Regularized Spatial Filtering Method (R-SFM) for detection of Attention Deficit Hyperactivity Disorder (ADHD) from resting-state functional Magnetic Resonance Imaging (rs-fMRI)

Abstract

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental problem in children. Resting state functional magnetic resonance imaging (rs-fMRI) provides an important tool in understanding the aberrant functional mechanisms in ADHD patients and assist in clinical diagnosis. Recently, spatio-temporal decomposition via spatial filtering (Fukunaga-Koontz transform, ICA) have gained attention in the analysis of fMRI time-series data. Their ability to decompose the blood oxygen level dependent (BOLD) rs-fMRI time series data into discriminative spatial and temporal components have resulted in better classification accuracy and the ability to isolate the important brain circuits responsible for the observed differences in brain activity. However, they are prone to errors in the estimation of covariance matrices due to the significant presence of atypical samples in the ADHD dataset. In this paper, we present a regularization framework to obtain a robust estimation of the covariance matrices such that the effect of atypical samples is reduced. The resulting approach called as regularized spatial filtering method (R-SFM) further uses Mahalanobis whitening to lower the effect of two-way correlations while preserving the spatial arrangement of the data in the feature extraction process. R-SFM was evaluated on the benchmark ADHD200 dataset and not only obtained a 6% improvement in classification accuracy, but also a 66.66% decrease in standard deviation over the previously developed SFM approach. Also R-SFM produces higher specificity which results in lower misclassification of ADHD, thereby reducing the risk of misdiagnosis. These results clearly show that R-SFM provides an accurate and reliable tool for detection of ADHD from BOLD rs-fMRI time series data.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

“…A total of 517 studies presenting 555 development-purpose AI models were eligible for inclusion in the analysis…”

“… b Eighty-six AI models 30 , 32 , 34 , 36 , 42 , 44 , 46 , 53 , 59 , 60 , 61 , 63 , 69 , 70 , 72 , 81 , 82 , 88 , 91 , 117 , 149 , 151 , 156 , 162 , 163 , 164 , 169 , 170 , 172 , 180 , 182 , 187 , 191 , 197 , 212 , 213 , 220 , 221 , 247 , 254 , 258 , 260 , 261 , 263 , 269 , 272 , 278 , 280 , 283 , 284 , 285 , 287 , 290 , 301 , 305 , 307 , 319 , …”

“…The study protocol was registered at PROSPERO (CRD42022340624) and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline (Figure 1…”

“… a Of the 555 artificial intelligence (AI) models, 469 were tested at the internal sample and 86 at the independent external sample, including intellectual disability, autism spectrum disorder, attention-deficit/hyperactivity disorder,…”

Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

“…Their results represent the state of the art diagnostic performance on the Autism dataset. Recently, (Aradhya et al 2018) utilized a regularized SFM based technique to improve the accuracy in the automatic detection of accuracy of ADHD from rs-fMRI data. SFM and its deravative techniques have adopted the 'Fukunaga-Koontz 'transform (Fukunaga 2013) to mathematically derive the spatial transformation filter which is greatly dependent on the mean distribution of the training data.…”