BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ARNI - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://arni-institute.org
X-WR-CALDESC:Events for ARNI
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260202T150000
DTEND;TZID=America/New_York:20260202T160000
DTSTAMP:20260425T071958
CREATED:20260130T145319Z
LAST-MODIFIED:20260130T145319Z
UID:2310-1770044400-1770048000@arni-institute.org
SUMMARY:Speaker: Thuy Nguyen – ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title:A multimodal sleep foundation model for disease prediction \nAbstract: Sleep is a fundamental biological process with broad implications for physical and mental health\, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization\, generalizability and multimodal integration. To address these challenges\, we developed SleepFM\, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations. Trained on a curated dataset of over 585\,000 hours of PSG recordings from approximately 65\,000 participants across several cohorts\, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. From one night of sleep\, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected P < 0.01)\, including all-cause mortality (C-Index\, 0.84)\, dementia (0.85)\, myocardial infarction (0.81)\, heart failure (0.80)\, chronic kidney disease (0.79)\, stroke (0.78) and atrial fibrillation (0.78). Moreover\, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study—a dataset that was excluded from pretraining—and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks\, achieving mean F1 scores of 0.70–0.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence. This work shows that foundation models can learn the language of sleep from multimodal sleep recordings\, enabling scalable\, label-efficient analysis and disease prediction.
URL:https://arni-institute.org/event/speaker-thuy-nguyen-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:MILA\, A14
END:VEVENT
END:VCALENDAR