A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis

Abstract

Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.

Reduced neural connectivity in the caudate anterior head predicts hallucination severity in schizophrenia

“…In this first-of-its-kind report, we now deliver robust evidence revealing the caudate anterior head division is one region that contributes to the neural pathophysiology underlying hallucinations in schizophrenia. We have previously shown that it is possible to predict treatment response at the individual level using structural MRI and resting-state fMRI ( Hinkley et al, 2022 ; Kambeitz-Ilankovic et al, 2021 ; Haas et al, 2021b ). The next step in a larger sample would be to investigate the predictive accuracy of treatment response to deep brain stimulation for improving hallucination severity at the individual level using machine learning approaches based on resting state connectivity metrics and clinical measures, such as the PANSS, Psychotic Symptoms Rating Scales (PSYRATS) ( Haddock et al, 1999 ) and Auditory Vocal Hallucination Rating Scale (AVHRS) ( Bartels-Velthuis et al, 2012 ).…”

Distinct multimodal biological and functional profiles of symptom-based subgroups in recent-onset psychosis

“…Neurocognitive performance was assessed by mapping several cognitive batteries available in the PRONIA study onto the six domains from the MATRICS consensus cognitive battery: social cognition, working memory, speed of processing, verbal learning, reasoning, and attention [50], [51],…”

The potential of precision psychiatry: what is in reach?

“…ELECT-TDCS, involving transcranial direct current stimulation in major depression) 31 and models predicting response to cognitive–behavioural psychotherapy (CBT) 32 and cognitive training. 33 Moreover, digital psychotherapeutic interventions are increasingly common, and machine learning approaches have been used to predict symptom change in response to an internet intervention for depression. 34 Their predictions outperform linear regression models and use easily accessible clinical data, increasing the potential for clinical implementation.…”