The claustrum is highly interconnected with many structures in the brain, but the organizing principles governing its vast connectivity have yet to be fully explored. We investigated the defining characteristics and activity of single claustrum neurons, the nature of their relationship with the cortex, and their connectivity within the claustrum itself to gain a comprehensive view of claustral circuits. We show that the claustrum is composed of excitatory and inhibitory cell types that are connected through intraclaustral excitatory synapses, especially between neurons with disparate projection targets. Investigations of corticoclaustral innervation demonstrated that axons from the cortex localize to distinct dorsoventral modules depending on their region of origin. In vitro dual-color optogenetic mapping experiments revealed that individual claustrum neurons integrated inputs from more than one cortical region in a cell type- and projection target-specific manner. Integration in claustrum neurons was observed to be particularly common between areas of the frontal cortex and less so for sensory, motor, and association cortices. Finally, activity in claustrum axons in midline cortical areas recorded with in vivo two-photon calcium imaging showed responses to multimodal sensory stimuli. Our findings shed light on the organizing principles of claustrum circuits, demonstrating that individual claustrum neurons integrate cortical inputs and redistribute this information back to cortex after performing output target- and cell type-dependent local computations.

Background We have previously shown that wearable technology and machine learning techniques can accurately discriminate between progressive supranuclear palsy (PSP), Parkinson's disease, and healthy controls. To date these techniques have not been applied in longitudinal studies of disease progression in PSP. Objectives We aimed to establish whether data collected by a body‐worn inertial measurement unit (IMU) network could predict clinical rating scale scores in PSP and whether it could be used to track disease progression. Methods We studied gait and postural stability in 17 participants with PSP over five visits at 3‐month intervals. Participants performed a 2‐minute walk and an assessment of postural stability by standing for 30 seconds with their eyes closed, while wearing an array of six IMUs. Results Thirty‐two gait and posture features were identified, which progressed significantly with time. A simple linear regression model incorporating the three features with the clearest progression pattern was able to detect statistically significant progression 3 months in advance of the clinical scores. A more complex linear regression and a random forest approach did not improve on this. Conclusions The reduced variability of the models, in comparison to clinical rating scales, allows a significant change in disease status from baseline to be observed at an earlier stage. The current study sheds light on the individual features that are important in tracking disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

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