Biology subjects

Szalardy, O.

Publications and source records attributed to Szalardy, O..

13 recordsLinked to original sources

bioRxiv2024

Temporal expectations modulate coupling between frontal and sensory brain areas

Temporal prediction enhances perceptual processing by aligning neural excitability with expected sensory events. While local oscillatory mechanisms are known to support timing, less is understood about how large-scale functional brain networks dynamically coordinate predictive processes. In particular, it remains unclear how functional connectivity (FC)--the integration of information into network hubs--differs during expectation formation (post-cue) versus outcome evaluation (post-target), and how this varies across levels of predictability. To investigate this, we recorded electroencephalogram (EEG) while participants performed a cued auditory target-detection task with varying temporal predictability (80% and 50%). Event-related potential (ERP) results revealed that implicit temporal predictability primarily modulated later evaluative processes (P3b, frontal negativity), rather than early sensory components, consistent with context updating under uncertainty. FC was analyzed using a data-driven approach based on Normalized Directed Transfer Entropy (NDTE) applied to EEG difference waveforms between high- and low-predictability conditions. Connectivity was examined separately for the post-cue and post-target periods to distinguish prediction and evaluation phases. Behaviorally, higher temporal predictability facilitated faster reaction times. Connectivity analyses revealed largely overlapping but somewhat distinct network dynamics for prediction and evaluation phases of the signal processing.

neuroscience↗

bioRxiv2024

Sleep Deprivation in Mice: Looking Beyond the Slow Wave Rebound

Sleep is a fundamental process by which the brain achieves an optimal computational regime; it has been suggested that criticality is an appropriate theoretical framework in which such a process can be understood. Studying the critical and fractal dynamics of the brain involves modelling the nonlinearity of brain signals (such as EEG or ECoG) yielding, among others, the following metrics: spectral slope, spectral intercept, spectral knee, and normalized spectral entropy. Therefore, the present study investigates the nonlinear and critical dynamics of the brain in relation to sleep deprivation in mice, by comparing the sensitivity of the above-mentioned metrics to classical band- limited spectral indices. Mice were exposed to a 9 day-long sleep deprivation paradigm with baseline, sleep deprivation, and recovery phases. Spectral parameters were computed using the FOOOF algorithm. The results suggest that the classical approach (slow wave activity; 0.75-4.5 Hz) to neural signal processing differentiates between baseline sleep and rebound sleep only during the NREM phase. In contrast, the spectral slope and the spectral intercept both capture sleep deprivation related effects during REM and NREM episodes as well. This is particularly notable considering that the spectral knee is shifted towards higher frequencies, essentially rendering the spectral slope unreflective of slow wave activity - traditionally considered the biomarker of sleep homeostasis. Lastly, normalized spectral entropy fails to differentiate between baseline sleep and sleep following sleep deprivation in mice. These results support the sensitivity of fractal spectral parameters indexing the intricate balance between sleep and wake states.

neuroscience↗

bioRxiv2024

Heartbeat related activity in the anterior thalamus differs between phasic and tonic REM sleep periods

Rapid eye movement (REM) sleep is a fundamental sleep state associated with diverse functions from elemental physiological processes to higher order neurocognitive functions. A growing body of research indicates that REM sleep with eye movements (phasic REM) differs from REM periods without ocular activity (tonic) in terms of spontaneous and evoked neural responses. Studies using auditory stimulation consistently observed enhanced evoked responses in tonic versus phasic REM, indicating that the external processing is largely diminished when the eyes move during REM sleep. Whereas exteroceptive processing during sleep is widely studies, investigation on interoception (the processing of bodily signals) during sleep is scarce, and limited to scalp electroencephalographic recordings. Here we studied interoceptive processing in a group of epileptic patients (N = 11) by measuring their heartbeat-related neural activity in the anterior nucleus of the thalamus (ANT) during phasic and tonic REM sleep and resting wakefulness. Evoked potentials and beta-low gamma spectral power locked to the heartbeat were significantly different in phasic REM compared to tonic REM and wakefulness. Heartbeat-related neural signals exhibited pronounced inter-trial phase synchronization at lower (7-20 Hz) oscillatory activity in all vigilance states, but reduced gamma synchronization at later time points in phasic REM only. Tonic REM and wakefulness did not show significant differences in heartbeat-related activity in the ANT. Our findings indicate that heartbeat-related neural activity is detectable at the level of the ANT, showing distinct signatures of interoceptive processing in phasic REM compared to tonic REM and wakefulness.

neuroscience↗

bioRxiv2024

MIND WANDERING DURING IMPLICIT LEARNING IS ASSOCIATED WITH INCREASED PERIODIC EEG ACTIVITY AND IMPROVED EXTRACTION OF HIDDEN PROBABILISTIC PATTERNS

Mind wandering, occupying 30-50% of our waking time, remains an enigmatic phenomenon in cognitive neuroscience. Predominantly viewed negatively, mind wandering is often associated with detrimental impacts on attention-demanding (model-based) tasks in both natural settings and laboratory conditions. Mind wandering however, might not be detrimental for all cognitive domains. We proposed that mind wandering may facilitate model-free processes, such as probabilistic learning, which relies on the automatic acquisition of statistical regularities with minimal attentional demands. We administered a well-established implicit probabilistic learning task combined with mind wandering thought probes in healthy adults (N = 37, 30 females). To explore the neural correlates of mind wandering and probabilistic learning, participants were fitted with high-density electroencephalography. Our findings indicate that probabilistic learning was not only immune to periods of mind wandering, but was positively associated with it. Spontaneous, as opposed to deliberate mind wandering, was particularly beneficial for extracting the probabilistic patterns hidden in the visual stream. Additionally, cortical oscillatory activity in the low-frequency (slow and delta) range, indicative of covert sleep-like states, was associated with both mind wandering and improved probabilistic learning, particularly in the early stages of the task. Given the importance of probabilistic implicit learning in predictive processing, our findings provide novel insights into the potential cognitive benefits of task-unrelated thoughts in addition to shedding light on its neural mechanisms. This surprising benefit challenges the predominant view of mind wandering as solely detrimental and highlights its complex role in human cognition, especially in memory consolidation. Statement of significanceMind wandering poses an unresolved puzzle for cognitive neuroscience: it is associated with poor performance in various cognitive domains, yet humans spend 30-50% of their waking time mind wandering. We proposed that mind wandering may be beneficial for less attention-demanding cognitive processes requiring automatic, habitual learning. We assessed an implicit probabilistic learning task measuring the ability to extract (without awareness) hidden regularities from the information stream. Participants showed superior performance in probabilistic learning during periods of mind wandering, especially when such task-unrelated thoughts occurred spontaneously without intention. Moreover, mind wandering and probabilistic learning were both associated with slow frequency neural activity, suggesting that mind wandering may reflect a transient, offline state facilitating rapid learning and memory consolidation.

neuroscience↗

bioRxiv2023

Persistently increased post-stress activity of paraventricular thalamic neurons is essential for the emergence of stress-induced maladaptive behavior

ABSTRACTTraumatic events can immediately lead to debilitating symptoms collectively called Acute Stress Disorder (ASD), however the mechanisms of ASD are poorly understood. Using a rodent model of ASD here we identify a crucial communication bottleneck between the brainstem and the forebrain, the calretinin-positive neurons in the paraventricular thalamus (PVT/CR+), that controls ASD. We show that following a single acute stress event, the pre-sleep behavior of the mice is altered for several days in parallel with a persistent increase in the firing rate of PVT/CR+ cells. Optogenetic inhibition of PVT/CR+ neuronal activity after the stress event for one hour was sufficient to rescue both the ASD symptoms and the prolonged increase in PVT/CR+ firing rate. Inhibition applied 5 days later was still able to ameliorate some of the symptoms. These data suggest that post-stress activity of PVT/CR+ neurons play a critical role in mediating the acute forms of stress-related affective dysfunctions. One-Sentence SummaryPost-stress inhibition of paraventricular thalamic neurons prevents the emergence of acute stressed phenotype.

neuroscience↗

bioRxiv2023

The effects of aging and hearing impairment on listening in noise

Listening in a noisy environment (e.g., speech in noise) relies on the fundamental ability to extract coherence from the variable sensory input. This allows the detection active sound sources and their segregation of them from the rest of the scene (figure-ground segregation). Peripheral and central causes of age-related decline of listening in noise were assessed by a tone-cloud-based figure detection task. In two conditions differing in the amount of noise, figure detection performance was equalized between young, normal-hearing, and hearing-impaired elderly listeners by adapting the stimulation separately to the abilities of each person. Based on behavioral measures and event-related brain potentials (ERP), in the absence of cognitive deficits, aging alone does not appear to significantly deteriorate the ability to detect sound sources in noise, although ERPs show delayed perceptual processes and some expected deterioration in attention and/or executive functions. However, even mild hearing impairment substantially reduces the ability to segregate individual sound sources within a complex auditory scene, and susceptibility to masking noise increases together with the severity of the hearing deficit. Significance StatementThis work provides new information about the contributions of central and peripheral causes to the typical age-related decline of listening in a noisy environment. Behavioral and neurophysiological data collected in a well-controlled model of listening in noise suggest that aging alone does not significantly reduce the ability to detect sound sources in a complex auditory scene. However, even mild hearing impairment significantly reduces this ability. The stimulus paradigm used appears to be quite sensitive to hearing loss, making it potentially useful for the early detection of hearing problems.

neuroscience↗

bioRxiv2022

Scale-free and oscillatory spectral measures of sleep stages in humans

Power spectra of sleep electroencephalograms (EEG) comprise two main components: a decaying power-law corresponding to the aperiodic neural background activity, and spectral peaks present due to neural oscillations.Traditional band-based spectral methods ignore this fundamental structure of the EEG spectra and thus are susceptible to misrepresenting the underlying phenomena. A fitting method that attempts to separate and parameterize the aperiodic and periodic spectral components called fitting oscillations & one over f (FOOOF) was applied to a set of annotated whole-night sleep EEG recordings of 251 subjects from a wide age range (4-69 years). Most of the extracted parameters exhibited sleep stage sensitivity; significant main effects and interactions of sleep stage, age, sex, and brain region were found. The spectral slope (describing the steepness of the aperiodic component) showed especially large and consistent variability between sleep stages (and low variability between subjects), making it a candidate indicator of sleep states. The limitations and arisen problems of the FOOOF method are also discussed, possible solutions for some of them are suggested.

bioinformatics↗

bioRxiv2022

Speech prosody supports speaker selection and auditory stream segregation in a multi-talker situation

To process speech in a multi-talker environment, listeners need to segregate the mixture of incoming speech streams and focus their attention on one of them. Potentially, speech prosody could aid the segregation of different speakers, the selection of the desired speech stream, and detecting targets within the attended stream. For testing these issues, we recorded behavioral responses and extracted event-related potentials and functional brain networks from electroencephalographic signals recorded while participants listened to two concurrent speech streams, performing a lexical detection and a recognition memory task in parallel. Prosody manipulation was applied to the attended speech stream in one group of participants and to the ignored speech stream in another group. Naturally recorded speech stimuli were either intact, synthetically F0-flattened, or suppressed by the speaker. Results show that prosody - especially the parsing cues mediated by speech rate - facilitates stream selection, while playing a smaller role in auditory stream segmentation and target detection.

neuroscience↗

bioRxiv2022

Do we parse the background into separate streams in the cocktail party?

AO_SCPLOWBSTRACTC_SCPLOWIn the cocktail party situation, people with normal hearing usually follow a single speaker among multiple concurrent ones. However, there is no agreement in the literature as to whether the background is segregated into multiple streams/speakers. The current study varied the number of concurrent speech streams and investigated target detection and memory for the contents of a target stream as well as the processing of distractors. A male-spoken target stream was either presented alone (single-speech), together with one male-spoken (one-distractor), or a male- and a female-spoken distractor (two-distractor). Behavioral measures of target detection and content tracking performance as well as target- and distractor detection related ERPs were assessed. We found that the detection sensitivity and the target N2b amplitude decreased whereas the P3b amplitude increased from the single-speech to the concurrent speech streams conditions. Importantly, the behavioral distractor effect differed between the conditions with one- vs. two-distractor (distraction by the female speaker was lower than that of the male speaker in either condition) and the target N2b elicited in the presence of two distractors was significantly smaller than that elicited in the presence of one distractor. Further, the voltage in the N2b time window significantly differed between the one- and two-distractor conditions for the same (M2) speaker. These results show that speech processing was different in the presence of one vs. two distractors, and thus, the current data suggest that the two background speech streams were segregated from each other.

neuroscience↗

bioRxiv2021

Thalamic activity during scalp slow waves in humans

Slow waves are major pacemakers of NREM sleep oscillations. While slow waves themselves are mainly generated by cortical neurons, it is not clear what role thalamic activity plays in the generation of some oscillations grouped by slow waves, and to what extent thalamic activity during slow waves is itself driven by corticothalamic inputs. To address this question, we simultaneously recorded both scalp EEG and local field potentials from six thalamic nuclei (bilateral anterior, mediodorsal and ventral anterior) in fifteen epileptic patients (age-range: 17-64 years, 7 females) undergoing Deep Brain Stimulation Protocol and assessed the temporal evolution of thalamic activity relative to scalp slow waves using time-frequency analysis. We found that thalamic activity in all six nuclei during scalp slow waves is highly similar to what is observed on the scalp itself. Slow wave downstates are characterized by delta, theta and alpha activity and followed by beta, high sigma and low sigma activity during subsequent upstates. Gamma activity in the thalamus is not significantly grouped by slow waves. Theta and alpha activity appeared first on the scalp, but sigma activity appeared first in the thalamus. These effects were largely independent from the scalp region in which SWs were detected and the precise identity of thalamic nuclei. Our results indicate that while small thalamocortical neuron assemblies may initiate cortical oscillations, especially in the sleep spindle range, the large-scale neuronal activity in the thalamus which is detected by field potentials is principally driven by global cortical activity, and thus it is highly similar to what is observed on the scalp.

neuroscience↗

bioRxiv2021

Sleep spindles, ripples, and interictal epileptiform discharges in the human anterior and mediodorsal thalamus

Sleep spindles are major oscillatory components of Non-Rapid Eye Movement (NREM) sleep, reflecting hyperpolarization-rebound sequences of thalamocortical neurons, the inhibition of which is caused by the NREM-dependent activation of GABAergic neurons in the reticular thalamic nucleus. Reports suggest a link between sleep spindles and several forms of interictal epileptic discharges (IEDs) which are considered as expressions of pathological off-line neural plasticity in the central nervous system. Here we investigated the relationship between thalamic sleep spindles, IEDs and ripples in the anterior and mediodorsal nuclei (ANT and MD) of epilepsy patients. Whole-night LFP from the ANT and MD were co-registered with scalp EEG/polysomnography by using externalized leads in 15 epilepsy patients undergoing Deep Brain Stimulation protocol. Slow ([~]12 Hz) and fast ([~]14 Hz) sleep spindles were present in the human ANT and MD. Roughly, one third of thalamic sleep spindles were associated with IEDs or ripples. Both IED- and ripple-associated spindles were longer than pure spindles. IED-associated thalamic sleep spindles were characterized by broadband increase in thalamic and cortical activity, both below and above the spindle frequency range, whereas ripple-associated thalamic spindles exceeded pure spindles in terms of 80-200 Hz thalamic, but not cortical activity as indicated by time-frequency analysis. These result show that thalamic spindles coupled with IEDs are reflected at the scalp slow and beta-gamma oscillation as well. IED density during sleep spindles in the MD, but not in the ANT was identified as correlates of years spent with epilepsy, whereas no signs of pathological processes were correlated with measures of ripple and spindle association. Furthermore, the density of ripple-associated sleep spindles in the ANT showed a positive correlation with general intelligence. Our findings indicate the complex and multifaceted role of the human thalamus in sleep spindle-related physiological and pathological neural plasticity.

neuroscience↗

bioRxiv2021

Circadian modulation of human sleep spindle frequency

Homeostatic and circadian processes play a pivotal role in determining sleep structure, timing and quality. In sharp contrast with the wide accessibility of the EEG index of sleep homeostasis, an electrophysological measure of the circadian modulation of sleep is still non-available. Evidence suggests that sleep spindle frequencies decelerate during biological night. In order to test the feasibility of measuring this marker in common polysomnographic protocols, the Budapest-Munich database of sleep records (N = 251 healthy subjects, 122 females, age range: 4-69 years), as well as an afternoon nap sleep record database (N = 112 healthy subjects, 30 females, age range: 18-30 years) were analysed by the Individual Adjustment Method of sleep spindle analysis. Slow and fast sleep spindle frequencies were characterized by U-shaped overnight dynamics, with highest values in the first and the fourth-to-fifth sleep cycle and the lowest values in the middle of the sleeping period (cycles 2-3). Age-related attenuation of sleep spindle deceleration was evident. Estimated phases of the nadirs in sleep spindle frequencies were advanced in children as compared to other age groups. Additionally, nap sleep spindles were faster than night sleep spindles (0.57 and 0.39 Hz difference for slow and fast types, respectively). The fine frequency resolution analysis of sleep spindles is a feasible method of measuring the assumed circadian modulation of sleep. Moreover, age-related attenuation of circadian sleep modulation might be measurable by assessing the overnight dynamics in sleep spindle frequency. Phase of the minimal sleep spindle frequency is a putative biomarker of chronotype.

neuroscience↗

bioRxiv2020

A new set of composite, non-redundant electroencephalogram measures of non-rapid eye movement sleep based on the power law scaling of the Fourier spectrum

A novel method for deriving composite, non-redundant measures of non-rapid eye movement (NREM) sleep electroencephalogram (EEG) is developed on the basis of the power law scaling of the Fourier spectra. Measures derived are the spectral intercept, the slope (spectral exponent), as well as the maximal whitened spectral peak amplitude and frequency in the sleep spindle range. As a proof of concept, we apply these measures on a large sleep EEG dataset (N = 175; 81 females; age range: 17-60 years) with previously demonstrated effects of age, sex and intelligence. As predicted, aging is associated with decreased overall spectral slopes (increased exponents) and whitened spectral peak amplitudes in the spindle frequency range. In addition, age associates with decreased sleep spindle spectral peak frequencies in the frontal region. Women were characterized by higher spectral intercepts and higher spectral peak frequencies in the sleep spindle range. No sex differences in whitened spectral peak amplitudes of the sleep spindle range were found. Intelligence correlated positively with whitened spectral peak amplitudes of the spindle frequency range in women, but not in men. Last, age-related increases in spectral exponents did not differ in subjects with average and high intelligence. Our findings replicate and complete previous reports in the literature, indicating that the number of variables describing NREM sleep EEG can be effectively reduced in order to overcome redundancy and Type I statistical errors in future electrophysiological studies of sleep. Author summaryGiven the tight reciprocal relationship between sleep and wakefulness, the objective description of the complex neural activity patterns characterizing human sleep is of utmost importance in understanding the several facets of brain function, like sex differences, aging and cognitive abilities. Current approaches are either exclusively based on visual impressions expressed in graded levels of sleep depth (W, N1, N2, N3, REM), whereas computerized quantitative methods provide an almost infinite number of potential metrics, suffering from significant redundancy and arbitrariness. Our current approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) are characterized by coloured noise-like properties. That is, the contribution of different frequencies to the power spectrum of the signal are best described by power law functions with negative exponents. In addition, we assume, that stages N2-N3 are further characterized by additional non-random (non-noise like, sinusoidal) activity patterns, which are emerging at specific frequencies, called sleep spindles (9-18 Hz). By relying on these assumptions we were able to effectively reduce 191 spectral measures to 4: (1) the spectral intercept reflecting the overall amplitude of the signal, (2) the spectral slope reflecting the constant ratio of low over high frequency power, (3) the frequency of the maximal sleep spindle activity and (4) the amplitude of the sleep spindle spectral peak. These 4 measures were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

neuroscience↗