There is substantial heterogeneity in the development of depression from adolescence into adulthood. Yet, little is known about the risk factors underlying its various patterns of development. For instance, despite the discovery of genetic variants for depression, these discoveries have not accounted for the high degree of genetic covariation between multiple disorders, nor have they been applied to disambiguate its heterogeneous developmental presentations.
Slide of the Week
Bradley T Christian, PhD – Slide of the Week
Adults with Down syndrome (DS) are predisposed to Alzheimer’s disease (AD) and a characterization of glucose metabolism change throughout AD progression has yet to be performed in this population. Using FDG PET, regional glucose metabolism was evaluated across groups of cognitively stable DS (CS-DS), DS with mild cognitive impairment or Alzheimer’s disease (MCI-DS/AD), and healthy non-DS sibling controls.
Andrew Alexander, PhD – Slide of the Week
Demonstration of the ability of MPnRAGE to correct for severe motion artifacts in a 7 year old girl. Retrospective motion correction greatly reduced motion-induced blurring in both structural T1-weighted images and quantitative T1 maps. The correction greatly improves the reliability of brain imaging measurements in children. The plots indicate the estimated amount of head motions that were corrected.
Donna Werling, PhD – Slide of the Week
Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and post- natal development.
Xinyu Zhao, PhD – Slide of the Week
Title: Identification of FMR1-regulated molecular networks in human neurodevelopment Legend: Generation of FMR1-FLAG hPSCs using one-step seamless genome editing using CRISPR-Cas9, Neural differentiation of hPSCs into forebrain dorsal NPCs (dNPC) and ventral MGE-like NPCs (vNPC), …
Daifeng Wang, PhD – Slide of the Week
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks.
Houri K. Vorperian, PhD – Slide of the Week
We present a unified heat kernel smoothing framework for modeling 3D anatomical surface data extracted from medical images. Due to image acquisition and preprocessing noises, it is expected the medical imaging data is noisy. The surface data of the anatomical structures is regressed using the weighted linear combination of Laplace-Beltrami (LB) eigenfunctions to smooth out noisy data and perform statistical analysis.
Masatoshi Suzuki, DVM, PhD – Slide of the Week
Amyotrophic lateral sclerosis (ALS) is a late-onset neuromuscular disease with no cure and limited treatment options. Patients experience a gradual paralysis leading to death from respiratory complications on average only 2-5 years after diagnosis.
Audra Sterling, PhD – Slide of the Week
Title: Comparing tense and agreement productivity in boys with fragile X syndrome, children with developmental language disorder, and children with typical development Legend: Pattern of tense and agreement productivity across boys with fragile X syndrome (FXS), …
Jenny Saffran, PhD – Slide of the Week
Eye-gaze methods offer numerous advantages for studying cognitive processes in children with autism spectrum disorder (ASD), but data loss may threaten the validity and generalizability of results. Some eye-gaze systems may be more vulnerable to data loss than others, but to our knowledge, this issue has not been empirically investigated. In the current study, we asked whether automatic eye-tracking and manual gaze coding produce different rates of data loss or different results in a group of 51 toddlers with ASD.