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Palva, J. M.

Publications and source records attributed to Palva, J. M..

At least 19 recordsLinked to original sources

Working memory operations emerge from dynamic changes in neural subspace geometry

Working memory representations of multiple items are maintained within quasi-orthogonal neural subspaces, a mechanism thought to reduce interference between memory contents. Previous studies in both non-human and human primates have demonstrated the existence of such subspaces, but it remains unclear how cognitive operations are performed on the information they encode. One hypothesis proposes that these operations are implemented through dynamic changes in subspace geometry, yet direct empirical evidence for this idea in the context of working memory is lacking. Furthermore, the neural mechanisms underlying such geometric reconfigurations remain unknown, particularly the role of oscillatory and aperiodic neural activity. Here, we addressed these questions using simultaneous magneto- and electroencephalography (M/EEG) recordings from healthy human participants performing a multi-item, multi-feature delayed match-to-sample visual WM task. Neural subspaces were estimated using dimensionality reduction techniques, and their geometry was characterized by quantifying the orthogonality between subspaces, distances between memory representations and subspace shapes. Participants maintained orientation and shape information simultaneously while task demands required either deprioritizing one feature or updating the representation of the prioritized feature. We found that subspace geometry was flexibly reshaped according to task demands: prioritization expanded representational subspaces, whereas de-prioritization shrunk them. In addition, subspaces representing different feature domains were generally oblique and became more orthogonal only under high cognitive demands. Finally, these task-dependent changes in subspace geometry were largely driven by interactions between aperiodic neural activity and slow oscillations in the delta and theta frequency bands.

neuroscience↗

Early life stress leads to an aberrant spread of neuronal avalanches in the prefrontal-amygdala network in males but not females

Development of the brain networks is highly vulnerable to stressful events. Early life stress (ELS) has been linked to multifaceted cognitive and emotional deficits in adulthood. Despite a growing body of evidence showing ELS-induced structural and functional changes in the prefrontal cortex (PFC) and basolateral amygdala (BLA), a circuit crucial for emotional processing, our knowledge of the resulting changes in the network dynamics is incomplete. Here, we investigate how maternal separation (MS) affects prefrontal-amygdala network in terms of neuronal avalanches, spatiotemporal clusters of activity, using simultaneous multielectrode recordings in the medial PFC (mPFC) and the BLA of urethane-anaesthetized juvenile (postnatal day (p) 14 - p15) and young adult (p50 - p 60) rats. Firstly, we show that MS leads to an intensified spread of activity within both regions as reflected in the higher mean branching ratios of the avalanches. Next, we demonstrate that most of the avalanches occur locally in one region, however, a small percentage of avalanches has clusters of activity in both regions simultaneously. We show that in MS animals prefrontal clusters followed by activity in the amygdala tend to be larger compared to controls and each event in the mPFC is followed by smaller number of events in the BLA, pointing towards impaired spread of activity from the mPFC to the BLA. Interestingly, avalanche spread from the BLA to the mPFC remains unaffected by MS. Abovementioned effects manifest only in adulthood and, intriguingly, only in males highlighting prolonged developmental and sex-dependent nature of ELS outcome. Significance statementBrain criticality implies that the brain self-organizers towards critical state, characterized by sustained activity propagation reflected in the unitary branching ratios of neuronal avalanches. Here we show how adverse events during early periods of network maturation, namely ELS, can disrupt developmental trajectories of the critical dynamics in the mPFC-BLA circuit in a sex-specific manner. This study broadens our understanding of the critical dynamics emergence in the prefrontal-limbic network and highlights ELS as a potential criticality control parameter.

neuroscience↗

REM sleep reconfigures large-scale network dynamics: a link to its suppressive role in epilepsy

Converging evidence suggests that human brain activity operates near a critical-like regime in which balanced excitation and inhibition support efficient large-scale communication. The brains proximity to criticality may be dynamically reset across the sleep-wake cycle and altered by epilepsy, leading to aberrant oscillatory dynamics. Building on recent work demonstrating a tripartite interaction between networks synchronization, oscillatory amplitude bistability, and cross-frequency coupling in the human brain that seems to favour epileptic activity, we examined how this interaction, and its underlying large-scale dynamics are modulated across vigilance states. We analyzed overnight recordings from 20 patients with drug-resistant epilepsy undergoing presurgical evaluation and selected overall 20 minutes of continuous, artifact free stereo-electroencephalography (SEEG) spanning REM sleep, NREM stages N2 and N3, and eyes-closed resting wakefulness. Across states, we quantified phase synchronization, phase-amplitude coupling, bistability and their correlation. REM sleep was consistently associated with a reduction of these dynamics relative to NREM sleep and wakefulness. Importantly, the canonical correlation between these measures -- reflecting the strength of the tripartite interaction -- was significantly weaker during REM sleep. These findings indicate that vigilance states modulate this previously identified multiscale interaction in human brain networks and suggest that the reduced epileptogenicity of REM sleep can be associated with a disruption of coordinated synchronization, coupling, and bistable dynamics at the large-scale network level.

neuroscience↗

Mitigating activity mixing with personalized whole-brain modeling

Mechanistic neuroimaging-based biomarkers based on localized brain activities or interactions are a central tenet in precision and personalized psychiatry. However, their accuracy may be limited by Activity Mixing that is a novel construct indicating the entanglement of any local neuronal activity with activities elsewhere in the network through long-range spatiotemporal correlations, which degrades the localization of brain-symptom associations. Here, we posit that the ill-posed inverse problem of activity mixing can be mitigated by fitting of generative whole-brain models. We developed a multi-objective fitting approach to estimate subject-specific local- and inter-areal brain-dynamics control parameters from neuroimaging observables. By integrating both synchronization and criticality metrics, this approach yields personalized parameters capture individual brain network dynamics more accurately than raw observables. In silico validation demonstrated that fitted model parameters improved brain-symptom correlation estimates by 30-85% and reduced false-negative rates by approximately [~]67% relative to conventional observables-based analyses. As in vivo proof-of-concept, resting-state magnetoencephalography (MEG) data from 230 patients with major depressive disorder (MDD) showed that aberrant brain criticality in the alpha-frequency band (11 Hz) was a significant predictor of disability with a correlation coefficient of 0.236 (95% confidence interval (CI) = [0.206, 0.266]) in 27 (CI = [23, 31]) significant cortical parcels. Model fitting both improved this correlation estimate by [~]56% up to 0.368 (CI = [0.341, 0.405]) and localized it [~]25% more narrowly to 20 (CI = [18, 22]) parcels. These findings suggest that model fitting can mitigate the effects of activity mixing and provide control-parameter estimates that delineate mechanistic biomarkers for brain disorders more accurately than the raw brain imaging observables.

neuroscience↗

CROCOpy - A Python toolbox for the analysis of CRitical Oscillations and COnnectivity

CROCOpy is a light-weighted toolbox for the assessment of neuronal oscillations, and multiple observables of functional connectivity (phase synchronization, amplitude coupling, and cross-frequency coupling) and critical dynamics (avalanches, long-range temporal correlations, bistability, and functional excitation-inhibition ratio). It was developed to simplify the analysis of continuous electrophysiological recordings and, in addition to metric computation, also includes methods for narrow-band filtering and statistical analysis. It is device-agnostic and supports both GPU and CPU computations. The toolbox also provides detailed tutorials.

neuroscience↗

Different developmental trajectories of critical dynamics in prefrontal cortex-amygdala circuitry

Neuronal avalanches, a hallmark of self-organized criticality, represent cascading activity among interconnected neurons. Despite their hypothesized role in neuronal maturation, developmental patterns of neuronal avalanches across interconnected brain regions in vivo remain unelucidated. We applied avalanche analysis to investigate the emergent features of functional connectivity in the developing medial prefrontal cortex-basolateral amygdala (mPFC-BLA) circuitry, using simultaneous electrophysiological recordings from the BLA and mPFC in juvenile and young adult anaesthetized rats. The avalanches were mainly confined to either mPFC or BLA, a small number spanned both regions. Prefrontal avalanches exhibited scale-free behavior with branching ratio close to one and decreasing over development; amygdaloid avalanche size distributions displayed a sharp peak at the maximum size, more pronounced in adulthood, which together with branching ratios over one suggests a deviation from criticality toward a supercritical state. Different dynamical regimes in the BLA and mPFC fit distinct functional demands of these regions.

neuroscience↗

Spectral patterns of MEG oscillatory coupling emerge from meta-stable dynamics with small coupling delays

Functional connectivity (FC) is a fundamental mechanism of neural communication, connecting distinct oscillating populations. Oscillatory networks exhibit heterogeneity across frequencies and coupling modes whose origins are not well understood, but have been suggested to involve a complex interplay of critical-like dynamics and structure-function coupling. We here utilized structural connectivity (SC) to tune a whole-brain computational model of delay-coupled damped oscillators near a Hopf bifurcation to match oscillations and FC as observed in resting-state magnetoencephalography (MEG) data. We assessed two forms of oscillation-based FC from empirical and model data, namely phase synchronization (PS) and amplitude coupling (AC). We found that both oscillations and FC best matched with empirical observations in a meta-stable regime which was characterized by small delays, realistic oscillation lifetimes, and FC with intermediate strength and high variability. How well MEG FC patterns were matched by the model varied between frequency bands and best fits were observed for high-alpha and beta-band networks. These fits could partially, but not fully, be explained by correlations with SC, implicating that both structure-function coupling and critical-like metastable dynamics underlie empirical FC, and the contributions of these mechanisms varies between different frequency bands in MEG data.

neuroscience↗

Neuronal oscillations predict un-cued task switching in the Wisconsin card sorting test

Cognitive flexibility is the ability to keep or update contextual cognitive representations. The underlying neural mechanisms and how it is influenced by individual sensitivity to feedback are not known. We measured cognitive flexibility under uncertainty with the Wisconsin Card Sorting Task and brain activity with magnetoencephalography (MEG). Using a behavioral sequential learning model, we show that variability in cognitive flexibility is predicted by individual reward and punishment sensitivity to feedback and anticipation and exploration tendencies. Uncovering their brain oscillatory signatures showed that individual learning speed is predicted by suppressed alpha-beta (7-32 Hz) and increased broad-band gamma (52-96 Hz) amplitudes along with concurrent large-scale alpha (7-13 Hz) desynchronization. Importantly, these oscillatory sub-processes were explained by individual levels of distinct feedback sensitivities. These results provide a novel account on neural sub-computations underlying flexible contextual switching between stable and unstable cognitive representations that predict the individual speed of rule learning.

neuroscience↗

Quantifying cortical maturational aspects during different vigilance states in preterm infants by advanced EEG analysis

Preterm birth is associated with numerous neurodevelopmental adverse outcomes, even in the absence of acquired lesions, as it occurs during a critical period of brain development. Clear organization of vigilance states can be recognized from 30-32 weeks postmenstrual age (PMA). In this study, we investigated whether spatial and temporal properties of neuronal oscillatory dynamics (i.e., phase synchronization, bistability, and cross-frequency coupling) during different vigilance states provide insights into cortical maturation in preterm infants born very low birth weight (VLBW) at low neurological risk and devoid of detectable brain lesions. We analyzed artifact-free video-polysomnographic data from 11 VLBW preterm infants (PMA at recording: 33.0 {+/-} 1.6 weeks) who underwent brain MRI at term-equivalent age. For each vigilance state, we computed the weighted Phase Lag Index (wPLI), Bistability Index (BiS), and Phase-Amplitude Coupling (PAC), both globally and across anterior and posterior regions, and examined their correlation with PMA at recording. wPLI, BiS, and PAC showed specific trends across vigilance states. BiS and PAC exhibited posterior-to-anterior differences and correlated with PMA. Our study suggests that these electrophysiological markers, particularly BiS and PAC, may serve as indices to monitor aspects of cortical maturation in VLBW at low neurological risk.

neuroscience↗

Sleep-Modulated Cross-Frequency Coupling Between δ Phase and β-γ Bistability: A System-Level Modulation of Epileptic Activity

ObjectiveWhile slow waves in {delta} (0.5-4 Hz) characterize NREM sleep, in patients with sleep-related epilepsy, seizures most frequently emerge during NREM stage 2, known to be promoted by {delta}-band instability. Meanwhile, the epileptogenic zone (EZ) shows localized bistability in {beta}-{gamma} band (15-200 Hz) neuronal oscillations--indicating a catastrophic shift toward seizure. We aim to clarify the mechanistic link between {delta}-band synchrony and {beta}-{gamma} band bistability in epilepsy. MethodsWe studied a cohort of fourteen patients with Sleep Hypermotor Epilepsy (22.3 {+/-} 10.8 years old; 7 males). 7-9-hour stereo-EEG sleep recordings were segmented into 10-minute of uninterrupted, interictal N2 and N3 epochs, and phase synchrony, phase-amplitude coupling (PAC), and bistability were assessed. Canonical correlation was examined to answer whether PAC links {delta}-phase to {beta}-{gamma} bistability. ResultsCompared to non-EZ, the EZ exhibited larger 15-200 Hz bistability along with stronger 2-8 Hz and 15-100 Hz synchrony throughout N2 and N3. Compared to N3, N2 showed stronger PAC between 2-30 Hz phases in the non-EZ and 5-150 Hz amplitudes in the EZ. Canonical correlations between {delta}-phase modulated PAC and both bistability and synchrony were identified during N2 (r = 0.86 and 0.82) and N3 (r = 0.84 and 0.80), with the strongest contributors being 2-4 Hz synchrony and bistability in 2-4 Hz and 15-200 Hz bands. Correlations between interictal spikes and canonical covariates of bistability and PAC (r2 = 0.62 for N2 and 0.56 for N3) validated their relevance to epileptogenicity. Significance{delta}-band synchrony and {beta}-{gamma} band bistability are not isolated epileptogenic mechanisms but likely act synergistically, playing a pivotal role in seizure generation through the coupling of {delta} phases and {beta}-{gamma} amplitudes across large networks, with significant contributions from non-epileptogenic tissues. Key pointsO_LIStrong {beta}-{gamma} bistability in neuronal oscillations localizes the EZ throughout N2 and N3 sleep. C_LIO_LIElevated {delta}-band phase synchrony characterizes the EZ and its functional neighbors throughout N2 and N3 sleep. C_LIO_LIa-band synchrony modulates local {beta}-{gamma} bistability through PAC, with significant contributions from non-EZ tissues. C_LI

neuroscience↗

Hierarchical model of human brain oscillations

The brain operates at the critical transition between order and disorder which supports optimal information processing. Whole-brain computational modeling is a powerful tool for uncovering the system-level mechanisms behind large-scale brain activity in both healthy and pathological states. However, most previous approaches have focused on either functional connectivity or criticality, making it difficult to capture both aspects simultaneously. Here, we introduce a new method based on a Hierarchical Kuramoto model that incorporates two levels of hierarchy. In our model, each node contains a large number of coupled oscillators, which allows us to examine both local synchronization and long-distance interactions between brain regions. The model produces critical-like dynamics marked by emergent long-range temporal correlations (LRTCs) and both inter-areal phase synchronization and amplitude correlations during the transition from asynchronous to synchronous states. Notably, structure-function coupling shows distinct patterns: correlations with structural connectivity peak at criticality for LRTCs and amplitude correlations, but decay for local and inter-areal phase synchronization. Comparisons with human resting-state magnetoencephalography (MEG) data reveal that the models behavior most closely resembles MEG phase synchronization and multi-peak power spectra on the subcritical side of an extended critical regime, supporting the hypothesis that the human brain operates in this state.

neuroscience↗

New vistas for epileptogenic zone (EZ) localization and interpretation

ObjectivePost-surgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30 to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). This suggests that the EZ entails a broader epileptogenic brain network (EpiNet) beyond the seizure-zone (SZ) that show seizure activity. MethodsWe first used computational modeling to identify putative complex-systems- and systems-neuroscience-driven mechanistic biomarkers for epileptogenicity. We then extracted these epileptogenicity biomarkers from stereo-EEG (SEEG) resting-state data from DRE patients and trained supervised classifiers to localize the SZ with these biomarkers against gold-standard clinical localization. To further explore the prevalence of these pathological biomarkers in an extended network outside of the clinically-identified SZ, we also used unsupervised classification. ResultsSupervised SZ-classification trained on individual features achieved accuracies of 0.6-0.7 areaunder-the-receiver-operating-characteristics curve (AUC). However, combining all criticality and synchrony features improved the AUC up to 0.85. Unsupervised classification uncovered an EpiNet-like cluster of brain regions with 51% of regions outside of SZ. Brain regions in this cluster engaged in inter-areal hypersynchrony and locally exhibited high amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure-risk regime revealed by computational modeling. SignificanceThe finding that combining biomarkers improves EZ localization shows that the different mechanistic biomarkers of epileptogenicity assessed here yield synergistic information. On the other hand, the discovery of SZ-like pathophysiological brain dynamics outside of the clinically-defined EZ provides experimental localization of an extended EpiNet. Key pointsO_LIWe advanced novel complex-systems- and systems-neuroscience-driven biomarkers for epileptogenicity C_LIO_LIIncreased bistability, inhibition, and power-low scaling exponents characterized our model operating in a high seizure-risk regime and SEEG oscillations in the seizure-zone (SZ) C_LIO_LICombining all biomarkers yielded more accurate supervised SZ-classification than using any individual biomarker alone C_LIO_LIUnsupervised classification revealed more extended pathological brain networks including the SZ and many non-seizure-zone areas that were previously considered healthy C_LI

physiology↗

Altered Brain Dynamics in idiopathic REM sleep behavior disorder: Implications for a continuum from prodromal to overt alpha-synucleinopathies

Idiopathic/isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of alpha-synucleinopathies. Cortical and sub-cortical brain modifications begin years before the emergence of overt neurodegenerative symptoms. To better understand the pathophysiological process impacting the brain from the prodromal to the overt stage of alpha-synucleinopathy, it is essential to assess iRBD patients over time. Recent evidence suggests that the human brain operates at an operating point near a critical phase transition between subcritical and supercritical phases in the systems state space to maintain cognitive and physiological performance. In contrast, a deviation from the critical regime leading to altered oscillatory dynamics has been observed in several pathologies. Here, we investigated if the alpha-synucleinopathy produces a deviation of the operating point already evident in the prodromal phase and if this shift correlates with biological and clinical disease severity. We analyzed a dataset of 59 patients with iRBD (age 69.61 {+/-} 6.98, 50 male) undergoing resting-state high-density EEG, presynaptic dopaminergic imaging, and clinical evaluations. Thirty-one patients (age 72.41 {+/-} 7.05, 31 male) also underwent clinical and instrumental follow-up (mean follow-up period 25.85 {+/-} 10.20 months). To localize the individual operating points along the excitation-inhibition (EI) continuum, we assessed both measures of neuronal EI balance and measures of critical brain dynamics such as long-range temporal correlation (LRTCs) and neuronal bistability in spontaneous narrow-band oscillations. Finally, we correlated critical brain dynamics and EI balance metrics with phase synchronization, nigro-striatal dopaminergic functioning, and clinical performances. Compared to 48 healthy subjects (age 70.25 {+/-} 10.15, 23 male), iRBD patients showed higher values of LRTCs and bistability in the 2-7 Hz band at diagnosis. Patients who eventually phenoconverted to overt alpha-synucleinopathy exhibited a more excitation-dominated (fEI > 1) condition than stable iRBD patients in 5-7 Hz. This higher excitation also directly correlated with phase synchronization in 2-7 Hz, further suggesting a shift of the operating point toward a supercritical state with the disease progression. Moreover, excitation-dominated state and low bistability were associated with deterioration of the nigro-striatal dopaminergic function and tended to correlate with stronger clinical symptoms. In conclusion, this study shows for the first time a deviation of the working point from inhibition-to excitation-dominated states along the continuum from prodromal to overt phases of the disease. These cortical brain dynamics modifications are associated with nigro-striatal dopaminergic impairment. These results increase our knowledge of the physiopathological process underlying alpha-synucleinopathies since prodromal stages, possibly providing new clues on disease-modifying strategies.

bioengineering↗

The influence of inter-regional delays in generating large-scale brain networks of phase synchronization

Large-scale networks of phase synchronization are considered to regulate the communication between brain regions fundamental to cognitive function, but the mapping to their structural substrates, i.e., the structure-function relationship, remains poorly understood. Biophysical Network Models (BNMs) have demonstrated the influences of local oscillatory activity and inter-regional anatomical connections in generating alpha-band (8-12 Hz) networks of phase synchronization observed with Electroencephalography (EEG) and Magnetoencephalography (MEG). Yet, the influence of inter-regional conduction delays remains unknown. In this study, we compared a BNM with standard "distance-dependent delays", which assumes constant conduction velocity, to BNMs with delays specified by two alternative methods accounting for spatially varying conduction velocities, "isochronous delays" and "mixed delays". We followed the Approximate Bayesian Computation (ABC) workflow, i) specifying neurophysiologically informed prior distributions of BNM parameters, ii) verifying the suitability of the prior distributions with Prior Predictive Checks, iii) fitting each of the three BNMs to alpha-band MEG resting-state data (N = 75) with Bayesian Optimisation for Likelihood-Free Inference (BOLFI), and iv) choosing between the fitted BNMs with ABC model comparison on a separate MEG dataset (N = 30). Prior Predictive Checks revealed the range of dynamics generated by each of the BNMs to encompass those seen in the MEG data, suggesting the suitability of the prior distributions. Fitting the models to MEG data yielded reliable posterior distributions of the parameters of each of the BNMs. Finally, model comparison revealed the BNM with "distance-dependent delays", as the most probable to describe the generation of alpha-band networks of phase synchronization seen in MEG. These findings suggest that distance-dependent delays contribute significantly to the neocortical architecture of human alpha-band networks of phase synchronization. Hence, our study illuminates the role of inter-regional delays in generating the large-scale networks of phase synchronization that might subserve the communication between regions vital to cognition. HighlightsO_LICompared methods to specify delays in Biophysical Network Models (BNMs) C_LIO_LIBNM with "distance-dependent" conduction delays more probable than alternatives C_LIO_LIBNMs with biologically informed prior distributions generate dynamics seen in MEG C_LIO_LIFitting BNMs yields reliable posterior distributions informed by MEG data (N = 75) C_LI

neuroscience↗

Sleep specific changes in infra-slow and respiratory frequency drivers of cortical EEG rhythms

Infra-slow fluctuations (ISFs, 0.008-0.1 Hz) characterize hemodynamic and electric potential signals from the human brain. ISFs are known to correlate with the amplitude dynamics of fast (> 1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood-brain barrier (BBB). Slow physiological pulsations such as respiration may also influence the amplitude dynamics of fast oscillations, but it remains uncertain if these processes track the fluctuations of fast cortical oscillations or act as their drivers. Moreover, possible effects of sleep and associated BBB permeability changes on such coupling are unknown. Here, we used non-invasive high-density full-band electroencephalography (EEG) in healthy human volunteers (N=21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling. The phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling evident during sleep. Causality analysis robustly showed that the phase of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states. However, the net direction of modulation was stronger during the awake state, despite the stronger power and phase-amplitude coupling of slow signals during sleep. These findings show that the ISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations. We propose that these slow physiological phases are involved in coordinating cortical excitability, which is a fundamental aspect of brain function. Significance StatementPreviously disregarded EEG infra-slow fluctuations (0.008-0.1 Hz) and slow physiological pulsations such as respiration have been attracting increasing research interest, which shows that both of these signals correlate with fast (> 1 Hz) neuronal oscillations. However, little has been known about the mechanisms underlying these interactions; for example, the direction of causality in this interaction has not hitherto been studied. Therefore, we investigated full-band EEG in healthy volunteers during wakefulness and sleep to determine if ISF and respiration phases drive neuronal amplitudes. Results showed that ISF and respiration are phase-amplitude coupled, and predict neuronal EEG rhythms. Thus, we conclude that fast neuronal rhythms in human brain are modulated by slower non-neural phenomena.

neuroscience↗

E/I unbalance and aberrant oscillation dynamics predict preclinical Alzheimer's disease

Alzheimers disease (AD) is a chronic, nonlinearly progressive neurodegenerative disease that affects multiple domains of behaviour and is the most common form of dementia. However, there is scarce understanding of its biological basis nor there are reliable markers for the earliest disease stages preceding AD. Here we investigated whether AD progression is predicted by increasingly aberrant critical brain dynamics driven by underlying E/I imbalance using magnetoencephalography (MEG) data from cross-sectional (N=343) and longitudinal (N=45) cohorts. As a hallmark of brain criticality, we quantified long-range temporal correlations (LRTCs) in neuronal oscillations and tracked changes in neuronal excitability. We demonstrate that attenuation and progressive changes of LRTCs characterize the earliest stages of disease progression and yield accurate classification to individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Our data indicate that pathological brain critical dynamics in AD progression provide a clinical marker for targeting specific treatments to individuals at increased risk.

neuroscience↗

Hippocampal neuronal synchronization in the rat - Dissociation of perception and associative learning.

Learning requires flexible switching between perceiving and encoding external sensory stimuli, making associations with representations already stored in memory (retrieval) and acting accordingly to adapt to the environment. Hippocampus is known to be central in creating mnemonic representations, but how it could support the integration of these processes of memory encoding and retrieval during repetitive learning experiences has remained unknown. In this study, we recorded local-field potentials (LFPs) from the dorsal hippocampus during classical trace eyeblink conditioning in rats. An auditory conditioned stimulus (CS) was followed by a periorbital shock-unconditioned stimulus (US). From the LFPs, we separated hippocampal oscillations that were linked with the variation in the trial-by-trial processing of the CS from those linked with the gradual learning of the CS-US association over daily sessions of training. Short latency transient theta and gamma band oscillations were associated with learning so that both the oscillation amplitudes (strength) and phase locking increased as a function of training. In contrast, long-latency sustained alpha and beta oscillations were associated with perception of CS. These data show divergent oscillatory neural signatures for perception and learning in hippocampal subfields.

neuroscience↗

Is mild ADHD beneficial: Brain criticality is maximal with moderate ADHD symptom scores

Attention-deficit/hyperactivity disorder (ADHD) is characterized by a continuum of symptoms including inattentiveness, hyperfocus, and hyperactivity, that are manifested, e.g., in increased reaction-time variability in continuous performance tasks (CPTs). Framework of brain criticality posits that brains operate in an extended regime of critical-like dynamics where neuronal and behavioral processes exhibit scale-free long-range temporal correlations (LRTCs) across hundreds of seconds. Whether shifts across critical-like brain states and parallel changes in LRTCs underlie ADHD symptoms has remained unexplored. We investigated whether brain-state shifts towards excitation-dominated dynamics and associated changes in LRTCs could explain ADHD symptoms and their continuum across individuals. We measured brain activity with magnetoencephalography (MEG) during resting and two CPTs from adult participants diagnosed with ADHD (N = 34) and neurotypical controls (NC) (N = 36) and characterized criticality with LRTCs of neuronal oscillations. ADHD patients exhibited stronger LRTCs than NC in low (5-20 Hz) and high (30-100 Hz) frequencies and dichotomous task effects. High-frequency LRTCs were positively correlated with ADHD symptoms, while beta-band (20-30 Hz) activity exhibited a quadratic correlation, peaking at moderate scores across the ADHD and NC cohorts being smaller for low and high scores. We demonstrate that ADHD is associated with shifts in brain criticality in resting-and task-state brain dynamics. Individuals with intermediate symptom scores across the NC and ADHD cohorts operated at peak criticality that is associated with a several functional benefits The progressive shift towards the supercritical side of the critical regime becomes detrimental only at moderate and severe symptoms.

neuroscience↗