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Item 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 MedicineINTRODUCTION: 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.Item Geometric Navigation of Axons in a Cerebral Pathway: Comparing dMRI with Tract Tracing and Immunohistochemistry(Oxford University Press, 2018-04-01) Mortazavi, Farzad; Oblak, Adrian L.; Morrison, Will Z.; Schmahmann, Jeremy D.; Stanley, H. Eugene; Wedeen, Van J.; Rosene, Douglas L.; Pathology and Laboratory Medicine, School of MedicineBrain fiber pathways are presumed to follow smooth curves but recent high angular resolution diffusion MRI (dMRI) suggests that instead they follow 3 primary axes often nearly orthogonal. To investigate this, we analyzed axon pathways under monkey primary motor cortex with (1) dMRI tractography, (2) axon tract tracing, and (3) axon immunohistochemistry. dMRI tractography shows the predicted crossings of axons in mediolateral and dorsoventral orientations and does not show axon turns in this region. Axons labeled with tract tracer in the motor cortex dispersed in the centrum semiovale by microscopically sharp axonal turns and/or branches (radii ≤15 µm) into 2 sharply defined orientations, mediolateral and dorsoventral. Nearby sections processed with SMI-32 antibody to label projection axons and SMI-312 antibody to label all axons revealed axon distributions parallel to the tracer axons. All 3 histological methods confirmed preponderant axon distributions parallel with dMRI axes with few axons (<20%) following smooth curves or diagonal orientations. These findings indicate that axons navigate deep white matter via microscopic sharp turns and branches between primary axes. They support dMRI observations of primary fiber axes, as well as the prediction that fiber crossings include navigational events not yet directly resolved by dMRI. New methods will be needed to incorporate coherent microscopic navigation into dMRI of connectivity.Item Human connectome module pattern detection using a new multi-graph MinMax cut model(Springer, 2014) Wang, De; Wang, Yang; Nie, Feiping; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of MedicineMany recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.Item Multi-omics for biomarker approaches in the diagnostic evaluation and management of abdominal pain and irritable bowel syndrome: what lies ahead(Taylor & Francis, 2023) Shin, Andrea; Kashyap, Purna C.; Medicine, School of MedicineReliable biomarkers for common disorders of gut-brain interaction characterized by abdominal pain, including irritable bowel syndrome (IBS), are critically needed to enhance care and develop individualized therapies. The dynamic and heterogeneous nature of the pathophysiological mechanisms that underlie visceral hypersensitivity have challenged successful biomarker development. Consequently, effective therapies for pain in IBS are lacking. However, recent advances in modern omics technologies offer new opportunities to acquire deep biological insights into mechanisms of pain and nociception. Newer methods for large-scale data integration of complementary omics approaches have further expanded our ability to build a holistic understanding of complex biological networks and their co-contributions to abdominal pain. Here, we review the mechanisms of visceral hypersensitivity, focusing on IBS. We discuss candidate biomarkers for pain in IBS identified through single omics studies and summarize emerging multi-omics approaches for developing novel biomarkers that may transform clinical care for patients with IBS and abdominal pain.Item Neurocognitive factors in sensory restoration of early deafness: a connectome model(Elsevier, 2016-05) Kral, A.; Kronenberger, W. G.; Pisoni, D. B.; O’Donoghue, G. M.; Psychiatry, School of MedicineProgress in biomedical technology (cochlear, vestibular, and retinal implants) has led to remarkable success in neurosensory restoration, particularly in the auditory system. However, outcomes vary considerably, even after accounting for comorbidity-for example, after cochlear implantation, some deaf children develop spoken language skills approaching those of their hearing peers, whereas other children fail to do so. Here, we review evidence that auditory deprivation has widespread effects on brain development, affecting the capacity to process information beyond the auditory system. After sensory loss and deafness, the brain's effective connectivity is altered within the auditory system, between sensory systems, and between the auditory system and centres serving higher order neurocognitive functions. As a result, congenital sensory loss could be thought of as a connectome disease, with interindividual variability in the brain's adaptation to sensory loss underpinning much of the observed variation in outcome of cochlear implantation. Different executive functions, sequential processing, and concept formation are at particular risk in deaf children. A battery of clinical tests can allow early identification of neurocognitive risk factors. Intervention strategies that address these impairments with a personalised approach, taking interindividual variations into account, will further improve outcomes.Item A novel structure-aware sparse learning algorithm for brain imaging genetics(Springer, 2014) Du, Lei; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L.; Huang, Heng; Inlow, Mark; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineBrain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.Item Pattern Visualization of Human Connectome Data(Europgraphics Association, 2012) Guo, Yishi; Wang, Yang; Fang, Shiaofen; Chao, Hongyang; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineThe human brain is a complex network with countless connected neurons, and can be described as a "connectome". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery. We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.Item Resolution and b value dependent structural connectome in ex vivo mouse brain(Elsevier, 2022) Crater, Stephanie; Maharjan, Surendra; Qi, Yi; Zhao, Qi; Cofer, Gary; Cook, James C.; Johnson, G. Allan; Wang, Nian; Radiology and Imaging Sciences, School of MedicineDiffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 μm isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 μm isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000s/mm2 to 8000 s/mm2), angular resolution (10 to 126), and spatial resolution (25 µm to 200 µm). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles is necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R2 > 0.99); a single-shell acquisition with b value of 3000 s/mm2 shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.Item The human connectome in Alzheimer disease - relationship to biomarkers and genetics(Springer Nature, 2021) Yu, Meichen; Sporns, Olaf; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineThe pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-β and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-β and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.Item A whole‐brain modeling approach to identify individual and group variations in functional connectivity(Wiley, 2021-01) Zhao, Yi; Caffo, Brian S.; Wang, Bingkai; Li, Chiang-Shan R.; Luo, Xi; Biostatistics, School of Public HealthResting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.