Vince D. Calhoun | bioRxiv Science

bioRxiv2020

Sparse parallel independent component analysis and its application to identify linked genomic and gray matter alterations underlying working memory impairment in attention-deficit/hyperactivity disorder

Most psychiatric disorders are highly heritable and associated with altered brain structural and functional patterns. Data fusion analyses on brain imaging and genetics, one of which is parallel independent component analysis (pICA), enable the link of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants in psychiatric disorders, it is usually challenging to reliably separate disorder-related genetic factors from the rest of the genome with the typical size of clinical samples. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources. The sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that the proposed spICA yields improved detection of independent sources and imaging-genomic associations compared to pICA. We applied spICA to gray matter volume (GMV) and single nucleotide polymorphism (SNP) data of 341 unrelated adults, including 127 controls, 167 attention-deficit/hyperactivity disorder (ADHD) cases, and 47 unaffected siblings. We identified one SNP source significantly and positively associated with a GMV source in superior/middle frontal regions. This association was replicated with a smaller effect size in 317 adolescents from ADHD families, including 188 individuals with ADHD and 129 unaffected siblings. The association was found to be more significant in ADHD families than controls, and stronger in adults and older adolescents than younger ones. The identified GMV source in superior/middle frontal regions was not correlated with head motion parameters and its loadings (expression levels) were reduced in adolescent (but not adult) individuals with ADHD. This GMV source was associated with working memory deficits in both adult and adolescent individuals with ADHD. The identified SNP component highlights SNPs in genes encoding long non-coding RNAs and SNPs in genes MEF2C, CADM2, and CADPS2, which have known functions relevant for modulating neuronal substrates underlying high-level cognition in ADHD.

bioengineering↗

bioRxiv2015

The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Neuroscience↗