Biology subjects

Forger, D. B.

Publications and source records attributed to Forger, D. B..

5 recordsLinked to original sources

bioRxiv2025

Realistic coupling enables flexible macroscopic traveling waves in the mouse cortex

Traveling waves are ubiquitous in neuronal systems across different spatial scales. While microscopic and mesoscopic waves are relatively well studied, the emergence of macroscopic traveling waves remains less understood. Here, by modeling the mouse cortex using spatial transcriptomic and connectivity data, we show that realistic cortical connectivity can generate a significantly higher level of macroscopic traveling waves than artificial local and uniform connectivity across multiple oscillation frequency bands, with the strongest advantage appearing in the theta, alpha, and beta frequency bands. By probing the model in different dynamic regimes, we find that macroscopic wave activity depends on both network connectivity and excitatory coupling strength, with a non-monotonic dependence on coupling. Together, our work shows how flexible macroscopic traveling waves can emerge in the mouse cortex and offers a computational framework to further study traveling waves in the mouse brain at the single-cell level.

neuroscience↗

bioRxiv2023

A Framework to Determine Active Connectivity within the Mouse Brain

Tremendous effort has focused on determining the physical connectivity within the mouse brain. However, the strength of connections within the brain constantly changes throughout the 24-hour day. Here, we combine experimental and computational methods to determine an "active connectivity" of the physical connections between the most active neurons. Brain cells of freely behaving mice are genetically marked with the activity- dependent TRAP2 system, imaged, digitized, and their connectivity is inferred from the latest brain atlases. We apply our methods to determine the most active networks in the early light and early dark hours of the day, two periods with distinct differences in sleep, wake, and feeding behavior. Increased signaling is seen through the visceral and agranular insular (AI) regions in the early day as peripheral stimuli are integrated. On the other hand, there is an increase in the activity of the retrosplenial cortex (RSP) and the anterior cingulate cortex (ACC) during the early night, when more sustained attention is required. Our framework carves a window to the three-dimensional networks of active connections in the mouse brain that underlie spontaneous behaviors or responses to environmental changes, thus providing the basis for direct computer simulations and analysis of such networks in the future.

neuroscience↗

bioRxiv2023

The combination of topological data analysis and mathematical modeling improves sleep stage prediction from consumer-grade wearables

Wearable devices have become commonplace tools for tracking behavioral and physiological parameters in real-world settings. Nonetheless, the practical utility of these data for clinical and research applications, such as sleep analysis, is hindered by their noisy, large-scale, and multidimensional characteristics. Here, we develop a neural network algorithm that predicts sleep stages by tracking topological features (TFs) of wearable data and model-driven clock proxies reflecting the circadian propensity for sleep. To evaluate its accuracy, we apply it to motion and heart rate data from the Apple Watch worn by subjects undergoing polysomnography (PSG) and compare the predicted sleep stages with the corresponding ground truth PSG records. We find that TFs and clock proxies can improve the overall performance of wake/REM/NREM sleep classification, particularly in identifying REM and NREM sleep (AUROC/AUPRC improvements > 9% and REM/NREM accuracy improvement " 12%). We find that this improvement is mainly attributed to the heart rate TFs. To confirm this, we compare the heart rate TFs between two groups, expected to have different cardiovascular conditions: younger, healthy subjects from the Apple Watch cohort and elderly subjects from the Multi-ethnic Study of Atherosclerosis cohort. Indeed, TFs largely vary across REM and NREM sleep in younger individuals while the variations disappear in elderly individuals, explaining the enhanced improvements in REM or NREM sleep classification problems only in the younger individuals. This study demonstrates the benefits of combining topological data analysis and mathematical modeling to extract hidden inputs of neural networks from puzzling wearable data.

bioinformatics↗

bioRxiv2021

Genomic heterogeneity affects the response to Daylight Saving Time

Circadian rhythms drive the timing of many physiological events in the 24-hour day. When individuals undergo an abrupt external shift (e.g., change in work schedule or travel across multiple time zones), circadian rhythms become misaligned with the new time and may take several days to adjust. Chronic circadian misalignment, e.g., as a result of shift work, has been shown to lead to several physical and mental health problems. Despite the serious health implications of circadian misalignment, relatively little is known about how genetic variation affects an individuals ability to shift to abrupt external changes. Accordingly, we use the one-hour advance from the onset of daylight saving time (DST) as a natural experiment to comprehensively study how individual heterogeneity affects the shift of sleep-wake rhythms in response to an abrupt external time change. We find that individuals genetically predisposed to a morning tendency adjust to the advance in a few days, while genetically predisposed evening-inclined individuals have not shifted. Observing differential effects by genetic disposition after a one-hour advance underscores the importance of heterogeneity in adaptation to external schedule shifts, and these genetic differences may affect how individuals adjust to jet lag or shift work as well.

genomics↗

bioRxiv2020

Characterizing the Human Heart Rate Circadian Pacemaker through Widely Available Wearable Devices

The effects of real-world stimuli and how they vary across individuals remains largely unknown for lack of a continuous real-world circadian marker. Here, we show how an underlying circadian rhythm in heart rate (CRHR) can be extracted from several widely used wearables. Analysis of over 130,000 days of data from medical interns on rotating shifts shows how CRHR dynamics are distinct from those of sleep-wake timing and vary greatly among individuals. Analysis of circadian timekeeping in travelers in 5 continents shows how the circadian timekeeping more carefully controls wake time rather than sleep time. We determine a personalized phase response curve (PRC) of CRHR to activity for each individual, representing the first passive and personalized determination of how human circadian timekeeping continuously changes due to real-world stimuli. These results collectively establish CRHR as a practical method to study circadian rhythms in the real world. eTOC BlurbWe show how the circadian rhythm in heart rate can be extracted from real world data collected by wearables. Studying data from a large cohort of medical interns working on shifts, we find very interesting dynamics of this circadian rhythm that are independent of the acute effects of activity or sleep-wake timing. These techniques can also determine personalized parameters of circadian timekeeping.

systems biology↗