Neurocognitive Artificial Neural Network Models Are Superior to Linear Models at Accounting for Dimensional Psychopathology

Abstract

In recent years, there has been debate about the optimal conceptualisation of psychopathology. Structural models of psychopathology have been developed to counter issues, including comorbidity and poor diagnostic stability prevalent within the traditional nosological approach. Regardless of the conceptualisation of psychological dysfunction, deficits in neurocognitive abilities have been claimed to be an aetiological feature of psychopathology. Explorations of the association between neurocognition and psychopathology have typically taken a linear approach, overlooking the potential interactive dynamics of neurocognitive abilities. Previously, we proposed a multidimensional hypothesis, where within-person interactions between neurocognitive domains are fundamental to understanding the role of neurocognition within psychopathology. In this study, we used previously collected psychopathology data for 400 participants on psychopathological symptoms, substance use, and performance on eight neurocognitive tasks and compared the predictive accuracy of linear models to artificial neural network models. The artificial neural network models were significantly more accurate than the traditional linear models at predicting actual (a) lower-level and (b) high-level dimensional psychopathology. These results provide support for the multidimensional hypothesis: that the study of non-linear interactions and compensatory neurocognitive profiles are integral to understanding the functional associations between neurocognition and of psychopathology.

Mental health consequences of COVID-19 suppression strategies in Victoria, Australia: a narrative review

“…Poor mental health is associated with a variety of biopsychosocial variables including social isolation, alcohol use, and deficits in cognitive ability. 34 – 37 Czeisler et al. 15 demonstrated significant changes in the behavior of adults during the lockdown, such as having a considerably increased screen time and circumventing COVID-19-related information.…”

“…Poor mental health is associated with a variety of biopsychosocial variables including social isolation, alcohol use, and deficits in cognitive ability. [34][35][36][37] Czeisler et al 15 demonstrated significant changes in the behavior of adults during the lockdown, such as having a considerably increased screen time and circumventing COVID-19-related information. 15 Additionally, 10% of participants reported new or increased substance use (e.g., alcohol, legal/ illegal drugs, and prescription drugs) to cope during the second wave of SARS-CoV-2 in Victoria.…”

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“…However, our results have provided a smaller set of potential key variables related to turnover intention that should inform future theory and model development and testing. This future research should use approaches including linear structural equation modelling, and dynamic alternatives such as network modelling, artificial neural networks, and computational modelling (e.g., see Haywood et al, 2022; Haywood & Baughman, 2021).…”

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“…Referring to internalizing disorders, deficits in the updating, in the working memory, and in the shifting mental set seem predisposed to depression and anxiety. Instead, shifting mental sets and working memory deficits are involved in developing externalizing disorders [ 51 , 52 , 53 ].…”