Functional connectivity analysis of cerebellum using spatially constrained spectral clustering

The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data

Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering

10.3389/fnins.2016.00188Frontiers in neuroscience10188GUSTO (Growing up towards Healthy Outcomes

Enhanced pre-frontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Traumatic brain injury (TBI) affects the structural connectivity, triggering the re-organization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain networks re-organization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severeTBI, comparing them to young typically developing control participants. In comparison to control participants, TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: 1) a subcortical network including part of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulum and precuneus; and 2) a task-positive network, involving regions of the dorsal attention system together with the dorsolateral and ventrolateral prefrontal regions

A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.Work supported by Ikerbasque: The Basque Foundation for Science, Euskampus at UPV/EHU, Gobierno Vasco (Saiotek SAIO13-PE13BF001) and Junta de Andalucía (P09-FQM-4682) to JMC; Ikerbasque Visiting Professor to SS; Junta de Andalucía (P09-FQM-4682) and Spanish Ministry of Economy and Competitiveness (FIS2013-43201-P) to MAM; the European Union’s Seventh Framework Programme (ICT-FET FP7/2007-2013, FET Young Explorers scheme) under grant agreement n. 284772 BRAIN BOW (www.brainbowproject.eu) and by the Joint Italy—Israel Laboratory on Neuroscience to PB. For results validation (figure S8), data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender

Support vector classification analysis of resting state functional connectivity fMRI

Since its discovery in 1995 resting state functional connectivity derived from functional MRI data has become a popular neuroimaging method for study psychiatric disorders. Current methods for analyzing resting state functional connectivity in disease involve thousands of univariate tests, and the specification of regions of interests to employ in the analysis. There are several drawbacks to these methods. First the mass univariate tests employed are insensitive to the information present in distributed networks of functional connectivity. Second, the null hypothesis testing employed to select functional connectivity dierences between groups does not evaluate the predictive power of identified functional connectivities. Third, the specification of regions of interests is confounded by experimentor bias in terms of which regions should be modeled and experimental error in terms of the size and location of these regions of interests. The objective of this dissertation is to improve the methods for functional connectivity analysis using multivariate predictive modeling, feature selection, and whole brain parcellation. A method of applying Support vector classification (SVC) to resting state functional connectivity data was developed in the context of a neuroimaging study of depression. The interpretability of the obtained classifier was optimized using feature selection techniques that incorporate reliability information. The problem of selecting regions of interests for whole brain functional connectivity analysis was addressed by clustering whole brain functional connectivity data to parcellate the brain into contiguous functionally homogenous regions. This newly developed famework was applied to derive a classifier capable of correctly seperating the functional connectivity patterns of patients with depression from those of healthy controls 90% of the time. The features most relevant to the obtain classifier match those previously identified in previous studies, but also include several regions not previously implicated in the functional networks underlying depression.Ph.D.Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthon

Identifying Changes of Functional Brain Networks using Graph Theory

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgement

Specific Frontostriatal Circuits for Impaired Cognitive Flexibility and Goal-Directed Planning in Obsessive-Compulsive Disorder: Evidence From Resting-State Functional Connectivity.

BACKGROUND: A recent hypothesis has suggested that core deficits in goal-directed behavior in obsessive-compulsive disorder (OCD) are caused by impaired frontostriatal function. We tested this hypothesis in OCD patients and control subjects by relating measures of goal-directed planning and cognitive flexibility to underlying resting-state functional connectivity. METHODS: Multiecho resting-state acquisition, combined with micromovement correction by blood oxygen level-dependent sensitive independent component analysis, was used to obtain in vivo measures of functional connectivity in 44 OCD patients and 43 healthy comparison subjects. We measured cognitive flexibility (attentional set-shifting) and goal-directed performance (planning of sequential response sequences) by means of well-validated, standardized behavioral cognitive paradigms. Functional connectivity strength of striatal seed regions was related to cognitive flexibility and goal-directed performance. To gain insights into fundamental network alterations, graph theoretical models of brain networks were derived. RESULTS: Reduced functional connectivity between the caudate and the ventrolateral prefrontal cortex was selectively associated with reduced cognitive flexibility. In contrast, goal-directed performance was selectively related to reduced functional connectivity between the putamen and the dorsolateral prefrontal cortex in OCD patients, as well as to symptom severity. Whole-brain data-driven graph theoretical analysis disclosed that striatal regions constitute a cohesive module of the community structure of the functional connectome in OCD patients as nodes within the basal ganglia and cerebellum were more strongly connected to one another than in healthy control subjects. CONCLUSIONS: These data extend major neuropsychological models of OCD by providing a direct link between intrinsically abnormal functional connectivity within dissociable frontostriatal circuits and those cognitive processes underlying OCD symptoms.This research was funded by a Wellcome Trust Senior Investigator Award (104631/Z/14/Z) awarded to T.W. Robbins. Work was completed at the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK, supported by a joint award from the Medical Research Council and Wellcome Trust (G00001354). M.M. Vaghi is supported by a Pinsent Darwin Scholarship in Mental Pathology and a Cambridge Home and EU Scholarship Scheme (CHESS) studentship. P.E. Vértes is supported by the Medical Research Council (grant no. MR/K020706/1). A.M. Apergis-Schoute is supported by the Wellcome Trust above. V. Voon is a Wellcome Trust Fellow