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

Haywood, D; Baughman, FD; Mullan, BA; Heslop, KR

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

Haywood, D

Baughman, FD Mullan, BA Heslop, KR

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Brain Sci, 2022, 12, (8), pp. 1060
Issue Date:: 2022-08-10

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Field	Value	Language
dc.contributor.author	Haywood, D https://orcid.org/0000-0002-9317-4135
dc.contributor.author	Baughman, FD
dc.contributor.author	Mullan, BA
dc.contributor.author	Heslop, KR
dc.date.accessioned	2024-05-06T07:04:29Z
dc.date.available	2022-08-09
dc.date.available	2024-05-06T07:04:29Z
dc.date.issued	2022-08-10
dc.identifier.citation	Brain Sci, 2022, 12, (8), pp. 1060
dc.identifier.issn	2076-3425
dc.identifier.issn	2076-3425
dc.identifier.uri	http://hdl.handle.net/10453/178686
dc.description.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.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Brain Sci
dc.relation.isbasedon	10.3390/brainsci12081060
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1109 Neurosciences, 1701 Psychology, 1702 Cognitive Sciences
dc.subject.classification	3209 Neurosciences
dc.subject.classification	5201 Applied and developmental psychology
dc.subject.classification	5202 Biological psychology
dc.title	Neurocognitive Artificial Neural Network Models Are Superior to Linear Models at Accounting for Dimensional Psychopathology.
dc.type	Journal Article
utslib.citation.volume	12
utslib.location.activity	Switzerland
utslib.for	1109 Neurosciences
utslib.for	1701 Psychology
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Health
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-06T07:04:25Z
pubs.issue	8
pubs.publication-status	Published online
pubs.volume	12
utslib.citation.issue	8

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.

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http://hdl.handle.net/10453/178686