The anatomical distance of functional connections predicts brain network topology in health and schizophrenia.

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive "pruning" of short-distance functional connections in schizophrenia.PEV is supported by the Medical Research Council (grant number MR/K020706/1). This work was supported by the Neuroscience in Psychiatry Network (NSPN) which is funded by a Wellcome Trust strategy award to the University of Cambridge and University College London. ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds stock in GSK.This is the final published version. It first appeared at http://onlinelibrary.wiley.com/doi/10.1111/jcpp.12365/full

Alien Hand, Restless Brain: Salience Network and Interhemispheric Connectivity Disruption Parallel Emergence and Extinction of Diagonistic Dyspraxia

International audienceDiagonistic dyspraxia (DD) is by far the most spectacular manifestation reported by sufferers of acute corpus callosum (CC) injury (so-called "split-brain"). In this form of alien hand syndrome, one hand acts at cross purposes with the other "against the patient's will". Although recent models view DD as a disorder of motor control, there is still little information regarding its neural underpinnings, due to widespread connectivity changes produced by CC insult, and the obstacle that non-volitional movements represent for task-based functional neuroimaging studies. Here, we studied patient AM, the first report of DD in patient with complete developmental CC agenesis. This unique case also offers the opportunity to study the resting-state connectomics of DD in the absence of diffuse changes subsequent to CC injury or surgery. AM developed DD following status epilepticus (SE) which resolved over a 2-year period. Whole brain functional connectivity (FC) was compared (Crawford-Howell [CH]) to 16 controls during the period of acute DD symptoms (Time 1) and after remission (Time 2). Whole brain graph theoretical models were also constructed and topological efficiency examined. At Time 1, disrupted FC was observed in inter-hemispheric and intra-hemispheric right edges, involving frontal superior and midline structures. Graph analysis indicated disruption of the efficiency of salience and right frontoparietal (FP) networks. At Time 2, after remission of diagnostic dyspraxia symptoms, FC and salience network changes had resolved. In sum, longitudinal analysis of connectivity in AM indicates that DD behaviors could result from disruption of systems that support the experience and control of volitional movements and the ability to generate appropriate behavioral responses to salient stimuli. This also raises the possibility that changes to large-scale functional architecture revealed by resting-state functional magnetic resonance imaging (fMRI) (rs-fMRI) may provide relevant information on the evolution of behavioral syndromes in addition to that provided by structural and task-based functional imaging

Review

Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue

Elucidating the complex organization of neural micro-domains in the locust Schistocerca gregaria using dMRI.

To understand brain function it is necessary to characterize both the underlying structural connectivity between neurons and the physiological integrity of these connections. Previous research exploring insect brain connectivity has typically used electron microscopy techniques, but this methodology cannot be applied to living animals and so cannot be used to understand dynamic physiological processes. The relatively large brain of the desert locust, Schistercera gregaria (Forksȧl) is ideal for exploring a novel methodology; micro diffusion magnetic resonance imaging (micro-dMRI) for the characterization of neuronal connectivity in an insect brain. The diffusion-weighted imaging (DWI) data were acquired on a preclinical system using a customised multi-shell diffusion MRI scheme optimized to image the locust brain. Endogenous imaging contrasts from the averaged DWIs and Diffusion Kurtosis Imaging (DKI) scheme were applied to classify various anatomical features and diffusion patterns in neuropils, respectively. The application of micro-dMRI modelling to the locust brain provides a novel means of identifying anatomical regions and inferring connectivity of large tracts in an insect brain. Furthermore, quantitative imaging indices derived from the kurtosis model that include fractional anisotropy (FA), mean diffusivity (MD) and kurtosis anisotropy (KA) can be extracted. These metrics could, in future, be used to quantify longitudinal structural changes in the nervous system of the locust brain that occur due to environmental stressors or ageing