Biology subjects

Frohlich, J.

Publications and source records attributed to Frohlich, J..

5 recordsLinked to original sources

bioRxiv2024

Brain signal complexity and aperiodicity predict human corticospinal excitability

BackgroundTranscranial magnetic stimulation (TMS) holds promise for brain modulation with relevant scientific and therapeutic applications, but it is limited by response variability. Targeting state-dependent EEG features such as phase and power shows potential, but uncertainty remains about the suitable brain states. ObjectiveThis study evaluated broadband EEG measures (BEMs), including the aperiodic exponent (AE) and entropy measures (CTW, LZ), as alternatives to band-limited features, such as power and phase, for predicting corticospinal excitability (CSE). MethodsTMS was delivered with randomly applied single pulses targeting the left primary motor cortex in 34 healthy participants while simultaneously recording EEG and EMG signals. Broadband and band-limited EEG features were evaluated for their ability to predict CSE using motor evoked potentials (MEPs) from the right extensor digitorum communis muscle as the outcome measure. ResultsBEMs (AE, CTW) significantly predicted CSE, comparable to beta-band power and phase, the most predictive and spatially specific band-limited markers of motor cortex CSE. Unlike these localized CSE markers at the site of stimulation, BEMs captured more global brain states and greater within-subject variability, indicating sensitivity to dynamic state changes. Notably, CTW was associated with high CSE, while AE was linked to low CSE. ConclusionThis study reveals BEMs as robust predictors of CSE that circumvent challenges of band-limited EEG features, such as narrowband filtering and phase estimation. They may reflect more general markers of brain excitability. With their slower timescale and broader sensitivity, BEMs are promising biomarkers for state-dependent TMS applications, particularly in therapeutic contexts.

neuroscience↗

bioRxiv2023

Neural entrainment induced by periodic audiovisual stimulation: A large-sample EEG study

Stroboscopic or "flicker" stimulation is a form of periodic visual stimulation that induces geometric hallucinations through closed eyelids. While the visual effects of this form of sensory stimulation have received considerable attention, few studies have investigated the neural entrainment effects of periodic visual stimulation. Here, we introduce two variants of the classic flicker paradigm while recording EEG to study neural entrainment effects in a large sample (over 80 participants per condition). In the first condition, we used multimodal stimulation composed of two simultaneous visual strobe frequencies paired with binaural beats which provided auditory stimulation at roughly the same frequency as the slower strobe. We compared this condition to sham stimulation, in which both strobes were set to very low frequencies and in which the binaural beats were absent. Additionally, we compared both conditions to a control group in which participants focused on their breathing during eyes-closed meditation (no stimulation). Our results demonstrate powerful evidence of neural entrainment at the frequency of the slower strobe in the experimental condition. Moreover, our findings resemble effects reported in prior literature using conventional non-invasive techniques for electromagnetic brain stimulation. We argue that stroboscopic stimulation should be further developed along these lines, e.g., as a potential therapeutic technique in psychiatric disorders.

neuroscience↗

bioRxiv2022

The complexity of event-related MEG signals decreases with maturation in human fetuses and newborns

The complexity of neural activity is a commonly used read-out of healthy functioning in cortical circuits. Prior work has linked neural complexity to the level of maternal care in preterm infants at risk for developing mental disorders, yet the evolution of neural complexity in early human development is largely unknown. We hypothesized that cortical dynamics would evolve to optimize information processing as birth approaches, thereby increasing the complexity of cortical activity. To test this hypothesis, we conducted the first ever study relating prenatal neural complexity to maturation. MEG recordings were obtained from a sample of fetuses and newborns, including longitudinal data before and after birth. Using cortical responses to auditory irregularities, we computed several entropy measures which reflect the complexity of the MEG signal. Despite our hypothesis, neural complexity significantly decreased with maturation in both fetuses and newborns. Furthermore, we found that complexity decreased significantly faster in male fetuses for most entropy measures. Our surprising results lay the groundwork for the first ever mapping of how neural complexity evolves in early human development, with important implications for future efforts to develop predictive biomarkers of psychiatric disorders based on the complexity of perinatal MEG signals.

neuroscience↗

bioRxiv2021

Consciousness is supported by near-critical cortical electrodynamics

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition, and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, for the first time, we empirically identify the specific critical point near which conscious cortical dynamics operate as the edge-of-chaos critical point, or the boundary between periodicity/stability and chaos/instability. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magne-toencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, GABAergic anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of cortical dynamics away from this critical point; conversely, we show that psychedelics may increase the information-richness of cortical activity by tuning cortical electrodynamics closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC), and show that assessing the proximity of cortical electrodynamics to the edge-of-chaos critical point may be clinically useful as a new biomarker of consciousness. Significance StatementWhat changes in the brain when we lose consciousness? One possibility is that the loss of consciousness corresponds to a transition of the brains electric activity away from edge-of-chaos criticality, or the knifes edge in between stability and chaos. Recent mathematical developments have produced novel tools for testing this hypothesis, which we apply for the first time to cortical recordings from diverse brain states. We show that the electric activity of the cortex is indeed poised near the boundary between stability and chaos during conscious states and transitions away from this boundary during unconsciousness, and that this transition disrupts cortical information processing.

neuroscience↗

bioRxiv2019

Emergence of consciousness and complexity amidst diffuse delta rhythms: the paradox of Angelman syndrome

Numerous theories link consciousness to informationally rich, complex neural dynamics. This idea is challenged by the observation that children with Angelman syndrome (AS), while fully conscious, display a hypersynchronous electroencephalogram (EEG) phenotype typical of information-poor dynamics associated with unconsciousness. If informational complexity theories are correct, then sufficiently complex dynamics must still exist during wakefulness and exceed that observed in sleep despite pathological delta (1 - 4 Hz) rhythms in children with AS. As characterized by multiscale metrics, EEGs from 35 children with AS feature significantly greater complexity during wakefulness compared with sleep, even when comparing the most pathological segments of wakeful EEG to the segments of sleep EEG least likely to contain conscious experiences, and when factoring out delta power differences across states. These findings support theories linking consciousness with complexity and warn against reverse inferring an absence of consciousness solely on the basis of clinical readings of EEG.

neuroscience↗