P.332 Large-scale brain networks functional connectivity characteristics of rTMS-therapy in chronic migraine patients

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

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Attentional Fluctuations Influence the Neural Fidelity and Connectivity of Stimulus Representations

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01306 ◽

2018 ◽

Vol 30 (9) ◽

pp. 1209-1228 ◽

Cited By ~ 6

Author(s):

David Rothlein ◽

Joseph DeGutis ◽

Michael Esterman

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Relevant Information ◽

Mental States ◽

Attention Task ◽

Default Mode ◽

Neural Representations ◽

Similarity Structure ◽

Similarity Matrices

Attention is thought to facilitate both the representation of task-relevant features and the communication of these representations across large-scale brain networks. However, attention is not “all or none,” but rather it fluctuates between stable/accurate (in-the-zone) and variable/error-prone (out-of-the-zone) states. Here we ask how different attentional states relate to the neural processing and transmission of task-relevant information. Specifically, during in-the-zone periods: (1) Do neural representations of task stimuli have greater fidelity? (2) Is there increased communication of this stimulus information across large-scale brain networks? Finally, (3) can the influence of performance-contingent reward be differentiated from zone-based fluctuations? To address these questions, we used fMRI and representational similarity analysis during a visual sustained attention task (the gradCPT). Participants ( n = 16) viewed a series of city or mountain scenes, responding to cities (90% of trials) and withholding to mountains (10%). Representational similarity matrices, reflecting the similarity structure of the city exemplars ( n = 10), were computed from visual, attentional, and default mode networks. Representational fidelity (RF) and representational connectivity (RC) were quantified as the interparticipant reliability of representational similarity matrices within (RF) and across (RC) brain networks. We found that being in the zone was characterized by increased RF in visual networks and increasing RC between visual and attentional networks. Conversely, reward only increased the RC between the attentional and default mode networks. These results diverge with analogous analyses using functional connectivity, suggesting that RC and functional connectivity in tandem better characterize how different mental states modulate the flow of information throughout the brain.

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Aberrant interactions of cortical networks in chronic migraine

Neurology ◽

10.1212/wnl.0000000000007577 ◽

2019 ◽

Vol 92 (22) ◽

pp. e2550-e2558 ◽

Cited By ~ 12

Author(s):

Gianluca Coppola ◽

Antonio Di Renzo ◽

Barbara Petolicchio ◽

Emanuele Tinelli ◽

Cherubino Di Lorenzo ◽

...

Keyword(s):

Functional Connectivity ◽

Chronic Migraine ◽

Large Scale ◽

Preventive Therapy ◽

Cortical Networks ◽

Executive Control Network ◽

Acute Medication ◽

Connectivity Patterns ◽

Mri Scans ◽

Group Ica

ObjectiveWe investigated resting-state (RS)-fMRI using independent component analysis (ICA) to determine the functional connectivity (FC) between networks in chronic migraine (CM) patients and their correlation with clinical features.MethodsTwenty CM patients without preventive therapy or acute medication overuse underwent 3T MRI scans and were compared to a group of 20 healthy controls (HC). We used MRI to collect RS data in 3 selected networks, identified using group ICA: the default mode network (DMN), the executive control network (ECN), and the dorsal attention system (DAS).ResultsCompared to HC, CM patients had significantly reduced functional connectivity between the DMN and the ECN. Moreover, in patients, the DAS showed significantly stronger FC with the DMN and weaker FC with the ECN. The higher the severity of headache, the increased the strength of DAS connectivity, and the lower the strength of ECN connectivity.ConclusionThese results provide evidence for large-scale reorganization of functional cortical networks in chronic migraine. They suggest that the severity of headache is associated with opposite connectivity patterns in frontal executive and dorsal attentional networks.

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Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

International Journal of Neural Systems ◽

10.1142/s0129065720500471 ◽

2020 ◽

Vol 30 (09) ◽

pp. 2050047

Author(s):

Lubin Wang ◽

Xianbin Li ◽

Yuyang Zhu ◽

Bei Lin ◽

Qijing Bo ◽

...

Keyword(s):

High Risk ◽

Functional Connectivity ◽

Symptom Severity ◽

Large Scale ◽

Brain Networks ◽

Brain Regions ◽

Support Vector ◽

Healthy Controls ◽

Intrinsic Functional Connectivity ◽

Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

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Novelty seeking and reward dependence-related large-scale brain networks functional connectivity variation during salience expectancy

Human Brain Mapping ◽

10.1002/hbm.23648 ◽

2017 ◽

Vol 38 (8) ◽

pp. 4064-4077 ◽

Cited By ~ 16

Author(s):

Shijia Li ◽

Liliana Ramona Demenescu ◽

Catherine M. Sweeney-Reed ◽

Anna Linda Krause ◽

Coraline D. Metzger ◽

...

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Networks ◽

Novelty Seeking ◽

Reward Dependence

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Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity

Schizophrenia Bulletin ◽

10.1093/schbul/sbx034 ◽

2017 ◽

Vol 44 (1) ◽

pp. 168-181 ◽

Cited By ~ 110

Author(s):

Debo Dong ◽

Yulin Wang ◽

Xuebin Chang ◽

Cheng Luo ◽

Dezhong Yao

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Meta Analysis ◽

Brain Networks ◽

Resting State Functional Connectivity

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Brain networks and cognitive impairment in Parkinson′s disease

10.1101/2020.12.14.422706 ◽

2020 ◽

Author(s):

Rosaria Rucco ◽

Anna Lardone ◽

marianna Liparoti ◽

Emahnuel Troisi Lopez ◽

Rosa De Micco ◽

...

Keyword(s):

Cognitive Impairment ◽

Functional Connectivity ◽

Large Scale ◽

Brain Activity ◽

Brain Networks ◽

Motor Impairment ◽

Brain Network ◽

Topological Features ◽

Delta Band ◽

Wide Range

Aim The aim of the present study is to investigate the relations between both functional connectivity and brain networks with cognitive decline, in patients with Parkinson′s disease (PD). Introduction PD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. Methods Starting from source-reconstructed resting-state magnetoencephalography data, we applied the PLM to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared to healthy subjects (HS). Furthermore, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. Results We found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschl′s gyrus and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared to PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared to HS and PD-NC patients, showed differences in multi frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the MoCA test and both the Diameter in delta band and the Tree Hierarchy in the alpha band. Conclusion Our work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.

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