Normative pathways in the functional connectome.

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.UK Medical Research Council (grant: G0802226) National Institute for Health Research (NIHR) (grant: 06-05-01) Alzheimer’s Research UK (ARUK- SRF2017B-1) Gates Cambridge Scholarshi

Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes

Alzheimer’s disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer’s spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes

Deep learning based pipeline for fingerprinting using brain functional MRI connectivity data

In this work we describe an appropriate pipeline for using deep-learning as a form of improving the brain functional connectivity-based fingerprinting process which is based in functional Magnetic Resonance Imaging (fMRI) data-processing results. This pipeline approach is mostly intended for neuroscientists, biomedical engineers, and physicists that are looking for an easy form of using fMRI-based Deep-Learning in identifying people, drastic brain alterations in those same people, and/or pathologic consequences to people’s brains. Computer scientists and engineers can also gain by noticing the data-processing improvements obtained by using the here-proposed pipeline. With our best approach, we obtained an average accuracy of 0.3132 ± 0.0129 and an average validation cost of 3.1422 ± 0.0668, which clearly outperformed the published Pearson correlation approach performance with a 50 Nodes parcellation which had an accuracy of 0.237.Thanks to Eduarda Sousa for support. NFL was supported by a fellowship of the project MEDPERSYST - POCI-01-0145-FEDER-016428, funded by Portugal’s FCT. This work was also supported by NORTE-01-0145-FEDER-000013, and NORTE 2020 under the Portugal 2020 Partnership Agreement through the FEDER, plus it was funded by the European Commission (FP7) “SwitchBox - Maintaining health in old age through homeostasis” (Contract HEALTH-F2-2010-259772), and co-financed by the Portuguese North Regional Operational Program (ON.2 – O Novo Norte), under the QREN through FEDER, and by the “Fundação Calouste Gulbenkian” (Portugal) (Contract grant number: P-139977; project “TEMPO - Better mental health during ageing based on temporal prediction of individual brain ageing trajectories”). We gratefully acknowledge the support of the NVIDIA Corporation with their donation of a Quadro P6000 board used in this research. This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Analysis of structural and functional brain networks

The brain is a representative example of a network. It consists of numerous spatially distributed regions that continuously exchange information through structural connections. In the past decade, an increasing number of studies have explored the brain network in both structural and functional aspects; they have begun to decipher complex brain wirings, as well as elucidate how the rich functionality emerges from this architecture. Based upon previous studies, this thesis addresses three critical gaps in the field. (I) Although it is known that the community structures of brain network are spatially overlapping, conventional studies have focused on grouping brain regions into communities such that each region belongs to only one community. Therefore, a recent “link community” concept was employed to disentangle those overlapping architectures. (II) Spatial independent component analysis (sICA) and k-means clustering are two representative data-driven algorithms used to analyze functional networks. However, it is still unclear how these two methods compare to each other in terms of their theoretical basis and biological relevance. Hence, the relationship between these two methods were investigated. (III) Despite the multi-scale functional organization of the brain, previous studies have primarily examined the large-scale networks of the entire brain. Complex neural activity patterns in relatively smaller spatial scales have been poorly understood. Therefore, the fine-scale spatiotemporal patterns within visual cortex were explored. The distinguishing results obtained in this study may provide new insights regarding the brain\u27s organization, as well as a better understanding of mathematical and statistical tools for functional and structural network analysis