal Seizures ◽

Epileptiform Discharges ◽

The Brain

Background: Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. Objective: To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. Methods: For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. Conclusion: The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.

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Unsupervised Pre-training of the Brain Connectivity Dynamic Using Residual D-Net

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-36718-3_51 ◽

2019 ◽

pp. 608-620

Author(s):

Youngjoo Seo ◽

Manuel Morante ◽

Yannis Kopsinis ◽

Sergios Theodoridis

Keyword(s):

Brain Connectivity ◽

The Brain

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Evolutionary modifications in human brain connectivity associated with schizophrenia

Brain ◽

10.1093/brain/awz330 ◽

2019 ◽

Vol 142 (12) ◽

pp. 3991-4002 ◽

Cited By ~ 13

Author(s):

Martijn P van den Heuvel ◽

Lianne H Scholtens ◽

Siemon C de Lange ◽

Rory Pijnenburg ◽

Wiepke Cahn ◽

...

Keyword(s):

Human Brain ◽

Brain Connectivity ◽

Brain Evolution ◽

Higher Order ◽

Brain Functions ◽

Human Brain Evolution ◽

The Brain

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

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A Robust and Reproducible Connectome Fingerprint of Ketamine is Highly Associated with the Connectomic Signature of Antidepressants

10.1101/2020.04.10.20061085 ◽

2020 ◽

Author(s):

Chadi G. Abdallah ◽

Kyung-Heup Ahn ◽

Lynnette A. Averill ◽

Samaneh Nemati ◽

Christopher L. Averill ◽

...

Keyword(s):

Brain Connectivity ◽

Imaging Study ◽

Seed Selection ◽

Major Depressive ◽

Brain Circuitry ◽

Brain Functional Connectivity ◽

The Brain ◽

Healthy Participants ◽

Placebo Cohort ◽

Model Approach

ABSTRACTOver the past decade, various N-Methyl-D-Aspartate modulators have failed in clinical trials, underscoring the challenges of developing novel rapid-acting antidepressants based solely on the receptor or regional targets of ketamine. Thus, identifying the effect of ketamine on the brain circuitry and networks is becoming increasingly critical. In this longitudinal functional magnetic resonance imaging study of data from 265 participants, we used a validated predictive model approach that allows the full assessment of brain functional connectivity, without the need for seed selection or connectivity summaries. First, we identified a connectome fingerprint (CFP) in healthy participants (Cohort A, n=25) during intravenous infusion of a subanesthetic dose of ketamine, compared to normal saline. We then demonstrated the robustness and reproducibility of the discovered Ketamine CFP in two separate healthy samples (Cohort B, n=22; Cohort C, n=18). Finally, we investigated the Ketamine CFP connectivity at 1-week post treatment in major depressive disorder patients randomized to 8 weeks of sertraline or placebo (Cohort D, n=200). We found a significant, robust, and reproducible Ketamine CFP, consistent with reduced connectivity within the primary cortices and within the executive network, but increased connectivity between the executive network and the rest of the brain. Compared to placebo, the Ketamine CFP connectivity changes at 1-week predicted response to sertraline at 8-weeks. In each of Cohort A-C, ketamine significantly increased connectivity in a previously identified Antidepressant CFP. Investigating the brain connectivity networks, we successfully identified a robust and reproducible ketamine biomarker that is related to the mechanisms of antidepressants.Graphical Abstract

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Determination of Dynamic Brain Connectivity via Spectral Analysis

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.655576 ◽

2021 ◽

Vol 15 ◽

Author(s):

Peter A. Robinson ◽

James A. Henderson ◽

Natasha C. Gabay ◽

Kevin M. Aquino ◽

Tara Babaie-Janvier ◽

...

Keyword(s):

Spectral Analysis ◽

Brain Structure ◽

Brain Connectivity ◽

Spatial Scales ◽

Effective Connectivity ◽

Physical Nature ◽

Building Blocks ◽

Brain Dynamics ◽

Compact Representation ◽

The Brain

Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.

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