N1-P2 Evoked Response as a Measure for Short-Term Visual Memory

We investigate the relationship of N1-P2 evoked response (peak-to-peak amplitude and time interval) with short-term visual memory in humans. Visual evoked responses obtained from 20 subjects (10 non-amnesic alcoholics and 10 non-alcoholics) are extracted from channel P8 referenced to channel Cz during the presentation of modified delayed matching-to-sample visual task using Snodgrass and Vanderwart picture set. Our results indicate that N1-P2 amplitudes are higher for non-matching (novel) stimuli as compared to matching stimuli for all the subjects. N1-P2 time interval is also shorter for the case of matching stimuli. This indicates that information processing is increased for the non-matching stimuli as compared to matching stimuli. These results are quite consistent with a number of related studies and we conclude that N1-P2 is related to short-term visual memory involved during object recognition. The results also indicate that N1-P2 amplitude is higher for non-alcoholics as compared to alcoholics, which indicates that some form of memory impairment exist in alcoholics

Generalisable long COVID subtypes: Findings from the NIH N3C and RECOVER programmes

Background: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. Methods: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. Findings: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. Funding: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz. © 2022 The AuthorsOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]