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

Generalized Slowing of Resting State Neural Oscillations in People with Schizophrenia

BACKGROUND

Recent interest in how neural oscillations reflect the flow of information through the brain has led to partitioning electroencephalography (EEG) recordings into periodic (i.e., oscillatory) and aperiodic (i.e., non-oscillatory) components. While both contribute to conventional measures of power within the frequencies that compose EEG recordings, the periodic aspect characterizes true oscillations - the speed of which is thought to be critical to efficient functioning of neural systems. Given evidence of EEG power abnormalities in schizophrenia, we sought to determine if the periodic aspect of EEG was aberrant in people with schizophrenia (SCZ) and could serve as a general measure of brain efficiency.

METHODS

Resting state EEGs were gathered from 104 SCZ and 105 healthy control participants. We used the fitting-oscillations-and-one-over-f (FOOOF) toolbox to remove aperiodic neural activity. We computed the cross-correlation between power spectra for individual participants and the mean power spectrum for all participants to quantify the relative speed of neural oscillations.

RESULTS

Periodic activity in SCZ was shifted toward lower frequencies compared to control participants during eyes closed rest. On average SCZ had a 0.55 Hz shift toward oscillatory slowing across the frequency spectrum which predicted worse perceptual reasoning. Slower neural oscillations were associated with weaker perceptual reasoning within SCZ.

CONCLUSIONS

Slowed periodic activity at rest is evident in schizophrenia and may represent inefficient functioning of neural circuits as reflected in worse perceptual reasoning. A slower pace of neural oscillations may be a general limitation on the transmission of information within the brain.

Peak alpha frequency in schizophrenia, bipolar disorder, and healthy volunteers: Associations with visual information processing and cognition

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BD) are associated with information processing abnormalities, including visual perceptual and cognitive impairments, that impact daily functioning. Recent work in healthy samples suggests that peak alpha frequency (PAF) is an electrophysiological index of visual information processing speed that is also correlated with cognitive ability. There is evidence that PAF is slowed in SCZ, but it remains unclear whether PAF is reduced in BD, or if slower PAF is associated with impaired visual perception and cognition in these clinical disorders.

METHODS

The current study recorded resting-state brain activity (both eyes open and closed) with electroencephalography (EEG) in 90 SCZ participants, 62 BD participants, and 69 healthy controls. Most participants also performed a visual perception task (backward masking) and cognitive testing (MATRICS Consensus Cognitive Battery).

RESULTS

We replicated previous findings of reduced PAF in SCZ compared with healthy controls. In contrast, PAF in BD did not significantly differ from healthy controls. Further, PAF was significantly correlated with performance on the perceptual and cognitive measures in SCZ, but not BD. PAF was also correlated with visual perception in the healthy control group, and showed a trend-level correlation with cognition.

CONCLUSIONS

Together, these results suggest that PAF deficits characterize SCZ, but not BD, and that individual differences in PAF relate to abnormalities in visual information processing and cognition in SCZ.

Slowed alpha oscillations and percept formation in psychotic psychopathology

Introduction

Psychosis is in part defined by disturbances in perception. Recent investigations have implicated the speed of alpha oscillations observed in brain electrical activity as reflective of a sampling rate of the visual environment and perception. Although both slowed alpha oscillations and aberrant percept formation are evident in disorders of psychotic psychopathology such as schizophrenia it is unclear whether slow alpha accounts for abnormal visual perception in these disorders.

Methods

To examine the role of the speed of alpha oscillations in perception in psychotic psychopathology we gathered resting-state magneto-encephalography data from probands with psychotic psychopathology (i.e., schizophrenia, schizoaffective disorder, and bipolar disorder with a history of psychosis), their biological siblings, and healthy controls. We appraised visual perceptual function without the confound of cognitive ability and effort through the use of a simple binocular rivalry task.

Results

We found a slowed pace of alpha oscillations in psychotic psychopathology that was associated with longer percept durations during binocular rivalry, consistent with the assertion that occipital alpha oscillations govern the rate of accumulation of visual information used to generate percepts. Alpha speed varied widely across individuals with psychotic psychopathology and was highly stable across several months indicating that it is likely a trait characteristic of neural function that is relevant to visual perception. Finally, a lower speed of alpha oscillation was associated with a lower IQ and greater disorder symptomatology implying that the effects of the endogenous neural oscillation on visual perception may have wider consequences for everyday functioning.

Discussion

Slowed alpha oscillations in individuals with psychotic psychopathology appear to reflect altered neural functions related to percept formation.

Randomized controlled trial of the glycine transporter 1 inhibitor PF-03463275 to enhance cognitive training and neuroplasticity in schizophrenia

N-methyl-d-aspartate glutamate receptor (NMDAR) hypofunction is implicated in the impaired neuroplasticity and cognitive impairments associated with schizophrenia (CIAS). We hypothesized that enhancing NMDAR function by inhibiting the glycine transporter-1 (GLYT1) would improve neuroplasticity and thereby augment benefits of non-pharmacological cognitive training (CT) strategies. This study examined whether co-administration of a GLYT1 inhibitor and computerized CT would have synergistic effects on CIAS. Stable outpatients with schizophrenia participated in this double-blind, placebo-controlled, within-subject, crossover augmentation study. Participants received placebo or GLYT1 inhibitor (PF-03463275) for two 5-week periods separated by 2 weeks of washout. PF-03463275 doses (40 or 60 mg twice daily) were selected to produce high GLYT1 occupancy. To limit pharmacodynamic variability, only cytochrome P450 2D6 extensive metabolizers were included. Medication adherence was confirmed daily. Participants received 4 weeks of CT in each treatment period. Cognitive performance (MATRICS Consensus Cognitive Battery) and psychotic symptoms (Positive and Negative Syndrome Scale) were assessed in each period. 71 participants were randomized. PF-03463275 in combination with CT was feasible, safe, and well-tolerated at the doses prescribed but did not produce greater improvement in CIAS compared to CT alone. PF-03463275 was not associated with improved CT learning parameters. Participation in CT was associated with improvement in MCCB scores.

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

Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

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

As the industry gradually enters the stage of unmanned and intelligent, factories in the future need to realize intelligent monitoring and diagnosis and maintenance of parts and components. In order to achieve this goal, it is first necessary to accurately identify and classify the parts in the factory. However, the existing literature rarely studies the classification and identification of parts of the entire factory. Due to the lack of existing data samples, this paper studies the identification and classification of small samples of industrial machine parts. In order to solve this problem, this paper establishes a convolutional neural network model based on the InceptionNet-V3 pretrained model through migration learning. Through experimental design, the influence of data expansion, learning rate and optimizer algorithm on the model effectiveness is studied, and the optimal model was finally determined, and the test accuracy rate reaches 99.74%. By comparing with the accuracy of other classifiers, the experimental results prove that the convolutional neural network model based on transfer learning can effectively solve the problem of recognition and classification of industrial machine parts with small samples and the idea of transfer learning can also be further promoted.