Browsing by Author "Goni, Joaquin"

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    Brain-wide structural connectivity alterations under the control of Alzheimer risk genes
    (Inderscience, 2020) Yan, Jingwen; Raja V, Vinesh; Huang, Zhi; Amico, Enrico; Nho, Kwangsik; Fang, Shiaofen; Sporns, Olaf; Wu, Yu-chien; Saykin, Andrew; Goni, Joaquin; Shen, Li; BioHealth Informatics, School of Informatics and Computing
    Background: Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown. Methods: We used the genotype and DTI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. After constructing the brain network for each subject, we extracted three types of link measures, including fiber anisotropy, fiber length and density. We then performed a targeted genetic association analysis of brain-wide connectivity measures using general linear regression models. Age at scan and gender were included in the regression model as covariates. For fair comparison of the genetic effect on different measures, fiber anisotropy, fiber length and density were all normalized with mean as 0 and standard deviation as one.We aim to discover the abnormal brain-wide network alterations under the control of 34 AD risk SNPs identified in previous large-scale genome-wide association studies. Results: After enforcing the stringent Bonferroni correction, rs10498633 in SLC24A4 were found to significantly associated with anisotropy, total number and length of fibers, including some connecting brain hemispheres. With a lower level of significance at 5e-6, we observed significant genetic effect of SNPs in APOE, ABCA7, EPHA1 and CASS4 on various brain connectivity measures.
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    Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks
    (Elsevier, 2016-12-22) Contreras, Joey A.; Goni, Joaquin; Risacher, Shannon L.; Amico, Enrico; Yoder, Karmen; Dzemidzic, Mario; West, John D.; McDonald, Brenna C.; Farlow, Martin R.; Sporns, Olaf; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of Medicine
    INTRODUCTION: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. METHODS: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). RESULTS: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. DISCUSSION: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.
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    Integrated Visualization of Human Brain Connectome Data
    (Springer, 2015-08) Li, Huang; Fang, Shiaofen; Goni, Joaquin; Contreras, Joey A.; Liang, Yanhua; Cai, Chengtao; West, John D.; Risacher, Shannon L.; Wang, Yang; Sporns, Olaf; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases.