Browsing by Author "Allen, Janerra D."
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Item Brain connectivity differences between typically developed and ADHD subjects using Energy Landscape Analysis of resting-state fMRI data(2022-07-18) Allen, Janerra D.; Choa, Fow-SenFunctional magnetic resonance imaging (fMRI) is an effective tool used to study neural systems and functional connectivity patterns within brain networks. Using resting-state fMRI data, we can uncover the functional connectivity differences in people with typically developed brains and brains of people with attention deficit hyperactivity disorder (ADHD). Segmenting the human brain into networks and analyzing the internetwork connectivity can help us identify which brain network regions are engaged and if they are working together. In this study, we used energy landscape analysis, a method that calculates and interprets multivariate time series data, such as resting-state fMRI, to investigate brain activity differences in typically developed, ADHD-Hyperactive/Impulsive, ADHD-Inattentive, and ADHDCombined subjects. The functional connectivity differences between the subgroups, analyzed separately, could be attributed to internetwork activity, and can possibly help identify biomarkers of ADHD. The internetwork connections consisted of the auditory network (AUD), attention network (ATN), default-mode network (DMN), frontoparietal network (FPN), salience network (SAN), sensorimotor network (SSM), and visual network (VIS). The activity patterns and disconnectivity graphs are obtained for each subject and the differences between groups are compared. Results suggest that DMN and VIS are strongly coupled for females with ADHD, whereas FPN and SAN are strongly coupled for males with ADHD. These cognitive differences may attribute to neural deficits and cognitive dysfunction in ADHD, such as trouble paying attention and inability to control behavior. The energy landscape analysis technique is a powerful tool for identifying differences between typically developed and ADHD subjects, which could help validate and encourage treatment options.Item Energy landscape analysis of fMRI data from Schizophrenic and healthy subjects(SPIE, 2021-04-12) Allen, Janerra D.; Varanasi, Sravani; Hong, Elliot; Choa, Fow-SenBrain connectivity biomarkers are powerful tools for not only identifying neuropsychiatric disorders in patients but also validating treatment effectiveness. In this work, we used energy landscape techniques to analyze resting state fMRI data collected from 107 healthy control (HC) and 86 Schizophrenia patients (SZ). Activity patterns and disconnectivity graphs were obtained from 264 ROIs and 180-second fMRI time course of each subject. Statistics of individual and subgroups’ inter-network and intra-network connections of Auditory Network (AUD), Attention Network (ATN), Default – Mode Network (DMN), Frontoparietal Network (FPN), Salience Network (SAN), Sensorimotor Network (SSM), and Visual Network (VIS) were analyzed. For inter-network results we found that the DMN and ATN of SZ are strongly coupled. But for HC, a stable brain states that the ATN, SAN, and FPN are coupled as a group and anti-correlated with the other coupled group of DMN, SSM, VIS, and AUD. For intra-networks we found that in FPN, controls have more flexibility to allow the Inferior Frontal Gyrus independently working together with the Superior Temporal Gyrus. In FPN we found that regions that process language and regions that process motor and planning can sometimes be decoupled in SZ. In SMN, some controls can accomplish a brain state to separate voluntary and autopilot activities. In VIS, controls have the ability to separate lower-level visual processing from working memory, motor planning, and guided coordination, whereas patients mixed some of them together, suggesting lack of self-awareness and self-constraint.