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Browsing by Author "Amico, Enrico"
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Item 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 ComputingBackground: 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.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 The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk(Elsevier, 2020-04) Amico, Enrico; Dzemidzic, Mario; Oberlin, Brandon G.; Carron, Claire R.; Harezlak, Jaroslaw; Goñi, Joaquín; Kareken, David A.; Neurology, School of MedicineHuman functional brain connectivity is usually measured either at “rest” or during cognitive tasks, ignoring life’s moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.Item Heritability Estimation of Reliable Connectomic Features*(Springer Nature, 2018-09) Xie, Linhui; Amico, Enrico; Salama, Paul; Wu, Yu-chien; Fang, Shiaofen; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín; Yan, Jingwen; Shen, Li; Radiology and Imaging Sciences, School of MedicineBrain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed ~5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.Item Joint Exploration and Mining of Memory-Relevant Brain Anatomic and Connectomic Patterns via a Three-Way Association Model(IEE, 2018-04) Yan, Jingwen; Liu, Kefei; Li, Huang; Amico, Enrico; Risacher, Shannon L.; Wu, Yu-Chien; Fang, Shiaofen; Sporns, Olaf; Saykin, Andrew J.; Goñi, Joaquín; Shen, Li; BioHealth Informatics, School of Informatics and ComputingEarly change in memory performance is a key symptom of many brain diseases, but its underlying mechanism remains largely unknown. While structural MRI has been playing an essential role in revealing potentially relevant brain regions, increasing availability of diffusion MRI data (e.g., Human Connectome Project (HCP)) provides excellent opportunities for exploration of their complex coordination. Given the complementary information held in these two imaging modalities, we hypothesize that studying them as a whole, rather than individually, and exploring their association will provide us valuable insights of the memory mechanism. However, many existing association methods, such as sparse canonical correlation analysis (SCCA), only manage to handle two-way association and thus cannot guarantee the selection of biomarkers and associations to be memory relevant. To overcome this limitation, we propose a new outcome-relevant SCCA model (OSCCA) together with a new algorithm to enable the three-way associations among brain connectivity, anatomic structure and episodic memory performance. In comparison with traditional SCCA, we demonstrate the effectiveness of our model with both synthetic and real data from the HCP cohort.Item Mapping the functional connectome traits of levels of consciousness(Elsevier, 2017-03) Amico, Enrico; Marinazzo, Daniele; DiPerri, Carol; Heine, Lizette; Annen, Jitka; Martial, Charlotte; Dzemidzic, Mario; Laureys, Steven; Goñi, Joaquín; Department of Neurology, School of MedicineExamining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a “resting” human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage.Item A morphospace of functional configuration to assess configural breadth based on brain functional networks(MIT Press, 2021-09) Duong-Tran, Duy; Abbas, Kausar; Amico, Enrico; Corominas-Murtra, Bernat; Dzemidzic, Mario; Kareken, David; Ventresca, Mario; Goñi, Joaquín; Neurology, School of MedicineThe quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.Item Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease(Wiley, 2021-08) Svaldi, Diana O.; Goñi, Joaquín; Abbas, Kausar; Amico, Enrico; Clark, David G.; Muralidharan, Charanya; Dzemidzic, Mario; West, John D.; Risacher, Shannon L.; Saykin, Andrew J.; Apostolova, Liana G.; Medicine, School of MedicineFunctional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.Item Tangent functional connectomes uncover more unique phenotypic traits(Elsevier, 2023-08-12) Abbas, Kausar; Liu, Mintao; Wang, Michael; Duong-Tran, Duy; Tipnis, Uttara; Amico, Enrico; Kaplan, Alan D.; Dzemidzic, Mario; Kareken, David; Ances, Beau M.; Harezlak, Jaroslaw; Goñi, Joaquín; Neurology, School of MedicineFunctional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).Item Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes(Springer, 2018-01) Svaldi, Diana O.; Goñi, Joaquín; Sanjay, Apoorva Bharthur; Amico, Enrico; Risacher, Shannon L.; West, John D.; Dzemidzic, Mario; Saykin, Andrew; Apostolova, Liana; Neurology, School of MedicineAlzheimer’s disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer’s spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.