Working Memory in People with Schizophrenia

Molecular Rhythmicity in Glia: Importance for Brain Health and Relevance to Psychiatric Disease

Circadian rhythms are approximate 24-hour rhythms present in nearly all aspects of human physiology, including proper brain function. These rhythms are produced at the cellular level through a transcriptional-translational feedback loop known as the molecular clock. Diurnal variation in gene expression has been demonstrated in brain tissue from multiple species, including humans, in both cortical and subcortical regions. Interestingly, these rhythms in gene expression have been shown to be disrupted across psychiatric disorders and may be implicated in their underlying pathophysiology. However, little is known regarding molecular rhythms in specific cell types in the brain and how they might be involved in psychiatric disease. Although glial cells (e.g., astrocytes, microglia, and oligodendrocytes) have been historically understudied compared to neurons, evidence of the molecular clock is found within each of these cell subtypes. Here, we review the current literature, which suggests that molecular rhythmicity is essential to functional physiologic outputs from each glial subtype. Furthermore, disrupted molecular rhythms within these cells and the resultant functional deficits may be relevant to specific phenotypes across psychiatric illnesses. Given that circadian rhythm disruptions have been so integrally tied to psychiatric disease, the molecular mechanisms governing these associations could represent exciting new avenues for future research and potential novel pharmacologic targets for treatment.

Repetitive Transcranial Magnetic Stimulation for Working Memory Deficits in Schizophrenia: A Systematic Review of Randomized Controlled Trials

Working memory (WM) deficits are a significant component of neurocognitive impairment in individuals with schizophrenia (SCZ). Two previous meta-analyses, conducted on randomized controlled trials (RCTs), examined the effectiveness of repetitive transcranial magnetic stimulation (rTMS) in addressing WM deficits in individuals diagnosed with SCZ. However, the conclusions drawn from these analyses were inconsistent. Additionally, the commonly used random effects (RE) models might underestimate statistical errors, attributing a significant portion of perceived heterogeneity between studies to variations in study quality. Therefore, this review utilized both RE and quality effects (QE) models to assess relevant RCTs comparing TMS with sham intervention in terms of clinical outcomes. A comprehensive literature search was conducted using PubMed and Scopus databases, resulting in the inclusion of 13 studies for data synthesis. Overall, regardless of whether the RE or QE model was used, eligible RCTs suggested that the TMS and sham groups exhibited comparable therapeutic effects after treatment. The current state of research regarding the use of rTMS as a treatment for WM deficits in patients with SCZ remains in its preliminary phase. Furthermore, concerning the mechanism of action, the activation of brain regions focused on the dorsolateral prefrontal cortex and alterations in gamma oscillations may hold significant relevance in the therapeutic application of rTMS for addressing WM impairments. Finally, we believe that the application of closed-loop neuromodulation may contribute to the optimization of rTMS for WM impairment in patients with SCZ.

Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia

Introduction

Patients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm.

Methods

We tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM.

Results

The DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs.

Discussion

These results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.

Sustained attention deficits in schizophrenia: Effect of memory load on the Identical Pairs Continuous Performance Test

Background

Sustained attention and vigilance impairments are well documented in people with schizophrenia (PSZ). The processes implicated in this impairment remain unclear. Here we investigated whether vigilance performance varied as a function of working memory load, and also examined the role of attentional lapsing that might arise from a loss of task set resulting in mind wandering.

Method

We examined Continuous Performance Test Identical Pairs (CPT-IP) data from a cumulative sample of 247 (PSZ) and 238 healthy control (HC) participants collected over a series of studies.

Results

PSZ performed more poorly that HC across conditions with signal/noise discrimination (d') decreasing with increasing working memory load across both groups However, there was a significant interaction of group and load suggesting that performance of PSZ was more negatively impacted by increasing load. We also found that PSZ has a significantly higher rate of attention lapsing than did HC.

Discussion

Our results suggest that difficulties maintaining task set and working memory limitations are implicated in the impairments observed on the Identical Pairs CPT. Difficulties with task set maintenance appear to explain the majority of between-group variance, with a more subtle impact of increasing working memory load.

Myelin dystrophy in the aging prefrontal cortex leads to impaired signal transmission and working memory decline: a multiscale computational study

Normal aging leads to myelin alternations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are often correlated with cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First we built a multicompartment pyramidal neuron model fit to monkey dlPFC data, with axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions, to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination in a population of neurons. Lasso regression identified distinctive parameter sets likely to modulate an axon's susceptibility to CV changes following demyelination versus remyelination. Next we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from electron microscopy up to behavior on aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.

Mouse Behavior on the Trial-Unique Nonmatching-to-Location (TUNL) Touchscreen Task Reflects a Mixture of Distinct Working Memory Codes and Response Biases

The trial-unique nonmatching to location (TUNL) touchscreen task shows promise as a translational assay of working memory (WM) deficits in rodent models of autism, ADHD, and schizophrenia. However, the low-level neurocognitive processes that drive behavior in the TUNL task have not been fully elucidated. In particular, it is commonly assumed that the TUNL task predominantly measures spatial WM dependent on hippocampal pattern separation, but this proposition has not previously been tested. In this project, we tested this question using computational modeling of behavior from male and female mice performing the TUNL task (N = 163 across three datasets; 158,843 trials). Using this approach, we empirically tested whether TUNL behavior solely measured retrospective WM, or whether it was possible to deconstruct behavior into additional neurocognitive subprocesses. Overall, contrary to common assumptions, modeling analyses revealed that behavior on the TUNL task did not primarily reflect retrospective spatial WM. Instead, behavior was best explained as a mixture of response strategies, including both retrospective WM (remembering the spatial location of a previous stimulus) and prospective WM (remembering an anticipated future behavioral response) as well as animal-specific response biases. These results suggest that retrospective spatial WM is just one of a number of cognitive subprocesses that contribute to choice behavior on the TUNL task. We suggest that findings can be understood within a resource-rational framework, and use computational model simulations to propose several task-design principles that we predict will maximize spatial WM and minimize alternative behavioral strategies in the TUNL task.SIGNIFICANCE STATEMENT Touchscreen tasks represent a paradigm shift for assessment of cognition in nonhuman animals by automating large-scale behavioral data collection. Their main relevance, however, depends on the assumption of functional equivalence to cognitive domains in humans. The trial-unique, delayed nonmatching to location (TUNL) touchscreen task has revolutionized the study of rodent spatial working memory. However, its assumption of functional equivalence to human spatial working memory is untested. We leveraged previously untapped single-trial TUNL data to uncover a novel set of hierarchically ordered cognitive processes that underlie mouse behavior on this task. The strategies used demonstrate multiple cognitive approaches to a single behavioral outcome and the requirement for more precise task design and sophisticated data analysis in interpreting rodent spatial working memory.