A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity

Machine and deep learning algorithms for classifying different types of dementia: A literature review

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.

Longitudinal changes in the functional connectivity of individuals at risk of Alzheimer's disease

First-degree relatives of Alzheimer's disease patients constitute a key population in the search for early markers. Our group identified functional connectivity differences between cognitively unimpaired individuals with and without a family history. In this unprecedented follow-up study, we examine whether family history is associated with a longitudinal increase in the functional connectivity of those regions. Moreover, this is the first work to correlate electrophysiological measures with plasma p-tau231 levels, a known pathology marker, to interpret the nature of the change. We evaluated 69 cognitively unimpaired individuals with a family history of Alzheimer's disease and 28 without, at two different time points, approximately 3 years apart, including resting state magnetoencephalography recordings and plasma p-tau231 determinations. Functional connectivity changes in both precunei and left anterior cingulate cortex in the high-alpha band were studied using non-parametric cluster-based permutation tests. Connectivity values were correlated with p-tau231 levels. Three clusters emerged in individuals with family history, exhibiting a longitudinal increase of connectivity. Notably, the clusters for both precunei bore a striking resemblance to those found in previous cross-sectional studies. The connectivity values at follow-up and the change in connectivity in the left precuneus cluster showed significant positive correlations with p-tau231. This study consolidates the use of electrophysiology, in combination with plasma biomarkers, to monitor healthy individuals at risk of Alzheimer's disease and emphasizes the value of combining noninvasive markers to understand the underlying mechanisms and track disease progression. This could facilitate the design of more effective intervention strategies and accurate progression assessment tools.

Long-Term Krill Oil Administration Alleviated Early Mild Cognitive Impairment in APP/PS1 Mice

SCOPE

Alzheimer's disease is an age-dependent neurodegenerative disorder. Mounting studies focus on the improvement of advanced cognitive impairment by dietary nutrients. Krill oil (KO), a rich source of DHA/EPA and astaxanthin, is effective in improving cognitive function. The study mainly investigates the protective effects of long-term KO administration on early cognitive impairment.

METHODS AND RESULTS

Results show that 2 months KO administration (50 and 100 mg kg-1 BW) can dramatically promote learning and memory abilities. Mechanism studies demonstrate that KO reduces amyloid β concentration by regulating the amyloidogenic pathway, inhibits neuro-inflammation via regulating TLR4-NLRP3 signaling pathway, and prevents neuron injure. KO supplementation also enhances gut barrier integrity, reduces serum lipopolysaccharide leakage, and alters the gut microbiota by reducing Helicobacteraceae, Lactobacillaceae proportion, increasing Dubosiella and Akkermansia relative abundance. Particularly, a significant increase of isovaleric acid, propionic acid, and acetic acid levels is observed after KO supplementation. Correlation analysis shows that short-chain fatty acids (SCFAs), gut microbiota, and cognitive function are strongly correlated.

CONCLUSIONS

The results reveal that KO relieves early mild cognitive impairment possibly for its role in mediating the gut microbiome-SCFAs-brain axis. Thus, KO may provide potential intervention strategies to prevent cognitive impairment in the early stages through the microbiota-gut-brain axis.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

The NeuroBel: A Screening Test for Verbal Language Impairment in Spanish-Speaking Elderly People With Cognitive Decline

PURPOSE

The NeuroBel is a short test that can detect cognitive decline using language tasks. This study replicated previous research using larger clinical samples from three Spanish-speaking countries.

METHOD

Eight tasks were used to analyze verbal language functioning using a psycholinguistic approach. A total of 232 elderly, monolingual Spanish speakers from Spain, Cuba, and Colombia participated in this study. Of these, 76 had Alzheimer's disease (AD) in the initial phase, 75 had mild cognitive impairment (MCI), and 81 did not exhibit cognitive impairment (healthy controls).

RESULTS

Significant differences were observed among the three clinical groups. The participants with AD and the participants with MCI had significantly lower NeuroBel scores than the control group on most of the tasks. However, repetition (in AD vs. MCI) and auditory lexical decision (in MCI vs. control) tasks were not significant in Tukey's post hoc tests. Discriminant analysis showed that 80.6% of the participants were correctly classified into the original groups and revealed the tasks that were the best and worst for differentiating among groups. The receiver-operating characteristic curves showed high sensitivity for AD and MCI. The area under the curve was .97 in the contrast of AD versus MCI + controls, .96 in the determination of overall cognitive decline (AD + MCI vs. controls), and .93 in the contrast of MCI and control groups.

CONCLUSION

This study confirmed that the NeuroBel is a suitable test for detecting cognitive decline based on language impairment in Spanish-speaking elderly people.

Dynamic functional connectivity MEG features of Alzheimer's disease

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

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 512-Ch Dual-Mode Microchip for Simultaneous Measurements of Electrophysiological and Neurochemical Activities

In the study of the brain, large and high-density microelectrode arrays have been widely used to study the behavior of neurotransmission. CMOS technology has facilitated these devices by enabling the integration of high-performance amplifiers directly on-chip. Usually, these large arrays measure only the voltage spikes resulting from action potentials traveling along firing neuronal cells. However, at synapses, communication between neurons occurs by the release of neurotransmitters, which cannot be measured on typical CMOS electrophysiology devices. Development of electrochemical amplifiers has resulted in the measurement of neurotransmitter exocytosis down to the level of a single vesicle. To effectively monitor the complete picture of neurotransmission, measurement of both action potentials and neurotransmitter activity is needed. Current efforts have not resulted in a device that is capable of the simultaneous measurement of action potential and neurotransmitter release at the same spatiotemporal resolution needed for a comprehensive study of neurotransmission. In this paper, we present a true dual-mode CMOS device that fully integrates 256-ch electrophysiology amplifiers and 256-ch electrochemical amplifiers, along with an on-chip 512 electrode microelectrode array capable of simultaneous measurement from all 512 channels.

Fortasyn Connect Improves Neuropsychiatric Symptoms in Patients with Mild Cognitive Impairment and Dementia: Results from a Retrospective Real-World Study

BACKGROUND

Behavioral and psychological symptoms of dementia (BPSD) manifest in the early stages of the disease and impair patients' and caregivers' quality of life.

OBJECTIVE

To assess the effectiveness of the nutritional supplement Fortasyn Connect on BPSD for 12 months in people with mild cognitive impairment (MCI) and dementia in clinical practice.

METHODS

Retrospective, national, single-center study of 236 patients (158 MCI and 78 dementia; 55.1% of AD etiology). BPSD were assessed with the Neuropsychiatric Inventory (NPI) at month 3, 6, and 12. Cognition (Mini-Mental State Examination, MMSE), depression (Geriatric Depression Scale, GDS), and everyday functioning (Blessed Dementia Scale, BLS-D; Rapid Disability Rating Scale 2, RDRS2) were also evaluated.

RESULTS

Total NPI score, caregiver impact, and symptoms of depression, anxiety, apathy, and irritability improved after 3, 6, and 12 months from Fortasyn Connect initiation (p <  0.001). NPI decreases were more pronounced when baseline NPI score was higher than >  20 points (p <  0.001). The benefit was independent of gender, age, diagnosis, etiology, or concomitant treatment (p <  0.0001), although larger decreases in NPI total score were observed in MCI patients (p <  0.0001). After 12 months, GDS scores decreased (p = 0.042), and MMSE, BLS-D, and RDRS 2 scores remained stable.

CONCLUSION

Fortasyn Connect improved BPSD over at least a year in patients with MCI and dementia. Depression, anxiety, apathy, and irritability were the symptoms that improved the most. The benefit was independent of patients' characteristics and treatment but was greater if prescribed early and when baseline NPI scores were higher.

Episodic memory dysfunction and hypersynchrony in brain functional networks in cognitively intact subjects and MCI: a study of 379 individuals

Delayed recall (DR) impairment is one of the most significant predictive factors in defining the progression to Alzheimer's disease (AD). Changes in brain functional connectivity (FC) could accompany this decline in the DR performance even in a resting state condition from the preclinical stages to the diagnosis of AD itself, so the characterization of the relationship between the two phenomena has attracted increasing interest. Another aspect to contemplate is the potential moderator role of the APOE genotype in this association, considering the evidence about their implication for the disease. 379 subjects (118 mild cognitive impairment (MCI) and 261 cognitively intact (CI) individuals) underwent an extensive evaluation, including MEG recording. Applying cluster-based permutation test, we identified a cluster of differences in FC and studied which connections drove such an effect in DR. The moderation effect of APOE genotype between FC results and delayed recall was evaluated too. Higher FC in beta band in the right occipital region is associated with lower DR scores in both groups. A significant anteroposterior link emerged in the seed-based analysis with higher values in MCI. Moreover, APOE genotype appeared as a moderator between beta FC and DR performance only in the CI group. An increased beta FC in the anteroposterior brain region appears to be associated with lower memory performance in MCI. This finding could help discriminate the pattern of the progression of healthy aging to MCI and the relation between resting state and memory performance.

A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset

Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions.

Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review

Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer's Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.

Causal Evidence for the Multiple Demand Network in Change Detection: Auditory Mismatch Magnetoencephalography across Focal Neurodegenerative Diseases

The multiple demand (MD) system is a network of fronto-parietal brain regions active during the organization and control of diverse cognitive operations. It has been argued that this activation may be a nonspecific signal of task difficulty. However, here we provide convergent evidence for a causal role for the MD network in the "simple task" of automatic auditory change detection, through the impairment of top-down control mechanisms. We employ independent structure-function mapping, dynamic causal modeling (DCM), and frequency-resolved functional connectivity analyses of MRI and magnetoencephalography (MEG) from 75 mixed-sex human patients across four neurodegenerative syndromes [behavioral variant fronto-temporal dementia (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), posterior cortical atrophy (PCA), and Alzheimer's disease mild cognitive impairment with positive amyloid imaging (ADMCI)] and 48 age-matched controls. We show that atrophy of any MD node is sufficient to impair auditory neurophysiological response to change in frequency, location, intensity, continuity, or duration. There was no similar association with atrophy of the cingulo-opercular, salience or language networks, or with global atrophy. MD regions displayed increased functional but decreased effective connectivity as a function of neurodegeneration, suggesting partially effective compensation. Overall, we show that damage to any of the nodes of the MD network is sufficient to impair top-down control of sensation, providing a common mechanism for impaired change detection across dementia syndromes.SIGNIFICANCE STATEMENT Previous evidence for fronto-parietal networks controlling perception is largely associative and may be confounded by task difficulty. Here, we use a preattentive measure of automatic auditory change detection [mismatch negativity (MMN) magnetoencephalography (MEG)] to show that neurodegeneration in any frontal or parietal multiple demand (MD) node impairs primary auditory cortex (A1) neurophysiological response to change through top-down mechanisms. This explains why the impaired ability to respond to change is a core feature across dementias, and other conditions driven by brain network dysfunction, such as schizophrenia. It validates theoretical frameworks in which neurodegenerating networks upregulate connectivity as partially effective compensation. The significance extends beyond network science and dementia, in its construct validation of dynamic causal modeling (DCM), and human confirmation of frequency-resolved analyses of animal neurodegeneration models.

Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke

Background

Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal.

Methods

For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level.

Findings

After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design).

Neurophysiological and Brain Structural Markers of Cognitive Frailty Differ from Alzheimer's Disease

With increasing life span and prevalence of dementia, it is important to understand the mechanisms of cognitive aging. Here, we focus on a subgroup of the population we term "cognitively frail," defined by reduced cognitive function in the absence of subjective memory complaints, or a clinical diagnosis of dementia. Cognitive frailty is distinct from cognitive impairment caused by physical frailty. It has been proposed to be a precursor to Alzheimer's disease, but may alternatively represent one end of a nonpathologic spectrum of cognitive aging. We test these hypotheses in humans of both sexes, by comparing the structural and neurophysiological properties of a community-based cohort of cognitive frail adults, to people presenting clinically with diagnoses of Alzheimer's disease or mild cognitive impairment, and community-based cognitively typical older adults. Cognitive performance of the cognitively frail was similar to those with mild cognitive impairment. We used a novel cross-modal paired-associates task that presented images followed by sounds, to induce physiological responses of novelty and associative mismatch, recorded by EEG/MEG. Both controls and cognitively frail showed stronger mismatch responses and larger temporal gray matter volume, compared with people with mild cognitive impairment and Alzheimer's disease. Our results suggest that community-based cognitively frail represents a spectrum of normal aging rather than incipient Alzheimer's disease, despite similar cognitive function. Lower lifelong cognitive reserve, hearing impairment, and cardiovascular comorbidities might contribute to the etiology of the cognitive frailty. Critically, community-based cohorts of older adults with low cognitive performance should not be interpreted as representing undiagnosed Alzheimer's disease.SIGNIFICANCE STATEMENT The current study investigates the neural signatures of cognitive frailty in relation to healthy aging and Alzheimer's disease. We focus on the cognitive aspect of frailty and show that, despite performing similarly to the patients with mild cognitive impairment, a cohort of community-based adults with poor cognitive performance do not show structural atrophy or neurophysiological signatures of Alzheimer's disease. Our results call for caution before assuming that cognitive frailty represents latent Alzheimer's disease. Instead, the cognitive underperformance of cognitively frail adults could result in cumulative effects of multiple psychosocial risk factors over the lifespan, and medical comorbidities.

Cognitive Training Modulates Brain Hypersynchrony in a Population at Risk for Alzheimer's Disease

BACKGROUND

Recent studies demonstrated that brain hypersynchrony is an early sign of dysfunction in Alzheimer's disease (AD) that can represent a proxy for clinical progression. Conversely, non-pharmacological interventions, such as cognitive training (COGTR), are associated with cognitive gains that may be underpinned by a neuroprotective effect on brain synchrony.

OBJECTIVE

To study the potential of COGTR to modulate brain synchrony and to eventually revert the hypersynchrony phenomenon that characterizes preclinical AD.

METHODS

The effect of COGTR was examined in a sample of healthy controls (HC, n = 41, 22 trained) and individuals with subjective cognitive decline (SCD, n = 49, 24 trained). Magnetoencephalographic (MEG) activity and neuropsychological scores were acquired before and after a ten-week COGTR intervention aimed at improving cognitive function and daily living performance. Functional connectivity (FC) was analyzed using the phase-locking value. A mixed-effects ANOVA model with factors time (pre-intervention/post-intervention), training (trained/non-trained), and diagnosis (HC/SCD) was used to investigate significant changes in FC.

RESULTS

We found an average increase in alpha-band FC over time, but the effect was different in each group (trained and non-trained). In the trained group (HC and SCD), we report a reduction in the increase in FC within temporo-parietal and temporo-occipital connections. In the trained SCD group, this reduction was stronger and showed a tentative correlation with improved performance in different cognitive tests.

CONCLUSION

COGTR interventions could mitigate aberrant increases in FC in preclinical AD, promoting brain synchrony normalization in groups at a higher risk of developing dementia.

Routine magnetoencephalography in memory clinic patients: A machine learning approach

INTRODUCTION

We report the routine application of magnetoencephalography (MEG) in a memory clinic, and its value in the discrimination of patients with Alzheimer's disease (AD) dementia from controls.

METHODS

Three hundred sixty-six patients visiting our memory clinic underwent MEG recording. Source-reconstructed MEG data were visually assessed and evaluated in the context of clinical findings and other diagnostic markers. We analyzed the diagnostic accuracy of MEG spectral measures in the discrimination of individual AD dementia patients (n = 40) from subjective cognitive decline (SCD) patients (n = 40) using random forest models.

RESULTS

Best discrimination was obtained using a combination of relative theta and delta power (accuracy 0.846, sensitivity 0.855, specificity 0.837). The results were validated in an independent cohort. Hippocampal and thalamic regions, besides temporal-occipital lobes, contributed considerably to the model.

DISCUSSION

MEG has been implemented successfully in the workup of memory clinic patients and has value in diagnostic decision-making.

Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology

Our incomplete understanding of the link between Alzheimer's Disease pathology and symptomatology is a crucial obstacle for therapeutic success. Recently, translational studies have begun to connect the dots between protein alterations and deposition, brain network dysfunction and cognitive deficits. Disturbance of neuronal activity, and in particular an imbalance in underlying excitation/inhibition (E/I), appears early in AD, and can be regarded as forming a central link between structural brain pathology and cognitive dysfunction. While there are emerging (non-)pharmacological options to influence this imbalance, the complexity of human brain dynamics has hindered identification of an optimal approach. We suggest that focusing on the integration of neurophysiological aspects of AD at the micro-, meso- and macroscale, with the support of computational network modeling, can unite fundamental and clinical knowledge, provide a general framework, and suggest rational therapeutic targets.

A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks

Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.

Temporal Lobe Epilepsy and Alzheimer's Disease: From Preclinical to Clinical Evidence of a Strong Association

Increasing evidence coming from both experimental and humans' studies strongly suggest the existence of a link between epilepsy, in particular temporal lobe epilepsy (TLE), and Alzheimer's disease (AD). Patients with mild cognitive impairment and AD are more prone to have seizures, and seizures seem to facilitate amyloid-β and tau deposits, thus promoting neurodegenerative processes. Consistent with this view, long-lasting drug-resistant TLE and AD have been shown to share several pathological and neuroimaging features. Even if studies addressing prevalence of interictal and subclinical epileptiform activity in these patients are not yet conclusive, their findings raise the possibility that epileptiform activity might negatively impact memory and hasten cognitive decline, either directly or by association with unrecognized silent seizures. In addition, data about detrimental effect of network hyperexcitability in temporal regions in the premorbid and early stages ofADopen up newtherapeutic opportunities for antiseizure medications and/or antiepileptic strategies that might complement or enhance existing therapies, and potentially modify disease progression. Here we provide a review of evidence linking epileptiform activity, network hyperexcitability, and AD, and their role promoting and accelerating neurodegenerative process. Finally, the effects of antiseizure medications on cognition and their optimal administration in patients with AD are summarized.

Role of Magnetoencephalography in the Early Stages of Alzheimer Disease

As synaptic dysfunction is an early manifestation of Alzheimer disease (AD) pathology, magnetoencephalography (MEG) is capable of detecting disruptions by assessing the synchronized oscillatory activity of thousands of neurons that rely on the integrity of neural connections. MEG findings include slowness of the oscillatory activity, accompanied by a reduction of the alpha band power, and dysfunction of the functional networks. These findings are associated with the neuropathology of the disease and cognitive impairment. These neurophysiological biomarkers predict which patients with mild cognitive impairment will develop dementia. MEG has demonstrated its utility as a noninvasive biomarker for early detection of AD.

A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG

In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

High-dimensional brain-wide functional connectivity mapping in magnetoencephalography

BACKGROUND

Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.

NEW METHOD

We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis.

RESULTS

We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer's disease.

CONCLUSIONS

Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.

The Wisdom and Vision From the ACMEGS Inaugural Decade

Concise history of fascinating magnetoencephalography (MEG) technology and catalog of very selected milestone preclinical and clinical MEG studies are provided as the background. The focus is the societal context defining a journey of MEG to and through clinical practice and formation of the American Clinical MEG Society (ACMEGS). We aspired to provide an objective historic perspective and document contributions of many professionals while focusing on the role of ACMEGS in the growth and maturation of clinical MEG field. The ACMEGS was born (2006) out of inevitability to address two vital issues-fair reimbursement and proper clinical acceptance. A beacon of accountable MEG practice and utilization is now an expanding professional organization with the highest level of competence in practice of clinical MEG and clinical credibility. The ACMEGS facilitated a favorable disposition of insurances toward MEG in the United States by combining the national replication of the grassroots efforts and teaming up with the strategic partners-particularly the American Academy of Neurology (AAN), published two Position Statements (2009 and 2017), the world's only set of MEG Clinical Practice Guidelines (CPGs; 2011) and surveys of clinical MEG practice (2011 and 2020) and use (2020). In addition to the annual ACMEGS Course (2012), we directly engaged MEG practitioners through an Invitational Summit (2019). The Society remains focused on the improvements and expansion of clinical practice, education, clinical training, and constructive engagement of vendors in these issues and pivotal studies toward additional MEG indications. The ACMEGS not only had the critical role in the progress of Clinical MEG in the United States and beyond since 2006 but positioned itself as the field leader in the future.

Detection of early stages of Alzheimer's disease based on MEG activity with a randomized convolutional neural network

The early detection of Alzheimer's disease can potentially make eventual treatments more effective. This work presents a deep learning model to detect early symptoms of Alzheimer's disease using synchronization measures obtained with magnetoencephalography. The proposed model is a novel deep learning architecture based on an ensemble of randomized blocks formed by a sequence of 2D-convolutional, batch-normalization and pooling layers. An important challenge is to avoid overfitting, as the number of features is very high (25755) compared to the number of samples (132 patients). To address this issue the model uses an ensemble of identical sub-models all sharing weights, with a final stage that performs an average across sub-models. To facilitate the exploration of the feature space, each sub-model receives a random permutation of features. The features correspond to magnetic signals reflecting neural activity and are arranged in a matrix structure interpreted as a 2D image that is processed by 2D convolutional networks. The proposed detection model is a binary classifier (disease/non-disease), which compared to other deep learning architectures and classic machine learning classifiers, such as random forest and support vector machine, obtains the best classification performance results with an average F1-score of 0.92. To perform the comparison a strict validation procedure is proposed, and a thorough study of results is provided.

Neural oscillations and brain stimulation in Alzheimer's disease

Aging is associated with alterations in cognitive processing and brain neurophysiology. Whereas the primary symptom of amnestic mild cognitive impairment (aMCI) is memory problems greater than normal for age and education, patients with Alzheimer's disease (AD) show impairments in other cognitive domains in addition to memory dysfunction. Resting-state electroencephalography (rsEEG) studies in physiological aging indicate a global increase in low-frequency oscillations' power and the reduction and slowing of alpha activity. The enhancement of slow and the reduction of fast oscillations, and the disruption of brain functional connectivity, however, are characterized as major rsEEG changes in AD. Recent rodent studies also support human evidence of age- and AD-related changes in resting-state brain oscillations, and the neuroprotective effect of brain stimulation techniques through gamma-band stimulations. Cumulatively, current evidence moves toward optimizing rsEEG features as reliable predictors of people with aMCI at risk for conversion to AD and mapping neural alterations subsequent to brain stimulation therapies. The present paper reviews the latest evidence of changes in rsEEG oscillations in physiological aging, aMCI, and AD, as well as findings of various brain stimulation therapies from both human and non-human studies.

Dissociated neuronal phase- and amplitude-coupling patterns in the human brain

Coupling of neuronal oscillations may reflect and facilitate the communication between neuronal populations. Two primary neuronal coupling modes have been described: phase-coupling and amplitude-coupling. Theoretically, both coupling modes are independent, but so far, their neuronal relationship remains unclear. Here, we combined MEG, source-reconstruction and simulations to systematically compare cortical amplitude-coupling and phase-coupling patterns in the human brain. Importantly, we took into account a critical bias of amplitude-coupling measures due to phase-coupling. We found differences between both coupling modes across a broad frequency range and most of the cortex. Furthermore, by combining empirical measurements and simulations we ruled out that these results were caused by methodological biases, but instead reflected genuine neuronal amplitude coupling. Our results show that cortical phase- and amplitude-coupling patterns are non-redundant, which may reflect at least partly distinct neuronal mechanisms. Furthermore, our findings highlight and clarify the compound nature of amplitude coupling measures.

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91-97%, sensitivity: 100%, and specificity: 77-93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.

Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography

Introduction

An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting-state acquisition and analysis to facilitate the pooling of data from different sites.

Methods

Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI.

Results

The spectral analyses confirmed frequency-specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results.

Discussion

This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large-scale collaborative projects.

Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment

The need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI) is evident. MCI patients have a high risk of developing Alzheimer's disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings on the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys) function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, 𝜃, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine 90 anatomical regions of interest (ROIs). A DFCG was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 MCI patients and 22 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments, which further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. An estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). The NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (n μstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded in gaining a high classification accuracy on the blind dataset (85%), which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could properly manipulate the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

Magnetoencephalography applied to the study of Alzheimer's disease

Magnetoencephalography (MEG) is a relatively modern neuroimaging technique able to study normal and pathological brain functioning with temporal resolution in the order of milliseconds and adequate spatial resolution. Although its clinical applications are still relatively limited, great advances have been made in recent years in the field of dementia and Alzheimer's disease (AD) in particular. In this chapter, we briefly describe the physiological phenomena underlying MEG brain signals and the different metrics that can be computed from these data in order to study the alterations disrupting brain activity not only in demented patients, but also in the preclinical and prodromal stages of the disease. Changes in non-linear brain dynamics, power spectral properties, functional connectivity and network topological changes observed in AD are narratively summarized in the context of the pathophysiology of the disease. Furthermore, the potential of MEG as a potential biomarker to identify AD pathology before dementia onset is discussed in the light of current knowledge and the relationship between potential MEG biomarkers and current established hallmarks of the disease is also reviewed. To this aim, findings from different approaches such as resting state or during the performance of different cognitive paradigms are discussed.Lastly, there is an increasing interest in current scientific literature in promoting interventions aimed at modifying certain lifestyles, such as nutrition or physical activity among others, thought to reduce or delay AD risk. We discuss the utility of MEG as a potential marker of the success of such interventions from the available literature.

Oscillatory hyperactivity and hyperconnectivity in young APOE-ɛ4 carriers and hypoconnectivity in Alzheimer's disease

We studied resting-state oscillatory connectivity using magnetoencephalography in healthy young humans (N = 183) genotyped for APOE-ɛ4, the greatest genetic risk for Alzheimer's disease (AD). Connectivity across frequencies, but most prevalent in alpha/beta, was increased in APOE-ɛ4 in a set of mostly right-hemisphere connections, including lateral parietal and precuneus regions of the Default Mode Network. Similar regions also demonstrated hyperactivity, but only in gamma (40-160 Hz). In a separate study of AD patients, hypoconnectivity was seen in an extended bilateral network that partially overlapped with the hyperconnected regions seen in young APOE-ɛ4 carriers. Using machine-learning, AD patients could be distinguished from elderly controls with reasonable sensitivity and specificity, while young APOE-e4 carriers could also be distinguished from their controls with above chance performance. These results support theories of initial hyperconnectivity driving eventual profound disconnection in AD and suggest that this is present decades before the onset of AD symptomology.

M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer's Disease: A Review From the ML Perspective

This paper reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). The first part of this paper is devoted to reviewing the recently emerged machine learning (ML) algorithms based on electroencephalography (EEG) and magnetoencephalography (MEG) modalities. In particular, the methods are categorized by different types of neuromarkers. The second part of the review is dedicated to a series of investigations that further highlight the differences between these two modalities. First, several source reconstruction methods are reviewed and their source-level performances explored, followed by an objective comparison between EEG and MEG from multiple perspectives. Finally, a number of the most recent reports on classification of MCI/AD during resting state using EEG/MEG are documented to show the up-to-date performance for this well-recognized data collecting scenario. It is noticed that the MEG modality may be particularly effective in distinguishing between subjects with MCI and healthy controls, a high classification accuracy of more than 98% was reported recently; whereas the EEG seems to be performing well in classifying AD and healthy subjects, which also reached around 98% of the accuracy. A number of influential factors have also been raised and suggested for careful considerations while evaluating the ML-based diagnosis systems in the real-world scenarios.

The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease

Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.

Magnetoencephalography With Optically Pumped 4He Magnetometers at Ambient Temperature

In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with optically pumped magnetometers (OPMs) based on the parametric resonance of ⁴He atoms. The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-temperature (the ⁴He vapor is neither cooled nor heated). The sensor achieves a sensitivity of 210 fT/ √ Hz in the bandwidth [2-300 Hz]. MEG simulation studies with a brain phantom were cross-validated with real MEG measurements on a healthy subject. For both studies, MEG signal was recorded consecutively with OPMs and superconducting quantum interference devices (SQUIDs) used as reference sensors. For healthy subject MEG recordings, three MEG proofs of concept were carried out: auditory evoked fields, visual evoked fields, and spontaneous activity. M100 peaks have been detected on evoked responses recorded by both OPMs and SQUIDs with no significant difference in latency. Concerning spontaneous activity, an attenuation of the signal power between 8-12 Hz (alpha band) related to eyes opening has been observed with OPM similarly to SQUID. All these results confirm that the room temperature vector ⁴He OPMs can record MEG signals and provide reliable information on brain activity.

Healthy and Pathological Brain Aging: From the Perspective of Oscillations, Functional Connectivity, and Signal Complexity

Healthy aging is associated with impairment in cognitive information processing. Several neuroimaging methods such as functional magnetic resonance imaging, positron emission tomography and near-infrared spectroscopy have been used to explore healthy and pathological aging by relying on hemodynamic or metabolic changes that occur in response to brain activity. Since electroencephalography (EEG) and magnetoencephalography (MEG) are able to measure neural activity directly with a high temporal resolution of milliseconds, these neurophysiological techniques are particularly important to investigate the dynamics of brain activity underlying neurocognitive aging. It is well known that age is a major risk factor for Alzheimer's disease (AD), and that synaptic dysfunction represents an early sign of this disease associated with hallmark neuropathological findings. However, the neurophysiological mechanisms underlying AD are not fully elucidated. This review addresses healthy and pathological brain aging from a neurophysiological perspective, focusing on oscillatory activity changes during the resting state, event-related potentials and stimulus-induced oscillatory responses during cognitive or motor tasks, functional connectivity between brain regions, and changes in signal complexity. We also highlight the accumulating evidence on age-related EEG/MEG changes and biological markers of brain neurodegeneration, including genetic factors, structural abnormalities on magnetic resonance images, and the biochemical changes associated with Aβ deposition and tau pathology.

A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)

Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities.

The Role of Magnetoencephalography in the Early Stages of Alzheimer's Disease

The ever increasing proportion of aged people in modern societies is leading to a substantial increase in the number of people affected by dementia, and Alzheimer’s Disease (AD) in particular, which is the most common cause for dementia. Throughout the course of the last decades several different compounds have been tested to stop or slow disease progression with limited success, which is giving rise to a strong interest toward the early stages of the disease. Alzheimer’s disease has an extended an insidious preclinical stage in which brain pathology accumulates slowly until clinical symptoms are observable in prodromal stages and in dementia. For this reason, the scientific community is focusing into investigating early signs of AD which could lead to the development of validated biomarkers. While some CSF and PET biomarkers have already been introduced in the clinical practice, the use of non-invasive measures of brain function as early biomarkers is still under investigation. However, the electrophysiological mechanisms and the early functional alterations underlying preclinical Alzheimer’s Disease is still scarcely studied. This work aims to briefly review the most relevant findings in the field of electrophysiological brain changes as measured by magnetoencephalography (MEG). MEG has proven its utility in some clinical areas. However, although its clinical relevance in dementia is still limited, a growing number of studies highlighted its sensitivity in these preclinical stages. Studies focusing on different analytical approaches will be reviewed. Furthermore, their potential applications to establish early diagnosis and determine subsequent progression to dementia are discussed.

Neurophysiological signatures of Alzheimer's disease and frontotemporal lobar degeneration: pathology versus phenotype

The disruption of brain networks is characteristic of neurodegenerative dementias. However, it is controversial whether changes in connectivity reflect only the functional anatomy of disease, with selective vulnerability of brain networks, or the specific neurophysiological consequences of different neuropathologies within brain networks. We proposed that the oscillatory dynamics of cortical circuits reflect the tuning of local neural interactions, such that different pathologies are selective in their impact on the frequency spectrum of oscillations, whereas clinical syndromes reflect the anatomical distribution of pathology and physiological change. To test this hypothesis, we used magnetoencephalography from five patient groups, representing dissociated pathological subtypes and distributions across frontal, parietal and temporal lobes: amnestic Alzheimer's disease, posterior cortical atrophy, and three syndromes associated with frontotemporal lobar degeneration. We measured effective connectivity with graph theory-based measures of local efficiency, using partial directed coherence between sensors. As expected, each disease caused large-scale changes of neurophysiological brain networks, with reductions in local efficiency compared to controls. Critically however, the frequency range of altered connectivity was consistent across clinical syndromes that shared a likely underlying pathology, whilst the localization of changes differed between clinical syndromes. Multivariate pattern analysis of the frequency-specific topographies of local efficiency separated the disorders from each other and from controls (accuracy 62% to 100%, according to the groups' differences in likely pathology and clinical syndrome). The data indicate that magnetoencephalography has the potential to reveal specific changes in neurophysiology resulting from neurodegenerative disease. Our findings confirm that while clinical syndromes have characteristic anatomical patterns of abnormal connectivity that may be identified with other methods like structural brain imaging, the different mechanisms of neurodegeneration also cause characteristic spectral signatures of physiological coupling that are not accessible with structural imaging nor confounded by the neurovascular signalling of functional MRI. We suggest that these spectral characteristics of altered connectivity are the result of differential disruption of neuronal microstructure and synaptic physiology by Alzheimer's disease versus frontotemporal lobar degeneration.

How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.

Tau burden and the functional connectome in Alzheimer's disease and progressive supranuclear palsy

Alzheimer's disease and progressive supranuclear palsy (PSP) represent neurodegenerative tauopathies with predominantly cortical versus subcortical disease burden. In Alzheimer's disease, neuropathology and atrophy preferentially affect 'hub' brain regions that are densely connected. It was unclear whether hubs are differentially affected by neurodegeneration because they are more likely to receive pathological proteins that propagate trans-neuronally, in a prion-like manner, or whether they are selectively vulnerable due to a lack of local trophic factors, higher metabolic demands, or differential gene expression. We assessed the relationship between tau burden and brain functional connectivity, by combining in vivo PET imaging using the ligand AV-1451, and graph theoretic measures of resting state functional MRI in 17 patients with Alzheimer's disease, 17 patients with PSP, and 12 controls. Strongly connected nodes displayed more tau pathology in Alzheimer's disease, independently of intrinsic connectivity network, validating the predictions of theories of trans-neuronal spread but not supporting a role for metabolic demands or deficient trophic support in tau accumulation. This was not a compensatory phenomenon, as the functional consequence of increasing tau burden in Alzheimer's disease was a progressive weakening of the connectivity of these same nodes, reducing weighted degree and local efficiency and resulting in weaker 'small-world' properties. Conversely, in PSP, unlike in Alzheimer's disease, those nodes that accrued pathological tau were those that displayed graph metric properties associated with increased metabolic demand and a lack of trophic support rather than strong functional connectivity. Together, these findings go some way towards explaining why Alzheimer's disease affects large scale connectivity networks throughout cortex while neuropathology in PSP is concentrated in a small number of subcortical structures. Further, we demonstrate that in PSP increasing tau burden in midbrain and deep nuclei was associated with strengthened cortico-cortical functional connectivity. Disrupted cortico-subcortical and cortico-brainstem interactions meant that information transfer took less direct paths, passing through a larger number of cortical nodes, reducing closeness centrality and eigenvector centrality in PSP, while increasing weighted degree, clustering, betweenness centrality and local efficiency. Our results have wide-ranging implications, from the validation of models of tau trafficking in humans to understanding the relationship between regional tau burden and brain functional reorganization.

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer's Disease

Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer's disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.

Network Disruption in the Preclinical Stages of Alzheimer’s Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment

Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease

Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.