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

Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder.

INTRODUCTION: The human functional connectome is a graphical representation, consisting of nodes connected by edges, of the inter-relationships of blood oxygenation-level dependent (BOLD) time-series measured by MRI from regions encompassing the cerebral cortices and, often, the cerebellum. Semi-metric analysis of the weighted, undirected connectome distinguishes an edge as either direct (metric), such that there is no alternative path that is accumulatively stronger, or indirect (semi-metric), where one or more alternative paths exist that have greater strength than the direct edge. The sensitivity and specificity of this method of analysis is illustrated by two case-control analyses with independent, matched groups of adolescents with autism spectrum conditions (ASC) and major depressive disorder (MDD). RESULTS: Significance differences in the global percentage of semi-metric edges was observed in both groups, with increases in ASC and decreases in MDD relative to controls. Furthermore, MDD was associated with regional differences in left frontal and temporal lobes, the right limbic system and cerebellum. In contrast, ASC had a broadly increased percentage of semi-metric edges with a more generalised distribution of effects and some areas of reduction. In summary, MDD was characterised by localised, large reductions in the percentage of semi-metric edges, whilst ASC is characterised by more generalised, subtle increases. These differences were corroborated in greater detail by inspection of the semi-metric backbone for each group; that is, the sub-graph of semi-metric edges present in >90% of participants, and by nodal degree differences in the semi-metric connectome. CONCLUSION: These encouraging results, in what we believe is the first application of semi-metric analysis to neuroimaging data, raise confidence in the methodology as potentially capable of detection and characterisation of a range of neurodevelopmental and psychiatric disorders.This study was funded by the UK Medial Research Council (grants: G0802226 and G0701919), the National Institute for Health Research (NIHR) (grant: 06/05/01) and the Behavioural and Clinical Neuroscience Institute (BCNI), University of Cambridge. The BCNI is jointly funded by the Medical Research Council and the Wellcome Trust. Additional support was received from the NIHR Cambridge Biomedical Research Centre. CCH is supported by a Parke Davis Fellowship from the University of Cambridge and resides at Columbia University.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.013638

A Winding Road: Alzheimer’s Disease Increases Circuitous Functional Connectivity Pathways

Neuroimaging has been successful in characterizing the pattern of cerebral atrophy that accompanies the progression of Alzheimer’s disease (AD). Examination of functional connectivity, the strength of signal synchronicity between brain regions, has gathered pace as another way of understanding changes to the brain that are associated with AD. It appears to have good sensitivity and detect effects that precede cognitive decline, and thus offers the possibility to understand the neurobiology of the disease in its earliest phases. However, functional connectivity analyzes to date generally consider only the strongest connections, with weaker links ignored. This proof-of-concept study compared patients with mild-to-moderate AD (N = 11) and matched control individuals (N = 12) based on functional connectivities derived from blood-oxygenation level dependent (BOLD) sensitive functional MRI acquired during resting wakefulness. All positive connectivities irrespective of their strength were included. Transitive closures of the resulting connectome were calculated that classified connections as either direct or indirect. Between-group differences in the proportion of indirect paths were observed. In AD, there was broadly increased indirect connectivity across greater spatial distances. Furthermore, the indirect pathways in AD had greater between-subject topological variance than controls. The prevailing characterization of AD as being a disconnection syndrome is refined by the observation that direct links between regions that are impaired are perhaps replaced by an increase in indirect functional pathways that is only detectable through inclusion of connections across the entire range of connection strengths

Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder.

The human functional connectome is a graphical representation, consisting of nodes connected by edges, of the inter-relationships of blood oxygenation-level dependent (BOLD) time-series measured by MRI from regions encompassing the cerebral cortices and, often, the cerebellum. Semi-metric analysis of the weighted, undirected connectome distinguishes an edge as either direct (metric), such that there is no alternative path that is accumulatively stronger, or indirect (semi-metric), where one or more alternative paths exist that have greater strength than the direct edge. The sensitivity and specificity of this method of analysis is illustrated by two case-control analyses with independent, matched groups of adolescents with autism spectrum conditions (ASC) and major depressive disorder (MDD).Significance differences in the global percentage of semi-metric edges was observed in both groups, with increases in ASC and decreases in MDD relative to controls. Furthermore, MDD was associated with regional differences in left frontal and temporal lobes, the right limbic system and cerebellum. In contrast, ASC had a broadly increased percentage of semi-metric edges with a more generalised distribution of effects and some areas of reduction. In summary, MDD was characterised by localised, large reductions in the percentage of semi-metric edges, whilst ASC is characterised by more generalised, subtle increases. These differences were corroborated in greater detail by inspection of the semi-metric backbone for each group; that is, the sub-graph of semi-metric edges present in >90% of participants, and by nodal degree differences in the semi-metric connectome.These encouraging results, in what we believe is the first application of semi-metric analysis to neuroimaging data, raise confidence in the methodology as potentially capable of detection and characterisation of a range of neurodevelopmental and psychiatric disorders

Semi-metric percentages and backbones.

<p>(a) Sagittal, axial and coronal projections of nodes coloured according to the modules in which they are contained. (b) Between-group comparisons (patient groups relative to controls) for whole brain, left and right hemisphere SMP displayed as box-and-whisker plots identifying the median by the central line, the 25^th and 75^th percentile ranges by the limits of the box, and the minimum and maximum range (excluding outliers) by the limits of the whiskers. Outliers are individually displayed and defined as values >1.5 the interquartile range from the 25% and 75% quartiles. (c) Sagittal projections of the left and right hemispheres of the semi-metric backbones for each group. The thickness of the edges represents the percentage of participants within each group with a semi-metric edge at that location, with percentages > 90% omitted.</p

Overview of network properties and analysis.

<p>(a) Histogram of correlation coefficients (i.e. edge weights) for each group. (b) Schematic diagram of a simple network with a semi-metric connection between nodes 1 and 2 (dashed edge) due to a shorter indirect path comprising edges between nodes 2 and 3 and 3 and 1 (solid edges). (c) The distribution of number of edges for semi-metric paths for each group. (d) Proximity matrices averaged across participants, for each group. (e) Axial projections of metric and semi-metric backbones for the control group. The thickness of the edges represents the percentage of participants within each group with a semi-metric edge at that location, with percentages > 90% omitted.</p

Demographic characteristics of the participants.

<p>SD = standard deviation</p><p>Demographic characteristics of the participants.</p

Node degree and node disruption indices.

<p>Sagittal, axial and coronal projections (left-to-right) of nodes for comparisons of node degree in the semi-metric network for each between-group comparisons: (a) ASC vs. controls; (b) MDD vs controls. The radius of the node is proportional to the average degree difference and the colour denotes the direction of the effect; red indicating increases and green decreases, relative to controls. Plots of the difference in mean degree between (c) ASC and (d) MDD, and controls against mean degree for controls, for the semi-metric network. Node disruption indices are defined as the slope of the regression lines, plotted on each graph.</p