Corticostriatal connectivity fingerprints:Probability maps based on resting-state functional connectivity

Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.status: publishe

Task-related edge density (TED) - a new method for revealing large-scale network formation in fMRI data of the human brain

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges in a brain network that differentially respond in unison to a task onset and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.Comment: 21 pages, 11 figure

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

Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.

BACKGROUND: The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. METHOD: Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. RESULTS: Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. CONCLUSIONS: Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.The study was funded by the Wellcome Trust Fellowship grant for V.V. (093705/Z/10/Z) and the Cambridge National Institute for Health Research (NIHR) Biomedical Research Centre. We thank Dr Ameera Patel for providing the H-O470 brain parcellation atlas in the present study.This is the final version of the article. It first appeared from Cambridge University Press via https://doi.org/10.1017/S003329171600264

Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections

Functional magnetic resonance imaging recordings in the resting-state (RS) from the human brain are characterized by spontaneous low-frequency fluctuations in the blood oxygenation level dependent signal that reveal functional connectivity (FC) via their spatial synchronicity. This RS study applied network analysis to compare FC between late-life depression (LLD) patients and control subjects. Raw cross-correlation matrices (CM) for LLD were characterized by higher FC. We analyzed the small-world (SW) and modular organization of these networks consisting of 110 nodes each as well as the connectivity patterns of individual nodes of the basal ganglia. Topological network measures showed no significant differences between groups. The composition of top hubs was similar between LLD and control subjects, however in the LLD group posterior medial-parietal regions were more highly connected compared to controls. In LLD, a number of brain regions showed connections with more distant neighbors leading to an increase of the average Euclidean distance between connected regions compared to controls. In addition, right caudate nucleus connectivity was more diffuse in LLD. In summary, LLD was associated with overall increased FC strength and changes in the average distance between connected nodes, but did not lead to global changes in SW or modular organization

Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility.

Discrete yet overlapping frontal-striatal circuits mediate broadly dissociable cognitive and behavioural processes. Using a recently developed multi-echo resting-state functional MRI (magnetic resonance imaging) sequence with greatly enhanced signal compared to noise ratios, we map frontal cortical functional projections to the striatum and striatal projections through the direct and indirect basal ganglia circuit. We demonstrate distinct limbic (ventromedial prefrontal regions, ventral striatum - VS, ventral tegmental area - VTA), motor (supplementary motor areas - SMAs, putamen, substantia nigra) and cognitive (lateral prefrontal and caudate) functional connectivity. We confirm the functional nature of the cortico-striatal connections, demonstrating correlates of well-established goal-directed behaviour (involving medial orbitofrontal cortex - mOFC and VS), probabilistic reversal learning (lateral orbitofrontal cortex - lOFC and VS) and attentional shifting (dorsolateral prefrontal cortex - dlPFC and VS) while assessing habitual model-free (SMA and putamen) behaviours on an exploratory basis. We further use neurite orientation dispersion and density imaging (NODDI) to show that more goal-directed model-based learning (MBc) is also associated with higher mOFC neurite density and habitual model-free learning (MFc) implicates neurite complexity in the putamen. This data highlights similarities between a computational account of MFc and conventional measures of habit learning. We highlight the intrinsic functional and structural architecture of parallel systems of behavioural control.VV and NAH are Wellcome Trust (WT) intermediate Clinical Fellows. LM is in receipt of an MRC studentship. The BCNI is supported by a WT and MRC grant. ETB is employed part-time by the University of Cambridge and part-time by GSK PLC and is a shareholder of GSK. TWR is a consultant for Cambridge Cognition, Eli Lilly, GSK, Merck, Sharpe and Dohme, Lundbeck, Teva and Shire Pharmaceuticals. He is or has been in receipt of research grants from Lundbeck, Eli Lilly and GSK and is an editor for Springer-Verlag (Psychopharmacology). The remaining authors declare no competing financial interests. The study was funded by the Wellcome Trust Fellowship grant for VV (093705/Z/10/Z) and Cambridge NIHR Biomedical Research Centre.This is the final version of the article. It was first available from Elsevier via http://dx.doi.org/10.1016/j.cortex.2015.11.00