Biology subjects

Lendner, J. D.

Publications and source records attributed to Lendner, J. D..

6 recordsLinked to original sources

bioRxiv2022

An evolutionary conserved division-of-laborbetween hippocampal and neocortical sharp-wave ripplesorganizes information transfer during sleep

The hippocampal sharp-wave ripple (SW-R) is the key substrate of the hippocampal-neocortical dialogue underlying memory formation. Recently, it became evident that SW-R are not unique to archicortex, but constitute a wide-spread neocortical phenomenon. To date, little is known about morphological and functional similarities between archi- and neocortical SW-R. Leveraging intracranial recordings from the human hippocampus and prefrontal cortex during sleep, our results reveal region-specific functional specializations, albeit a near-uniform morphology. While hippocampal SW-R trigger directional hippocampal-to-neocortical information flow, neocortical SW-R reduce information flow to minimize interference. At the population level, hippocampal SW-R confined population dynamics to a low-dimensional subspace, while neocortical SW-R diversified the population response; functionally uncoupling the hippocampal-neocortical network. Critically, our replication in rodents demonstrated the same division-of-labor between archi-and neocortical SW-R. These results uncover an evolutionary preserved mechanism where coordinated interplay between hippocampal and neocortical SW-R temporally segregates hippocampal information transfer from neocortical processing.

neuroscience↗

bioRxiv2022

Spectral slope and neural complexity as robust markers of task demand and brain state during sleep and wakefulness

Spectral slope and Lempel-Ziv complexity are affected in many neurophysiological disorders and are modulated by sleep, anesthesia, and aging. Yet, few studies have explored the relationship between these two parameters. We evaluated the impact of sleep stage and task-engagement (resting, attention and memory) on spectral slope and Lempel-Ziv complexity in a narrow- (30 - 45Hz) and broadband (1 - 45Hz) frequency range in 28 healthy males (21.54 {+/-} 1.90 years) over three recordings. Only in the broadband range, the slope steepens and complexity decreases continuously from wakefulness to N3. However, REM sleep is best discriminated by the narrowband slope. Importantly, slope and complexity also differentiate between tasks during wakefulness. While the narrowband complexity decreases across tasks, the slope is flattening with task engagement in both frequency ranges. In general, broadband slope and complexity are strongly positively correlated, but we observe a dissociation between them in the narrowband range. Critically, only the narrowband slope is associated with better Go/Nogo task performance. Our results demonstrate that slope and complexity are both powerful indices of sleep depth, task engagement and cognitive performance. While the broadband range is better suited to discriminate between brain states, especially the narrowband slope is a unique marker of task performance.

neuroscience↗

bioRxiv2022

Human REM sleep controls neural excitability in support of memory formation

Sleep oscillations provide a key substrate to facilitate memory processing, the underlying mechanism of which may involve the overnight homeostatic regulation of plasticity at a synaptic and whole-network level. However, there remains a lack of human data demonstrating if and how sleep enhances memory consolidation and associated neural homeostasis. We combined intracranial recordings and scalp electroencephalography (EEG) in humans to reveal a new role for rapid eye movement (REM) sleep in promoting the homeostatic recalibration of optimal excitation/inhibition-balance. Moreover, the extent of this REM-sleep homeostatic recalibration predicted the success of overnight memory consolidation, expressly the modulation of hippocampal-- neocortical excitability favoring remembering rather than forgetting. The findings describe a novel, fundamental role of human REM sleep in maintaining neural homeostasis, thereby enhancing long-term memory.

neuroscience↗

bioRxiv2022

Changes of oscillatory and aperiodic neuronal activity in working memory following anaesthesia: a prospective observational study

BackgroundAnaesthesia and surgery can lead to cognitive decline, especially in the elderly. However, to date, the neurophysiological underpinnings of perioperative cognitive decline remain unknown. MethodsWe included male patients, who were 60 years or older scheduled for elective radical prostatectomy under general anaesthesia. We obtained neuropsychological (NP) tests as well as a visual match-to-sample working memory (WM) task with concomitant 62-channel scalp electroencephalography (EEG) before and after surgery. ResultsA total number of 26 patients completed neuropsychological assessments and EEG pre- and postoperatively. Behavioural performance declined in the neuropsychological assessment after anaesthesia (total recall; t-tests: t25 = -3.25, Bonferroni-corrected p = 0.015 d = -0.902), while WM performance showed a dissociation between match and mis-match accuracy (rmANOVA: match*session F1,25 = 3.866, p = 0.060). Distinct EEG signatures tracked behavioural performance: Better performance in the NP assessment was correlated with an increase of non-oscillatory (aperiodic) activity, reflecting increased cortical activity (cluster permutation tests: total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), while WM accuracy was tracked by distinct temporally-structured oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (cluster permutation tests: matches: p < 0.001, mis-matches: p = 0.022). ConclusionsOscillatory and non-oscillatory (aperiodic) activity in perioperative scalp EEG recordings track distinct features of perioperative cognition. Aperiodic activity provides a novel electrophysiological biomarker to identify patients at risk for developing perioperative neurocognitive decline.

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

An Electrophysiological Marker of Arousal Level in Humans

Deep non-rapid eye movement sleep (NREM) - also called slow wave sleep (SWS) - and general anesthesia are prominent states of reduced arousal linked to the occurrence of slow oscillations in the electroencephalogram (EEG). Rapid eye movement (REM) sleep, however, is also associated with a diminished arousal level, but is characterized by a desynchronized, wake-like EEG. This observation challenges the notion of oscillations as the main physiological mediator of reduced arousal. Using intracranial and surface EEG recordings in four independent data sets, we establish the 1/f spectral slope as an electrophysiological marker that accurately delineates wakefulness from anesthesia, SWS and REM sleep. The spectral slope reflects the non-oscillatory, scale-free measure of neural activity and has been proposed to index the local balance between excitation and inhibition. Taken together, these findings reconcile the long-standing paradox of reduced arousal in both REM and NREM sleep and provide a common unifying physiological principle -- a shift in local Excitation/ Inhibition balance -- to explain states of reduced arousal such as sleep and anesthesia in humans.\n\nSignificance StatementThe clinical assessment of arousal levels in humans depends on subjective measures such as responsiveness to verbal commands. While non-rapid eye movement (NREM) sleep and general anesthesia share some electrophysiological markers, rapid eye movement sleep (REM) is characterized by a wake-like electroencephalogram. Here, we demonstrate that non-oscillatory, scale-free electrical brain activity -- recorded from both scalp electroencephalogram and intracranial recordings in humans -- reliably tracks arousal levels during both NREM and REM sleep as well as under general anesthesia with propofol. Our findings suggest that non-oscillatory brain activity can be used effectively to monitor vigilance states.

neuroscience↗