Effects of SYN1Q555X mutation on cortical gray matter microstructure

The different clinical facets of SYN1-related neurodevelopmental disorders

Synapsin-I (SYN1) is a presynaptic phosphoprotein crucial for synaptogenesis and synaptic plasticity. Pathogenic SYN1 variants are associated with variable X-linked neurodevelopmental disorders mainly affecting males. In this study, we expand on the clinical and molecular spectrum of the SYN1-related neurodevelopmental disorders by describing 31 novel individuals harboring 22 different SYN1 variants. We analyzed newly identified as well as previously reported individuals in order to define the frequency of key features associated with these disorders. Specifically, behavioral disturbances such as autism spectrum disorder or attention deficit hyperactivity disorder are observed in 91% of the individuals, epilepsy in 82%, intellectual disability in 77%, and developmental delay in 70%. Seizure types mainly include tonic-clonic or focal seizures with impaired awareness. The presence of reflex seizures is one of the most representative clinical manifestations related to SYN1. In more than half of the cases, seizures are triggered by contact with water, but other triggers are also frequently reported, including rubbing with a towel, fever, toothbrushing, fingernail clipping, falling asleep, and watching others showering or bathing. We additionally describe hyperpnea, emotion, lighting, using a stroboscope, digestive troubles, and defecation as possible triggers in individuals with SYN1 variants. The molecular spectrum of SYN1 variants is broad and encompasses truncating variants (frameshift, nonsense, splicing and start-loss variants) as well as non-truncating variants (missense substitutions and in-frame duplications). Genotype-phenotype correlation revealed that epileptic phenotypes are enriched in individuals with truncating variants. Furthermore, we could show for the first time that individuals with early seizures onset tend to present with severe-to-profound intellectual disability, hence highlighting the existence of an association between early seizure onset and more severe impairment of cognitive functions. Altogether, we present a detailed clinical description of the largest series of individuals with SYN1 variants reported so far and provide the first genotype-phenotype correlations for this gene. A timely molecular diagnosis and genetic counseling are cardinal for appropriate patient management and treatment.

A systematic review and meta-analysis of imaging genetics studies of specific reading disorder

The imaging genetics of specific reading disabilities (SRD) is an emerging field that aims to characterize the disabilities' neurobiological causes, including atypical brain structure and function and distinct genetic architecture. The present review aimed to summarize current imaging genetics studies of SRD, characterize the effect sizes of reported results by calculating Cohen's d, complete a Fisher's Combined Probability Test for genes featured in multiple studies, and determine areas for future research. Results demonstrate associations between SRD risk genes and reading network brain phenotypes. The Fisher's test revealed promising results for the genes DCDC2, KIAA0319, FOXP2, SLC2A3, and ROBO1. Future research should focus on exploratory approaches to identify previously undiscovered genes. Using comprehensive neuroimaging (e.g., functional and effective connectivity) and genetic (e.g., sequencing and epigenetic) techniques, and using larger samples, diverse stages of development, and longitudinal investigations, would help researchers understand the neurobiological correlates of SRD to improve early identification.

NODDI in clinical research

Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.

In Vivo Imaging of Gray Matter Microstructure in Major Psychiatric Disorders: Opportunities for Clinical Translation

Postmortem studies reveal that individuals with major neuropsychiatric disorders such as schizophrenia and autism spectrum disorder have gray matter microstructural abnormalities. These include abnormalities in neuropil organization, expression of proteins supporting neuritic and synaptic integrity, and myelination. Genetic and postmortem studies suggest that these changes may be causally linked to the pathogenesis of these disorders. Advances in diffusion-weighted magnetic resonance image (dMRI) acquisition techniques and biophysical modeling allow for the quantification of gray matter microstructure in vivo. While several biophysical models for imaging microstructural properties are available, one in particular, neurite orientation dispersion and density imaging (NODDI), holds great promise for clinical applications. NODDI can be applied to both gray and white matter and requires only a single extra shell beyond a standard dMRI acquisition. Since its development only a few years ago, the NODDI algorithm has been used to characterize gray matter microstructure in schizophrenia, Alzheimer's disease, healthy aging, and development. These investigations have shown that microstructural findings in vivo, using NODDI, align with postmortem findings. Not only do NODDI and other advanced dMRI-based modeling methods provide a window into the brain previously only available postmortem, but they may be more sensitive to certain brain changes than conventional magnetic resonance imaging approaches. This opens up exciting new possibilities for clinicians to more rapidly detect disease signatures and allows earlier intervention in the course of the disease. Given that neurites and gray matter microstructure have the capacity to rapidly remodel, these novel dMRI-based methods represent an opportunity to noninvasively monitor neuroplastic changes posttherapy within much shorter time scales.

Effects of SYN1Q555X mutation on cortical gray matter microstructure

A new Q555X mutation on the SYN1 gene was recently found in several members of a family segregating dyslexia, epilepsy, and autism spectrum disorder. To describe the effects of this mutation on cortical gray matter microstructure, we performed a surface-based group study using novel diffusion and quantitative multiparametric imaging on 13 SYN1_Q555X mutation carriers and 13 age- and sex-matched controls. Specifically, diffusion kurtosis imaging (DKI) and neurite orientation and dispersion and density imaging (NODDI) were used to analyze multi-shell diffusion data and obtain parametric maps sensitive to tissue structure, while quantitative metrics sensitive to tissue composition (T1, T2* and relative proton density [PD]) were obtained from a multi-echo variable flip angle FLASH acquisition. Results showed significant microstructural alterations in several regions usually involved in oral and written language as well as dyslexia. The most significant changes in these regions were lowered mean diffusivity and increased fractional anisotropy. This study is, to our knowledge, the first to successfully use diffusion imaging and multiparametric mapping to detect cortical anomalies in a group of subjects with a well-defined genotype linked to language impairments, epilepsy and autism spectrum disorder (ASD).