2Stroke is the leading cause of adult disability worldwide, with up to two-thirds 3 of individuals experiencing long-term disabilities. Large-scale neuroimaging 4 studies have shown promise in identifying robust biomarkers (e.g., measures 5 of brain structure) of long-term stroke recovery following rehabilitation. 6However, analyzing large rehabilitation-related datasets is problematic due to 7 barriers in accurate stroke lesion segmentation. Manually-traced lesions are 8 currently the gold standard for lesion segmentation on T1-weighted MRIs, but 9 are labor intensive and require anatomical expertise. While algorithms have 10 been developed to automate this process, the results often lack accuracy. 11Newer algorithms that employ machine-learning techniques are promising, yet 12 these require large training datasets to optimize performance. 1.1 will be a useful resource to assess and improve the accuracy of current 19 lesion segmentation methods.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

Immersive, head-mounted virtual reality (HMD-VR) provides a unique opportunity to understand how changes in sensory environments affect motor learning. However, potential differences in mechanisms of motor learning and adaptation in HMD-VR versus a conventional training (CT) environment have not been extensively explored. Here, we investigated whether adaptation on a visuomotor rotation task in HMD-VR yields similar adaptation effects in CT and whether these effects are achieved through similar mechanisms. Specifically, recent work has shown that visuomotor adaptation may occur via both an implicit, error-based internal model and a more cognitive, explicit strategic component. We sought to measure both overall adaptation and balance between implicit and explicit mechanisms in HMD-VR versus CT. Twenty-four healthy individuals were placed in either HMD-VR or CT and trained on an identical visuomotor adaptation task that measured both implicit and explicit components. Our results showed that the overall timecourse of adaption was similar in both HMD-VR and CT. However, HMD-VR participants utilized a greater cognitive strategy than CT, while CT participants engaged in greater implicit learning. These results suggest that while both conditions produce similar results in overall adaptation, the mechanisms by which visuomotor adaption occurs in HMD-VR appear to be more reliant on cognitive strategies.

Chronic stress is an established risk factor in the development of addiction. Addiction is characterized by a progressive transition from casual drug use to habitual and compulsive drug use. The ability of chronic stress to facilitate the transition to addiction may be mediated by increased engagement of the neurocircuitries underlying habitual behavior and addiction. In the present study, striatal morphology was evaluated after two weeks of chronic variable stress in male Sprague-Dawley rats. Dendritic complexity of medium spiny neurons was visualized and quantified with Golgi staining in the dorsolateral and dorsomedial striatum, as well as in the nucleus accumbens core and shell. In separate cohorts, the effects of chronic stress on habitual behavior and the acute locomotor response to methamphetamine were also assessed. Chronic stress resulted in increased dendritic complexity in the dorsolateral striatum and nucleus accumbens core, regions implicated in habitual behavior and addiction, while decreased complexity was found in the nucleus accumbens shell, a region critical for the initial rewarding effects of drugs of abuse. Chronic stress did not affect dendritic complexity in the dorsomedial striatum. A parallel shift toward habitual learning strategies following chronic stress was also identified. There was an initial reduction in acute locomotor response to methamphetamine, but no lasting effect as a result of chronic stress exposure. These findings suggest that chronic stress may facilitate the recruitment of habit- and addiction-related neurocircuitries through neuronal restructuring in the striatum.

The results suggest that older adults' EM deficits could potentially be ameliorated by incorporating their superior knowledge to supplement relatively ineffective attentional refreshing in WM.