The unique anatomical and functional features of principal and interneuron populations are critical for the appropriate function of neuronal circuits. Cell type-specific properties are encoded by selective gene expression programs that shape molecular repertoires and synaptic protein complexes. However, the nature of such programs, particularly for post-transcriptional regulation at the level of alternative splicing is only beginning to emerge. We here demonstrate that transcripts encoding the synaptic adhesion molecules neurexin-1,2,3 are commonly expressed in principal cells and interneurons of the mouse hippocampus but undergo highly differential, cell type-specific alternative splicing. Principal cell-specific neurexin splice isoforms depend on the RNA-binding protein Slm2. By contrast, most parvalbumin-positive (PV⁺) interneurons lack Slm2, express a different neurexin splice isoform and co-express the corresponding splice isoform-specific neurexin ligand Cbln4. Conditional ablation of Nrxn alternative splice insertions selectively in PV⁺ cells results in elevated hippocampal network activity and impairment in a learning task. Thus, PV-cell-specific alternative splicing of neurexins is critical for neuronal circuit function

The cerebellum integrates descending motor commands and sensory information to generate predictions and detect errors during ongoing behaviors. Cerebellar computation has been proposed to control motor but also non-motor behaviors, including reward expectation and cognitive flexibility. However, the organization and functional contribution of cerebellar output channels are incompletely understood. Here, we elaborate the cell-type specificity of a broad connectivity matrix from the deep cerebellar nuclei (DCN) to the dorsal striatum in mice. Cerebello-striatal connections arise from all deep cerebellar subnuclei and are relayed through intralaminar thalamic nuclei (ILN). In the dorsal striatum, these connections target medium spiny neurons, but also ChAT-positive interneurons, a class of tonically active interneurons implicated in shifting and updating behavioral strategies. Chemogenetic silencing of cerebello-striatal connectivity modifies function of striatal ChAT-positive interneurons. We propose that cerebello-striatal connections relay cerebellar computation to striatal circuits for goal-directed behaviors.

Vitamin D deficiency/insufficiency is a public health issue around the world. According to epidemiological studies, low vitamin D levels have been associated with an increased risk of some neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Animal models reveal that vitamin D has a variety of impacts on the synapses and circuits in the brain. A lack of vitamin D affects the expression of synaptic proteins, as well as the synthesis and metabolism of various neurotransmitters. Depending on where vitamin D receptors (VDRs) are expressed, vitamin D may also regulate certain neuronal circuits through the endocannabinoid signaling, mTOR pathway and oxytocin signaling. While inconsistently, some data suggest that vitamin D supplementation may be able to reduce the core symptoms of ASD and ADHD. This review emphasizes vitamin D’s role in the synaptic and circuit mechanisms of neurodevelopmental disorders including ASD and ADHD. Future application of vitamin D in these disorders will depend on both basic research and clinical studies, in order to make the transition from the bench to the bedside.

A heat-stable microparticle platform improves oral micronutrient delivery.

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n = 415 ASD patients (males n = 357), and n = 574 typical development (TD) controls (males n = 410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ∼75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.