A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis.
BACKGROUND
Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.


METHODS
To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).


RESULTS
In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.


CONCLUSIONS
The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Republic of Korea; Division of Mathematical Models, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Sunghyon
S
Kyeong
Division of Mathematical Models, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Seonjeong
S
Park
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Keun-Ah
KA
Cheon
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Republic of Korea.
Jae-Jin
JJ
Kim
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Dong-Ho
DH
Song
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Eunjoo
E
Kim
D000293:Adolescent; D001008:Anxiety Disorders / Q000453:epidemiology; D001289:Attention Deficit Disorder with Hyperactivity / Q000453:epidemiology / Q000473:pathology* / Q000503:physiopathology / Q000523:psychology; D019958:Attention Deficit and Disruptive Behavior Disorders / Q000453:epidemiology; D002648:Child; D002681:China / Q000453:epidemiology; D015897:Comorbidity; D019955:Conduct Disorder / Q000453:epidemiology; D063132:Connectome; D003866:Depressive Disorder / Q000453:epidemiology; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007360:Intelligence; D007859:Learning Disabilities / Q000453:epidemiology; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000473:pathology / Q000503:physiopathology; D009519:New York City / Q000453:epidemiology; D010641:Phenotype; D020323:Tics / Q000453:epidemiology