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March 9, 2015
Boston Connection March 2015

Tags: Boston Connection

Genetic Changes Shaping the Human Brain.

Bae BI1, Jayaraman D1, Walsh CA2.

Dev Cell. 2015 Feb 23;32(4):423-434.

The development and function of our brain are governed by a genetic blueprint, which reflects dynamic changes over the history of evolution. Recent progress in genetics and genomics, facilitated by next-generation sequencing and single-cell sorting, has identified numerous genomic loci that are associated with a neuroanatomical or neurobehavioral phenotype. Here, we review some of the genetic changes in both protein-coding and noncoding regions that affect brain development and evolution, as well as recent progress in brain transcriptomics. Understanding these genetic changes may provide novel insights into neurological and neuropsychiatric disorders, such as autism and schizophrenia.

Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning.

Kosmicki JA1, Sochat V2, Duda M3, Wall DP3.

Transl Psychiatry. 2015 Feb 24;5:e514

Although the prevalence of autism spectrum disorder (ASD) has risen sharply in the last few years reaching 1 in 68, the average age of diagnosis in the United States remains close to 4-well past the developmental window when early intervention has the largest gains. This emphasizes the importance of developing accurate methods to detect risk faster than the current standards of care. In the present study, we used machine learning to evaluate one of the best and most widely used instruments for clinical assessment of ASD, the Autism Diagnostic Observation Schedule (ADOS) to test whether only a subset of behaviors can differentiate between children on and off the autism spectrum. ADOS relies on behavioral observation in a clinical setting and consists of four modules, with module 2 reserved for individuals with some vocabulary and module 3 for higher levels of cognitive functioning. We ran eight machine learning algorithms using stepwise backward feature selection on score sheets from modules 2 and 3 from 4540 individuals. We found that 9 of the 28 behaviors captured by items from module 2, and 12 of the 28 behaviors captured by module 3 are sufficient to detect ASD risk with 98.27% and 97.66% accuracy, respectively. A greater than 55% reduction in the number of behaviorals with negligible loss of accuracy across both modules suggests a role for computational and statistical methods to streamline ASD risk detection and screening. These results may help enable development of mobile and parent-directed methods for pre

Autism and the synapse: emerging mechanisms and mechanism-based therapies.

Ebrahimi-Fakhari D1, Sahin M.

Curr Opin Neurol. 2015 Apr;28(2):91-102.

 Purpose of Review: Recent studies have implicated hundreds of genetic variants in the cause of autism spectrum disorder (ASD). Genes involved in 'monogenic' forms of syndromic ASD converge on common pathways that are involved in synaptic development, plasticity and signaling. In this review, we discuss how these 'developmental synaptopathies' inform our understanding of the molecular disease in ASD and highlight promising approaches that have bridged the gap between the bench and the clinic.

Recent Findings: Accumulating evidence suggests that synaptic deficits in syndromic and nonsyndromic ASD can be mapped to gene mutations in pathways that control synaptic protein synthesis and degradation, postsynaptic scaffold architecture and neurotransmitter receptors. This is recapitulated in models of Fragile X syndrome (FXS), Tuberous Sclerosis Complex (TSC), Angelman syndrome and Phelan-McDermid syndrome (PMS), all of which cause syndromic ASD. Important recent advances include the development of mouse models and patient-derived induced pluripotent stem cell (iPSC) lines that enable a detailed investigation of synaptic deficits and the identification of potential targets for therapy. Examples of the latter include mGluR5 antagonists in FXS, mTOR inhibitors in TSC and insulin-like growth factor 1 (IGF-1) in PMS.

Summary: Identifying converging pathways in syndromic forms of ASD will uncover novel therapeutic targets for non-syndromic ASD. Insights into developmental synaptopathies will lead to rational development of mechanism-based therapies and clinical trials that may provide a blueprint for other common pathways implicated in the molecular neuropathology of ASD.

A systematic review of molecular imaging (PET and SPECT) in autism spectrum disorder: Current state and future research opportunities.

Z├╝rcher NR1, Bhanot A2, McDougle CJ3, Hooker JM4.

Neurosci Biobehav Rev. 2015 Feb 12. pii: S0149-7634(15)00047-0.

Non-invasive positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are techniques used to quantify molecular interactions, biological processes and protein concentration and distribution. In the central nervous system, these molecular imaging techniques can provide critical insights into neurotransmitter receptors and their occupancy by neurotransmitters or drugs. In recent years, there has been an increase in the number of studies that have investigated neurotransmitters in autism spectrum disorders (ASDs), while earlier studies mostly focused on cerebral blood flow and glucose metabolism. The underlying and contributing mechanisms of ASD are largely undetermined and ASD diagnosis relies on the behavioral phenotype. Discovery of biochemical endophenotypes would represent a milestone in autism research that could potentially lead to ASD subtype stratification and the development of novel therapeutic drugs. This review characterizes the prior use of molecular imaging by PET and SPECT in ASD, addresses methodological challenges and highlights areas of future opportunity for contributions from molecular imaging to understand ASD pathophysiology.


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