Discriminating coupling between structural connectivity and functional connectivity in the brain networks of juvenile myoclonic epilepsy

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

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Brain network dynamics correlate with personality traits

10.1101/702266 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aya Kabbara ◽

Veronique Paban ◽

Arnaud Weill ◽

Julien Modolo ◽

Mahmoud Hassan

Keyword(s):

Functional Connectivity ◽

Personality Traits ◽

Resting State ◽

Brain Networks ◽

Neural Substrates ◽

Brain Network ◽

Functional Brain ◽

Human Connectome Project ◽

Functional Brain Networks ◽

The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

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Comparing the temporal relationship of structural and functional connectivity changes in different adult human brain networks: a single-case study

Wellcome Open Research ◽

10.12688/wellcomeopenres.14572.1 ◽

2018 ◽

Vol 3 ◽

pp. 50 ◽

Cited By ~ 1

Author(s):

Takamitsu Watanabe ◽

Geraint Rees

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Large Scale ◽

Single Case ◽

Brain Networks ◽

Structural Connectivity ◽

Time Lags ◽

Single Case Study ◽

Adult Human

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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Intra- and Inter-Modular Connectivity Alterations in the Brain Structural Network of Spinocerebellar Ataxia Type 3

Entropy ◽

10.3390/e21030317 ◽

2019 ◽

Vol 21 (3) ◽

pp. 317 ◽

Cited By ~ 1

Author(s):

Chi-Wen Jao ◽

Bing-Wen Soong ◽

Tzu-Yun Wang ◽

Hsiu-Mei Wu ◽

Chia-Feng Lu ◽

...

Keyword(s):

Spinocerebellar Ataxia ◽

Brain Networks ◽

Structural Connectivity ◽

Three Dimensional ◽

Brain Network ◽

Spinocerebellar Ataxia Type ◽

Compensatory Mechanisms ◽

Spinocerebellar Ataxia Type 3 ◽

Type 3 ◽

The Brain

In addition to cerebellar degeneration symptoms, patients with spinocerebellar ataxia type 3 (SCA3) exhibit extensive involvements with damage in the prefrontal cortex. A network model has been proposed for investigating the structural organization and functional mechanisms of clinical brain disorders. For neural degenerative diseases, a cortical feature-based structural connectivity network can locate cortical atrophied regions and indicate how their connectivity and functions may change. The brain network of SCA3 has been minimally explored. In this study, we investigated this network by enrolling 48 patients with SCA3 and 48 healthy subjects. A novel three-dimensional fractal dimension-based network was proposed to detect differences in network parameters between the groups. Copula correlations and modular analysis were then employed to categorize and construct the structural networks. Patients with SCA3 exhibited significant lateralized atrophy in the left supratentorial regions and significantly lower modularity values. Their cerebellar regions were dissociated from higher-level brain networks, and demonstrated decreased intra-modular connectivity in all lobes, but increased inter-modular connectivity in the frontal and parietal lobes. Our results suggest that the brain networks of patients with SCA3 may be reorganized in these regions, with the introduction of certain compensatory mechanisms in the cerebral cortex to minimize their cognitive impairment syndrome.

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Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

Scientific Reports ◽

10.1038/s41598-019-56834-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Milou Straathof ◽

Michel R. T. Sinke ◽

Theresia J. M. Roelofs ◽

Erwin L. A. Blezer ◽

R. Angela Sarabdjitsingh ◽

...

Keyword(s):

Functional Connectivity ◽

Structure Function ◽

Rat Brain ◽

Resting State ◽

Structural Connectivity ◽

Distinct Structure ◽

Macro Scale ◽

Function Relationship ◽

The Brain ◽

Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional and macro-scale diffusion-based structural connectivity, but no significant correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Zooming in on individual connections, we found strong functional connectivity in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Although characteristics of the applied techniques may affect where structural and functional networks (dis)agree, distinct structure-function relationships across the brain could also have a biological basis.

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A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

10.1101/2021.08.12.456134 ◽

2021 ◽

Author(s):

SUBBA REDDY OOTA ◽

Archi Yadav ◽

Arpita Dash ◽

Surampudi Bapi Raju ◽

Avinash Sharma

Keyword(s):

Deep Learning ◽

Functional Connectivity ◽

Complex Dynamics ◽

Brain Activity ◽

Structural Connectivity ◽

Reaction Diffusion ◽

Complex Nature ◽

Relational Structures ◽

Human Connectome Project ◽

The Brain

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of human brain function.

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Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults

Cerebral Cortex ◽

10.1093/cercor/bhaa179 ◽

2020 ◽

Vol 30 (12) ◽

pp. 6206-6223

Author(s):

Cheryl L Grady ◽

Jenny R Rieck ◽

Daniel Nichol ◽

Douglas D Garrett

Keyword(s):

Older Adults ◽

Functional Connectivity ◽

Brain Networks ◽

Brain Network ◽

Whole Brain ◽

The Face ◽

Brain Correlates ◽

Brain And Behavior ◽

Face Stimuli ◽

The Brain

Abstract Degrading face stimuli reduces face discrimination in both young and older adults, but the brain correlates of this decline in performance are not fully understood. We used functional magnetic resonance imaging to examine the effects of degraded face stimuli on face and nonface brain networks and tested whether these changes would predict the linear declines seen in performance. We found decreased activity in the face network (FN) and a decrease in the similarity of functional connectivity (FC) in the FN across conditions as degradation increased but no effect of age. FC in whole-brain networks also changed with increasing degradation, including increasing FC between the visual network and cognitive control networks. Older adults showed reduced modulation of this whole-brain FC pattern. The strongest predictors of within-participant decline in accuracy were changes in whole-brain network FC and FC similarity of the FN. There was no influence of age on these brain-behavior relations. These results suggest that a systems-level approach beyond the FN is required to understand the brain correlates of performance decline when faces are obscured with noise. In addition, the association between brain and behavior changes was maintained into older age, despite the dampened FC response to face degradation seen in older adults.

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