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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls

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Autor(es):
Stamatis, Caitlin A. [1, 2] ; Batistuzzo, Marcelo C. [3] ; Tanamatis, Tais [3] ; Miguel, Euripedes C. [3] ; Hoexter, Marcelo Q. [3] ; Timpano, Kiara R. [1]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Miami, Dept Psychol, Coral Gables, FL 33124 - USA
[2] Weill Cornell Med, NewYork Presbyterian Hosp, 425 W 61st St Penthouse Floor, New York, NY 10065 - USA
[3] Univ Sao Paulo, Dept Psychiat, Sao Paulo, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: BRITISH JOURNAL OF CLINICAL PSYCHOLOGY; v. 60, n. 1 DEC 2020.
Citações Web of Science: 0
Resumo

Objectives While theoretical models link obsessive-compulsive disorder (OCD) with executive function deficits, empirical findings from the neuropsychological literature remain mixed. These inconsistencies are likely exacerbated by the challenge of high-dimensional data (i.e., many variables per subject), which is common across neuropsychological paradigms and necessitates analytical advances. More unique to OCD is the heterogeneity of symptom presentations, each of which may relate to distinct neuropsychological features. While researchers have traditionally attempted to account for this heterogeneity using a symptom-based approach, an alternative involves focusing on underlying symptom motivations. Although the most studied symptom motivation involves fear of harmful events, 60-70% of patients also experience sensory phenomena, consisting of uncomfortable sensations or perceptions that drive compulsions. Sensory phenomena have received limited attention in the neuropsychological literature, despite evidence that symptoms motivated by these experiences may relate to distinct cognitive processes. Methods Here, we used a supervised machine learning approach to characterize neuropsychological processes in OCD, accounting for sensory phenomena. Results Compared to logistic regression and other algorithms, random forest best differentiated healthy controls (n = 59; balanced accuracy = .70), patients with sensory phenomena (n = 29; balanced accuracy = .59), and patients without sensory phenomena (n = 46; balanced accuracy = .62). Decision-making best distinguished between groups based on sensory phenomena, and among the patient subsample, those without sensory phenomena uniquely displayed greater risk sensitivity compared to healthy controls (d = .07, p = .008). Conclusions Results suggest that different cognitive profiles may characterize patients motivated by distinct drives. The superior performance and generalizability of the newer algorithms highlights the utility of considering multiple analytic approaches when faced with complex data. Practitioner points Practitioners should be aware that sensory phenomena are common experiences among patients with OCD. OCD patients with sensory phenomena may be distinguished from those without based on neuropsychological processes. (AU)

Processo FAPESP: 08/57896-8 - O Instituto Nacional da Psiquiatria do Desenvolvimento: uma nova abordagem para a psiquiatria tendo como foco as nossas crianças e o seu futuro
Beneficiário:Eurípedes Constantino Miguel Filho
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 17/50041-6 - Risco de distúrbios do espectro obsessivo-compulsivo: considerações clínicas, ambientais, familiares e endofenotípicas
Beneficiário:Eurípedes Constantino Miguel Filho
Linha de fomento: Auxílio à Pesquisa - Regular