scholarly journals Structural connectivity analysis using Finsler geometry

2018 ◽  
Author(s):  
Tom Dela Haije ◽  
Peter Savadjiev ◽  
Andrea Fuster ◽  
Robert T. Schultz ◽  
Ragini Verma ◽  
...  
Keyword(s):  
Geodesic Distance ◽  
Finsler Geometry ◽  
Structural Connectivity ◽  
Population Data ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Finsler Metric ◽  
Human Connectome Project ◽  
Using Data ◽  
Riemannian Framework

AbstractIn this work we demonstrate how Finsler geometry—and specifically the related geodesic tractography— can be levied to analyze structural connections between different brain regions. We present new theoretical developments which support the definition of a novel Finsler metric and associated con-nectivity measures, based on closely related works on the Riemannian framework for diffusion MRI. Using data from the Human Connectome Project, as well as population data from an autism spectrum disorder study, we demonstrate that this new Finsler metric, together with the new connectivity measures, results in connectivity maps that are much closer to known tract anatomy compared to previous geodesic connectivity methods. Our implementation can be used to compute geodesic distance and connectivity maps for segmented areas, and is publicly available.

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.

145 Tractography-defined Subregions of Human Nucleus Accumbens Predict Acute Anxiolytic Response to Selective Stimulation

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 234-235
Author(s):  
Sam Cartmell ◽  
Qiyuan Tian ◽  
Nolan Williams ◽  
Kai Miller ◽  
Grant Yang ◽  
...  
Keyword(s):  
Nucleus Accumbens ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Reward Processing ◽  
Clinical Effects ◽  
Obsessive Compulsive ◽  
Electrical Currents ◽  
Compulsive Disorder ◽  
Human Connectome Project ◽  
Monopolar Stimulation

Abstract INTRODUCTION The nucleus accumbens (NAc) serves as a key node in reward processing underlying motivated behavior, and its dysfunction is implicated in a host of psychiatric disorders that are amenable to deep brain stimulation (DBS). On the basis of histochemical and structural connectivity data most thoroughly conducted in animals, the NAc is divided into two primary subregions, core (dorsolateral) and shell (ventromedial), which have dissociable afferent and efferent connections and functions. To characterize NAc subregions, the current study used high-resolution diffusion tractography to delineate core and shell and assessed the immediate clinical implications. METHODS Multimodal MRI data from 245 healthy, unrelated subjects was obtained from the Human Connectome Project database. Freesurfer-generated brain regions were used to perform probabilistic tractography using every NAc voxel as seed and every other Freesurfer region as target. NAc voxels with similar connectivity fingerprints were grouped using k-means clustering. This procedure was also performed retrospectively on two other datasets of lesser quality, including that of one patient with obsessive-compulsive disorder who underwent bilateral DBS lead placement targeting the NAc. The final position of the DBS leads relative to tractography-defined subregions was determined, and the effect of monopolar stimulation on self-reported anxiety utilizing subregion-specific contacts was assessed utilizing a Likert scale. RESULTS >Tractography-based segmentation of the NAc produced ventromedial and dorsolateral subregions across subjects and datasets, consistent with prior histochemical evidence from humans. At electrical currents as low as 1.6 mA, monopolar stimulation of dorsolateral but not ventromedial subregions produced an acute reduction in self-reported anxiety. CONCLUSION NAc subregions that resemble histologically defined core and shell with dissociable acute clinical effects can be produced with tractography-based segmentation. These results have implications for DBS targeting of the NAc, and may help to explain variances in clinical outcome among patients receiving NAc DBS to date.

scholarly journals The within-subject application of diffusion tensor MRI and CLARITY reveals brain structural changes in Nrxn2 deletion mice

2018 ◽  
Author(s):  
Eleftheria Pervolaraki ◽  
Adam L. Tyson ◽  
Francesca Pibiri ◽  
Steven L. Poulter ◽  
Amy C. Reichelt ◽  
...  
Keyword(s):  
Anterior Cingulate Cortex ◽  
Orbitofrontal Cortex ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Anterior Cingulate ◽  
Diffusion Tensor Mri ◽  
Socially Relevant

AbstractBackgroundOf the many genetic mutations known to increase the risk of autism spectrum disorder, a large proportion cluster upon synaptic proteins. One such family of presynaptic proteins are the neurexins (NRXN), and recent genetic and mouse evidence has suggested a causative role for NRXN2 in generating altered social behaviours. Autism has been conceptualised as a disorder of atypical connectivity, yet how single-gene mutations affect such connectivity remains under-explored. To attempt to address this, we have developed a quantitative analysis of microstructure and structural connectivity leveraging diffusion tensor MRI (DTI) with high-resolution 3D imaging in optically cleared (CLARITY) brain tissue in the same mouse, applied here to the Nrxn2α knockout (KO) model.MethodsFixed brains of Nrxn2α KO mice underwent DTI using 9.4T MRI, and diffusion properties of socially-relevant brain regions were quantified. The same tissue was then subjected to CLARITY to immunolabel axons and cell bodies, which were also quantified.ResultsDTI revealed decreases in fractional anisotropy and increases in apparent diffusion coefficient in the amygdala (including the basolateral nuclei), the anterior cingulate cortex, the orbitofrontal cortex and the hippocampus. Radial diffusivity of the anterior cingulate cortex and orbitofrontal cortex was significantly increased in Nrxn2α KO mice, as were tracts between the amygdala and the orbitofrontal cortex. Using CLARITY, we find significantly altered axonal orientation in the amygdala, orbitofrontal cortex and the anterior cingulate cortex, which was unrelated to cell density.ConclusionsOur findings demonstrate that deleting a single neurexin gene (Nrxn2α) induces atypical structural connectivity within socially-relevant brain regions. More generally, our combined within-subject DTI and CLARITY approach presents a new, more sensitive method of revealing hitherto undetectable differences in the autistic brain.

scholarly journals Network Curvature as a Hallmark of Brain Structural Connectivity

2017 ◽  
Author(s):  
Hamza Farooq ◽  
Yongxin Chen ◽  
Tryphon T. Georgiou ◽  
Allen Tannenbaum ◽  
Christophe Lenglet
Keyword(s):  
Ricci Curvature ◽  
Biological Networks ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Or Efficiency ◽  
Healthy Development ◽  
Structural Networks

AbstractStudies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

scholarly journals Network alignment and similarity reveal atlas-based topological differences in structural connectomes

2020 ◽  
Author(s):  
Matteo Frigo ◽  
Emilio Cruciani ◽  
David Coudert ◽  
Rachid Deriche ◽  
Emanuele Natale ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
Graph Isomorphism ◽  
Brain Regions ◽  
Jaccard Index ◽  
Brain Atlas ◽  
Resonance Imaging ◽  
Mathematical Properties ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri ◽  
A New Technique

The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available.

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 Surface-Based Connectivity Integration

2020 ◽  
Author(s):  
Martin Cole ◽  
Kyle Murray ◽  
Etienne St-Onge ◽  
Benjamin Risk ◽  
Jianhui Zhong ◽  
...  
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Structural Connectivity ◽  
Imaging Biomarkers ◽  
Biological Sex ◽  
Human Connectome Project ◽  
Very High Spatial Resolution ◽  
Using Data ◽  
The Brain ◽  
New Framework

AbstractThere has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. However, several fundamental problems are still not well considered when conducting such connectome integration analyses, e.g., “Which structure (e.g., gray matter, white matter, white surface or pial surface) should be used for defining SC and FC and exploring their relationships”, “Which brain parcellation should be used”, and “How do the SC and FC correlate with each other and how do such correlations vary in different locations of the brain?”. In this work, we develop a new framework called surface-based connectivity integration (SBCI) to facilitate the integrative analysis of SC and FC with a re-thinking of these problems. We propose to use the white surface (the interface of white matter and gray matter) to build both SC and FC since diffusion signals are in the white matter while functional signals are more present in the gray matter. SBCI also represents both SC and FC in a continuous manner at very high spatial resolution on the white surface, avoiding the need of pre-specified atlases which may bias the comparison of SC and FC. Using data from the Human Connectome Project, we show that SBCI can create reproducible, high quality SC and FC, in addition to three novel imaging biomarkers reflective of the similarity between SC and FC throughout the brain, called global, local, and discrete SC-FC coupling. Further, we demonstrate the usefulness of these biomarkers in finding group effects due to biological sex throughout the brain.

scholarly journals Network curvature as a hallmark of brain structural connectivity

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamza Farooq ◽  
Yongxin Chen ◽  
Tryphon T. Georgiou ◽  
Allen Tannenbaum ◽  
Christophe Lenglet
Keyword(s):  
Financial Market ◽  
Biological Networks ◽  
Healthy Subjects ◽  
Structural Changes ◽  
Brain Connectivity ◽  
Empirical Studies ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Structural Networks

Abstract Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.

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

2018 ◽  
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.

scholarly journals The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models

2021 ◽  
Vol 11 (4) ◽  
pp. 487
Author(s):  
Giulia Ricci ◽  
Elisa Magosso ◽  
Mauro Ursino
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Nonlinear Phenomena ◽  
Neural Mass ◽  
Using Data ◽  
Cortical Regions ◽  
The Relationship ◽  
Connectivity Strength

Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions.

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