Neurocognitive Pattern Analysis Reveals Classificatory Hierarchy of Attention Deficits in Schizophrenia

Abstract

Attention deficits, among other cognitive deficits, are frequently observed in schizophrenia. Although valid and reliable neurocognitive tasks have been established to assess attention deficits in schizophrenia, the hierarchical value of those tests as diagnostic discriminants on a single-subject level remains unclear. Thus, much research is devoted to attention deficits that are unlikely to be translated into clinical practice. On the other hand, a clear hierarchy of attention deficits in schizophrenia could considerably aid diagnostic decisions and may prove beneficial for longitudinal monitoring of therapeutic advances. To propose a diagnostic hierarchy of attention deficits in schizophrenia, we investigated several facets of attention in 86 schizophrenia patients and 86 healthy controls using a set of established attention tests. We applied state-of-the-art machine learning algorithms to determine attentive test variables that enable an automated differentiation between schizophrenia patients and healthy controls. After feature preranking, hypothesis building, and hypothesis validation, the polynomial support vector machine classifier achieved a classification accuracy of 90.70% ± 2.9% using psychomotor speed and 3 different attention parameters derived from sustained and divided attention tasks. Our study proposes, to the best of our knowledge, the first hierarchy of attention deficits in schizophrenia by identifying the most discriminative attention parameters among a variety of attention deficits found in schizophrenia patients. Our results offer a starting point for hierarchy building of schizophrenia-associated attention deficits and contribute to translating these concepts into diagnostic and therapeutic practice on a single-subject level.

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients

“…Previous machine learning studies have generated encouraging diagnostic accuracies >85% (e.g. Shen et al ., 2014; Chu et al ., 2016; Santos-Mayo et al ., 2017; Xiao et al ., 2017) as well as prediction of the clinical outcome (Zarogianni et al ., 2017). However, most previous studies have been unimodal and performed in medicated and more chronic patient samples, in which the variation in data is greater than at first illness presentation.…”

Restoring activity in the thalamic reticular nucleus improves sleep architecture and reduces Aβ accumulation in mice

“…All of these domains are affected in AD (38,48,49). These domains are affected also in schizophrenia (50)(51)(52)(53), suggesting that there may be some commonalities underlying neuropsychiatric symptoms observed in diverse neurological disorders. Activity-marker mapping indicates that TRN activity is reduced in young transgenic mice expressing human amyloid precursor protein (APP) (54), a mouse model that is widely used to study the role of APP/A in the pathophysiology of AD (55).…”

The effects of abnormal visual experience on neurodevelopmental disorders

“…Attention plays an important role in guiding perception. Attentional deficits in schizophrenia have been widely explored (Carment et al., 2019; Galaverna et al., 2012; Gur et al., 2007; Shen et al., 2014) and are important aspects to consider when investigating this disorder. For instance, attentional deficits in schizophrenic patients have been investigated by measuring schizophrenic patients’ inhibitory control and other experimental parameters measuring attention (focused attention, selective attention, sustained attention) (Galaverna et al., 2012).…”