Biology subjects

Schiff, N. D.

Publications and source records attributed to Schiff, N. D..

4 recordsLinked to original sources

bioRxiv2025

A clinical grade neurostimulation implant for hierarchical control of physiological activity

Bioelectronic implants for neurostimulation aim to steer disordered neurophysiological processes back towards a healthy state. However, physiology is subject to biological rhythms, including the circadian rhythm and the sleep-wake cycle. These predictable rhythms affect disease symptomatology, biomarkers used in closed-loop therapies, and a physiological systems expected response to stimulation. Therefore, therapeutic devices should incorporate feedforward elements to align algorithm parameters with predictable changes in physiological state, as a parallel of physiological rheostatic control. Here we introduce the DyNeuMo-2c, the first clinical-grade implant capable of delivering closed-loop neurostimulation while flexibly changing its functional configuration according to time of day. The device can chronically measure brain activity and motion state to track potential biomarker patterns in natural, out-of-clinic settings, allowing identification and targeting of patient-specific chronotypes. The system implements a hierarchical control flow, with baseline therapy set by a circadian scheduler, and adaptive policies layered to take effect based on specific biomarkers indicating patient and disease state. Using a benchtop validation setup, we demonstrate that the system has the required capabilities for delivering time-contingent closed-loop therapy in two established clinical use cases: Parkinsons disease and epilepsy. Next, we deploy the system in vivo to deliver closed-loop deep brain stimulation in a healthy non-human primate model of vigilance, highlighting the importance of synchronisation between device operation and physiological state in various conditions (task performance, unconstrained behaviour, and sleep). Time-of-day-dependent adaptation of closed-loop stimulation enabled modulation of both vigilance and behaviour. Overall, the novel device architecture provides a proof-of-concept for delivering time-contingent therapy in chronic therapeutic settings where biological rhythms are of key importance.

neuroscience↗

bioRxiv2020

CardioPy: An open-source heart rate variabilitytoolkit for single-lead EKG

Background and ObjectiveHeart rate variability (HRV) is a promising clinical marker of health and disease. Although HRV methodology is relatively straightforward, accurate detection of R-peaks remains a significant methodological challenge; this is especially true for single-lead EKG signals, which are routinely collected alongside EEG monitoring and for which few software options exist. Most developed algorithms with favorable R-peak detection profiles require significant mathematical and computational proficiency for implementation, providing a significant barrier for clinical research. Our objective was to address these challenges by developing a simple, free, and open-source software package for HRV analysis of single-lead EKG signals. MethodsCardioPy was developed in python and optimized for short-term (5-minute) single-lead EKG recordings. CardioPys R-peak detection trades full automation and algorithmic complexity for an adaptive thresholding mechanism, manual artifact removal and parameter adjustment. Standard time and frequency domain analyses are included, such that CardioPy may be used as a stand-alone HRV analysis package. An example use-case of HRV across wakefulness and sleep is presented and results validated against the widely used Kubios HRV software. ResultsHRV analyses were conducted in 66 EKG segments collected from five healthy individuals. Parameter optimization was conducted or each segment, requiring ~1-3 minutes of manual inspection time. With optimization, CardioPys R-peak detection algorithm achieved a mean sensitivity of 100.0% (SD 0.05%) and positive predictive value of 99.8% (SD 0.20%). HRV results closely matched those produced by Kubios HRV, both by eye and by quantitative comparison; CardioPy power spectra explained an average of 99.7% (SD 0.50%) of the variance present in Kubios spectra. HRV analyses showed significant group differences between brain states; SDNN, low frequency power, and low frequency-to-high frequency ratio were reduced in slow wave sleep compared to wakefulness. ConclusionsCardioPy provides an accessible and transparent tool for HRV analyses. Manual parameter optimization and artifact removal allow granular control over data quality and a highly reproducible analytic pipeline, despite additional time requirements. Future versions are slated to include automatic parameter optimization and a graphical user interface, further reducing analysis time and improving accessibility.

physiology↗

bioRxiv2018

Identifiability in connectome based neural mass models

The spatial-temporal patterns of neuronal dynamics emerge from the network of coordinated brain regions, this structure-function relationship of the brain can be described mathematically by biophysical models of coupled brain regions connected by white matter tractography. Implementations of such models have focused on reproducing functional connectivity extracted from functional magnetic resonance imaging (fMRI), but these efforts are limited by the temporal resolution of fMRI data and the reduction of time course recordings into phenomenological functional connectivity maps. Here, we optimize parameters of a neural mass model (NMM) to best fit region-wise power spectra across the whole brain estimated from source localized electroencephalography (EEG). NMM models with global parameters were not able to fully reproduce region-wise power spectra, with or without the inclusion of structural connectivity information. In contrast, without the inclusion of structural connectivity information, independent oscillators at each brain region are able to reproduce region-wise power spectra. But the addition of structural connectivity and transmission delays to the NMM does not improve overall power spectra fit. Connectome-based NMM implementations with regional parameters lead to high dimensional network models that produce non-unique results. Inherent parameter identifiability problem in network models poses challenges for using such models as diagnostic tools for neurological diseases.

neuroscience↗

bioRxiv2017

Common neural responses to narrative speech indisorders of consciousness

ObjectiveClinical assessment of auditory attention in patients with disorders of consciousness is often limited by motor impairment. Here, we employ inter-subject correlations among electroencephalography responses to naturalistic speech in order to assay auditory attention among patients and healthy controls.\n\nMethodsElectroencephalographic data were recorded from 20 subjects with disorders of consciousness and 14 healthy controls during of two narrative audio stimuli, presented both forwards and time-reversed. Inter-subject correlation of evoked electroencephalography signals were calculated, comparing responses of both groups to those of the healthy control subjects. This analysis was performed blinded and subsequently compared to the diagnostic status of each patient based on the Coma Recovery Scale-Revised.\n\nResultsSubjects with disorders of consciousness exhibit significantly lower inter-subject correlation than healthy controls during narrative speech. Additionally, while healthy subjects had higher inter-subject correlation values in forward vs. backwards presentation, neural responses did not vary significantly with the direction of playback in subjects with disorders of consciousness. Increased inter-subject correlation values in the backward speech condition were noted with improving disorder of consciousness diagnosis, both in cross-sectional analysis and in a subset of patients with longitudinal data.\n\nInterpretationInter-subject correlation of neural responses to narrative speech audition differentiates healthy controls from patients and appears to index clinical diagnoses in disorders of consciousness.

neuroscience↗