OUP accepted manuscript

From simulation to clinic: Assessing the required channel count for effective clinical use of OPM-MEG systems

The channel count in an Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG) system plays a pivotal role in determining its overall performance. While existing research consistently highlights that a greater number of channels enhances system capabilities, practical constraints such as sensor placement on the head, inter-channel interference, and cost-efficiency impose limitations on channel scalability. Additionally, the optimal channel count required for clinical applications of OPM-MEG remains unclear. In this study, we systematically investigate the impact of channel count on OPM-MEG performance by integrating simulations, phantom experiments, and human MEG experiments. Four configurations with varying channel counts (16, 32, 64, and 128) are evaluated. Specifically, systems with fewer channels (e.g., 16 channels) encounter significant challenges in meeting the demands of clinical MEG applications. In contrast, a 64-channel OPM-MEG system demonstrates performance metrics-such as signal-to-noise ratio (SNR) and localization accuracy-that are comparable to those of a 306-channel Superconducting Quantum Interference Device MEG (SQUID-MEG) system. Notably, a 128-channel OPM-MEG system surpasses the 306-channel SQUID-MEG system, achieving superior results. This work provides a detailed exploration of the relationship between channel count and OPM-MEG system performance, analyzing how many channels of the OPM-MEG system are suitable for clinical applications. By combining simulation-based evaluations with empirical measurements, we found that it is crucial to carefully select the appropriate number of channels based on the specific usage requirements in clinical applications.

Uncovering stage-specific neural and molecular progression in Alzheimer's disease: Implications for early screening

INTRODUCTION

Understanding molecular, neuroanatomical, and neurophysiological changes in cognitive decline is crucial for comprehending Alzheimer's disease (AD) progression and facilitating objective staging and early screening.

METHODS

We enrolled 277 participants and employed a multimodal approach, integrating genomics, metagenomics, metabolomics, magnetic resonance imaging (MRI), and electroencephalogram (EEG) to investigate the AD continuum, from subjective cognitive decline (SCD) through mild cognitive impairment (MCI) to advanced AD.

RESULTS

Key markers and mechanisms were identified for each stage: initial neurophysiological deficits in SCD with compensatory metabolomic responses, gut-brain axis dysregulation in MCI, and extensive metabolic disruption and multisystem breakdown in AD. Using random forest models, we identified specific feature combinations that achieved predictive areas under the curve (AUCs) of 0.78 for SCD, 0.84 for MCI, and 0.98 for AD, highlighting EEG as a particularly effective early screening tool.

DISCUSSION

This study elucidates AD's pathophysiological progression and highlights the potential of machine learning-assisted multimodal strategies for early detection and staging.

HIGHLIGHTS

Early electroencephalogram (EEG) changes and compensatory metabolomic responses define subjective cognitive decline (SCD) stage. In mild cognitive impairment (MCI), gut-brain axis dysfunction alters microbial diversity and functional pathways. In Alzheimer's disease (AD), systemic breakdown disruption enables near-perfect machine learning (ML) detection. Random forest models yield predictive areas under the curve (AUCs) of 0.78 (SCD), 0.84 (MCI), 0.98 (AD). EEG is a convenient, cost-efficient marker for early screening.

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.

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.

The role of parvalbumin interneuron dysfunction across neurodegenerative dementias

Parvalbumin-positive (PV+) basket neurons are fast-spiking, non-adapting inhibitory interneurons whose oscillatory activity is essential for regulating cortical excitation/inhibition balance. Their dysfunction results in cortical hyperexcitability and gamma rhythm disruption, which have recently gained substantial traction as contributing factors as well as potential therapeutic targets for the treatment of Alzheimer's Disease (AD). Recent evidence indicates that PV+ cells are also impaired in Frontotemporal Dementia (FTD) and Dementia with Lewy bodies (DLB). However, no attempt has been made to integrate these findings into a coherent pathophysiological framework addressing the contribution of PV+ interneuron dysfunction to the generation of cortical hyperexcitability and gamma rhythm disruption in FTD and DLB. To fill this gap, we epitomized the most recent evidence on PV+ interneuron impairment in AD, FTD, and DLB, focusing on its contribution to the generation of cortical hyperexcitability and gamma oscillatory disruption and their interplay with misfolded protein accumulation, neuronal death, and clinical symptoms' onset. Our work deepens the current understanding concerning the role of PV+ interneuron dysfunction across neurodegenerative dementias, highlighting commonalities and differences among AD, FTD, and DLB, thus paving the way for identifying novel biomarkers and potential therapeutic targets for the treatment of these diseases.

Prevention and Treatment Strategies for Alzheimer's Disease: Focusing on Microglia and Astrocytes in Neuroinflammation

Alzheimer's disease (AD) is a fatal neurodegenerative disease characterized by its insidious onset and progressive development, making it the most common form of dementia. Despite its prevalence, the exact causes and mechanisms responsible for AD remain unclear. Recent studies have highlighted that inflammation in the central nervous system (CNS) plays a crucial role in both the initiation and progression of AD. Neuroinflammation, an immune response within the CNS triggered by glial cells in response to various stimuli, such as nerve injury, infection, toxins, or autoimmune reactions, has emerged as a significant factor alongside amyloid deposition and neurofibrillary tangles (NFTs) commonly associated with AD. This article aims to provide an overview of the most recent research regarding the involvement of neuroinflammation in AD, with a particular focus on elucidating the specific mechanisms involving microglia and astrocytes. By exploring these intricate processes, a new theoretical framework can be established to further probe the impact of neuroinflammation on the development and progression of AD. Through a deeper understanding of these underlying mechanisms, potential targets for therapeutic interventions and novel treatment strategies can be identified in the ongoing battle against AD.

Toward a more comprehensive understanding of network centrality disruption in amnestic mild cognitive impairment: a MEG multilayer approach

BACKGROUND

Alzheimer's Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies.

METHODS

Our study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI.

RESULTS

The results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions' activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions' activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions.

CONCLUSIONS

Our findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Cannabidiol ameliorates mitochondrial disease via PPARγ activation in preclinical models

Mutations in mitochondrial energy-producing genes lead to a heterogeneous group of untreatable disorders known as primary mitochondrial diseases (MD). Leigh syndrome (LS) is the most common pediatric MD and is characterized by progressive neuromuscular affectation and premature death. Here, we show that daily cannabidiol (CBD) administration significantly extends lifespan and ameliorates pathology in two LS mouse models, and improves cellular function in fibroblasts from LS patients. CBD delays motor decline and neurodegenerative signs, improves social deficits and breathing abnormalities, decreases thermally induced seizures, and improves neuropathology in affected brain regions. Mechanistically, we identify peroxisome proliferator-activated receptor gamma (PPARγ) as a key nuclear receptor mediating CBD's beneficial effects, while also providing proof of dysregulated PPARγ expression and activity as a common feature in both mouse neurons and fibroblasts from LS patients. Taken together, our results provide the first evidence for CBD as a potential treatment for LS.

Applications of OPM-MEG for translational neuroscience: a perspective

Magnetoencephalography (MEG) allows the non-invasive measurement of brain activity at millisecond precision combined with localization of the underlying generators. So far, MEG-systems consisted of superconducting quantum interference devices (SQUIDS), which suffer from several limitations. Recent technological advances, however, have enabled the development of novel MEG-systems based on optically pumped magnetometers (OPMs), offering several advantages over conventional SQUID-MEG systems. Considering potential improvements in the measurement of neuronal signals as well as reduced operating costs, the application of OPM-MEG systems for clinical neuroscience and diagnostic settings is highly promising. Here we provide an overview of the current state-of-the art of OPM-MEG and its unique potential for translational neuroscience. First, we discuss the technological features of OPMs and benchmark OPM-MEG against SQUID-MEG and electroencephalography (EEG), followed by a summary of pioneering studies of OPMs in healthy populations. Key applications of OPM-MEG for the investigation of psychiatric and neurological conditions are then reviewed. Specifically, we suggest novel applications of OPM-MEG for the identification of biomarkers and circuit deficits in schizophrenia, dementias, movement disorders, epilepsy, and neurodevelopmental syndromes (autism spectrum disorder and attention deficit hyperactivity disorder). Finally, we give an outlook of OPM-MEG for translational neuroscience with a focus on remaining methodological and technical challenges.

Dopamine neuron degeneration in the Ventral Tegmental Area causes hippocampal hyperexcitability in experimental Alzheimer's Disease

Early and progressive dysfunctions of the dopaminergic system from the Ventral Tegmental Area (VTA) have been described in Alzheimer's Disease (AD). During the long pre-symptomatic phase, alterations in the function of Parvalbumin interneurons (PV-INs) are also observed, resulting in cortical hyperexcitability represented by subclinical epilepsy and aberrant gamma-oscillations. However, it is unknown whether the dopaminergic deficits contribute to brain hyperexcitability in AD. Here, using the Tg2576 mouse model of AD, we prove that reduced hippocampal dopaminergic innervation, due to VTA dopamine neuron degeneration, impairs PV-IN firing and gamma-waves, weakens the inhibition of pyramidal neurons and induces hippocampal hyperexcitability via lower D2-receptor-mediated activation of the CREB-pathway. These alterations coincide with reduced PV-IN numbers and Perineuronal Net density. Importantly, L-DOPA and the selective D2-receptor agonist quinpirole rescue p-CREB levels and improve the PV-IN-mediated inhibition, thus reducing hyperexcitability. Moreover, similarly to quinpirole, sumanirole - another D2-receptor agonist and a known anticonvulsant - not only increases p-CREB levels in PV-INs but also restores gamma-oscillations in Tg2576 mice. Conversely, blocking the dopaminergic transmission with sulpiride (a D2-like receptor antagonist) in WT mice reduces p-CREB levels in PV-INs, mimicking what occurs in Tg2576. Overall, these findings support the hypothesis that the VTA dopaminergic system integrity plays a key role in hippocampal PV-IN function and survival, disclosing a relevant contribution of the reduced dopaminergic tone to aberrant gamma-waves, hippocampal hyperexcitability and epileptiform activity in early AD.

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.

Abnormal gamma phase-amplitude coupling in the parahippocampal cortex is associated with network hyperexcitability in Alzheimer's disease

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.

Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio?

A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-10003-x.

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model

Background

There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance.

Objective

We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach.

Methods

We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology.

Results

The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics.

Conclusions

Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.

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.

Network Hyperexcitability in Early Alzheimer's Disease: Is Functional Connectivity a Potential Biomarker?

Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer's disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4-8 Hz and the 8-13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.

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

An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

Background

Electroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.

Methods

Related publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.

Results

Between 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.

Conclusion

The application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.