Nav1.2, a voltage-gated sodium channel subunit encoded by the Scn2a gene, has been implicated in various brain disorders, including epilepsy, autism spectrum disorder, intellectual disability, and schizophrenia. Nav1.2 is known to regulate the generation of action potentials in the axon initial segment and their propagation along axonal pathways. Nav1.2 also regulates synaptic integration and plasticity by promoting back-propagation of action potentials to dendrites, but whether Nav1.2 deletion in mice affects neuronal excitability, synaptic transmission, synaptic plasticity, and/or disease-related animal behaviors remains largely unclear. Here, we report that mice heterozygous for the Scn2a gene ( Scn2a +/- mice) show decreased neuronal excitability and suppressed excitatory synaptic transmission in the presence of network activity in the hippocampus. In addition, Scn2a +/- mice show suppressed hippocampal long-term potentiation (LTP) in association with impaired spatial learning and memory, but show largely normal locomotor activity, anxiety-like behavior, social interaction, repetitive behavior, and whole-brain excitation. These results suggest that Nav1.2 regulates hippocampal neuronal excitability, excitatory synaptic drive, LTP, and spatial learning and memory in mice.

No abstract

No abstract

Brain structural morphology varies over the aging trajectory, and the prediction of a person’s age using brain morphological features can help the detection of an abnormal aging process. Neuroimaging-based brain age is widely used to quantify an individual’s brain health as deviation from a normative brain aging trajectory. Machine learning approaches are expanding the potential for accurate brain age prediction but are challenging due to the great variety of machine learning algorithms. Here, we aimed to compare the performance of the machine learning models used to estimate brain age using brain morphological measures derived from structural magnetic resonance imaging scans. We evaluated 27 machine learning models, applied to three independent datasets from the Human Connectome Project (HCP, n = 1113, age range 22–37), the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, n = 601, age range 18–88), and the Information eXtraction from Images (IXI, n = 567, age range 19–86). Performance was assessed within each sample using cross-validation and an unseen test set. The models achieved mean absolute errors of 2.75–3.12, 7.08–10.50, and 8.04–9.86 years, as well as Pearson’s correlation coefficients of 0.11–0.42, 0.64–0.85, and 0.63–0.79 between predicted brain age and chronological age for the HCP, Cam-CAN, and IXI samples, respectively. We found a substantial difference in performance between models trained on the same data type, indicating that the choice of model yields considerable variation in brain-predicted age. Furthermore, in three datasets, regularized linear regression algorithms achieved similar performance to nonlinear and ensemble algorithms. Our results suggest that regularized linear algorithms are as effective as nonlinear and ensemble algorithms for brain age prediction, while significantly reducing computational costs. Our findings can serve as a starting point and quantitative reference for future efforts at improving brain age prediction using machine learning models applied to brain morphometric data.

No abstract