Individual Alpha Peak Frequency Moderates Transfer of Learning in Cognitive Remediation of Schizophrenia

Abstract

Objective:Meta-analyses report moderate effects across cognitive remediation (CR) trials in schizophrenia. However, individual responses are variable, with some participants showing no appreciable gain in cognitive performance. Furthermore, reasons for heterogeneous outcome are undetermined. We examine the extent to which CR outcome is attributable to near learning—direct gains in trained cognitive tasks—while also exploring factors influencing far transfer of gains during training to external cognitive measures.Method:Thirty-seven schizophrenia outpatients were classified as CR responders and non-responders according to change in MATRICS Consensus Cognitive Battery composite score following 20 sessions of computer-based training. Metrics of near learning during training, as well as baseline demographic, clinical, cognitive, and electroencephalographic (EEG) measures, were examined as predictors of responder status.Results:Significant post-training improvement in cognitive composite score (Cohen’s d = .41) was observed across the sample, with n = 21 and n = 16 classified as responders and non-responders, respectively. Near learning was evidenced by significant improvement on each training exercise with practice; however, learning did not directly predict responder status. Group-wise comparison of responders and non-responders identified two factors favoring responders: higher EEG individual alpha frequency (IAF) and lower antipsychotic dosing. Tested in moderation analyses, IAF interacted with learning to predict improvement in cognitive outcome.Conclusion:CR outcome in schizophrenia is not directly explained by learning during training and appears to depend on latent factors influencing far transfer of trained abilities. Further understanding of factors influencing transfer of learning is needed to optimize CR efficacy.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

“…Moreover, one study reported a positive correlation between individual alpha peak frequency (IAPF) and measures of speed of processing, perceptual reasoning, working memory, verbal reasoning and attention in SCZ [ 113 ]. Interestingly, one study investigating the effects of cognitive remediation found no significant correlation at baseline (pre-treatment) between IAPF and a global score of cognition but found that higher values in IAPF at baseline predicted higher responsiveness to the intervention and greater improvements on cognitive skills after the completion of the therapy sessions [ 111 ]. Finally, a decreased alpha band connectivity in the prefrontal cortex reported in FES as compared to controls was found to be correlated with cognitive processing impairments [ 101 ].…”

“…Studies on resting state and evoked alpha power produced heterogeneous results [ 66 , 111 , 113 , 122 ], and it is therefore difficult to draw conclusions. Most of the studies, however, used standard alpha frequency instead of individual alpha peak frequency (IAPF) which may cause heterogeneity in the results of studies investigating the association of alpha frequency and cognitive functions.…”

“…In fact, IAPF has been shown to be much more reliable and reproducible across sessions and cognitive tasks than standard alpha frequency measurements [ 113 , 273 , 274 ]. Studies investigating IAPF have reported a reduction in subjects with schizophrenia and some studies have hypothesized that dysfunctional timing of alpha waves could be at the core of defective cognitive processing [ 111 , 113 , 275 ]. Thus, the vast inter-subject’s variability in the standard frequency alpha range in both physiological and pathological conditions places some restrains in its use as assessment tools of cognitive impairments.…”

Slowed alpha oscillations and percept formation in psychotic psychopathology

“…Researchers have shown that people with psychotic psychopathology such as schizophrenia have difficulty in adaptively forming percepts from external stimuli ( Feigenson et al, 2014 ; Spencer and Ghorashi, 2014 ; Keane et al, 2016 ; McCleery et al, 2020 ; Longenecker et al, 2021 ; Osborne et al, 2022 ). Also, the pace of the alpha oscillations is slowed in people with schizophrenia, which is typically measured as the frequency of the alpha peak in the frequency spectrum of the resting EEG ( Yeum and Kang, 2018 ; Murphy and Öngür, 2019 ; Castelluccio et al, 2020 ). We have shown that lower alpha peak frequencies are associated with lower intelligence scores that may be mediated by the ability to distinguish visually degraded images of numerals from one another, thereby suggesting that the pace of alpha oscillations is relevant to perceptual anomalies and tied to cognitive deficits in schizophrenia ( Ramsay et al, 2021 ).…”

Recognition of industrial machine parts based on transfer learning with convolutional neural network

“…In the field of industrial machine parts recognition, there are few algorithms related to its specific research. In order to solve the problem of recognition and classification of industrial machine parts under the condition of small samples, a recognition method of industrial machine parts based on transfer learning [8][9][10][11][12][13][14][15] with CNN is proposed. Not only can further improve the recognition accuracy, but also save the time of model training.…”