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Schizophrenia is a complex mental disorder whose pathophysiological mechanisms remain yet unclear. Various lines of evidence converge on a temporal disorder with temporal imprecision occurring in the millisecond range of the ongoing phase cycles. However, the intertrial phase coherence (ITPC) often used to index such temporal imprecision in EEG, is by itself not able to capture temporal irregularities in the range of around 10 milliseconds. This is due to its static calculation with the averaging over trials. To obtain a more dynamic measures in the millisecond range, we introduce 1. The precision index (PI) as temporally more precise measure, and 2. a novel more dynamic method to calculate the ITPC in temporally resolved way, i.e., dITPC. We show that schizophrenia subjects show decreased PI during deviant tones in an auditory oddball task which shows strong but not one to one correlation with the ITPC. Moreover, we demonstrate that schizophrenia subjects showed higher latencies and frequencies over the course of time in the dITPC. Finally, employing multiple regression models, we show that the latency of the dITPC, as calculated dynamically across both standard and deviant tones, predicts the PI deficits in the deviant tones. Together, our findings demonstrate temporal alterations in the phase dynamics of schizophrenia with temporal irregularities in the dynamic background predicting temporal imprecision in the lower millisecond range in the more cognitive foreground.
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http://dx.doi.org/10.1038/s41398-025-03510-4 | DOI Listing |
Wellcome Open Res
August 2025
Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA.
Arenaviruses and Hantaviruses, primarily hosted by rodents and shrews, represent significant public health threats due to their potential for zoonotic spillover into human populations. Despite their global distribution, the full impact of these viruses on human health remains poorly understood, particularly in regions like Africa, where data is sparse. Both virus families continue to emerge, with pathogen evolution and spillover driven by anthropogenic factors such as land use change, climate change, and biodiversity loss.
View Article and Find Full Text PDFPLoS Biol
September 2025
Center for Neural Science, Department of Biology and Department of Psychology, New York University, New York, New York, United States of America.
Investigating social and independent behavior structure in early life is critical for understanding development and brain maturation in social mammals. However, this investigation necessitates monitoring animals over weeks to months often with subsecond time resolution creating challenges for both lab studies focused on brief observation periods and field studies in which animal tracking can be imprecise. Here we used machine vision and two-week long continuous behavior recordings of families of gerbils, a highly social rodent, in large, undisturbed home environments to quantify the behavioral development of individual pups.
View Article and Find Full Text PDFJ Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.
bioRxiv
August 2025
Department of Biomedical Engineering, Johns Hopkins University.
Neural population activity is often stereotyped into recurring activity patterns, i.e., neural motifs, which can be seen as the fundamental building blocks in sensory processing and cognition.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
Urban environments undergo continuous changes due to natural processes and human activities, which necessitates robust methods for monitoring changes in land cover and infrastructure for sustainable developments. Change detection in remote sensing plays a pivotal role in analyzing these temporal variations and supports various applications, including environmental monitoring. Many deep learning-based methods have been widely investigated for change detection in the literature.
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