The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach.
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsive behaviors, which typically manifest during childhood. This study employs functional magnetic resonance imaging (fMRI) to use spontaneous brain acti...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 203 - 217 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471478&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471478 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471478 184471478 184471478 10.1007/s10278-024-01189-5 184471478 ppf: 203 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The Use of fMRI Regional Analysis to Automatically Detect ADHD Through a 3D CNN-Based Approach. aug: au: Gülhan, Perihan Gülşah Özmen, Güzin affil: https://ror.org/045hgzm75 Department of Electrical and Electronics Engineering, Institute of Science, Selcuk University, Konya, Turkey sug: subj: Attention Deficit Hyperactivity Disorder Diagnosis Brain Radiography Magnetic Resonance Imaging Convolutional Neural Networks Automation Decision Support Systems, Clinical Human Male Female Child Adolescence Adult Comparative Studies Descriptive Statistics Deep Learning Imaging, Three-Dimensional Resource Databases Attention Deficit Hyperactivity Disorder Physiopathology Attention Deficit Hyperactivity Disorder Classification Funding Source Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsive behaviors, which typically manifest during childhood. This study employs functional magnetic resonance imaging (fMRI) to use spontaneous brain activity for classifying individuals with ADHD, focusing on a 3D convolutional neural network (CNN) architecture to facilitate the design of decision support systems. We developed a novel deep learning model based on 3D CNNs using the ADHD-200 database, which comprises datasets from NeuroImage (NI), New York University (NYU), and Peking University (PU). We used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) data in three dimensions and performed a fivefold cross-validation to address the dataset imbalance. We aimed to verify the efficacy of our proposed 3D CNN by contrasting it with a fully connected neural network (FCNN) architecture. The 3D CNN achieved accuracy rates of 76.19% (NI), 69.92% (NYU), and 70.77% (PU) for fALFF data. The FCNN model yielded lower accuracy rates across all datasets. For generalizability, we trained on NI and NYU datasets and tested on PU. The 3D CNN achieved 69.48% accuracy on fALFF outperforming the FCNN. Our results demonstrate that using 3D CNNs for classifying fALFF data is an effective approach for diagnosing ADHD. Also, FCNN confirmed the efficiency of the designed model. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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