Browsing by Author "Sporns, Olaf"

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    Aging, beta‐amyloid deposition, and brain functional connectivity decline
    (Wiley, 2025-01-09) Yi, Dahyun; Chumin, Evgeny J.; Byun, Min Soo; Cha, Woo-Jin; Ahn, Hyejin; Kim, Yu Kyeong; Kang, Koung Mi; Sohn, Chul-Ho; Risacher, Shannon L.; Sporns, Olaf; Nho, Kwangsik; Saykin, Andrew J.; Lee, Dong Young; KBASE; Radiology and Imaging Sciences, School of Medicine
    Background: Changes in brain network organization are influenced by aging. Accumulation of amyloid‐beta (Aβ) and neurodegeneration in the neocortex are also expected to alter neuronal networks. Therefore, we examined the relationship between aging and brain functional connectivity (FC), as well as the effect of brain Aβ on this relationship. Method: Resting state functional MRI (rsfMRI) from 594 participants spanning age and diagnostic severity of AD from the Korean Brain Aging Study for the Early Diagnosis and Prediction of AD (KBASE) was preprocessed as previously described in studies conducted at the Indiana AD Research Center (Chumin 2021, 2023). Cortical FC data from 200 regions (Schaefer 2018) grouped into 7 canonical resting state networks (RSN; Yeo 2011) was used to compute a network segregation measure (ratio of within‐ to between‐network connectivity (Chan 2014); here used as an index of FC) across all RSNs. Additionally, a subsample of older participants was classified as Aβ positive or negative based on global amyloid in Centiloid units (Klunk 2015). Result: Intrinsic network connectivity was reduced with increasing age beginning in young adulthood (Fig, left), resulting in a dedifferentiated, or less segregated, network architecture (t = ‐4.79, p = 0.000002). The relationship between age and network segregation was significant in the Aβ negative group (t = ‐4.09, p = 0.00005); however, such relationship was not found in the Aβ positive group (Fig, right). Fitted Aβ values were significantly different (Welch Two Sample t‐test: p < 2.2e‐16). Conclusion: This preliminary study elucidates age‐related decline of brain FC, quantified as network segregation, from young adulthood to late‐life, wherein RSN communication become less coherent, manifesting as a degeneration of FC structure. Such age‐related reduction pattern of brain connectivity appears disappear under the presence of pathological Aβ deposition in brain.
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    Alterations in White Matter Microstructure and Connectivity in Young Adults with Alcohol Use Disorder
    (Wiley, 2019) Chumin, Evgeny J.; Grecco, Gregory G.; Dzemidzic, Mario; Cheng, Hu; Finn, Peter; Sporns, Olaf; Newman, Sharlene D.; Yoder, Karmen K.; Radiology and Imaging Sciences, School of Medicine
    Background Magnetic resonance imaging (MRI) studies have shown differences in volume and structure in the brains of individuals with alcohol use disorder (AUD). Most research has focused on neuropathological effects of alcohol that appear after years of chronic alcohol misuse. However, few studies have investigated white matter (WM) microstructure and diffusion MRI‐based (DWI) connectivity during early stages of AUD. Therefore, the goal of this work was to investigate WM integrity and structural connectivity in emerging adulthood AUD subjects using both conventional DWI metrics and a novel connectomics approach. Methods Twenty‐two AUD and eighteen controls (CON) underwent anatomical and diffusion MRI. Outcome measures were scalar diffusion metrics and structural network connectomes. Tract Based Spatial Statistics was used to investigate group differences in diffusion measures. Structural connectomes were used as input into a community structure procedure to obtain a co‐classification index matrix (an indicator of community association strength) for each subject. Differences in co‐classification and structural connectivity (indexed by streamline density) were assessed via the Network Based Statistics Toolbox. Results AUD had higher FA values throughout the major WM tracts, but also had lower FA values in WM tracts in the cerebellum and right insula (pTFCE < 0.05). Mean diffusivity was generally lower in the AUD group (pTFCE < 0.05). AUD had lower co‐classification of nodes between ventral attention and default mode networks, and higher co‐classification between nodes of visual, default mode, and somatomotor networks. Additionally, AUD had higher fiber density between an adjacent pair of nodes within the default mode network. Conclusion Our results indicate that emerging adulthood AUD subjects may have differential patterns of FA and distinct differences in structural connectomes compared to CON. These data suggest that such alterations in microstructure and structural connectivity may uniquely characterize early stages of AUD and/or a predisposition for development of AUD.
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    Association between brain tau deposition and default mode network connectivity in cognitively normal older adults
    (Wiley, 2025-01-09) Cha, Woo-Jin; Yi, Dahyun; Chumin, Evgeny J.; Byun, Min Soo; Jung, Joon Hyung; Ahn, Hyejin; Kim, Yu Kyeong; Lee, Yun-Sang; Kang, Koung Mi; Sohn, Chul-Ho; Risacher, Shannon L.; Sporns, Olaf; Nho, Kwangsik; Saykin, Andrew J.; Lee, Dong Young; KBASE Research Group; Radiology and Imaging Sciences, School of Medicine
    Background: Alzheimer’s disease (AD) pathology occurs in the brain before manifestation of significant cognitive decline. Growing evidence suggests that brain networks such as default mode network (DMN) or salience network, identified through resting‐state functional magnetic resonance imaging (MRI), are affected by AD pathology. In this study, we investigated the relationship between network segregation and the key in vivo AD pathologies including beta‐amyloid (Aβ) and tau deposition in old adults with no cognitive impairment. Method: A total 283 older adults with normal cognition aging from 55 to 87 were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV‐1451 PET for measuring tau deposition, structural MRI, and resting‐state functional MRI for measuring functional connectivity (FC). For PET scans, standard uptake value ratio (SUVR) was used for the analyses; combined regions of inferior cerebellum and pons were used as the reference region when obtaining SUVRs. For FC, segregation values (ratios between median z‐transformed Pearson correlation of within‐ and between‐network connectivity) for overall and the seven individual resting state networks were computed (Table). The relationships between Aβ or tau deposition and network connectivity segregation were examined through cross‐sectional approach using multiple regression analyses. In the analyses, Aβ or tau deposition was used as an independent variable and segregation values of the networks were used as dependent variables. Result: Tau deposition had a significant negative association with the DMN segregation (β = ‐0.249, p = 0.007); but, tau had no relationships with any other networks (Table). Aβ deposition was not associated with any segregation values for the seven brain networks (Table). Conclusion: Our finding suggests that impaired functional connectivity of DMN is closely linked to tau deposition even in cognitively unimpaired older individuals.
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    BECA: A Software Tool for Integrated Visualization of Human Brain Data
    (Springer, 2017) Li, Huang; Fang, Shiaofen; Zigon, Bob; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquin; Shen, Li; Computer and Information Science, School of Science
    Visualization plays an important role in helping neuroscientist understanding human brain data. Most publicly available software focuses on visualizing a specific brain imaging modality. Here we present an extensible visualization platform, BECA, which employ a plugin architecture to facilitate rapid development and deployment of visualization for human brain data. This paper will introduce the architecture and discuss some important design decisions in implementing the BECA platform and its visualization plugins.
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    Brain explorer for connectomic analysis
    (Springer, 2017-08-23) Li, Huang; Fang, Shiaofen; Contreras, Joey A.; West, John D.; Risacher, Shannon L.; Wang, Yang; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Visualization plays a vital role in the analysis of multimodal 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 anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. 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.
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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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    Characterizing neurodegeneration in the human connectome: a network science study of hereditary diffuse leukoencephalopathy with spheroids
    (Office of the Vice Chancellor for Research, 2015-04-17) Contreras, Joey; Rishacher, Shannon L.; West, John D.; Wu, Yu-Chien; Wang, Yang; Murrell, Jill R.; Dzemidzic, Mario; Farlow, Martin R.; Unverzagt, Frederik; Ghetti, Bernardino; Matthews, Brandy R.; Quaid, Kimberly A.; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín
    Abstract The effect of white matter neurodegeneration on the human connectome and its functional implications is an important topic with clinical applicability of advanced brain network analysis. The aim of this study was to evaluate integration and segregation changes in structural connectivity (SC) that arise as consequence of white matter lesions in hereditary diffuse leukoencephalopathy with spheroids (HDLS). Also, we assessed the relationship between HDLS induced structural changes and changes in restingstate functional connectivity (rsFC). HDLS is a rare autosomal dominant neurodegenerative disorder caused by mutations in the CSF1R gene. HDLS is characterized by severe white matter damage leading to prominent subcortical lesions detectable by structural MRI. Spheroids, an important feature of HDLS, are axonal swellings indicating damage. HDLS causes progressive motor and cognitive decline. The clinical symptoms of HDLS are often mistaken for other diseases such as Alzheimer’s disease, frontotemporal dementia, atypical Parkinsonism or multiple sclerosis. Our study is focused on the follow-up of two siblings, one being a healthy control (HC) and the other one being an HDLS patient. In this study, deterministic fiber-tractography of diffusion MRI with multi-tensor modeling was used in order to obtain reliable and reproducible SC matrices. Integration changes were measured by means of SC shortest-paths (including distance and number of edges), whereas segregation and community organization were measured by means of a multiplex modularity analysis on the SC matrices. Additionally, rsFC was modeled using state of the art preprocessing methods including motion regressors and scrubbing. This allowed us to characterize functional changes associated to the disease. Major integration disruption involved superior frontal (L,R), caudal middle frontal (R), precentral (L,R), inferior parietal (R), insula (R) and paracentral (L) regions. Major segregation changes were characterized by the disruption of a large bilateral module that was observed in the HC that includes the frontal pole (L,R), medial orbitofrontal (L,R), rostral middle frontal (L), superior frontal (L,R), precentral (L,R), paracentral (L,R), rostral anterior cingulate (L,R), caudal anterior cingulate (L,R), posterior cingulate (L,R), postcentral (L), precuneus (L,R), lateral orbitofrontal (R) and parsorbitalis (R). The combination of tractography and network analysis permitted the detection and characterization of profound cortical to cortical changes in integration and segregation associated with HDLS white matter lesions and its relationship with rsFC. Our preliminary findings suggest that advanced network analytic approaches show promising sensitivity to known white matter pathology and progression. Further Indiana Alzheimer Disease Center Symposium. March 6, 2015. research is needed to address the specificity of network profiles for differentiation among white matter pathologies and diseases.
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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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    Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis
    (Elsevier, 2013-03-22) Kim, Dae-Jin; Bolbecker, Amanda R.; Howell, Josselyn; Rass, Olga; Sporns, Olaf; Hetrick, William P.; Breier, Alan; O'Donnell, Brian F.; Psychiatry, School of Medicine
    Disruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity.
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    Edge time series components of functional connectivity and cognitive function in Alzheimer's disease
    (Springer, 2024) Chumin, Evgeny J.; Cutts, Sarah A.; Risacher, Shannon L.; Apostolova, Liana G.; Farlow, Martin R.; McDonald, Brenna C.; Wu, Yu‑Chien; Betzel, Richard; Saykin, Andrew J.; Sporns, Olaf; Radiology and Imaging Sciences, School of Medicine
    Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer’s disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer’s Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer’s disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.