Estimating biological accuracy of DSM for attention deficit/hyperactivity disorder based on multivariate analysis for small samples

Abstract

Objective To estimate whether the “Diagnostic and Statistical Manual of Mental Disorders” (DSM) is biologically accurate for the diagnosis of Attention Deficit/ Hyperactivity Disorder (ADHD) using a biological-based classifier built by a special method of multivariate analysis of a large dataset of a small sample (much more variables than subjects), holding neurophysiological, behavioral, and psychological variables. Methods Twenty typically developing boys and 19 boys diagnosed with ADHD, aged 10–13 years, were examined using the Attentional Network Test (ANT) with recordings of event-related potentials (ERPs). From 774 variables, a reduced number of latent variables (LVs) were extracted with a clustering of variables method (CLV), for further reclassification of subjects using the k-means method. This approach allowed a multivariate analysis to be applied to a significantly larger number of variables than the number of cases. Results From datasets including ERPs from the mid-frontal, mid-parietal, right frontal, and central scalp areas, we found 82% of agreement between DSM and biological-based classifications. The kappa index between DSM and behavioral/psychological/neurophysiological data was 0.75, which is regarded as a “substantial level of agreement”. Discussion The CLV is a useful method for multivariate analysis of datasets with much less subjects than variables. In this study, a correlation is found between the biological-based classifier and the DSM outputs for the classification of subjects as either ADHD or not. This result suggests that DSM clinically describes a biological condition, supporting its validity for ADHD diagnostics.

Neurophysiological and behavioral correlates of alertness impairment and compensatory processes in ADHD evidenced by the Attention Network Test

“…In a concomitant study, we reclassified these subjects based on a multivariate analysis of a dataset resulting from ANT application. We observed a “substantial level of agreement” between DSM and the phenomenological dimensions of ANT (behavioral, psychological, and neurophysiological, similar to what is described in the present study): kappa index = 0.72 [48], and the specificity and sensitivity of the DSM-IV-TR regarding biological variables were 75% and 89%, respectively [49].…”

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

“…One-hundred fifty of 555 models (27.0%; 95% CI, 23.3%-30.7%) were rated with poor reporting quality in the applications domain across 2 signaling questions. 26 , 28 , 29 , 32 , 36 , 38 , 41 , 45 , 50 , 51 , 60 , 65 , 69 , 74 , 78 , 82 , 84 , 94 , 97 , 99 , 105 , 107 , 108 , 112 , 117 , 119 , 120 , 125 , 132 , 133 , 134 , 138 , 139 , 141 , 143 , 146 , 150 , 159 , 172 , 183 , 186 , 190 , 193 , 194 , 200 , 203 , 204 , 214 , 219 , 220 , 222 , 223 , 228 , 229 , 230 , 232 , 233 , 241 ,…”

“…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…”

Social and labor stressors influencing disability in Adult Attention Deficit/Hyperactivity

“…A major source of discussion throughout society is Attention Deficit Hyperactivity Disorder (ADHD). Despite the evidence that ADHD is a well-defined mental phenomenology by biological bases [8–10], its pathological character is quite controversial, both inside academic walls [11,12] and according to common sense [13]. Nevertheless, ADHD is organized as a mental disorder in DSM while there is significative impairment [2].…”