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Item Altered amygdala-cortical connectivity in individuals with Cannabis use disorder(Sage, 2021) Aloi, Joseph; McCusker, Marie C.; Lew, Brandon J.; Schantell, Mikki; Eastman, Jacob A.; Frenzel, Michaela R.; Wilson, Tony W.; Psychiatry, School of MedicineBackground: Cannabis is one of the most commonly used substances in the United States. Prior literature using task-based functional magnetic resonance imaging (fMRI) has identified that individuals with Cannabis use disorder (CUD) show impairments in emotion processing circuitry. However, whether the functional networks involving these regions are also altered in CUD remains poorly understood. Aims: Investigate changes in resting-state functional connectivity (rsFC) in regions related to emotional processing in CUD. Methods: Sixty-two participants completed resting-state fMRI, including 21 with CUD, 20 with histories of illicit substance use but no current CUD diagnosis, and 21 with no history of illicit substance use. Whole-brain seed-based connectivity analyses were performed and one-way analyses of covariance (ANCOVAs) were conducted to detect group differences in the bilateral amygdalae, hippocampi, and the anterior and posterior cingulate cortices. Results: The CUD group exhibited significant reductions in rsFC between the amygdala and the cuneus, paracentral lobule, and supplementary motor area, and between the cingulate cortices and the occipital and temporal lobes. There were no significant group differences in hippocampal functional connectivity. In addition, CUD symptom counts based on the Structured Clinical Interview for DSM-5 (SCID) and the Cannabis Use Disorders Identification Test (CUDIT) significantly correlated with multiple connectivity metrics. Conclusion: These data expand on emerging literature indicating that CUD is associated with dysfunction in the neural circuits underlying emotion processing. Dysfunction in emotion processing circuits may play a role in the behavioral impairments seen in emotion processing tasks in individuals with CUD, and the severity of CUD symptoms appears to be directly related to the degree of dysfunction in these circuits.Item The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement(Elsevier, 2017-05) Weiner, Michael W.; Veitch, Dallas P.; Aisen, Paul S.; Beckett, Laurel A.; Cairns, Nigel J.; Green, Robert C.; Harvey, Danielle; Jack, Clifford R., Jr.; Jagust, William; Morris, John C.; Petersen, Ronald C.; Salazar, Jennifer; Saykin, Andrew J.; Shaw, Leslie M.; Toga, Arthur W.; Trojanowski, John Q.; Radiology and Imaging Sciences, School of MedicineINTRODUCTION: The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI-3, which began on August 1, 2016, is a 5-year renewal of the current ADNI-2 study. METHODS: ADNI-3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition. RESULTS: Multimodal analyses will provide insight into AD pathophysiology and disease progression. DISCUSSION: ADNI-3 will aim to inform AD treatment trials and facilitate development of AD disease-modifying treatments.Item Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control(Elsevier, 2022) Zhao, Yi; Nebel, Mary Beth; Caffo, Brian S.; Mostofsky, Stewart H.; Rosch, Keri S.; Biostatistics and Health Data Science, School of MedicineBackground: Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study we apply a novel whole-matrix regression approach referred to as Covariate Assisted Principal (CAP) regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Methods: Participants included 8-12 year-old children with ADHD (n=115, 29 girls) and typically developing controls (n=102, 35 girls) who completed a resting-state fMRI scan and a go/no-go task (GNG). We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau). Results: The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models suggesting some specificity to the covariates of interest. Conclusions: These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.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 Differences in functional connectivity distribution after transcranial direct-current stimulation: A connectivity density point of view(Wiley, 2023) Tang, Bohao; Zhao, Yi; Venkataraman, Archana; Tsapkini, Kyrana; Lindquist, Martin A.; Pekar, James; Caffo, Brian; Biostatistics, School of Public HealthIn this manuscript, we consider the problem of relating functional connectivity measurements viewed as statistical distributions to outcomes. We demonstrate the utility of using the distribution of connectivity on a study of resting-state functional magnetic resonance imaging association with an intervention. The method uses the estimated density of connectivity between nodes of interest as a functional covariate. Moreover, we demonstrate the utility of the procedure in an instance where connectivity is naturally considered an outcome by reversing the predictor/response relationship using case/control methodology. The method utilizes the density quantile, the density evaluated at empirical quantiles, instead of the empirical density directly. This improved the performance of the method by highlighting tail behavior, though we emphasize that by being flexible and non-parametric, the technique can detect effects related to the central portion of the density. To demonstrate the method in an application, we consider 47 primary progressive aphasia patients with various levels of language abilities. These patients were randomly assigned to two treatment arms, transcranial direct-current stimulation and language therapy versus sham (language therapy only), in a clinical trial. We use the method to analyze the effect of direct stimulation on functional connectivity. As such, we estimate the density of correlations among the regions of interest and study the difference in the density post-intervention between treatment arms. We discover that it is the tail of the density, rather than the mean or lower order moments of the distribution, that demonstrates a significant impact in the classification. The new approach has several benefits. Among them, it drastically reduces the number of multiple comparisons compared with edge-wise analysis. In addition, it allows for the investigation of the impact of functional connectivity on the outcomes where the connectivity is not geometrically localized.Item 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 MedicineUnderstanding 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.Item Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer’s Disease(medRxiv, 2023-11-18) 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 MedicineUnderstanding 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.Item Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation(National Academy of Science, 2023) Chai, Ya; Gehrman, Philip; Yu, Meichen; Mao, Tianxin; Deng, Yao; Rao, Joy; Shi, Hui; Quan, Peng; Xu, Jing; Zhang, Xiaocui; Lei, Hui; Fang, Zhuo; Xu, Sihua; Boland, Elaine; Goldschmied, Jennifer R.; Barilla, Holly; Goel, Namni; Basner, Mathias; Thase, Michael E.; Sheline, Yvette I.; Dinges, David F.; Detre, John A.; Zhang, Xiaochu; Rao, Hengyi; Radiology and Imaging Sciences, School of MedicineSleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.Item Functional connectivity in frontostriatal networks differentiate offspring of parents with substance use disorders from other high-risk youth(Elsevier, 2021) Kwon, Elizabeth; Hummer, Tom; Andrews, Katharine D.; Finn, Peter; Aalsma, Matthew; Bailey, Allen; Hanquier, Jocelyne; Wang, Ting; Hulvershorn, Leslie; Psychiatry, School of MedicineBackground: Family history (FH) of substance use disorders (SUDs) is known to elevate SUD risk in offspring. However, the influence of FH SUDs has been confounded by the effect of externalizing psychopathologies in the addiction risk neuroimaging literature. Thus, the current study aimed to assess the association between parental SUDs and offspring functional connectivity in samples matched for psychopathology and demographics. Methods: Ninety 11-12-year-old participants with externalizing disorders were included in the study (48 FH+, 42 FH-). We conducted independent component analyses (ICA) and seed-based analyses (orbitofrontal cortex; OFC, nucleus accumbens (NAcc), dorsolateral prefrontal cortex) with resting state data. Results: FH+ adolescents showed stronger functional connectivity between the right lateral OFC seed and anterior cingulate cortex compared to FH- adolescents (p < 0.05, corrected). Compared to FH-, FH+ adolescents showed stronger negative functional connectivity between the left lateral OFC seed and right postcentral gyrus and between the left NAcc seed and right middle occipital gyrus (p < 0.05, corrected). Poorer emotion regulation was associated with more negative connectivity between right occipital/left NAcc among FH+ adolescents based on the seed-based analysis. FH- adolescents had stronger negative functional connectivity between ventral attention/salience networks and dorsal attention/visuospatial networks in the ICA. Conclusions: Both analytic methods found group differences in functional connectivity between brain regions associated with executive functioning and regions associated with sensory input (e.g., postcentral gyrus, occipital regions). We speculate that families densely loaded for SUD may confer risk by altered neurocircuitry that is associated with emotion regulation and valuation of external stimuli beyond what would be explained by externalizing psychopathology alone.Item Gene co-expression changes underlying the functional connectomic alterations in Alzheimer's disease(BMC, 2022-04-23) He, Bing; Gorijala, Priyanka; Xie, Linhui; Cao, Sha; Yan, Jingwen; BioHealth Informatics, School of Informatics and ComputingBackground: There is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer's disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured. Methods: Leveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions. Results: Differential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average. Conclusions: This work applies a new biclustering method to search for a subset of genes with altered co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development.