fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network

Silva, Senuri De; Dayarathna, Sanuwani; Ariyarathne, Gangani; Meedeniya, Dulani; Jayarathna, Sampath

doi:10.4018/ijehmc.2021010106

Cited by 25 publications

(10 citation statements)

References 27 publications

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“…Riaz et al 27 calculated FCs as features by using the affinity propagation (AP) clustering and the density peak (DP) algorithm. Riaz et al 28 58 as features of the model in their study. The correlation, generated through the seed-based correlation approach, between ROIs was calculated by the average time series.…”

Section: Feature Extractionmentioning

confidence: 99%

A review of ADHD detection studies with machine learning methods using rsfMRI data

Taspinar,

Ozkurt

2024

NMR in Biomedicine

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Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school‐age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non‐invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)‐based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD‐200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.

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Section: Feature Extractionmentioning

confidence: 99%

A review of ADHD detection studies with machine learning methods using rsfMRI data

Taspinar,

Ozkurt

2024

NMR in Biomedicine

View full text Add to dashboard Cite

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“…Principal Components Analysis, Autoregression, Linear Embeddings, Autoencoders (Cordes & Nandy, 2006;Huang et al, 2018;Mannfolk et al, 2010;Pereira et al, 2009) being of difficult interpretation for clinicians and hard to generalize. Research in fMRI has been recently characterized by a higher reliance on Neural Networks (Suk et al, 2016) and Embeddings (Sidhu, 2019), with the most promising results coming from CNN (Meszlényi et al, 2017;Sarraf et al, 2019;Tahmassebi et al, 2018;Zhao et al, 2018), especially in the field of Computational Psychiatry (Ariyarathne et al, 2020;El Gazzar et al, 2019;Oh et al, 2019;Silva et al, 2021). Convolutional Neural Networks design follows biological research and the study of the receptive field by the visual cortex (Hubel & Wiesel, 1959), their first development establishing the groundwork for the field of computer vision (Denker et al, 1989;LeCun et al, 1989).…”

Section: Technical Contributionsmentioning

confidence: 99%

The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO)

Tarchi

Damiani

Vittori

et al. 2021

Brain Imaging and Behavior

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Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the focus of recent literature preferentially lies on three lines of research, namely: functional connectivity, network analysis and spectral analysis. Data was gathered from the UCLA Consortium for Neuropsychiatric Phenomics. The sample was composed by 130 neurotypicals, 50 participants diagnosed with Schizophrenia, 49 with Bipolar disorder and 43 with ADHD. Single fMRI scans were reduced in their dimensionality by a novel method (i-ECO) averaging results per Region of Interest and through an additive color method (RGB): local connectivity values (Regional Homogeneity), network centrality measures (Eigenvector Centrality), spectral dimensions (fractional Amplitude of Low-Frequency Fluctuations). Average images per diagnostic group were plotted and described. The discriminative power of this novel method for visualizing and analyzing fMRI results in an integrative manner was explored through the usage of convolutional neural networks. The new methodology of i-ECO showed between-groups differences that could be easily appreciated by the human eye. The precision-recall Area Under the Curve (PR-AUC) of our models was > 84.5% for each diagnostic group as evaluated on the test-set – 80/20 split. In conclusion, this study provides evidence for an integrative and easy-to-understand approach in the analysis and visualization of fMRI results. A high discriminative power for psychiatric conditions was reached. This proof-of-work study may serve to investigate further developments over more extensive datasets covering a wider range of psychiatric diagnoses.

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“…Attention Deficit/Hyperactivity disorder (ADHD) is characterized by symptoms presenting in a heterogeneous manner across individuals, including attention deficits, impulsivity, and hyper-activity (American Psychiatric Association, 2013 ). Functional Magnetic Resonance Imaging (fMRI) proved to be a powerful tool for exploring the neurobiological correlates of ADHD symptoms and behaviors (Damiani et al, 2020 ; Iravani et al, 2021 ; Qian et al, 2018 ; Rosch et al, 2018 ; Silva et al, 2021 ; Tarchi et al, 2021 ). Specifically, fMRI highlighted the importance of how each region is functionally connected to the rest of the brain.…”

Section: Introductionmentioning

confidence: 99%

Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study

Tarchi

Damiani

Fantoni

et al. 2022

Brain Imaging and Behavior

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Eigenvector-Centrality (EC) has shown promising results in the field of Psychiatry, with early results also pertaining to ADHD. Parallel efforts have focused on the description of aberrant interhemispheric coordination in ADHD, as measured by Voxel-Mirrored-Homotopic-Connectivity (VMHC), with early evidence of altered Resting-State fMRI. A sample was collected from the ADHD200-NYU initiative: 86 neurotypicals and 89 participants with ADHD between 7 and 18 years old were included after quality control for motion. After preprocessing, voxel-wise EC and VMHC values between diagnostic groups were compared, and network-level values from 15 functional networks extracted. Age, ADHD severity (Connor’s Parent Rating-Scale), IQ (Wechsler-Abbreviated-Scale), and right-hand dominance were correlated with EC/VMHC values in the whole sample and within groups, both at the voxel-wise and network-level. Motion was controlled by censoring time-points with Framewise-Displacement > 0.5 mm, as well as controlling for group differences in mean Framewise-Displacement values. EC was significantly higher in ADHD compared to neurotypicals in the left inferior Frontal lobe, Lingual gyri, Peri-Calcarine cortex, superior and middle Occipital lobes, right inferior Occipital lobe, right middle Temporal gyrus, Fusiform gyri, bilateral Cuneus, right Precuneus, and Cerebellum (FDR-corrected-p = 0.05). No differences were observed between groups in voxel-wise VMHC. EC was positively correlated with ADHD severity scores at the network level (at p-value < 0.01, Inattentive: Cerebellum rho = 0.273; Hyper/Impulsive: High-Visual Network rho = 0.242, Cerebellum rho = 0.273; Global Index Severity: High-Visual Network rho = 0.241, Cerebellum rho = 0.293). No differences were observed between groups for motion (p = 0.443). While EC was more related to ADHD psychopathology, VMHC was consistently and negatively correlated with age across all networks.

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fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network

Cited by 25 publications

References 27 publications

A review of ADHD detection studies with machine learning methods using rsfMRI data

A review of ADHD detection studies with machine learning methods using rsfMRI data

The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO)

Centrality and interhemispheric coordination are related to different clinical/behavioral factors in attention deficit/hyperactivity disorder: a resting-state fMRI study

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