Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Canada's Student Mental Health Network: Protocol for a Comprehensive Program Evaluation.

BACKGROUND: Prevalence estimates for mental health-related problems, including above-average stress, psychological distress, and symptoms of mental illnesses have increased significantly among Canadian postsecondary students. As demand for downstream mental treatment has surpassed many institutions' abilities to deliver timely care, there is a need for innovative upstream supports that foster mental health promotion and mental illness prevention among this population. OBJECTIVE: Supported by an extensive network of student volunteers, Canada's Student Mental Health Network is a virtual, one-stop shop for centralized mental health education and evidence-based resources tailored to postsecondary students. This article describes a protocol for the comprehensive evaluation of the Network. METHODS: Development of the Network was developed using a participatory action research framework. Network content is created and curated by students and reviewed by subject matter experts. The proposed program evaluation will include both a formative process evaluation and a summative impact assessment to determine the feasibility, acceptability, and utility of the Network in addition to assessing change in the 3 primary outcomes of interest: mental health literacy, perceived social support, and help-seeking behavior. Participants will be recruited directly from the Network website using a "rolling" recruitment approach to allow for continuous data collection and evaluation. A combination of qualitative (ie, interviews) and quantitative (ie, surveys) methods of data collection will be used. RESULTS: The process of evaluation of the Network will begin in September 2022, collecting data for 1 year. In September 2023, the impact evaluation will begin using the same follow-up schedule. Data collection will then remain ongoing to facilitate the continued evaluation of the Network. Reports detailing evaluation data will be released annually. CONCLUSIONS: The Network is a novel and innovative method of delivering universal mental health promotion to Canadian postsecondary students by providing centralized and freely accessible mental health education and resources, created by students and validated by subject matter experts. The continued creation and curation of resources for the Network will be ongoing to meet the evolving needs of the target population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41521.

Improving the Prevention, Detection, and Management of Delirium in Adult Inpatients; an Interprofessional Consultative Team Pilot Project.

Delirium is a common, often preventable fluctuating state of cognition associated with increased morbidity and mortality. This report describes the implementation of an interprofessional consultative Delirium Team formed to improve the prevention, detection, and management of delirium in a community hospital. Team members consulted refered inpatients with delirium to establish a care plan and provide recommendations for pharmacological and non-pharmacological management. The team also offered delirium-related education to unit staff, patients, and caregivers. Consultations were initially completed by the team Nurse Practitioner or Occupational Therapist, and complex patients were discussed with the team Geriatrician and Psychiatrist at rounds to optimize specialist input. Of the 160 patients managed by the team over the 8-month study period, two-thirds of referred patients did not require specialist consultation for their delirium management. Strategies most often recommended by experts for managing delirium were related to medical management, social/cognitive engagement, and functional mobility. Two-thirds of all recommendations made by the team were implemented. Barriers and facilitators to implementation and improving unit staff adherence are further described. The consultative Delirium Team is a promising model that should be further explored for managing an aging population in a capacity-limited medical system.

Predictors of shorter- and longer-term mortality after COVID-19 presentation among dialysis patients: parallel use of machine learning models in Latin and North American countries.

BACKGROUND: We developed machine learning models to understand the predictors of shorter-, intermediate-, and longer-term mortality among hemodialysis (HD) patients affected by COVID-19 in four countries in the Americas. METHODS: We used data from adult HD patients treated at regional institutions of a global provider in Latin America (LatAm) and North America who contracted COVID-19 in 2020 before SARS-CoV-2 vaccines were available. Using 93 commonly captured variables, we developed machine learning models that predicted the likelihood of death overall, as well as during 0-14, 15-30, > 30 days after COVID-19 presentation and identified the importance of predictors. XGBoost models were built in parallel using the same programming with a 60%:20%:20% random split for training, validation, & testing data for the datasets from LatAm (Argentina, Columbia, Ecuador) and North America (United States) countries. RESULTS: Among HD patients with COVID-19, 28.8% (1,001/3,473) died in LatAm and 20.5% (4,426/21,624) died in North America. Mortality occurred earlier in LatAm versus North America; 15.0% and 7.3% of patients died within 0-14 days, 7.9% and 4.6% of patients died within 15-30 days, and 5.9% and 8.6% of patients died > 30 days after COVID-19 presentation, respectively. Area under curve ranged from 0.73 to 0.83 across prediction models in both regions. Top predictors of death after COVID-19 consistently included older age, longer vintage, markers of poor nutrition and more inflammation in both regions at all timepoints. Unique patient attributes (higher BMI, male sex) were top predictors of mortality during 0-14 and 15-30 days after COVID-19, yet not mortality > 30 days after presentation. CONCLUSIONS: Findings showed distinct profiles of mortality in COVID-19 in LatAm and North America throughout 2020. Mortality rate was higher within 0-14 and 15-30 days after COVID-19 in LatAm, while mortality rate was higher in North America > 30 days after presentation. Nonetheless, a remarkable proportion of HD patients died > 30 days after COVID-19 presentation in both regions. We were able to develop a series of suitable prognostic prediction models and establish the top predictors of death in COVID-19 during shorter-, intermediate-, and longer-term follow up periods.

Trajectories of clinical and laboratory characteristics associated with COVID-19 in hemodialysis patients by survival.

INTRODUCTION: The clinical impact of COVID-19 has not been established in the dialysis population. We evaluated the trajectories of clinical and laboratory parameters in hemodialysis (HD) patients. METHODS: We used data from adult HD patients treated at an integrated kidney disease company who received a reverse transcription polymerase chain reaction (RT-PCR) test to investigate suspicion of a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection between May 1 and September 1, 2020. Nonparametric smoothing splines were used to fit data for individual trajectories and estimate the mean change over time in patients testing positive or negative for SARS-CoV-2 and those who survived or died within 30 days of first suspicion or positive test date. For each clinical parameter of interest, the difference in average daily changes between COVID-19 positive versus negative group and COVID-19 survivor versus nonsurvivor group was estimated by fitting a linear mixed effects model based on measurements in the 14 days before (i.e., Day -14 to Day 0) Day 0. RESULTS: There were 12,836 HD patients with a suspicion of COVID-19 who received RT-PCR testing (8895 SARS-CoV-2 positive). We observed significantly different trends (p < 0.05) in pre-HD systolic blood pressure (SBP), pre-HD pulse rate, body temperature, ferritin, neutrophils, lymphocytes, albumin, and interdialytic weight gain (IDWG) between COVID-19 positive and negative patients. For COVID-19 positive group, we observed significantly different clinical trends (p < 0.05) in pre-HD pulse rate, lymphocytes, neutrophils, and albumin between survivors and nonsurvivors. We also observed that, in the group of survivors, most clinical parameters returned to pre-COVID-19 levels within 60-90 days. CONCLUSION: We observed unique temporal trends in various clinical and laboratory parameters among HD patients who tested positive versus negative for SARS-CoV-2 infection and those who survived the infection versus those who died. These trends can help to define the physiological disturbances that characterize the onset and course of COVID-19 in HD patients.

Effectiveness of Interprofessional Consultation-Based Interventions for Delirium: A Scoping Review.

BACKGROUND: Interprofessional geriatric consultation teams and multicomponent interventions are established models for delirium care. They are combined in interprofessional consultative delirium team interventions; however, insight into this novel approach is lacking. OBJECTIVE: To describe the effectiveness and core components of consultation-based interventions for delirium. METHOD: Ovid MEDLINE, EMBASE, PsycINFO, CINAHL, and ProQuest. Data on core intervention components, outcomes, facilitators, and barriers were extracted. RESULTS: 10 studies were included. Core intervention components were systematic delirium screening, ongoing consultation, implementation of non-pharmacologic and pharmacological interventions, and staff education. Of the included studies, 1/6 found a significant reduction in delirium incidence, 1/2 a reduction in delirium duration, and 2/3 found a reduction in falls. Facilitators and barriers to implementation were discussed. CONCLUSION: There was consistency in team structure and core components, however intervention operationalization and effectiveness varied widely. There is some evidence that this model is effective for reducing delirium and its sequelae.

The Clp1 R140H mutation alters tRNA metabolism and mRNA 3' processing in mouse models of pontocerebellar hypoplasia.

Homozygous mutation of the RNA kinase CLP1 (cleavage factor polyribonucleotide kinase subunit 1) causes pontocerebellar hypoplasia type 10 (PCH10), a pediatric neurodegenerative disease. CLP1 is associated with the transfer RNA (tRNA) splicing endonuclease complex and the cleavage and polyadenylation machinery, but its function remains unclear. We generated two mouse models of PCH10: one homozygous for the disease-associated Clp1 mutation, R140H, and one heterozygous for this mutation and a null allele. Both models exhibit loss of lower motor neurons and neurons of the deep cerebellar nuclei. To explore whether Clp1 mutation impacts tRNA splicing, we profiled the products of intron-containing tRNA genes. While mature tRNAs were expressed at normal levels in mutant mice, numerous other products of intron-containing tRNA genes were dysregulated, with pre-tRNAs, introns, and certain tRNA fragments up-regulated, and other fragments down-regulated. However, the spatiotemporal patterns of dysregulation do not correlate with pathogenicity for most altered tRNA products. To elucidate the effect of Clp1 mutation on precursor messenger RNA (pre-mRNA) cleavage, we analyzed poly(A) site (PAS) usage and gene expression in Clp1R140H/- spinal cord. PAS usage was shifted from proximal to distal sites in the mutant mouse, particularly in short and closely spaced genes. Many such genes were also expressed at lower levels in the Clp1R140H/- mouse, possibly as a result of impaired transcript maturation. These findings are consistent with the hypothesis that select genes are particularly dependent upon CLP1 for proper pre-mRNA cleavage, suggesting that impaired mRNA 3' processing may contribute to pathogenesis in PCH10.

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

BACKGROUND: Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals. METHOD: We conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient's relative blood volume (RBV) decreases at a rate of at least - 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records. RESULTS: Treatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data. CONCLUSIONS: The findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.

Artificial intelligence enabled applications in kidney disease.

Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end-stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists' medical decision-making, but instead assist them in providing optimal personalized care for their patients.

Machine Learning for Prediction of Patients on Hemodialysis with an Undetected SARS-CoV-2 Infection.

Background: We developed a machine learning (ML) model that predicts the risk of a patient on hemodialysis (HD) having an undetected SARS-CoV-2 infection that is identified after the following ≥3 days. Methods: As part of a healthcare operations effort, we used patient data from a national network of dialysis clinics (February-September 2020) to develop an ML model (XGBoost) that uses 81 variables to predict the likelihood of an adult patient on HD having an undetected SARS-CoV-2 infection that is identified in the subsequent ≥3 days. We used a 60%:20%:20% randomized split of COVID-19-positive samples for the training, validation, and testing datasets. Results: We used a select cohort of 40,490 patients on HD to build the ML model (11,166 patients who were COVID-19 positive and 29,324 patients who were unaffected controls). The prevalence of COVID-19 in the cohort (28% COVID-19 positive) was by design higher than the HD population. The prevalence of COVID-19 was set to 10% in the testing dataset to estimate the prevalence observed in the national HD population. The threshold for classifying observations as positive or negative was set at 0.80 to minimize false positives. Precision for the model was 0.52, the recall was 0.07, and the lift was 5.3 in the testing dataset. Area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) for the model was 0.68 and 0.24 in the testing dataset, respectively. Top predictors of a patient on HD having a SARS-CoV-2 infection were the change in interdialytic weight gain from the previous month, mean pre-HD body temperature in the prior week, and the change in post-HD heart rate from the previous month. Conclusions: The developed ML model appears suitable for predicting patients on HD at risk of having COVID-19 at least 3 days before there would be a clinical suspicion of the disease.

Postsecondary Mental Health Policy in Canada: A Scoping Review of the Grey Literature: Politique de santé mentale post-secondaire au Canada: un examen de la portée de la littérature grise.

OBJECTIVE: Concerns surrounding the mental health and well-being of Canadian postsecondary students have increased in recent years, with data suggesting increases in the prevalence of self-reported stress and psychological distress. Strategies to address postsecondary mental health have emerged at the national, provincial, and institutional levels. While reviews of the academic literature on the subject have been conducted, a detailed review of the grey literature has not. The objective of this study was to map the current state of grey literature related to current or recommended action supporting postsecondary mental health and well-being in Canada, with a focus on policy documents and guiding frameworks. METHODS: We conducted a review following Arksey and O'Malley's 5-step framework for scoping reviews, as well as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our search was restricted to documents with a primary focus on postsecondary mental health, a national or provincial scope, and publication date between 2000 and 2019. RESULTS: While a national policy or guiding framework applicable to all postsecondary institutions across Canada does not yet exist, recommendations for policy at both the national and provincial levels were well aligned, emphasizing the need for a comprehensive approach to addressing mental health services through the use of a whole-campus approach that encompasses both upstream and downstream services. CONCLUSION: Postsecondary sector stakeholders should consider how existing policy documents and guiding frameworks can be used to inform evidence-based, institutionally specific action on postsecondary mental health. More work is required to align the fragmented action occurring across Canada and incentivize postsecondary institutions to create a sustainable, effective strategy to address the increasingly complex and unique mental health needs of their students, staff, and faculty.

A longitudinal study of event related potentials and correlations with psychosocial functioning and clinical features in first episode psychosis patients.

BACKGROUND: Deficits in auditory event-related potentials (ERPs), brain responses to stimuli indexing different cognitive processes, have been demonstrated widely in chronic schizophrenia (SZ) patients though much less is known about these responses across the early course of psychosis. The present study examined multiple ERP components in first episode psychosis (FEP) patients longitudinally and investigated the relationships between ERPs, psychosocial functioning, and clinical features over time. METHODS: N1, P2, P3a, and P3b ERPs were elicited using a three-stimulus (novelty) auditory oddball paradigm. FEP patients included SZ-spectrum and psychotic bipolar disorder (BD) diagnoses. Data were collected from 41 patients at baseline, 20 patients at 12-month follow-up, 14 at 24-month follow-up, and 29 healthy control subjects. RESULTS: N1 and P2 ERPs were intact across the early stages of psychosis. Baseline P2 was significantly larger in BD than SZ patients. Reduced P3a and P3b ERPs were found in patients followed longitudinally and are stable over time. ERPs tracked distinct aspects of symptomology and medication, though specific associations were inconsistent across time. Baseline P3a amplitude predicted later psychosocial functioning. The pattern of correlations between ERP components in patients differed from controls. DISCUSSION: Baseline P3a ERP, and PANSS general score were significant and independent predictors of later MCAS functioning at 12-month. Overall, individuals with worse functioning and greater symptomology produced smaller amplitudes. Our results highlight the heterogeneity within the FEP population. Correlation patterns among ERPs are similar between patients and controls. P3a and P3b amplitudes appear to link with higher-order cognitive and psychosocial functioning.

The Firing Rate Speed Code of Entorhinal Speed Cells Differs across Behaviorally Relevant Time Scales and Does Not Depend on Medial Septum Inputs.

The firing rate of speed cells, a dedicated subpopulation of neurons in the medial entorhinal cortex (MEC), is correlated with running speed. This correlation has been interpreted as a speed code used in various computational models for path integration. These models consider firing rate to be linearly tuned by running speed in real-time. However, estimation of firing rates requires integration of spiking events over time, setting constraints on the temporal accuracy of the proposed speed code. We therefore tested whether the proposed speed code by firing rate is accurate at short time scales using data obtained from open-field recordings in male rats and mice. We applied a novel filtering approach differentiating between speed codes at multiple time scales ranging from deciseconds to minutes. In addition, we determined the optimal integration time window for firing-rate estimation using a general likelihood framework and calculated the integration time window that maximizes the correlation between firing rate and running speed. Data show that these time windows are on the order of seconds, setting constraints on real-time speed coding by firing rate. We further show that optogenetic inhibition of either cholinergic, GABAergic, or glutamatergic neurons in the medial septum/diagonal band of Broca does not affect modulation of firing rates by running speed at each time scale tested. These results are relevant for models of path integration and for our understanding of how behavioral activity states may modulate firing rates and likely information processing in the MEC.SIGNIFICANCE STATEMENT Path integration is the most basic form of navigation relying on self-motion cues. Models of path integration use medial septum/diagonal band of Broca (MSDB)-dependent MEC grid-cell firing patterns as the neurophysiological substrate of path integration. These models use a linear speed code by firing rate, but do not consider temporal constraints of integration over time for firing-rate estimation. We show that firing-rate estimation for speed cells requires integration over seconds. Using optogenetics, we show that modulation of firing rates by running speed is independent of MSDB inputs. These results enhance our understanding of path integration mechanisms and the role of the MSDB for information processing in the MEC.

Reconciling the different faces of hippocampal theta: The role of theta oscillations in cognitive, emotional and innate behaviors.

The theta oscillation (5-10Hz) is a prominent behavior-specific brain rhythm. This review summarizes studies showing the multifaceted role of theta rhythm in cognitive functions, including spatial coding, time coding and memory, exploratory locomotion and anxiety-related behaviors. We describe how activity of hippocampal theta rhythm generators - medial septum, nucleus incertus and entorhinal cortex, links theta with specific behaviors. We review evidence for functions of the theta-rhythmic signaling to subcortical targets, including lateral septum. Further, we describe functional associations of theta oscillation properties - phase, frequency and amplitude - with memory, locomotion and anxiety, and outline how manipulations of these features, using optogenetics or pharmacology, affect associative and innate behaviors. We discuss work linking cognition to the slope of the theta frequency to running speed regression, and emotion-sensitivity (anxiolysis) to its y-intercept. Finally, we describe parallel emergence of theta oscillations, theta-mediated neuronal activity and behaviors during development. This review highlights a complex interplay of neuronal circuits and synchronization features, which enables an adaptive regulation of multiple behaviors by theta-rhythmic signaling.

Regulation of mRNA Translation in Neurons-A Matter of Life and Death.

Dynamic regulation of mRNA translation initiation and elongation is essential for the survival and function of neural cells. Global reductions in translation initiation resulting from mutations in the translational machinery or inappropriate activation of the integrated stress response may contribute to pathogenesis in a subset of neurodegenerative disorders. Aberrant proteins generated by non-canonical translation initiation may be a factor in the neuron death observed in the nucleotide repeat expansion diseases. Dysfunction of central components of the elongation machinery, such as the tRNAs and their associated enzymes, can cause translational infidelity and ribosome stalling, resulting in neurodegeneration. Taken together, dysregulation of mRNA translation is emerging as a unifying mechanism underlying the pathogenesis of many neurodegenerative disorders.

Systemic administration of two different anxiolytic drugs decreases local field potential theta frequency in the medial entorhinal cortex without affecting grid cell firing fields.

Neurons coding spatial location (grid cells) are found in medial entorhinal cortex (MEC) and demonstrate increasing size of firing fields and spacing between fields (grid scale) along the dorsoventral axis. This change in grid scale correlates with differences in theta frequency, a 6-10Hz rhythm in the local field potential (LFP) and rhythmic firing of cells. A relationship between theta frequency and grid scale can be found when examining grid cells recorded in different locations along the dorsoventral axis of MEC. When describing the relationship between theta frequency and grid scale, it is important to account for the strong positive correlation between theta frequency and running speed. Plotting LFP theta frequency across running speeds dissociates two components of this relationship: slope and intercept of the linear fit. Change in theta frequency through a change in the slope component has been modeled and shown experimentally to affect grid scale, but the prediction that change in the intercept component would not affect grid scale has not been tested experimentally. This prediction about the relationship of intercept to grid scale is the primary hypothesis tested in the experiments presented here. All known anxiolytic drugs decrease hippocampal theta frequency despite their differing mechanisms of action. Specifically, anxiolytics decrease the intercept of the theta frequency-running speed relationship in the hippocampus. Here we demonstrate that anxiolytics decrease the intercept of the theta frequency-running speed relationship in the MEC, similar to hippocampus, and the decrease in frequency through this change in intercept does not affect grid scale.

REST corepressors RCOR1 and RCOR2 and the repressor INSM1 regulate the proliferation-differentiation balance in the developing brain.

The transcriptional events that lead to the cessation of neural proliferation, and therefore enable the production of proper numbers of differentiated neurons and glia, are still largely uncharacterized. Here, we report that the transcription factor Insulinoma-associated 1 (INSM1) forms complexes with RE1 Silencing Transcription factor (REST) corepressors RCOR1 and RCOR2 in progenitors in embryonic mouse brain. Mice lacking both RCOR1 and RCOR2 in developing brain die perinatally and generate an abnormally high number of neural progenitors at the expense of differentiated neurons and oligodendrocyte precursor cells. In addition, Rcor1/2 deletion detrimentally affects complex morphological processes such as closure of the interganglionic sulcus. We find that INSM1, a transcription factor that induces cell-cycle arrest, is coexpressed with RCOR1/2 in a subset of neural progenitors and forms complexes with RCOR1/2 in embryonic brain. Further, the Insm1-/- mouse phenocopies predominant brain phenotypes of the Rcor1/2 knockout. A large number of genes are concordantly misregulated in both knockout genotypes, and a majority of the down-regulated genes are targets of REST. Rest transcripts are up-regulated in both knockouts, and reducing transcripts to control levels in the Rcor1/2 knockout partially rescues the defect in interganglionic sulcus closure. Our findings indicate that an INSM1/RCOR1/2 complex controls the balance of proliferation and differentiation during brain development.

Cholinergic modulation of cognitive processing: insights drawn from computational models.

Acetylcholine plays an important role in cognitive function, as shown by pharmacological manipulations that impact working memory, attention, episodic memory, and spatial memory function. Acetylcholine also shows striking modulatory influences on the cellular physiology of hippocampal and cortical neurons. Modeling of neural circuits provides a framework for understanding how the cognitive functions may arise from the influence of acetylcholine on neural and network dynamics. We review the influences of cholinergic manipulations on behavioral performance in working memory, attention, episodic memory, and spatial memory tasks, the physiological effects of acetylcholine on neural and circuit dynamics, and the computational models that provide insight into the functional relationships between the physiology and behavior. Specifically, we discuss the important role of acetylcholine in governing mechanisms of active maintenance in working memory tasks and in regulating network dynamics important for effective processing of stimuli in attention and episodic memory tasks. We also propose that theta rhythm plays a crucial role as an intermediary between the physiological influences of acetylcholine and behavior in episodic and spatial memory tasks. We conclude with a synthesis of the existing modeling work and highlight future directions that are likely to be rewarding given the existing state of the literature for both empiricists and modelers.

Neural correlates of long-term object memory in the mouse anterior cingulate cortex.

Damage to the hippocampal formation results in a profound temporally graded retrograde amnesia, implying that it is necessary for memory acquisition but not its long-term storage. It is therefore thought that memories are transferred from the hippocampus to the cortex for long-term storage in a process called systems consolidation (Dudai and Morris, 2000). Where in the cortex this occurs remains an open question. Recent work (Frankland et al., 2005; Vetere et al., 2011) suggests the anterior cingulate cortex (ACC) as a likely candidate area, but there is little direct electrophysiological evidence to support this claim. Previously, we demonstrated object-associated firing correlates in caudal ACC during tests of recognition memory and described evidence of neuronal responses to where an object had been following a brief delay. However, long-term memory requires evidence of more durable representations. Here we examined the activity of ACC neurons while testing for long-term memory of an absent object. Mice explored two objects in an arena and then were returned 6 h later with one of the objects removed. Mice continued to explore where the object had been, demonstrating memory for that object. Remarkably, some ACC neurons continued to respond where the object had been, while others developed new responses in the absent object's location. The incidence of absent-object responses by ACC neurons was greatly increased with increased familiarization to the objects, and such responses were still evident 1 month later. These data strongly suggest that the ACC contains neural correlates of consolidated object/place association memory.