Cannabidiol attenuates seizure susceptibility and behavioural deficits in adult CDKL5R59X knock-in mice.

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is caused by a loss-of-function mutation in CDKL5 gene, encoding a serine-threonine kinase highly expressed in the brain. CDD manifests with early-onset epilepsy, autism, motor impairment and severe intellectual disability. While there are no known treatments for CDD, the use of cannabidiol has recently been introduced into clinical practice for neurodevelopmental disorders. Given the increased clinical utilization of cannabidiol, we examined its efficacy in the CDKL5R59X knock-in (R59X) mice, a CDD model based on a human mutation that exhibits both lifelong seizure susceptibility and behavioural deficits. We found that cannabidiol pre-treatment rescued the increased seizure susceptibility in response to the chemoconvulsant pentylenetetrazol (PTZ), attenuated working memory and long-term memory impairments, and rescued social deficits in adult R59X mice. To elucidate a potential mechanism, we compared the developmental hippocampal and cortical expression of common endocannabinoid (eCB) targets in R59X mice and their wild-type littermates, including cannabinoid type 1 receptor (CB1R), transient receptor potential vanilloid type 1 (TRPV1) and 2 (TRPV2), G-coupled protein receptor 55 (GPR55) and adenosine receptor 1 (A1R). Many of these eCB targets were developmentally regulated in both R59X and wild-type mice. In addition, adult R59X mice demonstrated significantly decreased expression of CB1R and TRPV1 in the hippocampus, and TRPV2 in the cortex, while TRPV1 was increased in the cortex. These findings support the potential for dysregulation of eCB signalling as a plausible mechanism and therapeutic target in CDD, given the efficacy of cannabidiol to attenuate hyperexcitability and behavioural deficits in this disorder.

Histopathologic brain age estimation via multiple instance learning.

Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.