Dysfunction in different phases of working memory in schizophrenia: evidence from ERP recordings

Long-term findings on working memory neural dynamics in healthcare workers after mild COVID-19

OBJECTIVE

Understanding the long-term impact of Coronavirus Disease 2019 (COVID-19) on cognitive function, even in mild cases, is critical to the well-being of individuals, especially for healthcare workers who are at increased risk of exposure to the virus. To the best of our knowledge, the electrophysiological activity underlying cognitive functioning has not yet been explored.

METHODS

Seventy-seven healthcare workers took part in the study (43 with mild infection about one year before the study and 34 uninfected). To assess cognitive status, event-related potentials (ERPs) and behavioural responses were recorded while participants performed a working memory task.

RESULTS

COVID-19 participants exhibited a distinct neural pattern with lower parieto-occipital N1 amplitudes and higher frontal P2 amplitudes as compared to non-infected healthcare workers. We found no behavioural differences (reaction times and error rates) in working memory functioning between groups.

CONCLUSIONS

This neural pattern suggests the presence of a decrement of processing resources linked to the encoding of sensory information (N1), followed by the enhanced of the P2 response which could be interpreted as the activation of compensation mechanism in COVID-19 participants.

SIGNIFICANCE

The current findings point out that ERPs could serve as valuable neural indices for detecting distinctive patterns in working memory functioning of COVID-19 participants, even in mild cases. However, further research is required to precisely ascertain the long-term cognitive effects of COVID-19 beyond one-year post-infection.

Finding normal-to-better neurocognitive indexes in individuals with schizotypal traits using a social role task

Schizophrenia patients make more errors and have longer reaction times (RTs) than healthy controls in most cognitive tasks. Deficits are also observed in subclinical participants having high scores on the schizotypal personality questionnaire (SPQ). They are accompanied by smaller amplitudes of the event-related brain potentials (ERPs) that index attention and semantic- and working-memory. These functions are thus thought to be impaired in individuals having various schizophrenia attributes (SzAs). Nevertheless, normal RTs were recently found in SzAs during a particular self-referential task where half of the stimuli were names of extraordinary social roles (e.g., genius). Each name (ordinary or extraordinary) was presented individually, and participants were asked to decide whether or not they would consider themselves performing the role at any moment of their lives. To further test an absence of cognitive deficits in this task, the ERPs elicited by names of social roles were also examined in 175 healthy participants. The absence of longer RTs in high- than in low-SPQs was replicated. Moreover, the ERPs of high SPQs had larger occipital N1s, larger P2s and larger occipital N400s than those of low SPQs while late positive potentials (LPPs) were of similar amplitudes. Such results are consistent with clinical observations of greater attention and faster processing of stimuli related to extraordinary/delusional beliefs. Further studies should test whether the cognitive deficits found in SzAs are due to the use of tasks and stimuli that are less within their focus of interest than within that of healthy controls.

Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network

Schizophrenia is a severe mental illness that imposes considerable economic burden on families and society. However, its clinical diagnosis primarily relies on scales and doctors' clinical experience and lacks an objective and accurate diagnostic approach. In recent years, graph convolutional neural networks (GCN) have been used to assist in psychiatric diagnosis owing to their ability to learn spatial-association information. Therefore, this study proposes a schizophrenia automatic recognition model based on graph convolutional neural network. Herein, the resting-state electroencephalography (EEG) data of 103 first-episode schizophrenia patients and 92 normal controls (NCs) were obtained. The automatic recognition model was trained with a nodal feature matrix that comprised the time and frequency-domain features of the EEG signals and local features of the brain network. The most significant regions that contributed to the model classification were identified, and the correlation between the node topological features of each significant region and clinical evaluation metrics was explored. Experiments were conducted to evaluate the performance of the model using 10-fold cross-validation. The best performance in the theta frequency band with a 6 s epoch length and phase-locked value. The recognition accuracy was 90.01%. The most significant region for identifying with first-episode schizophrenia patients and NCs was located in the parietal lobe. The results of this study verify the applicability of the proposed novel method for the identification and diagnosis of schizophrenia.

Prospective memory in early and established psychosis: An Indian perspective

Individuals affected by psychosis often have deficits in several neurocognitive functions. Prospective memory (PM), the ability to remember to do things, is crucial for activities of daily living, social and occupational functioning, but very few studies have attempted to examine this domain of functioning in people with psychosis, particularly in India. A total of 71 patients with psychosis, (both early and established psychosis), and 140 age, gender and education-matched healthy controls were assessed using the Positive and Negative Symptom Scale, Hospital Anxiety and Depression scale, and Addenbrooke's Cognitive Examination. PM was assessed using the Cambridge Prospective Memory Test and the Prospective and Retrospective Memory Questionnaire (PRMQ). Group differences were evaluated using Mann-Whitney U-tests. Significantly greater cognitive deficits, higher anxiety and depression were evident in the psychosis group compared with controls. The psychosis group performed significantly poorer on both time- and event-based tests in CAMPROMPT than controls. These differences remained when controlling for age, education, general cognitive functioning and mood. The subjective measure of PM (PRMQ) did not differentiate the two groups. The PM performance of early and established psychosis patients was similar. Comparisons with cross-cultural data (PRMQ UK norms and CAMPROMPT and PRMQ Chinese data) revealed important differences in PM performance. Individuals with psychosis have significant deficits in both time- and event-based PM. CAMPROMPT emerged as a more sensitive PM measure compared with PRMQ. Results from cross-cultural comparisons underscore the need for cultural contextualization of assessments.

Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs

Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in aberrant predictive processing, as schizophrenic patients show relatively less attenuated neuronal activity to self-produced tones, than healthy controls. Here, we tested the hypothesis that this aberrant predictive mechanism would manifest itself in the temporal irregularity of neuronal signals. For that purpose, we here introduce an event-related potential (ERP) study model analysis that consists of an EEG real-time model equation, eeg(t) and a frequency Laplace transformed Transfer Function (TF) equation, eeg(s). Combining circuit analysis with control and cable theory, we estimate the temporal model representations of auditory ERPs to reveal neural mechanisms that make predictions about self-generated sensations. We use data from 49 schizophrenic patients (SZ) and 32 healthy control (HC) subjects in an auditory ‘prediction’ paradigm; i.e., who either pressed a button to deliver a sound tone (epoch a), or just heard the tone without button press (epoch b). Our results show significantly higher degrees of temporal irregularity or imprecision between different trials of the ERP from the Cz electrode (N100, P200) in SZ compared to HC (Levene’s test, p < 0.0001) as indexed by altered latency, lower similarity/correlation of single trial time courses (using dynamic time warping), and longer settling times to reach steady state in the intertrial interval. Using machine learning, SZ vs HC could be highly accurately classified (92%) based on the temporal parameters of their ERPs’ TF models, using as features the poles of the TF rational functions. Together, our findings show temporal irregularity or imprecision between single trials to be abnormally increased in SZ. This may indicate a general impairment of SZ, related to precisely predicting the sensory consequences of one’s actions.

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.

Altered sub-processes of working memory in fibromyalgia patients: An ERP study using N-back task

OBJECTIVE

Cognitive dysfunction in fibromyalgia has become a key symptom considered by patients as more disabling than pain itself. Experimental evidence from neuropsychological and neuroimaging studies indicates that such cognitive impairments are especially robust when patients need to set in motion working memory processes, suggesting the existence of an altered functioning underlying the cerebral cortices of the frontoparietal memory network. However, the temporal dynamics of working memory sub-processes have not yet been explored in fibromyalgia.

SUBJECTS

Thirty-six right-handed women participated in the experiment: comprising eighteen patients with fibromyalgia and eighteen healthy controls.

METHODS

Event-related potentials (ERPs) and behavioural responses were recorded while participants were engaged in a 2-back working memory task. Principal Component Analyses (PCA) were used to define and quantify the ERP components associated with working memory processes.

RESULTS

Fibromyalgia patients exhibited worse performance than the control group, as revealed by their number of errors in the working memory task. Moreover, both scalp parieto-occipital P2 and parieto-occipital P3 amplitudes were lower for patients than for healthy control participants. Regression analyses revealed that lower P3 amplitudes were observed in those patients with fibromyalgia reporting higher pain ratings.

CONCLUSIONS

Current results suggest that both encoding of information (as reflected by P2) and subsequently, context updating and the replacement (as seen in lower P3 amplitudes), as a part of working memory sub-processes, are impaired in fibromyalgia. Studying the temporal dynamics of working memory using ERP methodology is a helpful approach to detect specific cognitive impaired mechanisms in this chronic pain syndrome. These new data could be used to develop more specific treatments adapted for each patient.

Dynamic Changes of Brain Networks during Working Memory Tasks in Schizophrenia

Electroencephalograph (EEG) signals and graph theory measures have been widely used to characterize the brain functional networks of healthy individuals and patients by calculating the correlations between different electrodes over an entire time series. Although EEG signals have a high temporal resolution and can provide relatively stable results, the process of constructing and analyzing brain functional networks is inevitably complicated by high time complexity. Our goal in this research was to distinguish the brain function networks of schizophrenia patients from those of healthy participants during working memory tasks. Consequently, we utilized a method involving microstates, which are each characterized by a unique topography of electric potentials over an entire channel array, to reduce the dimension of the EEG signals during working memory tasks and then compared and analyzed the brain functional networks using the microstates time series (MTS) and original time series (OTS) of the schizophrenia patients and healthy individuals. We found that the right frontal and parietal-occipital regions neurons of the schizophrenia patients were less active than those of the healthy participants during working memory tasks. Notably, compared with OTS, the time needed to construct the brain functional networks was significantly reduced by using MTS. In conclusion, our results show that, like OTS, MTS can well distinguish the brain functional network of schizophrenia patients from those of healthy individuals during working memory tasks while greatly decreasing time complexity. MTS can thus provide a method for characterizing the original time series for the construction and analysis of EEG brain functional networks.

Brain Abnormalities in Different Phases of Working Memory in Schizophrenia: An Integrative Multi-Modal MRI Study

The current study aimed to examine both gray matter and functional activity changes in schizophrenia by combing both structural and task-related functional magnetic resonance imaging (fMRI). Nineteen patients with schizophrenia and 17 controls were recruited. The fMRI scan was performed while performing a working memory (WM) task. In terms of task performance, accuracy did not differ between groups, but there were significant differences in reaction time. Compared with controls, patients exhibited decreased functional activation in prefrontal areas, insula, lingual gyrus, and superior temporal gyrus during different phases of WM. The subcallosal cortex showed increased activation. Intriguingly, a structural-functional correlation was found in the left dorsolateral prefrontal cortex, anterior cingulate cortex, and subcallosal cortex in patients when performing high-load WM task. This study demonstrated both impaired gray matter volume and functional activation during WM in schizophrenia, suggesting structural and functional impairments. The structural-functional correlation in schizophrenia suggested that structural damage in schizophrenia might induce a decreased ability to modulate functional response in accordance with increasing task difficulty.

Using ERPs to explore the impact of affective distraction on working memory stages in schizophrenia

Research on individuals with schizophrenia (SCZ) shows a variety of emotional and cognitive deficits. We examined the hypothesis that ineffective emotional interference control may impact working memory (WM) performance by disrupting information encoding, maintenance, or retrieval in SCZ. Twenty-eight SCZ and 28 matched healthy controls (HC) performed the visual and verbal delayed-matching-to-sample task (DMST) with trials preceded by negative and nonemotional visual distractors. Event-Related Potentials associated with affective stimuli processing (Late Positive Potential-LPP) and WM-encoding (target-P3), maintenance (Negative Slow Wave-NSW), and retrieval (probe-P3) were analyzed. Patients showed overall worse DMST accuracy than HC. Emotional distraction negatively impacted accuracy during the verbal DMST in both groups combined. Both groups also displayed similar LPP modulation during the presentation of emotional distractors. HC showed enhanced NSW after presentation of a negative distraction, whereas this did not occur in SCZ. Comparable effects of emotional distraction were found for WM-encoding and retrieval in both groups. While emotional and neutral stimuli differentially impacted WM-maintenance on the neural level in HC, we did not observe this effect in SCZ, even though both groups showed similar behavioral and neurophysiological reactions to affective stimuli. Deficits in inhibitory mechanisms in SCZ may be responsible for this effect and may have particular relevance for WM-maintenance difficulties.

Adjustment of Synchronization Stability of Dynamic Brain-Networks Based on Feature Fusion

When the brain is active, the neural activities of different regions are integrated on various spatial and temporal scales; this is termed the synchronization phenomenon in neurobiological theory. This synchronicity is also the main underlying mechanism for information integration and processing in the brain. Clinical medicine has found that some of the neurological diseases that are difficult to cure have deficiencies or abnormalities in the whole or local integration processes of the brain. By studying the synchronization capabilities of the brain-network, we can intensively describe and characterize both the state of the interactions between brain regions and their differences between people with a mental illness and a set of controls by measuring the rapid changes in brain activity in patients with psychiatric disorders and the strength and integrity of their entire brain network. This is significant for the study of mental illness. Because static brain network connection methods are unable to assess the dynamic interactions within the brain, we introduced the concepts of dynamics and variability in a constructed EEG brain functional network based on dynamic connections, and used it to analyze the variability in the time characteristics of the EEG functional network. We used the spectral features of the brain network to extract its synchronization features and used the synchronization features to describe the process of change and the differences in the brain network's synchronization ability between a group of patients and healthy controls during a working memory task. We propose a method based on the fusion of traditional features and spectral features to achieve an adjustment of the patient's brain network synchronization ability, so that its synchronization ability becomes consistent with that of healthy controls, theoretically achieving the purpose of the treatment of the diseases. Studying the stability of brain network synchronization can provide new insights into the pathogenic mechanism and cure of mental diseases and has a wide range of potential applications.

Mechanisms Underlying Visuospatial Working Memory Impairments in Schizophrenia

Working memory deficits are observed in the vast majority of individuals diagnosed with schizophrenia and those at risk for the disorder. Working memory impairments are present during the prodromal stage and persist throughout the course of schizophrenia. Given the importance of cognition in functional outcome, working memory deficits are an important therapeutic target for schizophrenia. This chapter examines mechanisms underlying working memory deficits in schizophrenia, focusing on the roles of perception and attention in the encoding process. Lastly, we present a comprehensive discussion of neural oscillation and internal noise in the context of the etiology of working memory deficits in schizophrenia and introduce noninvasive treatment strategies that could improve encoding processes.

Evidence for a differential contribution of early perceptual and late cognitive processes during encoding to episodic memory impairment in schizophrenia

OBJECTIVES

Schizophrenia is characterised by significant episodic memory impairment that is thought to be related to problems with encoding, however the neuro-functional mechanisms underlying these deficits are not well understood. The present study used a subsequent recognition memory paradigm and event-related potentials (ERPs) to investigate temporal aspects of episodic memory encoding deficits in schizophrenia.

METHODS

Electroencephalographic data was recorded in 24 patients and 19 healthy controls whilst participants categorised single words as pleasant/unpleasant. ERPs were generated to subsequently recognised versus unrecognised words on the basis of a forced-choice recognition memory task. Subsequent memory effects were examined with the late positive component (LPP). Group differences in N1, P2, N400 and LPP were examined for words correctly recognised.

RESULTS

Patients performed more poorly than controls on the recognition task. During encoding patients had significantly reduced N400 and LPP amplitudes than controls. LPP amplitude correlated with task performance however amplitudes did not differ between patients and controls as a function of subsequent memory. No significant differences in N1 or P2 amplitude or latency were observed.

CONCLUSIONS

The present results indicate that early sensory processes are intact and dysfunctional higher order cognitive processes during encoding are contributing to episodic memory impairments in schizophrenia.

Deficits in attentional processing of fearful facial expressions in schizophrenic patients

Impaired attentional processing of negative facial expressions is prominent in schizophrenia and has been shown to be associated with patients’ social dysfunctions. However, little is known about when and which specific attention deficits influence social functions. Given the dynamic feature of attention, it is necessary to investigate the attention deficits in negative emotional processing unfolding in time. The current study used event-related potentials (ERPs) to investigate the temporal dynamics of attention deficits in emotion perception and their potential relationship with emotional/social impairments in neuroleptic naive schizophrenic patients. Two specific attention deficits were identified and were found to be associated with emotional/social impairments. More specifically, the deficit in orienting attention (evidenced with the reduced P1 amplitude) was correlated with expressive deficits, while the deficit in executive control of attention (evidenced with the reduced P3 amplitude) was correlated with avolition/asociality. Together, these findings may provide novel insights into the core pathophysiological processes and offer objective biomarkers for specific emotional/social impairments in schizophrenia. It is also hoped that this study helps to bridge the gap between basic cognitive deficits and relative high-level social dysfunctions in schizophrenic patients.

Impaired retrieval processes evident during visual working memory in schizophrenia

Prominent working memory (WM) deficits have been observed in people with schizophrenia (PSZ) across multiple sensory modalities, including the visuospatial realm. Electrophysiological abnormalities noted during early visual processing as well as later cognitive functions in PSZ may underlie deficiencies in WM ability, though the mechanisms linking behavior to neural responses are not well understood. WM dysfunction has also been observed in biological relatives of PSZ (REL) and therefore may be a manifestation of genetic liability for the disorder. We administered a delayed response visuospatial WM task to 23 PSZ, 30 of their REL, and 37 healthy controls (CTRL) to better understand the contributions of neural abnormalities to WM performance deficits associated with schizophrenia. PSZ performed more poorly on the WM task and failed to effectively process distractor stimuli as well as CTRL and REL. N1 electrophysiological responses to probes during retrieval differentiated the type and locations of stimuli presented during encoding in CTRL. Retrieval N1 responses in PSZ, however, failed to do so, while retrieval responses in REL showed more pronounced differentiation of stimulus features during encoding. Furthermore, neural responses during retrieval predicted behavioral performance in PSZ and REL, but not CTRL. These results suggest that retrieval processes are particularly important to efficient visuospatial WM function in PSZ and REL, and support further investigation of WM retrieval as a potential target for improving overall WM function through clinical intervention.