BDNF and TrkB Mediate the Improvement from Chronic Stress-induced Spatial Memory Deficits and CA3 Dendritic Retraction.

The brain is capable of improving from a chronically stressed state. The hippocampus in particular appears to "recover" from chronic stress-induced morphological and functional deficits following a post-stress rest period of several weeks. We previously found that hippocampal brain-derived neurotrophic factor (BDNF) was necessary for spatial ability to improve following a post-stress rest period. The following studies are the first to investigate the involvement of BDNF and its TrkB receptor on the recovery process following the end of chronic stress, as it pertains to hippocampal dendritic retraction and spatial memory deficits. In the first study, hippocampal BDNF was downregulated via RNA interference and then hippocampal CA3 and CA1 dendritic complexity were evaluated following chronic stress and a post-stress rest period in male Sprague-Dawley rats. Downregulating hippocampal BDNF prevented the enhancement of CA3 apical dendritic complexity following the rest period. Moreover, chronic stress and downregulated BDNF in the post-stress rest group led to regionally specific enhancements in CA1 dendritic complexity. In the second study, we tested whether the TrkB receptor was involved by administering daily systemic injections of ANA-12, a TrkB receptor antagonist, during the three-week post-stress rest period. ANA-12 prevented the improvement in spatial ability and CA3 apical dendritic complexity following the post-stress rest period. These data demonstrate that hippocampal BDNF acting via its TrkB receptor is necessary during the post-stress rest period in order to improve the impaired hippocampal structural and cognitive outcomes that occur in response to chronic stress.

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.

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.