scholarly journals Revisiting correlation-based functional connectivity and its relationship with structural connectivity

2020 ◽  
Vol 4 (4) ◽  
pp. 1235-1251
Author(s):  
Raphael Liégeois ◽  
Augusto Santos ◽  
Vincenzo Matta ◽  
Dimitri Van De Ville ◽  
Ali H. Sayed
Keyword(s):  
Functional Connectivity ◽  
Functional Data ◽  
Structural Connectivity ◽  
Functional Time Series ◽  
Functional Dynamics ◽  
Human Connectome Project ◽  
Brain Structure And Function ◽  
Partial Correlations ◽  
Using Data ◽  
And Function

Patterns of brain structural connectivity (SC) and functional connectivity (FC) are known to be related. In SC-FC comparisons, FC has classically been evaluated from correlations between functional time series, and more recently from partial correlations or their unnormalized version encoded in the precision matrix. The latter FC metrics yield more meaningful comparisons to SC because they capture ‘direct’ statistical dependencies, that is, discarding the effects of mediators, but their use has been limited because of estimation issues. With the rise of high-quality and large neuroimaging datasets, we revisit the relevance of different FC metrics in the context of SC-FC comparisons. Using data from 100 unrelated Human Connectome Project subjects, we first explore the amount of functional data required to reliably estimate various FC metrics. We find that precision-based FC yields a better match to SC than correlation-based FC when using 5 minutes of functional data or more. Finally, using a linear model linking SC and FC, we show that the SC-FC match can be used to further interrogate various aspects of brain structure and function such as the timescales of functional dynamics in different resting-state networks or the intensity of anatomical self-connections.

scholarly journals Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

scholarly journals Survivors of SARS-CoV-2 Infection Show Neuropsychiatric Sequelae Measured by Surveys, Neurocognitive Testing, and Magnetic Resonance Imaging: Preliminary Results

2021 ◽  
Author(s):  
Laura M. Hack ◽  
Jacob Brawer ◽  
Megan Chesnut ◽  
Xue Zhang ◽  
Max Wintermark ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Functional Connectivity ◽  
Brain Structure ◽  
Functional Neuroimaging ◽  
Structure And Function ◽  
Self Report ◽  
Neurocognitive Testing ◽  
Preliminary Results ◽  
Brain Structure And Function ◽  
And Function

AbstractA significant number of individuals experience physical, cognitive, and mental health symptoms in the months after acute infection with SARS-CoV-2, the virus that causes COVID-19. This study assessed depressive and anxious symptoms, cognition, and brain structure and function in participants with symptomatic COVID-19 confirmed by PCR testing (n=100) approximately three months following infection, leveraging self-report questionnaires, objective neurocognitive testing, and structural and functional neuroimaging data. Preliminary results demonstrated that over 1/5 of our cohort endorsed clinically significant depressive and/or anxious symptoms, and >40% of participants had cognitive impairment on objective testing across multiple domains, consistent with ‘brain-fog’. While depression and one domain of quality of life (physical functioning) were significantly different between hospitalized and non-hospitalized participants, anxiety, cognitive impairment, and most domains of functioning were not, suggesting that the severity of SARS-CoV-2 infection does not necessarily relate to the severity of neuropsychiatric outcomes and impaired functioning in the months after infection. Furthermore, we found that the majority of participants in a subset of our cohort who completed structural and functional neuroimaging (n=15) had smaller olfactory bulbs and sulci in conjunction with anosmia. We also showed that this subset of participants had dysfunction in attention network functional connectivity and ventromedial prefrontal cortex seed-based functional connectivity. These functional imaging dysfunctions have been observed previously in depression and correlated with levels of inflammation. Our results support and extend previous findings in the literature concerning the neuropsychiatric sequelae associated with long COVID. Ongoing data collection and analyses within this cohort will allow for a more comprehensive understanding of the longitudinal relationships between neuropsychiatric symptoms, neurocognitive performance, brain structure and function, and inflammatory and immune profiles.

scholarly journals A Connectomic Atlas of the Human Cerebrum—Chapter 8: The Posterior Cingulate Cortex, Medial Parietal Lobe, and Parieto-Occipital Sulcus

2018 ◽  
Vol 15 (suppl_1) ◽  
pp. S350-S371 ◽  
Author(s):  
Cordell M Baker ◽  
Joshua D Burks ◽  
Robert G Briggs ◽  
Andrew K Conner ◽  
Chad A Glenn ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Functional Significance ◽  
Parietal Lobe ◽  
Structural Connectivity ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Posterior Cingulate ◽  
Human Connectome Project

ABSTRACT In this supplement, we build on work previously published under the Human Connectome Project. Specifically, we seek to show a comprehensive anatomic atlas of the human cerebrum demonstrating all 180 distinct regions comprising the cerebral cortex. The location, functional connectivity, and structural connectivity of these regions are outlined, and where possible a discussion is included of the functional significance of these areas. In part 8, we specifically address regions relevant to the posterior cingulate cortex, medial parietal lobe, and the parieto-occipital sulcus.

scholarly journals A Connectomic Atlas of the Human Cerebrum—Chapter 7: The Lateral Parietal Lobe

2018 ◽  
Vol 15 (suppl_1) ◽  
pp. S295-S349 ◽  
Author(s):  
Cordell M Baker ◽  
Joshua D Burks ◽  
Robert G Briggs ◽  
Andrew K Conner ◽  
Chad A Glenn ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Functional Connectivity ◽  
Functional Significance ◽  
Parietal Lobe ◽  
Structural Connectivity ◽  
Human Connectome Project

ABSTRACT In this supplement, we build on work previously published under the Human Connectome Project. Specifically, we seek to show a comprehensive anatomic atlas of the human cerebrum demonstrating all 180 distinct regions comprising the cerebral cortex. The location, functional connectivity, and structural connectivity of these regions are outlined, and where possible a discussion is included of the functional significance of these areas. In part 7, we specifically address regions relevant to the lateral parietal lobe.

scholarly journals Age-dependent differences in functional brain networks are atypical in Tourette syndrome

2020 ◽  
Author(s):  
Ashley N. Nielsen ◽  
Caterina Gratton ◽  
Soyoung Kim ◽  
Jessica A. Church ◽  
Kevin J. Black ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Tourette Syndrome ◽  
Time Course ◽  
Neurodevelopmental Disorder ◽  
Developmental Differences ◽  
Functional Networks ◽  
Functional Neuroanatomy ◽  
Brain Structure And Function ◽  
Functional Brain Networks ◽  
And Function

AbstractTourette syndrome (TS) is a neurodevelopmental disorder characterized by motor and vocal tics. TS is complex, with symptoms that involve sensory, motor, and top-down control processes and that fluctuate over the course of development. While many have studied atypical brain structure and function associated with TS, the neural substrates supporting the complex range and time-course of symptoms is largely understudied. Here, we used functional connectivity MRI to examine functional networks across the whole-brain in children and adults with TS. To investigate the functional neuroanatomy of childhood and adulthood TS, we separately considered the sets of connections within each functional network and those between each pair of functional networks. We tested whether developmental stage (child, adult), diagnosis (TS, control), or an interaction between these factors was present among these connections. We found that developmental changes for most functional networks in TS were unaltered (i.e., developmental differences in TS were similar to those in typically developing children and adults). However, there were several within-network and cross-network connections that exhibited either “divergent” or “attenuated” development in TS. Connections involving the somatomotor, cingulo-opercular, auditory, dorsal attention, and default mode networks diverged from typical development in TS, demonstrating enhanced functional connectivity in adulthood TS. In contrast, connections involving the basal ganglia, thalamus, cerebellum, auditory, visual, reward, and ventral attention networks showed attenuated developmental differences in TS. These results suggest that adulthood TS is characterized by increased functional connectivity among functional networks that support cognitive control and attention, which may be implicated in suppressing, producing, and attending to tics. In contrast, subcortical systems that have been implicated in the initiation and production of tics may be immature in adulthood TS. Jointly, our results reveal how several cortical and subcortical functional networks interact and differ across development in TS.

scholarly journals Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity

2021 ◽  
Author(s):  
Josh Neudorf ◽  
Shaylyn Kress ◽  
Ron Borowsky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Functional Connectivity ◽  
Brain Research ◽  
Structural Connectivity ◽  
Model Performance ◽  
Future Research ◽  
Individual Level ◽  
Human Connectome Project ◽  
Deep Learning Model

AbstractAlthough functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals Two classes of functional connectivity in dynamical processes in networks

2021 ◽  
Vol 18 (183) ◽  
Author(s):  
Venetia Voutsa ◽  
Demian Battaglia ◽  
Louise J. Bracken ◽  
Andrea Brovelli ◽  
Julia Costescu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Structural Connectivity ◽  
Ecological Systems ◽  
Dynamical Processes ◽  
Structure And Dynamics ◽  
Theoretical View ◽  
And Function ◽  
The Relationship ◽  
Simultaneous Activity

The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines—from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity—one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes—excitations, regular and chaotic oscillators—and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

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

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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