Clinical assessment of DSM-IV anxiety disorders in fragile X syndrome: prevalence and characterization

Fragile X syndrome (FXS) is the most common form of inherited intellectual disability (ID). Anxiety and social withdrawal are considered core features of the FXS phenotype, yet there is limited diagnostic evidence of the prevalence of formal anxiety disorders in FXS. This study assessed the prevalence of anxiety disorders in a sample of 58 males and 39 females with FXS (ages 5.0–33.3 years). Participants’ parents completed the Anxiety Disorders Interview Schedule (ADIS-IV), a clinical interview based on DSM-IV criteria, and the Anxiety Depression and Mood Scale (ADAMS), a psychiatric disorders screening instrument normed in ID. We conducted cognitive (IQ) and autism (AUT) assessments and surveyed medication use. Despite a high rate of psychopharmacological treatment, 86.2% of males and 76.9% of females met criteria for an anxiety disorder, with social phobia and specific phobia the most commonly diagnosed. Proband status, gender, and IQ were not significantly related to any anxiety disorders, however significantly higher rates of a few anxiety disorders were found in older age and AUT groups. Significant correlations between ADIS diagnoses and ADAMS scores provided cross-validation of instruments, indicating that the ADIS is suitable for use in FXS. A greater percentage of our sample met criteria for most anxiety disorders than has been reported in other ID groups or the general population. The rate of anxiety compared to general ID suggests that the FMR1 full mutation confers an especially high risk for these disorders, regardless of factors commonly associated with FXS clinical involvement. A thorough clinical assessment and treatment of anxiety should be included in the FXS standard of care

FMR1 premutation and full mutation molecular mechanisms related to autism

Fragile X syndrome (FXS) is caused by an expanded CGG repeat (>200 repeats) in the 5′ un-translated portion of the fragile X mental retardation 1 gene (FMR1) leading to a deficiency or absence of the FMR1 protein (FMRP). FMRP is an RNA-binding protein that regulates the translation of a number of other genes that are important for synaptic development and plasticity. Furthermore, many of these genes, when mutated, have been linked to autism in the general population, which may explain the high comorbidity that exists between FXS and autism spectrum disorders (ASD). Additionally, premutation repeat expansions (55 to 200 CGG repeats) may also give rise to ASD through a different molecular mechanism that involves a direct toxic effect of FMR1 mRNA. It is believed that RNA toxicity underlies much of the premutation-related involvement, including developmental concerns like autism, as well as neurodegenerative issues with aging such as the fragile X-associated tremor ataxia syndrome (FXTAS). RNA toxicity can also lead to mitochondrial dysfunction, which is common in older premutation carriers both with and without FXTAS. Many of the problems with cellular dysregulation in both premutation and full mutation neurons also parallel the cellular abnormalities that have been documented in idiopathic autism. Research regarding dysregulation of neurotransmitter systems caused by the lack of FMRP in FXS, including metabotropic glutamate receptor 1/5 (mGluR1/5) pathway and GABA pathways, has led to new targeted treatments for FXS. Preliminary evidence suggests that these new targeted treatments will also be beneficial in non-fragile X forms of autism

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

Functional Prestin Transduction of Immature Outer Hair Cells from Normal and Prestin-Null Mice

Prestin is a membrane protein in the outer hair cell (OHC) that has been shown to be essential for electromotility. OHCs from prestin-null mice do not express prestin, do not have a nonlinear capacitance (the electrical signature of electromotility), and are smaller in size than wild-type OHCs. We sought to determine whether prestin-null OHCs can be transduced to incorporate functional prestin protein in a normal fashion. A recombinant helper-dependent adenovirus expressing prestin and green fluorescent protein (HDAd–prestin–GFP) was created and tested in human embryonic kidney cells (HEK cells). Transduced HEK cells demonstrated membrane expression of prestin and nonlinear capacitance. HDAd–prestin–GFP was then applied to cochlear sensory epithelium explants harvested from wild-type and prestin-null mice at postnatal days 2–3, the age at which native prestin is just beginning to become functional in wild-type mice. At postnatal days 4–5, we investigated transduced OHCs for (1) their prestin expression pattern as revealed by immunofluorescence; (2) their cell surface area as measured by linear capacitance; and (3) their prestin function as indicated by nonlinear capacitance. HDAd–prestin–GFP efficiently transduced OHCs of both genotypes and prestin protein localized to the plasma membrane. Whole-cell voltage clamp studies revealed a nonlinear capacitance in transduced wild-type and prestin-null OHCs, but not in non-transduced cells of either genotype. Prestin transduction did not increase the linear capacitance (cell surface area) for either genotype. In peak nonlinear capacitance, voltage at peak nonlinear capacitance, charge density of the nonlinear capacitance, and shape of the voltage-capacitance curves, the transduced cells of the two genotypes resembled each other and previously reported data from adult wild-type mouse OHCs. Thus, prestin introduced into prestin-deficient OHCs segregates normally to the cell membrane and generates a normal nonlinear capacitance, indicative of normal prestin function