Human Verbal Memory Encoding Is Hierarchically Distributed in a Continuous Processing Stream.

Processing of memory is supported by coordinated activity in a network of sensory, association, and motor brain regions. It remains a major challenge to determine where memory is encoded for later retrieval. Here, we used direct intracranial brain recordings from epilepsy patients performing free recall tasks to determine the temporal pattern and anatomical distribution of verbal memory encoding across the entire human cortex. High γ frequency activity (65-115 Hz) showed consistent power responses during encoding of subsequently recalled and forgotten words on a subset of electrodes localized in 16 distinct cortical areas activated in the tasks. More of the high γ power during word encoding, and less power before and after the word presentation, was characteristic of successful recall and observed across multiple brain regions. Latencies of the induced power changes and this subsequent memory effect (SME) between the recalled and forgotten words followed an anatomical sequence from visual to prefrontal cortical areas. Finally, the magnitude of the memory effect was unexpectedly found to be the largest in selected brain regions both at the top and at the bottom of the processing stream. These included the language processing areas of the prefrontal cortex and the early visual areas at the junction of the occipital and temporal lobes. Our results provide evidence for distributed encoding of verbal memory organized along a hierarchical posterior-to-anterior processing stream

Application of deep learning models to improve ulcerative colitis endoscopic disease activity scoring under multiple scoring systems

Background and Aims Lack of clinical validation and inter-observer variability are two limitations of endoscopic assessment and scoring of disease severity in patients with ulcerative colitis [UC]. We developed a deep learning [DL] model to improve, accelerate and automate UC detection, and predict the Mayo Endoscopic Subscore [MES] and the Ulcerative Colitis Endoscopic Index of Severity [UCEIS]. Methods A total of 134 prospective videos [1550 030 frames] were collected and those with poor quality were excluded. The frames were labelled by experts based on MES and UCEIS scores. The scored frames were used to create a preprocessing pipeline and train multiple convolutional neural networks [CNNs] with proprietary algorithms in order to filter, detect and assess all frames. These frames served as the input for the DL model, with the output being continuous scores for MES and UCEIS [and its components]. A graphical user interface was developed to support both labelling video sections and displaying the predicted disease severity assessment by the artificial intelligence from endoscopic recordings. Results Mean absolute error [MAE] and mean bias were used to evaluate the distance of the continuous model’s predictions from ground truth, and its possible tendency to over/under-predict were excellent for MES and UCEIS. The quadratic weighted kappa used to compare the inter-rater agreement between experts’ labels and the model’s predictions showed strong agreement [0.87, 0.88 at frame-level, 0.88, 0.90 at section-level and 0.90, 0.78 at video-level, for MES and UCEIS, respectively]. Conclusions We present the first fully automated tool that improves the accuracy of the MES and UCEIS, reduces the time between video collection and review, and improves subsequent quality assurance and scoring

Quality indicators in inflammatory bowel disease

Inflammatory bowel disease (IBD), which includes Crohn's disease and ulcerative colitis, is a chronic, debilitating, and expensive condition affecting millions of people globally. There is significant variation in the quality of care for patients with IBD across North America, Europe, and Asia; this variation suggests poor quality of care due to overuse, underuse, or misuse of health services and disparity of outcomes. Several initiatives have been developed to reduce variation in care delivery and improve processes of care, patient outcomes, and reduced healthcare costs. These initiatives include the development of quality indicator sets to standardize care across organizations, and learning health systems to enable data sharing between doctors and patients, and sharing of best practices among providers. These programs have been variably successful in improving patient outcomes and reducing healthcare utilization. Further studies are needed to demonstrate the long-term impact and applicability of these efforts in different geographic areas around the world, as regional variations in patient populations, societal preferences, and costs should inform local quality improvement efforts

Potential Treatments for SARS‐CoV‐2 Infection

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155523/1/cld969_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155523/2/cld969.pd

Clinical applications of artificial intelligence and machine learning- based methods in inflammatory bowel disease

Our objective was to review and exemplify how selected applications of artificial intelligence (AI) might facilitate and improve inflammatory bowel disease (IBD) care and to identify gaps for future work in this field. IBD is highly complex and associated with significant variation in care and outcomes. The application of AI to IBD has the potential to reduce variation in healthcare delivery and improve quality of care. AI refers to the ability of machines to mimic human intelligence. The range of AI’s ability to perform tasks that would normally require human intelligence varies from prediction to complex decision- making that more closely resembles human thought. Clinical applications of AI have been applied to study pathogenesis, diagnosis, and patient prognosis in IBD. Despite these advancements, AI in IBD is in its early development and has tremendous potential to transform future care.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166331/1/jgh15405_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/166331/2/jgh15405.pd

The Relationship Between Opioid Use and Healthcare Utilization in Patients With Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: Pain is commonly experienced by patients with inflammatory bowel disease (IBD). Unfortunately, pain management is a challenge in IBD care, as currently available analgesics are associated with adverse events. Our understanding of the impact of opioid use on healthcare utilization among IBD patients remains limited. METHODS: A systematic search was completed using PubMed, Embase, the Cochrane Library, and Scopus through May of 2020. The exposure of interest was any opioid medication prescribed by a healthcare provider. Outcomes included readmissions rate, hospitalization, hospital length of stay, healthcare costs, emergency department visits, outpatient visits, IBD-related surgeries, and IBD-related medication utilization. Meta-analysis was conducted on study outcomes reported in at least 4 studies using random-effects models to estimate pooled relative risk (RR) and 95% confidence interval (CI). RESULTS: We identified 1969 articles, of which 30 met inclusion criteria. Meta-analysis showed an association between opioid use and longer length of stay (mean difference, 2.25 days; 95% CI, 1.29-3.22), higher likelihood of prior IBD-related surgery (RR, 1.72; 95% CI, 1.32-2.25), and higher rates of biologic use (RR, 1.38; 95% CI, 1.13-1.68) but no difference in 30-day readmissions (RR, 1.17; 95% CI, 0.86-1.61), immunomodulator use (RR, 1.13; 95% CI, 0.89-1.44), or corticosteroid use (RR, 1.36; 95% CI, 0.88-2.10) in patients with IBD. On systematic review, opioid use was associated with increased hospitalizations, healthcare costs, emergency department visits, outpatient visits, and polypharmacy. DISCUSSION: Opioids use among patients with IBD is associated with increased healthcare utilization. Nonopioid alternatives are needed to reduce burden on the healthcare system and improve patient outcomes

A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection

Computational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic regression prediction model was built using spectral features of intracranial electroencephalography (iEEG) from the selected electrodes. We demonstrate our method on iEEG data from 37 human subjects performing free recall verbal short-term memory tasks. The method achieves a 36.3% reduction in the number of electrodes used for prediction, resulting in a 64.9 % reduction in inference computation time with just a 0.3 % loss in prediction performance compared to the case when all electrodes were used. The electrodes selected using our method provided improved prediction performance compared to those electrodes that were not selected in 31 out of 37 patients. Building upon this observation, we also developed a method to identify the subjects for whom the proposed electrode selection method would be beneficial