Unique mapping of structural and functional connectivity on cognition

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.

Download Full-text

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

10.1101/2021.09.01.458609 ◽

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.

Download Full-text

New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

10.1101/489997 ◽

2018 ◽

Author(s):

Paolo Finotteli ◽

Caroline Garcia Forlim ◽

Paolo Dulio ◽

Leonie Klock ◽

Alessia Pini ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Connectivity ◽

Brain Networks ◽

Structural Connectivity ◽

Network Models ◽

Brain Regions ◽

Cortical Network ◽

Future Research ◽

Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

Download Full-text

Discordant attributes of structural and functional connectivity in a two-layer multiplex network

10.1101/273136 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sol Lim ◽

Filippo Radicchi ◽

Martijn P van den Heuvel ◽

Olaf Sporns

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Structural Connectivity ◽

Brain Regions ◽

Resting State Networks ◽

Structural Failure ◽

Resonance Imaging ◽

Multiplex Network ◽

Human Connectome Project ◽

Magnetic Resonance Imaging Mri

AbstractSeveral studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine whether node strength assortativity within and between the SC and FC layer may confer increased robustness against structural failure. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may explain laterality of some neurological dysfunctions and recovery.

Download Full-text

Heritability and interindividual variability of regional structure-function coupling

Nature Communications ◽

10.1038/s41467-021-25184-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zijin Gu ◽

Keith Wakefield Jamison ◽

Mert Rory Sabuncu ◽

Amy Kuceyeski

Keyword(s):

Functional Connectivity ◽

Structure Function ◽

Regional Differences ◽

Regional Scale ◽

Brain Regions ◽

Diffusion Weighted Mri ◽

Regional Structure ◽

Human Connectome Project ◽

Structural Connections ◽

The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

Download Full-text

Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

10.1101/2021.05.11.443529 ◽

2021 ◽

Author(s):

David Pascucci ◽

Maria Rubega ◽

Joan Rue-Queralt ◽

Sebastien Tourbier ◽

Patric Hagmann ◽

...

Keyword(s):

Functional Connectivity ◽

Diffusion Tensor ◽

Meta Analysis ◽

Structural Connectivity ◽

Brain Regions ◽

Noise Perturbation ◽

Eeg Data ◽

Functional Brain Networks ◽

Anatomical Priors ◽

And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

Download Full-text

Changes in functional connectivity associated with facial expression processing over the working adult lifespan

10.31234/osf.io/yrkbw ◽

2021 ◽

Author(s):

Thomas Murray ◽

Justin O'Brien ◽

Veena Kumari

Keyword(s):

Functional Connectivity ◽

Facial Expressions ◽

Brain Connectivity ◽

Negative Emotions ◽

Brain Regions ◽

Emotional Expressions ◽

Expression Recognition ◽

Frontal Pole ◽

Adult Lifespan ◽

Age Related

The recognition of negative emotions from facial expressions is shown to decline across the adult lifespan, with some evidence that this decline begins around middle age. While some studies have suggested ageing may be associated with changes in neural response to emotional expressions, it is not known whether ageing is associated with changes in the network connectivity associated with processing emotional expressions. In this study, we examined the effect of participant age on whole-brain connectivity to various brain regions that have been associated with connectivity during emotion processing: the left and right amygdalae, medial prefrontal cortex (mPFC), and right posterior superior temporal sulcus (rpSTS). The study involved healthy participants aged 20-65 who viewed facial expressions displaying anger, fear, happiness, and neutral expressions during functional magnetic resonance imaging (fMRI). We found effects of age on connectivity between the left amygdala and voxels in the occipital pole and cerebellum, between the right amygdala and voxels in the frontal pole, and between the rpSTS and voxels in the orbitofrontal cortex, but no effect of age on connectivity with the mPFC. Furthermore, ageing was more greatly associated with a decline in connectivity to the left amygdala and rpSTS for negative expressions in comparison to happy and neutral expressions, consistent with the literature suggesting a specific age-related decline in the recognition of negative emotions. These results add to the literature surrounding ageing and expression recognition by suggesting that changes in underlying functional connectivity might contribute to changes in recognition of negative facial expressions across the adult lifespan.

Download Full-text

Spatial Patterns for Discriminative Estimation

10.1101/746891 ◽

2019 ◽

Author(s):

Alberto Llera ◽

Roselyne Chauvin ◽

Peter Mulders ◽

Jilly Naaijen ◽

Maarten Mennes ◽

...

Keyword(s):

Spatial Patterns ◽

Brain Connectivity ◽

Covariance Structure ◽

Brain Regions ◽

Linear Filters ◽

Cognitive Changes ◽

Experimental Conditions ◽

Cognitive States ◽

Human Connectome Project ◽

Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

Download Full-text

Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

10.1101/2020.02.04.934554 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alba Xifra-Porxas ◽

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Brain Activity ◽

Large Data ◽

Head Motion ◽

Time Varying ◽

Resting State Networks ◽

Human Connectome Project ◽

Noninvasive Biomarker ◽

Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

Download Full-text

Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

10.31234/osf.io/xp79b ◽

2021 ◽

Author(s):

Andrew Lynn ◽

Eric D. Wilkey ◽

Gavin Price

Keyword(s):

Functional Connectivity ◽

Predictive Modeling ◽

Brain Connectivity ◽

Network Connectivity ◽

Brain Regions ◽

Math Skills ◽

Whole Brain ◽

Building Models ◽

State Dependent ◽

Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

Download Full-text

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

Operative Neurosurgery ◽

10.1093/ons/opy262 ◽

2018 ◽

Vol 15 (suppl_1) ◽

pp. S350-S371 ◽

Cited By ~ 3

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.

Download Full-text