Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls.
Journal:
The British journal of clinical psychology
PMID:
33300635
Abstract
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.