Using structural connectivity to augment community structure in EEG functional connectivity

Mapping Intimacies ◽

10.1101/831743 ◽

2019 ◽

Cited By ~ 2

Author(s):

Katharina Glomb ◽

Emeline Mullier ◽

Margherita Carboni ◽

Maria Rubega ◽

Giannarita Iannotti ◽

...

Keyword(s):

Community Structure ◽

White Matter ◽

Functional Connectivity ◽

Structural Connectivity ◽

Brain Regions ◽

Source Analysis ◽

Eeg Signal ◽

Volume Conduction ◽

Structural Connectome ◽

Eeg Functional Connectivity

AbstractRecently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity (SC) in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between non-connected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG- and volume-conduction-free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known - and confirmed in this study - that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting “filtered” signal correlation matrices to those obtained from fMRI and find that such “graph filtering” improves the agreement between EEG and fMRI functional connectivity structure. This suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

Using structural connectivity to augment community structure in EEG functional connectivity

Network Neuroscience ◽

10.1162/netn_a_00147 ◽

2020 ◽

Vol 4 (3) ◽

pp. 761-787 ◽

Cited By ~ 3

Author(s):

Katharina Glomb ◽

Emeline Mullier ◽

Margherita Carboni ◽

Maria Rubega ◽

Giannarita Iannotti ◽

...

Keyword(s):

Community Structure ◽

White Matter ◽

Functional Connectivity ◽

Large Scale ◽

Structural Connectivity ◽

Brain Regions ◽

Source Analysis ◽

Volume Conduction ◽

Functional Relationships ◽

The Impact

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

10.1101/2021.12.14.21267815 ◽

2021 ◽

Author(s):

Ajay Peddada ◽

Kevin Holly ◽

Tejaswi D Sudhakar ◽

Christina Ledbetter ◽

Christopher E. Talbot ◽

...

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Temporal Lobe ◽

Resting State ◽

Classification Accuracy ◽

Structural Connectivity ◽

Brain Regions ◽

Group Differences ◽

Functional Connections ◽

White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.721206 ◽

2021 ◽

Vol 15 ◽

Author(s):

Parinaz Babaeeghazvini ◽

Laura M. Rueda-Delgado ◽

Jolien Gooijers ◽

Stephan P. Swinnen ◽

Andreas Daffertshofer

Keyword(s):

Magnetic Resonance Imaging ◽

White Matter ◽

Magnetic Resonance ◽

Functional Connectivity ◽

Spectral Power ◽

Structural Connectivity ◽

Brain Regions ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Mri ◽

And Diffusion

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

Communications Biology ◽

10.1038/s42003-021-01700-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Arian Ashourvan ◽

Preya Shah ◽

Adam Pines ◽

Shi Gu ◽

Christopher W. Lynn ◽

...

Keyword(s):

White Matter ◽

Maximum Entropy ◽

Large Scale ◽

Structural Connectivity ◽

Quantitative Relationship ◽

Brain Regions ◽

Maximum Entropy Model ◽

Entropy Model ◽

Activation Patterns ◽

Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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.

Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

Network Neuroscience ◽

10.1162/netn_a_00150 ◽

2020 ◽

Vol 4 (3) ◽

pp. 871-890

Author(s):

Arseny A. Sokolov ◽

Peter Zeidman ◽

Adeel Razi ◽

Michael Erb ◽

Philippe Ryvlin ◽

...

Keyword(s):

White Matter ◽

Dynamic Range ◽

Diffusion Processes ◽

Brain Connectivity ◽

Effective Connectivity ◽

Structural Connectivity ◽

Laplacian Matrix ◽

Graph Laplacian ◽

Brain Regions ◽

High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

EEG functional connectivity, axon delays and white matter disease

Clinical Neurophysiology ◽

10.1016/j.clinph.2014.04.003 ◽

2015 ◽

Vol 126 (1) ◽

pp. 110-120 ◽

Cited By ~ 42

Author(s):

Paul L. Nunez ◽

Ramesh Srinivasan ◽

R. Douglas Fields

Keyword(s):

White Matter ◽

Functional Connectivity ◽

White Matter Disease ◽

Eeg Functional Connectivity

Comparative Primate Connectomics

Brain Behavior and Evolution ◽

10.1159/000488886 ◽

2018 ◽

Vol 91 (3) ◽

pp. 170-179 ◽

Cited By ~ 12

Author(s):

James K. Rilling ◽

Martijn P. van den Heuvel

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Tool Use ◽

False Negative ◽

Structural Connectivity ◽

Cell Counting ◽

Primate Species ◽

Future Research ◽

Ecological Niches ◽

Human Specific

A connectome is a comprehensive map of neural connections of a species nervous system. While recent work has begun comparing connectomes across a wide breadth of species, we present here a more detailed and specific comparison of connectomes across the primate order. Long-range connections are thought to improve communication efficiency and thus brain function but are costly in terms of energy and space utilization. Methods for measuring connectivity in the brain include measuring white matter volume, histological cell counting, anatomical tract tracing, diffusion-weighted imaging and tractography, and functional connectivity in MRI. Comparisons of global white matter connectivity suggest that larger primate brains are less well connected than smaller primate brains, but that humans have more connections than expected for our cortical neuron number, which may be concentrated in the prefrontal cortex. Although there is significant overlap in structural connectivity between humans and nonhuman primates, human-specific connections are found in cortical areas involved with language, imitation, and tool use. Similar to structural connectivity, there is also widespread overlap between humans and macaques in resting state functional connectivity. However, there are again a number of human-specific connections in cortical regions involved in language, tool use, and empathy. Comparative connectomics also offers the opportunity to detect specializations of connectivity in other primate species besides humans. Future research should capitalize on the ability of diffusion tractography to measure connectivity in postmortem brains that could expand the representation of species beyond humans, chimpanzees, and rhesus macaques, and facilitate identification of connectivity-based adaptations to different social and ecological niches. This work will require careful attention to establishing cortical homologies across species and to improving tractography methods to limit detection of false-positive and false-negative connections. Finally, it will be important to attempt to establish the functional significance of variation in connectivity profiles by examining how these covary with behavior and cognition both across and within species.

Altered Resting-State Functional and White Matter Tract Connectivity in Stroke Patients With Dysphagia

Neurorehabilitation and Neural Repair ◽

10.1177/1545968313508227 ◽

2013 ◽

Vol 28 (3) ◽

pp. 260-272 ◽

Cited By ~ 12

Author(s):

Shasha Li ◽

Zhenxing Ma ◽

Shipeng Tu ◽

Muke Zhou ◽

Sihan Chen ◽

...

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Fractional Anisotropy ◽

Resting State ◽

Structural Connectivity ◽

White Matter Tract ◽

Stroke Patients ◽

Hemispheric Stroke ◽

Swallowing Dysfunction ◽

The Mean

Background. Swallowing dysfunction is intractable after acute stroke. Our understanding of the alterations in neural networks of patients with neurogenic dysphagia is still developing. Objective. The aim was to investigate cerebral cortical functional connectivity and subcortical structural connectivity related to swallowing in unilateral hemispheric stroke patients with dysphagia. Methods. We combined a resting-state functional connectivity with a white matter tract connectivity approach, recording 12 hemispheric stroke patients with dysphagia, 12 hemispheric stroke patients without dysphagia, and 12 healthy controls. Comparisons of the patterns in swallowing-related functional connectivity maps between patient groups and control subjects included ( a) seed-based functional connectivity maps calculated from the primary motor cortex (M1) and the supplementary motor area (SMA) to the entire brain, ( b) a swallowing-related functional connectivity network calculated among 20 specific regions of interest (ROIs), and ( c) structural connectivity described by the mean fractional anisotropy of fibers bound through the SMA and M1. Results. Stroke patients with dysphagia exhibited dysfunctional connectivity mainly in the sensorimotor-insula-putamen circuits based on seed-based analysis of the left and right M1 and SMA and decreased connectivity in the bilateral swallowing-related ROIs functional connectivity network. Additionally, white matter tract connectivity analysis revealed that the mean fractional anisotropy of the white matter tract was significantly reduced, especially in the left-to-right SMA and in the corticospinal tract. Conclusions. Our results indicate that dysphagia secondary to stroke is associated with disruptive functional and structural integrity in the large-scale brain networks involved in motor control, thus providing new insights into the neural remodeling associated with this disorder.

Unique mapping of structural and functional connectivity on cognition

10.1101/296913 ◽

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.