Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients

Disentangling sources of variability in decision-making

Even the most highly-trained observers presented with identical choice-relevant stimuli will reliably exhibit substantial trial-to-trial variability in the timing and accuracy of their choices. Despite being a pervasive feature of choice behaviour and a prominent phenotype for numerous clinical disorders, the capability to disentangle the sources of such intra-individual variability (IIV) remains limited. In principle, computational models of decision-making offer a means of parsing and estimating these sources, but methodological limitations have prevented this potential from being fully realized in practice. In this Review, we first discuss current limitations of algorithmic models for understanding variability in decision-making behaviour. We then highlight recent advances in behavioural paradigm design, novel analyses of cross-trial behavioural and neural dynamics, and the development of neurally grounded computational models that are now making it possible to link distinct components of IIV to well-defined neural processes. Taken together, we demonstrate how these methods are opening up new avenues for systematically analysing the neural origins of IIV, paving the way for a more refined, holistic understanding of decision-making in health and disease.

Deviation from Nash mixed equilibrium in repeated rock-scissors-paper reflect individual traits

Current psychiatric nosology is based on observed and self-reported symptoms. Heterogenous pathophysiological mechanisms may underlie similar symptoms leading to diagnosis not matching up to the neurobiology. Recent research has sought to move away from diagnoses by symptoms, to viewing aberrant mental health in terms of abnormal human neurobehavioral functioning and concurrent deviations in the pathophysiology. Human behavior in a social context is a core neurobehavioral function with large individual variation that may reflect genomic, metabolic or neurobiological variation, whose identification potentially yields more accurate targeting for the development of interventions and biomedical treatments. In this research, we describe an experimental framework that utilizes a zero-sum game of repeated Rock-Paper-Scissors played against an artificial intelligence agent as an assay of social interaction. Human deviation from the Nash Mixed Equilibrium strategy of play, the only guaranteed way to avoid exploitation, can be seen in the sequential dependence of hands. We hypothesize that this deviation represents humans mimicing randomness to avoid exploitation through constant adjustments of behavior, which we analyze in terms of a set of switching heuristic lag-1 conditional response rules. We quantify and interpret the set of rules subjects are able to utilize as mirroring individual traits. Subjects in the study also completed the Autism Quotient Abridged survey, and subscores of the social, imagination and routine factors were found to be predicted by a combination of behavioral features derived from game play.

EEG features associated with Alzheimer's disease and Frontotemporal dementia are not reflected by processed indices used in anesthesia monitoring

Patients with dementia face increased risks after general anesthesia. Improved perioperative electroencephalogram (EEG) monitoring techniques could aid in identifying vulnerable patients. However, current technology relies on processed indices to measure "depth-of-anesthesia". Analyzing OpenNeuro Dataset ds004504, we compared resting-state, eyes-closed EEG recordings of healthy controls (n = 27) with patients diagnosed with Alzheimer's disease (AD, n = 35) and Frontotemporal dementia (FTD, n = 23). We focused on prefrontal recordings. Analysis included spectral analysis, the "fitting-oscillations&-one-over-f"-algorithm for aperiodic and periodic signal features, as well as calculations of openibis, permutation entropy (PeEn), spectral entropy (SpEn), and spectral edge frequency (SEF). Spectral differences were pronounced, including a higher alpha/theta-ratio of controls (2.62 [95%CI: 1.54-3.62]) compared to both AD (0.55 [95%CI: 0.26-1.92], P < 0.001, AUC: 0.765 [0.642-0.888]) and FTD (0.83 [95%CI: 0.33-1.65], P = 0.007, AUC: 0.779 [0.652-0.907]). Oscillatory peak detection within the alpha frequency band was more robust in control (versus AD: P = 0.003, Cramér's V = 0.374; versus FTD: P = 0.003, Cramér's V = 0.414). Processed index parameters did not show a clear trend. FTD was associated with a higher prefrontal openibis (95.53 [95%CI: 93.43-97.39]) than control (91.98 [95%CI: 89.46-96.27], P = 0.033, AUC: 0.717 [0.572-0.862]) and an elevated SEF (23.68 [95%CI: 14.10-25.57] Hz) compared to AD (16.60 [95%CI: 14.22-22.22] Hz, P = 0.041, AUC: 0.676 [0.532-0.821]). AD and FTD are associated with EEG baseline abnormalities, and a standard prefrontal montage, as used intraoperatively, could present a promising technical screening approach for cognitive vulnerability. However, these EEG features are obscured by processed index parameters currently used in neuroanesthesia monitoring. OpenNeuro Dataset ds004504 "A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects" (doi: https://doi.org/10.18112/openneuro.ds004504.v1.0.7 ).

Measurement of Excitation-Inhibition Imbalance in Autism spectrum Disorder Using EEG Proxy Markers: A Pilot Study

Autism Spectrum Disorder (ASD) is a severe neurodevelopmental disorder characterized primarily by social impairments and repetitive behaviors. Imbalance in excitatory-inhibitory (E/I) activity within the central nervous system may be a key mechanism underlying ASD. Electroencephalography (EEG) is a useful tool for recording brain electrical signals, reflecting the activity of cortical neuron populations, and estimating both global and regional E/I balance. Various EEG methods can estimate E/I balance, including non-periodic exponent, corrected alpha power, sample entropy, average spatial phase synchronization (ASPS), and detrended fluctuation analysis (DFA) based on E/I indices. However, research on using EEG proxy markers to assess E/I imbalance in autism is limited, and there is no study indicating which method is most sensitive. Therefore, this study employed a high-density EEG acquisition system to collect data from a relatively large sample of autistic and typically developing (TD) children. We computed EEG proxy markers and used the Coefficient of Variation (CV) to compare the sensitivity of five EEG markers between the two groups. The results indicated that non-periodic exponent based on power spectra and corrected alpha power from non-periodic neural activity were more advantageous. The findings may provide theoretical support for the exploration of EEG biomarkers based on E/I balance theory.

Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition

The brain criticality hypothesis postulates that brain dynamics are set at a phase transition where information processing is optimized. Long-range temporal correlations (TCs) characterizing the dissipation of information within a signal have been shown to be a hallmark of brain criticality. However, the experimental link between cognitive performance, criticality, and thus TCs has remained elusive due to limitations in recording length and spatial and temporal resolution. In this study, we investigate multiday invasive EEG recordings of 104 persons with epilepsy (PwE) together with an extensive cognitive test battery. We show that short TCs predict cognitive impairment. Further, we show that heterogeneous factors, including interictal epileptiform discharges (IEDs), antiseizure medications (ASMs), and intermittent periods with slow-wave activity (SWSs), all act directly to perturb critical dynamics and thus cognition. Our work suggests critical dynamics to be the setpoint to measure optimal network function, thereby providing a unifying framework for the heterogeneous mechanisms impacting cognition in conditions like epilepsy.

Neurophysiological signatures of ageing: compensatory and compromised neural mechanisms

Spatiotemporal patterns of neural oscillations change with ageing, even in the cognitively unimpaired individual. Whether these neurophysiological changes represent ageing-related vulnerabilities or mechanisms that support cognitive resilience remains largely unknown. In this study, we used magnetoencephalography imaging to examine age-related changes of resting-state whole-brain neurophysiology in a well-characterized cohort of cognitively unimpaired individuals (n = 70; age range 52-87 years). We quantified spatial patterns of age-related changes in band-limited spectral power within delta-theta (2-7 Hz), alpha (8-12 Hz) and beta (13-30 Hz) bands and the spectral aperiodic slope (15-50 Hz), and examined how spectral changes are associated with cognitive abilities in healthy ageing. In a subset of individuals (n = 40) who were evaluated with a uniform battery of cognitive tests, using a partial least square regression approach, we examined the associations between age-related spectral changes and cognitive performance. We found that, with advancing age, delta-theta and beta spectral power reduces, while alpha spectral power increases. A periodic slope also showed reductions with ageing. Better cognitive scores were positively correlated with delta-theta reductions and alpha power increases associated with ageing, suggesting that these may represent compensatory neural mechanisms. Beta power reductions and spectral aperiodic slope changes, in contrast, correlated negatively with higher cognitive scores, suggesting that these may represent compromised neural mechanisms of ageing. Our findings highlighted that the neurophysiological changes that occur during later decades of life were distinct from the previously known lifespan changes. This study demonstrates the trajectories of neurophysiological changes in cognitive ageing explicitly relating to conserved and impaired neural mechanisms with important implications for identifying specific spectral changes in neurodegenerative processes in the context of ageing.

Longitudinal excitation-inhibition balance altered by sex and APOE-ε4

Neuronal hyperexcitation affects memory and neural processing across the Alzheimer's disease (AD) cognitive continuum. Levetiracetam, an antiepileptic, shows promise in improving cognitive impairment by restoring the neural excitation/inhibition balance in AD patients. We previously identified a hyper-excitable phenotype in cognitively unimpaired female APOE-ε4 carriers relative to male counterparts cross-sectionally. This sex difference lacks longitudinal validation; however, clarifying the vulnerability of female ε4-carriers could better inform antiepileptic treatment efficacy. Here, we investigated this sex-by-ε4 interaction using a longitudinal design. We used resting-state fMRI and diffusion tensor imaging collected longitudinally from 106 participants who were cognitively unimpaired for at least one scan event but may have been assessed to have clinical dementia ratings corresponding to early mild cognitive impairment over time. By including scan events where participants transitioned to mild cognitive impairment, we modeled the trajectory of the whole-brain excitation-inhibition ratio throughout the preclinical cognitively healthy continuum and extended to early impairment. A linear mixed model revealed a significant three-way interaction among sex, ε4-status, and time, with female ε4-carriers showing a significant hyper-excitable trajectory. These findings suggest a possible pathway for preventative therapy targeting preclinical hyperexcitation in female ε4-carriers.

A Shift Toward Supercritical Brain Dynamics Predicts Alzheimer's Disease Progression

Alzheimer's disease (AD) is the most common form of dementia with continuum of disease progression of increasing severity from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and lastly to AD. The transition from MCI to AD has been linked to brain hypersynchronization, but the underlying mechanisms leading to this are unknown. Here, we hypothesized that excessive excitation in AD disease progression would shift brain dynamics toward supercriticality across an extended regime of critical-like dynamics. In this framework, healthy brain activity during aging preserves operation at near the critical phase transition at balanced excitation-inhibition (E/I). To test this hypothesis, we used source-reconstructed resting-state MEG data from a cross-sectional cohort (N = 343) of individuals with SCD, MCI, and healthy controls (HC) as well as from a longitudinal cohort (N = 45) of MCI patients. We then assessed brain criticality by quantifying long-range temporal correlations (LRTCs) and functional EI (fE/I) of neuronal oscillations. LRTCs were attenuated in SCD in spectrally and anatomically constrained regions while this breakdown was progressively more widespread in MC. In parallel, fE/I was increased in the MCI but not in the SC cohort. Both observations also predicted the disease progression in the longitudinal cohort. Finally, using machine learning trained on functional (LRTCs, fE/I) and structural (MTL volumes) features, we show that LRTCs and f/EI are the most informative features for accurate classification of individuals with SCD while structural changes accurate classify the individuals with MCI. These findings establish that a shift toward supercritical brain dynamics reflects early AD disease progression.

Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field

To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.

Alzheimer's disease: A case study involving EEG-based fE/I ratio and pTau-181 protein analysis through nasal administration of Saraswata Ghrita

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs memory, language, and cognitive functions and currently has no definitive cure. Saraswata Ghrita (SG), a traditional Ayurvedic remedy administered nasally, offers a holistic approach and is believed to directly affect brain functions through its unique delivery route.

Objective

This study aimed to evaluate the effectiveness of SG in improving cognitive function and neurochemical biomarkers in a patient with AD. Key outcomes included electroencephalography-based excitation/inhibition (fE/I) ratio, and levels of phosphorylated Tau-181 (pTau-181), serotonin, dopamine, acetylcholine, and dehydroepiandrosterone (DHEA).

Methods

A 90-day proof-of-concept clinical trial was conducted with one AD patient. Nasal administration of SG was performed twice daily. Measurements included EEG spectral power analysis across 1-48 Hz, cognitive function assessed by Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Quality of Life in Alzheimer's Disease (QoL-AD) scales, and biochemical analyses of pTau-181, serotonin, dopamine, acetylcholine, and DHEA.

Results

Notable improvements were observed: ADAS-Cog score decreased from 40 to 36, QoL-AD score increased from 23 to 31, MMSE score improved from 13 to 18, and MoCA score increased from 8 to 13. Biochemical markers showed a decrease in pTau-181 (12.50 pg/ml to 6.28 pg/ml), an increase in acetylcholine (13.73 pg/ml to 31.83 pg/ml), while serotonin and DHEA levels rose, and dopamine levels decreased (39.14 pg/ml to 36.21 pg/ml).

Conclusions

SG demonstrated potential in enhancing cognitive functions and neurochemical markers in AD, with the nasal route proving safe and effective. These findings suggest the value of traditional Ayurvedic treatments in contemporary AD management.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer's disease: a magnetoencephalography study

Understanding the nature and onset of neurophysiological changes, and the selective vulnerability of central hub regions in the functional network, may aid in managing the growing impact of Alzheimer's disease on society. However, the precise neurophysiological alterations occurring in the pre-clinical stage of human Alzheimer's disease remain controversial. This study aims to provide increased insights on quantitative neurophysiological alterations during a true early stage of Alzheimer's disease. Using high spatial resolution source-reconstructed magnetoencephalography, we investigated regional and whole-brain neurophysiological changes in a unique cohort of 11 cognitively unimpaired individuals with pathogenic mutations in the presenilin-1 or amyloid precursor protein gene and a 1:3 matched control group (n = 33) with a median age of 49 years. We examined several quantitative magnetoencephalography measures that have been shown robust in detecting differences in sporadic Alzheimer's disease patients and are sensitive to excitation-inhibition imbalance. This includes spectral power and functional connectivity in different frequency bands. We also investigated hub vulnerability using the hub disruption index. To understand how magnetoencephalography measures change as the disease progresses through its pre-clinical stage, correlations between magnetoencephalography outcomes and various clinical variables like age were analysed. A comparison of spectral power between mutation carriers and controls revealed oscillatory slowing, characterized by widespread higher theta (4-8 Hz) power, a lower posterior peak frequency and lower occipital alpha 2 (10-13 Hz) power. Functional connectivity analyses presented a lower whole-brain (amplitude-based) functional connectivity in the alpha (8-13 Hz) and beta (13-30 Hz) bands, predominantly located in parieto-temporal hub regions. Furthermore, we found a significant hub disruption index for (phase-based) functional connectivity in the theta band, attributed to both higher functional connectivity in 'non-hub' regions alongside a hub disruption. Neurophysiological changes did not correlate with indicators of pre-clinical disease progression in mutation carriers after multiple comparisons correction. Our findings provide evidence that oscillatory slowing and functional connectivity differences occur before cognitive impairment in individuals with autosomal dominant mutations leading to early onset Alzheimer's disease. The nature and direction of these alterations are comparable to those observed in the clinical stages of Alzheimer's disease, suggest an early excitation-inhibition imbalance, and fit with the activity-dependent functional degeneration hypothesis. These insights may prove useful for early diagnosis and intervention in the future.

Spatiotemporal Dynamics of Periodic and Aperiodic Brain Activity Under Peripheral Nerve Stimulation With Acupuncture

Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.

The development of aperiodic neural activity in the human brain

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study (https://osf.io/gsru7), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males; n electrodes = 5691), we mapped aperiodic (1/ƒ-like) activity, a proxy of excitation:inhibition (E:I) balance with steeper slopes indexing inhibition and flatter slopes indexing excitation. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are differentially associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC inhibitory control during adolescence in the development of attention and memory.

Modifiable dementia risk associated with smaller white matter volume and altered 1/f aperiodic brain activity: cross-sectional insights from the LEISURE study

BACKGROUND

The rising prevalence of dementia necessitates identifying early neurobiological markers of dementia risk. Reduced cerebral white matter volume and flattening of the slope of the electrophysiological 1/f spectral power distribution provide neurobiological markers of brain ageing alongside cognitive decline. However, their association with modifiable dementia risk remains to be understood.

METHODS

A cross-sectional sample of 98 healthy older adults (79 females, mean age = 65.44) underwent structural magnetic resonance imaging (sMRI), resting-state electroencephalography (EEG), cognitive assessments and dementia risk scoring using the CogDrisk framework. Univariate and multivariate linear regression models were conducted to investigate the relationships between modifiable dementia risk and sMRI brain volumes, the exponent of EEG 1/f spectral power, and cognition, whilst controlling for non-modifiable factors.

RESULTS

Smaller global white matter volume (F(1,87) = 6.884, R2 = 0.073, P = .010), and not grey (F(1,87) = 0.540, R2 = 0.006, P = .468) or ventricle volume (F(1,87) = 0.087, R2 = 0.001, P = .769), was associated with higher modifiable dementia risk. A lower exponent, reflecting a flatter 1/f spectral power distribution, was associated with higher dementia risk at frontal (F(1,92) = 4.096, R2 = 0.043, P = .046) but not temporal regions. No significant associations were found between cognitive performance and dementia risk. In multivariate analyses, both white matter volume and the exponent of the 1/f spectral power distribution independently associated with dementia risk.

CONCLUSIONS

Structural and functional neurobiological markers of early brain ageing, but not cognitive function, are independently associated with modifiable dementia risk in healthy older adults.

GABA Signaling in Health and Disease in the Nervous System

Throughout development, gamma-aminobutyric acid, or GABA, plays a role in the proliferation, migration, and differentiation of neural progenitor cells [...].

Repetitive subconcussion results in disrupted neural activity independent of concussion history

Concussion is a public health crisis that results in a complex cascade of neurochemical changes that can have life-changing consequences. Subconcussions are generally considered less serious, but we now realize repetitive subconcussions can lead to serious neurological deficits. Subconcussions are common in contact sports and the military where certain personnel are exposed to repetitive occupational blast overpressure. Post-mortem studies show subconcussion is a better predictor than concussion for chronic traumatic encephalopathy-a progressive and fatal neurodegenerative tauopathy, only diagnosable post-mortem-thus, an in vivo biomarker would be transformative. Magnetoencephalography captures the dynamics of neuronal electrochemical action, and functional MRI shows that functional connectivity is associated with tauopathy patterns. Therefore, both imaging modalities could provide surrogate markers of tauopathy. In this cross-sectional study, we examined the effects of repetitive subconcussion on neuronal activity and functional connectivity using magnetoencephalography and functional MRI, and on neurological symptoms and mental health in a military sample. For magnetoencephalography and outcome analyses, 81 participants were split into 'high' and 'low' blast exposure groups using the generalized blast exposure value: n = 41 high blast (26.4-65.7 years; 4 females) and n = 40 low blast (28.0-63.3 years; 8 females). For functional MRI, two high blast male participants without data were excluded: n = 39 (29.6-65.7 years). Magnetoencephalography revealed disrupted neuronal activity in participants with a greater history of repetitive subconcussions, including neural slowing (higher delta activity) in right fronto-temporal lobes and subcortical regions (hippocampus, amygdala, caudate, pallidum and thalamus), and functional dysconnectivity in the posterior default mode network (lower connectivity at low and high gamma). These abnormalities were independent of concussion or traumatic stress history, and magnetoencephalography showed functional dysconnectivity not detected in functional MRI. Besides magnetoencephalography changes, those with higher blast exposure had poorer somatic and cognitive outcomes, with no blast-related differences in mental health or associations between neurological symptoms and neuronal activity. This study suggests that repetitive subconcussions have deleterious effects on brain function and that magnetoencephalography provides an avenue for both treatment targets by identifying affected brain regions and in prevention by identifying those at risk of cumulative subconcussive neurotrauma.

Challenges and Approaches in the Study of Neural Entrainment

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction.

Structural inequality and temporal brain dynamics across diverse samples

BACKGROUND

Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored.

METHODS

Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed.

FINDINGS

Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions.

CONCLUSION

These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.

Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy

PURPOSE

To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.

METHODS

MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.

RESULTS

Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.

CONCLUSION

HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

Dawson G, Marston H, Kas MJH. The aperiodic exponent of neural activity varies with vigilance state in mice and men

Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure disease-related changes in the 1/f signal. We demonstrate this by comparing EEG results from a longitudinal study in a genetic mouse model for synaptic dysfunction in schizophrenia and autism spectrum disorders to results from a large European cohort study with schizophrenia and mild Alzheimer's disease patients. The comparison shows when the 1/f is corrected for vigilance state there is a difference between groups, and this effect disappears when vigilance state is not corrected for. In conclusion, more attention should be paid to the vigilance state during analysis of EEG signals regardless of the species.

Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.

Brain health in diverse settings: How age, demographics and cognition shape brain function

Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.

Aperiodic component of EEG power spectrum and cognitive performance are modulated by education in aging

Recent studies have shown a growing interest in the so-called "aperiodic" component of the EEG power spectrum, which describes the overall trend of the whole spectrum with a linear or exponential function. In the field of brain aging, this aperiodic component is associated both with age-related changes and performance on cognitive tasks. This study aims to elucidate the potential role of education in moderating the relationship between resting-state EEG features (including aperiodic component) and cognitive performance in aging. N = 179 healthy participants of the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) dataset were divided into three groups based on age and education. Older adults exhibited lower exponent, offset (i.e. measures of aperiodic component), and Individual Alpha Peak Frequency (IAPF) as compared to younger adults. Moreover, visual attention and working memory were differently associated with the aperiodic component depending on education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, while an opposite trend was found in those with low education. While further investigation is needed, this study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.

Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

BACKGROUND

The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed.

METHODS

We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition.

RESULTS

Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity.

CONCLUSIONS

Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.

Assessing cortical excitability with electroencephalography: A pilot study with EEG-iTBS

BACKGROUND

Cortical excitability measures neural reactivity to stimuli, usually delivered via Transcranial Magnetic Stimulation (TMS). Excitation/inhibition balance (E/I) is the ongoing equilibrium between excitatory and inhibitory activity of neural circuits. According to some studies, E/I could be estimated in-vivo and non-invasively through the modeling of electroencephalography (EEG) signals and termed 'intrinsic excitability' measures. Several measures have been proposed (phase consistency in the gamma band, sample entropy, exponent of the power spectral density 1/f curve, E/I index extracted from detrend fluctuation analysis, and alpha power). Intermittent theta burst stimulation (iTBS) of the primary motor cortex (M1) is a non-invasive neuromodulation technique allowing controlled and focal enhancement of TMS cortical excitability and E/I of the stimulated hemisphere.

OBJECTIVE

Investigating to what extent E/I estimates scale with TMS excitability and how they relate to each other.

METHODS

M1 excitability (TMS) and several E/I estimates extracted from resting state EEG recordings were assessed before and after iTBS in a cohort of healthy subjects.

RESULTS

Enhancement of TMS M1 excitability, as measured through motor-evoked potentials (MEPs), and phase consistency of the cortex in high gamma band correlated with each other. Other measures of E/I showed some expected results, but no correlation with TMS excitability measures or strong consistency with each other.

CONCLUSIONS

EEG E/I estimates offer an intriguing opportunity to map cortical excitability non-invasively, with high spatio-temporal resolution and with a stimulus independent approach. While different EEG E/I estimates may reflect the activity of diverse excitatory-inhibitory circuits, spatial phase synchrony in the gamma band is the measure that best captures excitability changes in the primary motor cortex.

Unveiling the crucial neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies

Neurodevelopmental proteasomopathies represent a distinctive category of neurodevelopmental disorders (NDD) characterized by genetic variations within the 26S proteasome, a protein complex governing eukaryotic cellular protein homeostasis. In our comprehensive study, we identified 23 unique variants in PSMC5 , which encodes the AAA-ATPase proteasome subunit PSMC5/Rpt6, causing syndromic NDD in 38 unrelated individuals. Overexpression of PSMC5 variants altered human hippocampal neuron morphology, while PSMC5 knockdown led to impaired reversal learning in flies and loss of excitatory synapses in rat hippocampal neurons. PSMC5 loss-of-function resulted in abnormal protein aggregation, profoundly impacting innate immune signaling, mitophagy rates, and lipid metabolism in affected individuals. Importantly, targeting key components of the integrated stress response, such as PKR and GCN2 kinases, ameliorated immune dysregulations in cells from affected individuals. These findings significantly advance our understanding of the molecular mechanisms underlying neurodevelopmental proteasomopathies, provide links to research in neurodegenerative diseases, and open up potential therapeutic avenues.

Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes

Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.

Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes

BACKGROUND

Studies in animal models of Alzheimer's disease (AD) have provided valuable insights into the molecular and cellular processes underlying neuronal network dysfunction. Whether and how AD-related neurophysiological alterations translate between mice and humans remains however uncertain.

METHODS

We characterized neurophysiological alterations in mice and humans carrying AD mutations in the APP and/or PSEN1 genes, focusing on early pre-symptomatic changes. Longitudinal local field potential recordings were performed in APP/PS1 mice and cross-sectional magnetoencephalography recordings in human APP and/or PSEN1 mutation carriers. All recordings were acquired in the left frontal cortex, parietal cortex, and hippocampus. Spectral power and functional connectivity were analyzed and compared with wildtype control mice and healthy age-matched human subjects.

RESULTS

APP/PS1 mice showed increased absolute power, especially at higher frequencies (beta and gamma) and predominantly between 3 and 6 moa. Relative power showed an overall shift from lower to higher frequencies over almost the entire recording period and across all three brain regions. Human mutation carriers, on the other hand, did not show changes in power except for an increase in relative theta power in the hippocampus. Mouse parietal cortex and hippocampal power spectra showed a characteristic peak at around 8 Hz which was not significantly altered in transgenic mice. Human power spectra showed a characteristic peak at around 9 Hz, the frequency of which was significantly reduced in mutation carriers. Significant alterations in functional connectivity were detected in theta, alpha, beta, and gamma frequency bands, but the exact frequency range and direction of change differed for APP/PS1 mice and human mutation carriers.

CONCLUSIONS

Both mice and humans carrying APP and/or PSEN1 mutations show abnormal neurophysiological activity, but several measures do not translate one-to-one between species. Alterations in absolute and relative power in mice should be interpreted with care and may be due to overexpression of amyloid in combination with the absence of tau pathology and cholinergic degeneration. Future studies should explore whether changes in brain activity in other AD mouse models, for instance, those also including tau pathology, provide better translation to the human AD continuum.

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer's disease

Aims

Our aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.

Methods

A total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.

Results

(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.

Conclusion

Integrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.