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dc.contributor.advisorConstance Moore, PhD
dc.contributor.authorCzerniak, Suzanne M.
dc.date2022-08-11T08:08:44.000
dc.date.accessioned2022-08-23T16:06:27Z
dc.date.available2022-08-23T16:06:27Z
dc.date.issued2014-03-28
dc.date.submitted2014-06-06
dc.identifier.doi10.13028/M2V591
dc.identifier.urihttp://hdl.handle.net/20.500.14038/32064
dc.description.abstractAttention Deficit/Hyperactivity Disorder (ADHD) is a common psychiatric disorder of childhood that is characterized by symptoms of inattention, impulsivity/hyperactivity, or a combination of both. Intrinsic brain dysfunction in ADHD can be examined through various methods including resting state functional Magnetic Resonance Imaging (rs-fMRI), which investigates patients’ functional brain connections in the absence of an explicit task. To date, studies of group differences in resting brain connectivity between patients with ADHD and typically developing controls (TDCs) have revealed reduced connectivity within the Default Mode Network (DMN), a resting state network implicated in introspection, mind-wandering, and day-dreaming. However, few studies have addressed the use of resting state connectivity measures as a diagnostic aide for ADHD on the individual patient level. In the current work, we attempted first to characterize the differences in resting state networks, including the DMN and three attention networks (the salience network, the left executive network, and the right executive network), between a group of youth with ADHD and a group of TDCs matched for age, IQ, gender, and handedness. Significant over- and under-connections were found in the ADHD group in all of these networks compared with TDCs. We then attempted to use a support vector machine (SVM) based on the information extracted from resting state network connectivity to classify participants as “ADHD” or “TDC.” The IFGmiddle temporal network (66.8% accuracy), the parietal association network (86.6% specificity and 48.5% PPV), and a physiological noise component (sensitivity 39.7% and NPV 69.6%) performed the best classifications. Finally, we attempted to combine and utilize information from all the resting state networks that we identified to improve classification accuracy. Contrary to our hypothesis, classification accuracy decreased to 54-55% when this information was combined. Overall, the work presented here supports the theory that the ADHD brain is differently connected at rest than that of TDCs, and that this information may be useful for developing a diagnostic aid. However, because ADHD is such a heterogeneous disorder, each ADHD patient’s underlying brain deficits may be unique making it difficult to determine what connectivity information is diagnostically useful.
dc.language.isoen_US
dc.rightsCopyright is held by the author, with all rights reserved.
dc.subjectDissertations, UMMS
dc.subjectAdolescent
dc.subjectAttention Deficit Disorder with Hyperactivity
dc.subjectBrain
dc.subjectNeuroimaging
dc.subjectMagnetic Resonance Imaging
dc.subjectSupport Vector Machines
dc.subjectAdolescent
dc.subjectAttention Deficit Disorder with Hyperactivity
dc.subjectBrain
dc.subjectNeuroimaging
dc.subjectMagnetic Resonance Imaging
dc.subjectSupport Vector Machines
dc.subjectBehavioral Neurobiology
dc.subjectDiagnosis
dc.subjectMental Disorders
dc.titleADHD-200 Patient Characterization and Classification using Resting State Networks: A Dissertation
dc.typeDoctoral Dissertation
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1708&context=gsbs_diss&unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/gsbs_diss/706
dc.legacy.embargo2015-04-18T00:00:00-07:00
dc.identifier.contextkey5662809
refterms.dateFOA2022-08-27T04:50:56Z
html.description.abstract<p>Attention Deficit/Hyperactivity Disorder (ADHD) is a common psychiatric disorder of childhood that is characterized by symptoms of inattention, impulsivity/hyperactivity, or a combination of both. Intrinsic brain dysfunction in ADHD can be examined through various methods including resting state functional Magnetic Resonance Imaging (rs-fMRI), which investigates patients’ functional brain connections in the absence of an explicit task. To date, studies of group differences in resting brain connectivity between patients with ADHD and typically developing controls (TDCs) have revealed reduced connectivity within the Default Mode Network (DMN), a resting state network implicated in introspection, mind-wandering, and day-dreaming. However, few studies have addressed the use of resting state connectivity measures as a diagnostic aide for ADHD on the individual patient level. In the current work, we attempted first to characterize the differences in resting state networks, including the DMN and three attention networks (the salience network, the left executive network, and the right executive network), between a group of youth with ADHD and a group of TDCs matched for age, IQ, gender, and handedness. Significant over- and under-connections were found in the ADHD group in all of these networks compared with TDCs. We then attempted to use a support vector machine (SVM) based on the information extracted from resting state network connectivity to classify participants as “ADHD” or “TDC.” The IFGmiddle temporal network (66.8% accuracy), the parietal association network (86.6% specificity and 48.5% PPV), and a physiological noise component (sensitivity 39.7% and NPV 69.6%) performed the best classifications. Finally, we attempted to combine and utilize information from all the resting state networks that we identified to improve classification accuracy. Contrary to our hypothesis, classification accuracy decreased to 54-55% when this information was combined. Overall, the work presented here supports the theory that the ADHD brain is differently connected at rest than that of TDCs, and that this information may be useful for developing a diagnostic aid. However, because ADHD is such a heterogeneous disorder, each ADHD patient’s underlying brain deficits may be unique making it difficult to determine what connectivity information is diagnostically useful.</p>
dc.identifier.submissionpathgsbs_diss/706
dc.contributor.departmentPsychiatry
dc.description.thesisprogramClinical and Population Health Research


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