A resting state network in the motor control circuit of the basal ganglia

<p>Abstract</p> <p>Background</p> <p>In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond.</p> <p>Results</p> <p>An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 ± 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates.</p> <p>Conclusion</p> <p>Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved.</p

Impact of host age and parity on susceptibility to severe urinary tract infection in a murine model.

The epidemiology and bacteriology of urinary tract infection (UTI) varies across the human lifespan, but the reasons for these differences are poorly understood. Using established monomicrobial and polymicrobial murine UTI models caused by uropathogenic Escherichia coli (UPEC) and/or Group B Streptococcus (GBS), we demonstrate age and parity as inter-related factors contributing to UTI susceptibility. Young nulliparous animals exhibited 10-100-fold higher bacterial titers compared to older animals. In contrast, multiparity was associated with more severe acute cystitis in older animals compared to age-matched nulliparous controls, particularly in the context of polymicrobial infection where UPEC titers were ∼1000-fold higher in the multiparous compared to the nulliparous host. Multiparity was also associated with significantly increased risk of chronic high titer UPEC cystitis and ascending pyelonephritis. Further evidence is provided that the increased UPEC load in multiparous animals required TLR4-signaling. Together, these data strongly suggest that the experience of childbearing fundamentally and permanently changes the urinary tract and its response to pathogens in a manner that increases susceptibility to severe UTI. Moreover, this murine model provides a system for dissecting these and other lifespan-associated risk factors contributing to severe UTI in at-risk groups

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

This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.0136388Introduction:\ud 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). \ud \ud Results:\ud 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. \ud \ud Conclusion:\ud These encouraging results in what we believe is the first application of semi-metric analysis to neuroimaging data, raises 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

Research data supporting "Semi-Metric Topology of the Human Connectome"

There are two sheets in the Excel spreadsheet. The first is the demographic data (age and gender) for the sample included in the study. The second sheet contains the semi-metric percentages derived from resting state functional MRI for each individual extracted from regions of interest of the cortex and cerebellum.This work was supported by the MRC [grant numbers MRC G0802226 and MRC G0701919], the NIHR, the Wellcome Trust and Parke-Davis