Dr Shlomi Haar

Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia. The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease. A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. . Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models. Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

Directional selectivity during visually guided hand movements is a fundamental characteristic of neural populations in multiple motor areas of the primate brain. In the current study, we assessed how directional selectivity changes when reaching movements are dissociated from their visual feedback by rotating the visual field. We recorded simultaneous movement kinematics and fMRI activity while human subjects performed out-and-back movements to four peripheral targets before and after adaptation to a 45° visuomotor rotation. A classification algorithm was trained to identify movement direction according to voxel-by-voxel fMRI patterns in each of several brain areas. The direction of movements was successfully decoded with above-chance accuracy in multiple motor and visual areas when training and testing the classifier on trials within each condition, thereby demonstrating the existence of directionally selective fMRI patterns within each stage of the experiment. Most importantly, when training the classifier on baseline trials and decoding rotated trials, motor brain areas exhibited above-chance decoding according to the original movement direction and visual brain areas exhibited above-chance decoding according to the rotated visual target location, while posterior parietal cortex (PPC) exhibited chance-level decoding according to both. These results reveal that directionally selective fMRI patterns in motor system areas faithfully represent movement direction regardless of visual feedback, while fMRI patterns in visual system areas faithfully represent target location regardless of movement direction. Directionally selective fMRI patterns in PPC, however, were altered following adaptation learning, thereby suggesting that the novel visuomotor mapping, which was learned during visuomotor adaptation, is stored in PPC.

Substantial controversy exists regarding the presence and significance of anatomical abnormalities in autism spectrum disorders (ASD). The release of the Autism Brain Imaging Data Exchange (similar to 1000 participants, age 6-65 years) offers an unprecedented opportunity to conduct large-scale comparisons of anatomical MRI scans across groups and to resolve many of the outstanding questions. Comprehensive univariate analyses using volumetric, thickness, and surface area measures of over 180 anatomically defined brain areas, revealed significantly larger ventricular volumes, smaller corpus callosum volume (central segment only), and several cortical areas with increased thickness in the ASD group. Previously reported anatomical abnormalities in ASD including larger intracranial volumes, smaller cerebellar volumes, and larger amygdala volumes were not substantiated by the current study. In addition, multivariate classification analyses yielded modest decoding accuracies of individuals' group identity (< 60%), suggesting that the examined anatomical measures are of limited diagnostic utility for ASD. While anatomical abnormalities may be present in distinct subgroups of ASD individuals, the current findings show that many previously reported anatomical measures are likely to be of low clinical and scientific significance for understanding ASD neuropathology as a whole in individuals 6-35 years old.

The effort to determine morphological and anatomically defined neuronal characteristics from extracellularly recorded physiological signatures has been attempted with varying success in different brain areas. Recent studies have attempted such classification of cerebellar interneurons (CINs) based on statistical measures of spontaneous activity. Previously, such efforts in different brain areas have used supervised clustering methods based on standard parameterizations of spontaneous interspike interval (ISI) histograms. We worried that this might bias researchers toward positive identification results and decided to take a different approach. We recorded CINs from anesthetized cats. We used unsupervised clustering methods applied to a nonparametric representation of the ISI histograms to identify groups of CINs with similar spontaneous activity and then asked how these groups map onto different cell types. Our approach was a fuzzy C-means clustering algorithm applied to the Kullbach-Leibler distances between ISI histograms. We found that there is, in fact, a natural clustering of the spontaneous activity of CINs into six groups but that there was no relationship between this clustering and the standard morphologically defined cell types. These results proved robust when generalization was tested to completely new datasets, including datasets recorded under different anesthesia conditions and in different laboratories and different species (rats). Our results suggest the importance of an unsupervised approach in categorizing neurons according to their extracellular activity. Indeed, a reexamination of such categorization efforts throughout the brain may be necessary. One important open question is that of functional differences of our six spontaneously defined clusters during actual behavior.

Human motor learning is a neural process essential for acquiring new motor skills and adapting existing ones, which is fundamental to everyday activities. Neurological disorders such as Parkinson's Disease (PD) and stroke can significantly affect human motor functions. Identifying neural biomarkers for human motor learning is essential for advancing therapeutic strategies for such disorders. However, identifying specific neural biomarkers associated with motor learning has been challenging due to the complex nature of brain activity and the limitations of traditional data analysis techniques. In response to these challenges, we developed a novel Spatial Graph Neural Network (SGNN) model to predict motor learning outcomes from electroencephalogram (EEG) data using the spatial-temporal dynamics of brain activity. We used it to analyse EEG data collected during a visuomotor rotation (VMR) task designed to elicit distinct types of learning: error-based and reward-based. By doing so, we establish a controlled environment that allows for precisely investigating neural signatures associated with these learning processes. To understand the features learned by the SGNN, we used a set of spatial, spectral, and temporal explainability methods to identify the brain regions and temporal dynamics crucial for learning. These approaches offer comprehensive insights into the neural biomarkers, aligning with current literature and ablation studies, and pave the way for applying this methodology to find biomarkers from various brain signals and tasks.

Ipsilateral motor areas of cerebral cortex are active during arm movements and even reliably predict movement direction. Is coding similar during ipsilateral and contralateral movements? If so, is it in extrinsic (world-centered) or intrinsic (joint-configuration) coordinates? We addressed these questions by examining the similarity of multivoxel fMRI patterns in visuomotor cortical regions during unilateral reaching movements with both arms. The results of three complementary analyses revealed that fMRI response patterns were similar across right and left arm movements to identical targets (extrinsic coordinates) in visual cortices, and across movements with equivalent joint-angles (intrinsic coordinates) in motor cortices. We interpret this as evidence for the existence of distributed neural populations in multiple motor system areas that encode ipsilateral and contralateral movements in a similar manner: according to their intrinsic/joint coordinates. Cortical motor control exhibits clear lateralization: each hemisphere controls the motor output of the contralateral body. Nevertheless, neural populations in ipsilateral areas across the visuomotor hierarchy are active during unilateral movements. We show that fMRI response patterns in the motor cortices are similar for both arms if the movement direction is mirror-reversed across the midline. This suggests that in both ipsilateral and contralateral motor cortices, neural populations have effector-invariant coding of movements in intrinsic coordinates. This not only affects our understanding of motor control, it may serve in the development of brain machine interfaces that also use ipsilateral neural activity.

Deep brain stimulation (DBS) is an effective technique for treating motor symptoms in neurological conditions like Parkinson’s disease and dystonic and essential tremor (DT and ET). The DBS delivery could be improved if reliable biomarkers could be found. We propose a deep learning (DL) framework based on EEGNet to search for digital biomarkers in EEG recordings for discriminating neural response from changes in DBS parameters. Here we present a proof-of-concept by distinguishing left and right arm movement in raw EEG recorded during a DBS programming session of a DT patient. Based on the classification of 1s segments from six-channel EEG, we achieve an average accuracy of up to 93.8%. In addition, we propose a simple, yet effective model-agnostic filtering strategy for explaining the network’s performance, showing which frequency band features it mostly uses to classify the EEG.

Motor-learning literature focuses on simple laboratory-tasks due to their controlled manner and the ease to apply manipulations to induce learning and adaptation. Recently, we introduced a billiards paradigm and demonstrated the feasibility of real-world-neuroscience using wearables for naturalistic full-body motion-tracking and mobile-brain-imaging. Here we developed an embodied virtual-reality (VR) environment to our real-world billiards paradigm, which allows to control the visual feedback for this complex real-world task, while maintaining sense of embodiment. The setup was validated by comparing real-world ball trajectories with the trajectories of the virtual balls, calculated by the physics engine. We then ran our short-term motor learning protocol in the embodied VR. Subjects played billiard shots when they held the physical cue and hit a physical ball on the table while seeing it all in VR. We found comparable short-term motor learning trends in the embodied VR to those we previously reported in the physical real-world task. Embodied VR can be used for learning real-world tasks in a highly controlled environment which enables applying visual manipulations, common in laboratory-tasks and rehabilitation, to a real-world full-body task. Embodied VR enables to manipulate feedback and apply perturbations to isolate and assess interactions between specific motor-learning components, thus enabling addressing the current questions of motor-learning in real-world tasks. Such a setup can potentially be used for rehabilitation, where VR is gaining popularity but the transfer to the real-world is currently limited, presumably, due to the lack of embodiment.

The neurobehavioral mechanisms of human motor-control and learning evolved in free behaving, real-life settings, yet this is studied mostly in reductionistic lab-based experiments. Here we take a step towards a more real-world motor neuroscience using wearables for naturalistic full-body motion-tracking and the sports of pool billiards to frame a real-world skill learning experiment. First, we asked if well-known features of motor learning in lab-based experiments generalize to a real-world task. We found similarities in many features such as multiple learning rates, and the relationship between task-related variability and motor learning. Our data-driven approach reveals the structure and complexity of movement, variability, and motor learning, enabling an in-depth understanding of the structure of motor learning in three ways: First, while expecting most of the movement learning is done by the cue-wielding arm, we find that motor learning affects the whole body, changing motor-control from head to toe. Second, during learning, all subjects decreased their movement variability and their variability in the outcome. Subjects who were initially more variable were also more variable after learning. Lastly, when screening the link across subjects between initial variability in individual joints and learning, we found that only the initial variability in the right forearm supination shows a significant correlation to the subjects' learning rates. This is in-line with the relationship between learning and variability: while learning leads to an overall reduction in movement variability, only initial variability in specific task-relevant dimensions can facilitate faster learning.

Deep Brain Stimulation (DBS) is an invasive procedure used to alleviate motor symptoms in Parkinson's Disease (PD) patients. While brain activity can be used to optimise DBS parameters, the impact of DBS parameters on brain activity remains unclear. We aimed to identify the cortical neural response to changes in DBS parameters, which are sensitive to the effect of small changes in the stimulation parameters and could be used as neural biomarkers. We recorded in-clinic EEG data from seven hemispheres of PD patients during DBS programming sessions. Here we developed a siamese adaptation of the EEGNet deep learning architecture and trained it to distinguish whether two short (1-sec-long) segments of brain activity were taken with the same stimulation parameters, or if either the strength or location of the stimulation had changed. 13 independent models were trained independently in each hemisphere for stimulation amplitude or contact, and all achieved high accuracy with an average of 78%. Our models are sensitive to changes in brain activity recorded at the scalp of the patients following changes as small as 0.3mA in the DBS parameters. Next, we interpreted what our black-box AI models learned with an ablation-based explainability method, that extracts frequency bands learned by the models through a perturbation of the input's frequency spectrum. We found that fast Narrow-Band Gamma oscillations (60-90Hz), contributed most to the models across all 7 hemispheres. This work, using a data-driven approach, joins a recent body of evidence suggesting cortical Narrow-Band Gamma activity as the potential range for digital biomarkers for DBS optimization.

Objective: In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments. Methods: A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers. Results: On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0. Conclusion: Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait analysis, with quality similar to gold standard assessment. Significance: Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.

This is the code and data for the paper: Wu, Haar, Faisal (2021) Reproducing Human Motor Adaptation in Spiking Neural Simulation and known Synaptic Learning RulesRun files: 1. force_field.m - standard force field experiment with adaptation + baseline (Fig 4,5) 2. relearning.m - relearning experiment with adaptation + de-adaptation + adaptation (Fig 6a) 3. error-clamp.m - error clamp experiment with adaptation + de-adaptation + error clamp (Fig 6d) Lesion run files: 1. Lesion_aging.m - Aging brain (Fig 7a) 2. no_memory.m - Mossy finer damage (Fig 7b) 3. Lesion_cerebellar_cortex.m - Cerebellar cortex damage (Fig. 7c) 4. Purkinje_noise.m - large noise in purkinje cells (Fig 7d)

We developed an embodied Virtual Reality (eVR) environment and paradigm for studying sensorimotor learning mechanisms in real-world tasks. By using a real-world setting, i.e. playing pool billiard, we showed previously that subjects could learn by error-based and reward-based learning mechanisms, yet actually split into two distinct groups using one or the other mechanism primarily. Now, we isolated Error-based and Reward-based reinforcement learning by providing different partial visual feedback to two groups of subjects (that clamped either error or reward feedback), to understand how and whether the learning processes were different between groups. Visual feedback was manipulated in form of a rotation of the target ball’s trajectory, that requires from learners a cognitive abstraction that acts only on an object, in contrast to traditional visuomotor learning experiments which rotate the whole visual field. We found that subjects’ compensation for the perturbation and the learning curve differed between groups, with the error-based subjects showing exponential learning curve and achieving a higher improvement than reward subjects who presented a linear learning curve. Yet, no significant difference was found in inter-trial variability and success rate.

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium's ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Visuomotor rotations are learned through a combination of explicit strategy and implicit recalibration. However, measuring the relative contribution of each remains a challenge and the possibility of multiple explicit and implicit components complicates the issue. Recent interest has focused on the possibility that eye movements reflects explicit strategy. Here we compared eye movements during adaptation to two accepted measures of explicit learning: verbal report and the exclusion test. We found that while reporting, all subjects showed a match among all three measures. However, when subjects did not report their intention, the eye movements of some subjects suggested less explicit adaptation than what was measured in an exclusion test. Interestingly, subjects whose eye movements did match their exclusion could be clustered into the following two subgroups: fully implicit learners showing no evidence of explicit adaptation and explicit learners with little implicit adaptation. Subjects showing a mix of both explicit and implicit adaptation were also those where eye movements showed less explicit adaptation than did exclusion. Thus, our results support the idea of multiple components of explicit learning as only part of the explicit learning is reflected in the eye movements. Individual subjects may use explicit components that are reflected in the eyes or those that are not or some mixture of the two. Analysis of reaction times suggests that the explicit components reflected in the eye movements involve longer reaction times. This component, according to recent literature, may be related to mental rotation.

Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen's d = -0.13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use. Altered lateralized neurodevelopment may therefore be a feature of ASD, affecting widespread brain regions with diverse functions. Large-scale analysis was necessary to quantify subtle alterations of brain structural asymmetry in ASD.

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects explained their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities. Neural activity and movement kinematics are remarkably variable. Although intertrial variability is rarely studied, here, we demonstrate that individual human subjects exhibit distinct magnitudes of neural and kinematic variability that are reproducible across movements to different targets and when performing these movements with either arm. Furthermore, when examining the relationship between cortical variability and movement variability, we find that cortical fMRI variability in parietal cortex of individual subjects explained their movement extent variability. This enabled us to explain why some subjects performed more variable movements than others based on their cortical variability magnitudes.

Long-term levodopa use in Parkinson's disease is associated with declining efficacy and motor complications. Understanding its effects on brain reorganisation is vital for optimizing therapy. Using data from Parkinson's Progression Marker Initiative, we investigated levodopa's impact on resting-state functional connectivity within the cortico-basal ganglia-cerebellum system in 29 patients, under drug-naïve and levodopa-medicated conditions. Univariate comparisons of inter-regional connectivity between conditions were conducted, and multivariate combinations of connections within and between networks were assessed. No significant effect of levodopa was found using the univariate seed-based approach. However, the network connectivity pattern between basal ganglia and cerebellum showed robust classification power. Eigenvector Centrality Mapping (ECM) further identified functional hubs, with cerebellar hubs being the only ones within the cortico-basal ganglia-cerebellum system affected by medication. Our study provides further insight into the importance of inter-network functional connectivity in Parkinson's and the impact of levodopa medication, highlighting the often-overlooked role of the cerebellum.

Neuroimaging studies show structural differences in both cortical and subcortical brain regions in children and adults with autism spectrum disorder (ASD) compared with healthy subjects. Findings are inconsistent, however, and it is unclear how differences develop across the lifespan. The authors investigated brain morphometry differences between individuals with ASD and healthy subjects, cross-sectionally across the lifespan, in a large multinational sample from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) ASD working group. The sample comprised 1,571 patients with ASD and 1,651 healthy control subjects (age range, 2-64 years) from 49 participating sites. MRI scans were preprocessed at individual sites with a harmonized protocol based on a validated automated-segmentation software program. Mega-analyses were used to test for case-control differences in subcortical volumes, cortical thickness, and surface area. Development of brain morphometry over the lifespan was modeled using a fractional polynomial approach. The case-control mega-analysis demonstrated that ASD was associated with smaller subcortical volumes of the pallidum, putamen, amygdala, and nucleus accumbens (effect sizes [Cohen's d], 0.13 to -0.13), as well as increased cortical thickness in the frontal cortex and decreased thickness in the temporal cortex (effect sizes, -0.21 to 0.20). Analyses of age effects indicate that the development of cortical thickness is altered in ASD, with the largest differences occurring around adolescence. No age-by-ASD interactions were observed in the subcortical partitions. The ENIGMA ASD working group provides the largest study of brain morphometry differences in ASD to date, using a well-established, validated, publicly available analysis pipeline. ASD patients showed altered morphometry in the cognitive and affective parts of the striatum, frontal cortex, and temporal cortex. Complex developmental trajectories were observed for the different regions, with a developmental peak around adolescence. These findings suggest an interplay in the abnormal development of the striatal, frontal, and temporal regions in ASD across the lifespan.

Digital biomarkers based on accurate tracking of motor behaviour can provide a cost-effective, objective, and robust measure for Parkinson’s Disease progression, changes in care needs, and the effect of interventions. Markerless motion capture technology offers a promising approach for running it in the home. This technology uses depth sensors to capture movement unobtrusively and generate objective and quantifiable movement features. Here we present a 4-month long case study during which the patient visits our lab every month to perform mobility tasks and daily living tasks. Our data suggest accurate tracking of symptom fluctuations during both task types. This is a promising proof-of-concept towards passive tracking in-the-home of Parkinsonian symptom fluctuations.

Contemporary robotics gives us mechatronic capabilities for augmenting human bodies with extra limbs. However, how our motor control capabilities pose limits on such augmentation is an open question. We developed a Supernumerary Robotic 3rd Thumbs (SR3T) with two degrees-of-freedom controlled by the user's body to endow them with an extra contralateral thumb on the hand. We demonstrate that a pianist can learn to play the piano with 11 fingers within an hour. We then evaluate 6 naive and 6 experienced piano players in their prior motor coordination and their capability in piano playing with the robotic augmentation. We show that individuals' augmented performance with the SR3T could be explained by our new custom motor coordination assessment, the Human Augmentation Motor Coordination Assessment (HAMCA) performed pre-augmentation. Our work demonstrates how supernumerary robotics can augment humans in skilled tasks and that individual differences in their augmentation capability are explainable by their individual motor coordination abilities.

Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. Interregional profiles of group difference in cortical thickness between cases and controls. A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders. In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.

We discuss a new framework for understanding the structure of motor control. Our approach integrates existing models of motor control with the reality of hierarchical cortical processing and the parallel segregated loops that characterize cortical-subcortical connections. We also incorporate the recent claim that cortex functions via predictive representation and optimal information utilization. Our framework assumes that each cortical area engaged in motor control generates a predictive model of a different aspect of motor behavior. In maintaining these predictive models, each area interacts with a different part of the cerebellum and BG. These subcortical areas are thus engaged in domain-appropriate system identification and optimization. This refocuses the question of division of function among different cortical areas. What are the different aspects of motor behavior that are predictively modeled? We suggest that one fundamental division is between modeling of task and body whereas another is the model of state and action. Thus, we propose that the posterior parietal cortex, somatosensory cortex, premotor cortex, and motor cortex represent task state, body state, task action, and body action, respectively. In the second part of this review, we demonstrate how this division of labor can better account for many recent findings of movement encoding, especially in the premotor and posterior parietal cortices.

VR rehabilitation is an established field by now, however, it often refers to computer screen-based interactive rehabilitation activities. In recent years, there was an increased use of VR-headsets, which can provide an immersive virtual environment for real-world tasks, but they are lacking any physical interaction with the task objects and any proprioceptive feedback. Here, we focus on Embodied Virtual Reality (EVR), an emerging field where not only the visual input via VR-headset but also the haptic feedback is physically correct. This happens because subjects interact with physical objects that are veridically aligned in Virtual Reality. This technology lets us manipulate motor performance and motor learning through visual feedback perturbations. Bill-EVR is a framework that allows interventions in the performance of real-world tasks, such as playing pool billiard, engaging end-users in motivating life-like situations to trigger motor (re)learning - subjects see in VR and handle the real-world cue stick, the pool table and shoot physical balls. Specifically, we developed our platform to isolate and evaluate different mechanisms of motor learning to investigate its two main components, error-based and reward-based motor adaptation. This understanding can provide insights for improvements in neurorehabilitation: indeed, reward-based mechanisms are putatively impaired by degradation of the dopaminergic system, such as in Parkinson's disease, while error-based mechanisms are essential for recovering from stroke-induced movement errors. Due to its fully customisable features, our EVR framework can be used to facilitate the improvement of several conditions, providing a valid extension of VR-based implementations and constituting a motor learning tool that can be completely tailored to the individual needs of patients.

Many recent studies found signatures of motor learning in neural beta oscillations (13-30 Hz), and specifically in the post-movement beta rebound (PMBR). All these studies were in controlled laboratory-tasks in which the task designed to induce the studied learning mechanism. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase vs. decrease, respectively). Here, we explored the PMBR dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. Our EEG recordings highlight the opposing dynamics of PMBR magnitudes (increase vs. decrease) between different subjects performing the same task. The groups of subjects, defined by their neural dynamics, also showed behavioral differences expected for different learning mechanisms. Our results suggest that when faced with the complexity of the real-world different subjects might use different learning mechanisms for the same complex task. We speculate that all subjects combine multi-modal mechanisms of learning, but different subjects have different predominant learning mechanisms.

Attention Deficit Hyperactivity Disorder (ADHD) has been described as a disorder where frequent lapses of attention impair the ability of an individual to focus/attend in a sustained manner, thereby generating abnormally large intra-individual behavioral variability across trials. Indeed, increased reaction time (RT) variability is a fundamental behavioral characteristic of individuals with ADHD found across a large number of cognitive tasks. But what is the underlying neurophysiology that might generate such behavioral instability? Here, we examined trial-by-trial EEG response variability to visual and auditory stimuli while subjects' attention was diverted to an unrelated task at the fixation cross. Comparisons between adult ADHD and control participants revealed that neural response variability was significantly larger in the ADHD group as compared with the control group in both sensory modalities. Importantly, larger trial-by-trial variability in ADHD was apparent before and after stimulus presentation as well as in trials where the stimulus was omitted, suggesting that ongoing (rather than stimulus-evoked) neural activity is continuously more variable (noisier) in ADHD. While the patho-physiological mechanisms causing this increased neural variability remain unknown, they appear to act continuously rather than being tied to a specific sensory or cognitive process. (C) 2016 Elsevier Ltd. All rights reserved.

Recent technological developments in mobile brain and body imaging are enabling new frontiers of real-world neuroscience. Simultaneous recordings of body movement and brain activity from highly skilled individuals as they demonstrate their exceptional skills in real-world settings, can shed new light on the neurobehavioural structure of human expertise. Driving is a real-world skill which many of us acquire to different levels of expertise. Here we ran a case-study on a subject with the highest level of driving expertise-a Formula E Champion. We studied the driver's neural and motor patterns while he drove a sports car on the "Top Gear" race track under extreme conditions (high speed, low visibility, low temperature, wet track). His brain activity, eye movements and hand/foot movements were recorded. Brain activity in the delta, alpha, and beta frequency bands showed causal relation to hand movements. We herein demonstrate the feasibility of using mobile brain and body imaging even in very extreme conditions (race car driving) to study the sensory inputs, motor outputs, and brain states which characterise complex human skills.

Background To date the description of mechanically ventilated patients with Coronavirus Disease 2019 (COVID-19) has focussed on admission characteristics with no consideration of the dynamic course of the disease. Here, we present a data-driven analysis of granular, daily data from a representative proportion of patients undergoing invasive mechanical ventilation (IMV) within the United Kingdom (UK) to evaluate the complete natural history of COVID-19. Methods We included adult patients undergoing IMV within 48 hours of ICU admission with complete clinical data until death or ICU discharge. We examined factors and trajectories that determined disease progression and responsiveness to ARDS interventions. Our data visualisation tool is available as a web-based widget (https://www.CovidUK.ICU). Findings Data for 623 adults with COVID-19 who were mechanically ventilated between 01 March 2020 and 31 August 2020 were analysed. Mortality, intensity of mechanical ventilation and severity of organ injury increased with severity of hypoxaemia. Median tidal volume per kg across all mandatory breaths was 5.6 [IQR 4.7-6.6] mL/kg based on reported body weight, but 7.0 [IQR 6.0-8.4] mL/kg based on calculated ideal body weight. Non-resolution of hypoxaemia over the first week of IMV was associated with higher ICU mortality (59.4% versus 16.3%; P

•We performed a multicohort longitudinal brain atrophy analysis between PD and healthy ageing.•Increased cerebellar WM in PD at baseline - suggests potential compensatory mechanisms in prodromal and early PD.•Accelerated cerebellar WM atrophy over 2–3 years post PD diagnosis at Crus I and lobule IX. Brain atrophy is detected in early Parkinson's disease (PD) and accelerates over the first few years post-diagnosis. This was captured by multiple cross-sectional studies and a few longitudinal studies in early PD. Yet only a longitudinal study with a control group can capture accelerated atrophy in early PD and differentiate it from healthy ageing. Accordingly, we performed a multicohort longitudinal analysis between PD and healthy ageing, examining subcortical regions implicated in PD pathology, including the basal ganglia, thalamus, corpus callosum (CC), and cerebellum. Longitudinal volumetric analysis was performed on 56 early PD patients and 53 matched controls, with scans collected 2–3 years apart. At baseline, the PD group showed a greater volume in the pallidum, thalamus, and cerebellar white matter (WM), suggesting potential compensatory mechanisms in prodromal and early PD. After 2–3 years, accelerated atrophy in PD was observed in the putamen and cerebellar WM. Interestingly, healthy controls – but not PD patients – demonstrated a significant decline in Total Intracranial Volume (TIV), and atrophy in the thalamus and mid-CC. Between-group analysis revealed more severe atrophy in the right striatum and cerebellar WM in PD, and in the mid-posterior CC in controls. Using CEREbellum Segmentation (CERES) for lobule segmentation on the longitudinal PD cohort, we found a significant decline in the WM of non-motor regions in the cerebellum, specifically Crus I and lobule IX. Our results highlight an initial increase in cerebellar WM volume during prodromal PD, followed by significant degeneration over the first few years post-diagnosis.

Clinical trials of disease-modifying therapies in PD require valid and responsive primary outcome measures that are relevant to patients. The objective is to select a patient-centered primary outcome measure for disease-modification trials over three or more years. Experts in Parkinson's disease (PD), statistics, and health economics and patient and public involvement and engagement (PPIE) representatives reviewed and discussed potential outcome measures. A larger PPIE group provided input on their key considerations for such an endpoint. Feasibility, clinimetric properties, and relevance to patients were assessed and synthesized. Although initial considerations favored the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III in Off, feasibility, PPIE input, and clinimetric properties supported the MDS-UPDRS Part II. However, PPIE input also highlighted the importance of nonmotor symptoms, especially in the longer term, leading to the selection of the MDS-UPDRS Parts I + II sum score. The MDS-UPDRS Parts I + II sum score was chosen as the primary outcome for large 3-year disease-modification trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Interhemispheric functional connectivity abnormalities are often reported in autism and it is thus not surprising that structural defects of the corpus callosum (CC) are consistently found using both traditional MRI and DTI techniques. Past DTI studies however, have subdivided the CC into 2 or 3 segments without regard for where fibers may project to within the cortex, thus placing limitations on our ability to understand the nature, timing and neurobehavioral impact of early CC abnormalities in autism. Leveraging a unique cohort of 97 toddlers (68 autism; 29 typical) we utilized a novel technique that identified seven CC tracts according to their cortical projections. Results revealed that younger (

Are long pauses in the firing of cerebellar interneurons (CINs) related to Purkinje cell (PC) pauses? If PC pauses affect the larger network, then we should find a close relationship between CIN pauses and those in PCs. We recorded activity of 241 cerebellar cortical neurons (206 CINs and 35 PCs) in three anesthetized cats. One fifth of the CINs and more than half of the PCs were identified as pausing. Pauses in CINs and PCs showed some differences: CIN mean pause length was shorter, and, after pauses, only CINs had sustained reduction in their firing rate (FR). Almost all pausing CINs fell into same cluster when we used different methods of clustering CINs by their spontaneous activity. The mean spontaneous firing rate of that cluster was approximately 53 Hz. We also examined cross-correlations in simultaneously recorded neurons. Of 39 cell pairs examined, 14 (35 %) had cross-correlations significantly different from those expected by chance. Almost half of the pairs with two CINs showed statistically significant negative correlations. In contrast, PC/CIN pairs did not often show significant effects in the cross-correlation (12/15 pairs). However, for both CIN/CIN and PC/CIN pairs, pauses in one unit tended to correspond to a reduction in the firing rate of the adjacent unit. In our view, our results support the possibility that previously reported PC bistability is part of a larger network response and not merely a biophysical property of PCs. Any functional role for PC bistability should probably be sought in the context of the broader network.

Background: Large controversy exists regarding the potential existence and clinical significance of larger brain volumes in toddlers who later develop autism. Assessing this relationship is important for determining the clinical utility of early head circumference (HC) measures and for assessing the validity of the early overgrowth hypothesis of autism, which suggests that early accelerated brain development may be a hallmark of the disorder. Methods: We performed a retrospective comparison of HC, height, and weight measurements between 66 toddlers who were later diagnosed with autism and 66 matched controls. These toddlers represent an unbiased regional sample from a single health service provider in the southern district of Israel. On average, participating toddlers had >8 measurements between birth and the age of two, which enabled us to characterize individual HC, height, and weight development with high precision and fit a negative exponential growth model to the data of each toddler with exceptional accuracy. Results: The analyses revealed that HC sizes and growth rates were not significantly larger in toddlers with autism even when stratifying the autism group based on verbal capabilities at the time of diagnosis. In addition, there were no significant correlations between ADOS scores at the time of diagnosis and HC at any time-point during the first 2 years of life. Conclusions: These negative results add to accumulating evidence, which suggest that brain volume is not necessarily larger in toddlers who develop autism. We believe that conflicting results reported in other studies are due to small sample sizes, use of misleading population norms, changes in the clinical definition of autism over time, and/or inclusion of individuals with syndromic autism. While abnormally large brains may be evident in some individuals with autism and more clearly visible in MRI scans, converging evidence from this and other studies suggests that enlarged HC is not a common etiology of the entire autism population. Early HC measures, therefore, offer very limited clinical utility for assessment of autism risk in the general population.