Executive Impairment in Alcohol Use Disorder Reflects Structural Changes in Large-Scale Brain Networks: A Joint Independent Component Analysis on Gray-Matter and White-Matter Features

ABSTRACTResting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained animals of either sex and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. Using resting-state functional magnetic resonance imaging (rs-fMRI), in this exploratory study we found multiple resting-state networks in dogs, which resemble the pattern described in humans. We report the following dog resting-state networks: default mode network (DMN), visual network (VIS), sensorimotor network (SMN), combined auditory (AUD)-saliency (SAL) network and cerebellar network (CER). The DMN, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give us insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

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Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel independent component analysis with reference

NeuroImage ◽

10.1016/j.neuroimage.2013.05.073 ◽

2013 ◽

Vol 83 ◽

pp. 384-396 ◽

Cited By ~ 33

Author(s):

Jiayu Chen ◽

Vince D. Calhoun ◽

Godfrey D. Pearlson ◽

Nora Perrone-Bizzozero ◽

Jing Sui ◽

...

Keyword(s):

Independent Component Analysis ◽

Gray Matter ◽

Component Analysis ◽

Independent Component ◽

Guided Exploration ◽

Genomic Risk

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Mapping joint grey and white matter reductions in Alzheimer's disease using joint independent component analysis

Neuroscience Letters ◽

10.1016/j.neulet.2012.10.038 ◽

2012 ◽

Vol 531 (2) ◽

pp. 136-141 ◽

Cited By ~ 12

Author(s):

Xiaojuan Guo ◽

Yuan Han ◽

Kewei Chen ◽

Yan Wang ◽

Li Yao

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Independent Component Analysis ◽

Component Analysis ◽

Independent Component

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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

10.1101/2020.07.11.198622 ◽

2020 ◽

Author(s):

Kuaikuai Duan ◽

Jiayu Chen ◽

Vince D. Calhoun ◽

Wenhao Jiang ◽

Kelly Rootes-Murdy ◽

...

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Working Memory ◽

Independent Component Analysis ◽

Psychiatric Disorders ◽

Gray Matter ◽

Attention Deficit ◽

Component Analysis ◽

Independent Component ◽

Hyperactivity Disorder ◽

Common Genetic Variants

AbstractMost 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.

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Gray Matter Segmentation of Brain MRI Using Hybrid Enhanced Independent Component Analysis in Noisy and Noise Free Environment

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.47.75 ◽

2020 ◽

Vol 47 ◽

pp. 75-103

Author(s):

Shaik Basheera ◽

M. Satya Sai Ram

Keyword(s):

Independent Component Analysis ◽

Gray Matter ◽

Brain Mri ◽

Component Analysis ◽

Independent Component ◽

Noisy Environments ◽

Low Contrast ◽

Free Environment ◽

Medical Segmentation ◽

The Brain

Medical segmentation is the primary task performed to diagnosis the abnormalities in the human body. The brain is the complex organ and anatomical segmentation of brain tissues is a challenging task. In this paper, we used Enhanced Independent component analysis to perform the segmentation of gray matter. We used modified K means, Expected Maximization and Hidden Markov random field to provide better spatial correlation that overcomes in-homogeneity, noise and low contrast. Our objective is achieved in two steps (i) initially unwanted tissues are clipped from the MRI image using skull stripped Algorithm (ii) Enhanced Independent Component analysis is used to perform the segmentation of gray matter. We apply the proposed method on both T1w and T2w MRI to perform segmentation of gray matter at different noisy environments. We evaluate the the performance of our proposed system with Jaccard Index, Dice Coefficient and Accuracy. We further compared the proposed system performance with the existing frameworks. Our proposed method gives better segmentation of gray matter useful for diagnosis neurodegenerative disorders.

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