Browsing by Subject "functional connectivity"

Now showing 1 - 5 of 5
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    Developmental Resting State Functional Connectivity for Clinicians
    (Springer International Publishing, 2014-09-01) Hulvershorn, Leslie A.; Cullen, Kathryn R.; Francis, Michael; Westlund, Mindy; Department of Psychiatry, IU School of Medicine
    Resting state functional magnetic imaging (fMRI) is a novel means to examine functional brain networks. It allows investigators to identify functional networks defined by distinct, spontaneous signal fluctuations. Resting state functional connectivity (RSFC) studies examining child and adolescent psychiatric disorders are being published with increasing frequency, despite concerns about the impact of motion on findings. Here we review important RSFC findings on typical brain development and recent publications of child and adolescent psychiatric disorders. We close with a summary of the major findings and current strengths and limitations of RSFC studies.
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    Effect of Electro-Acupuncture and Moxibustion on Brain Connectivity in Patients with Crohn’s Disease: A Resting-State fMRI Study
    (Frontiers Media, 2017-11-17) Bao, Chunhui; Wang, Di; Liu, Peng; Shi, Yin; Jin, Xiaoming; Wu, Luyi; Zeng, Xiaoqing; Zhang, Jianye; Liu, Huirong; Wu, Huangan; Anatomy and Cell Biology, School of Medicine
    Acupuncture and moxibustion have been shown to be effective in treating Crohn’s disease (CD), but their therapeutic mechanisms remain unclear. Here we compared brain responses to either electro-acupuncture or moxibustion treatment in CD patients experiencing remission. A total of 65 patients were randomly divided into an electro-acupuncture group (n = 32) or a moxibustion group (n = 33), and treated for 12 weeks. Eighteen patients in the electro-acupuncture group and 20 patients in the moxibustion group underwent resting-state functional magnetic resonance imaging at baseline and after treatment. Seed-based analysis was used to compare the resting-state functional connectivity (rsFC) between bilateral hippocampus and other brain regions before and after the treatments, as well as between the two groups. The CD activity index (CDAI) and inflammatory bowel disease questionnaire (IBDQ) were used to evaluate disease severity and patient quality of life. Electro-acupuncture and moxibustion both significantly reduced CDAI values and increased IBDQ scores. In the electro-acupuncture group, the rsFC values between bilateral hippocampus and anterior middle cingulate cortex (MCC) and insula were significantly increased, and the changes were negatively correlated with the CDAI scores. In the moxibustion group, the rsFC values between bilateral hippocampus and precuneus as well as inferior parietal lobe (IPC) were significantly elevated, and the changes were negatively correlated with the CDAI scores. We conclude that the therapeutic effects of electro-acupuncture and moxibustion on CD may involve the differently modulating brain homeostatic afferent processing network and default mode network (DMN), respectively.
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    Functional network connectivity in early-stage schizophrenia
    (Elsevier, 2020-04) Hummer, Tom A.; Yung, Matthew G.; Goñi, Joaquín; Conroy, Susan K.; Francis, Michael M.; Mehdiyoun, Nicole F.; Breier, M. A. Alan; Psychiatry, School of Medicine
    Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans. Gray matter regions were organized into nine distinct functional networks. Functional connectivity was calculated between 278 gray matter regions for each subject. Network connectivity properties were defined by the mean and variance of correlations of all regions. Whole-brain network measures of global efficiency (reflecting overall interconnectedness) and locations of hubs (key regions for communication) were also determined. The control sample had greater connectivity between the following network pairs: somatomotor-limbic, somatomotor-default mode, dorsal attention-default mode, ventral attention-limbic, and ventral attention-default mode. The patient sample had greater variance in interactions between ventral attention network and other functional networks. Illness duration was associated with overall increases in the variability of network connections. The control group had higher global efficiency and more hubs in the cerebellum network, while patient group hubs were more common in visual, frontoparietal, or subcortical networks. Thus, reduced functional connectivity in patients was largely present between distinct networks, rather than within-networks. The implications of these findings for the pathophysiology of schizophrenia are discussed.
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    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 Medicine
    Functional 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.
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    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 Medicine
    Alzheimer’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.