Alzheimer's disease: The state of the art in resting-state magnetoencephalography

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.

Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia

BACKGROUND

Alzheimer's disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials.

METHODS

Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm.

RESULTS

The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic.

CONCLUSIONS

According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.

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.

Amnestic Mild Cognitive Impairment Is Associated With Frequency-Specific Brain Network Alterations in Temporal Poles

There is general agreement that the neuropathological processes leading to Alzheimer’s disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the “hippocampal memory” deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI.

Resilience to cognitive impairment in the oldest-old: design of the EMIF-AD 90+ study

BACKGROUND

The oldest-old (subjects aged 90 years and older) population represents the fastest growing segment of society and shows a high dementia prevalence rate of up to 40%. Only a few studies have investigated protective factors for cognitive impairment in the oldest-old. The EMIF-AD 90+ Study aims to identify factors associated with resilience to cognitive impairment in the oldest-old. In this paper we reviewed previous studies on cognitive resilience in the oldest-old and described the design of the EMIF-AD 90+ Study.

METHODS

The EMIF-AD 90+ Study aimed to enroll 80 cognitively normal subjects and 40 subjects with cognitive impairment aged 90 years or older. Cognitive impairment was operationalized as amnestic mild cognitive impairment (aMCI), or possible or probable Alzheimer's Disease (AD). The study was part of the European Medical Information Framework for AD (EMIF-AD) and was conducted at the Amsterdam University Medical Centers (UMC) and at the University of Manchester. We will test whether cognitive resilience is associated with cognitive reserve, vascular comorbidities, mood, sleep, sensory system capacity, physical performance and capacity, genetic risk factors, hallmarks of ageing, and markers of neurodegeneration. Markers of neurodegeneration included an amyloid positron emission tomography, amyloid β and tau in cerebrospinal fluid/blood and neurophysiological measures.

DISCUSSION

The EMIF-AD 90+ Study will extend our knowledge on resilience to cognitive impairment in the oldest-old by extensive phenotyping of the subjects and the measurement of a wide range of potential protective factors, hallmarks of aging and markers of neurodegeneration.

TRIAL REGISTRATION

Nederlands Trial Register NTR5867 . Registered 20 May 2016.

Early diagnosis of mild cognitive impairment and Alzheimer's with event-related potentials and event-related desynchronization in N-back working memory tasks

BACKGROUND AND OBJECTIVE

In this study we investigate whether or not event-related potentials (ERP) and/or event-related (de)synchronization (ERD/ERS) can be used to differentiate between 27 healthy elderly (HE), 21 subjects diagnosed with mild cognitive impairment (MCI) and 15 mild Alzheimer's disease (AD) patients.

METHODS

Using 32-channel EEG recordings, we measured ERP responses to a three-level (N-back, N = 0,1,2) visual working memory task. We also performed ERD analysis over the same EEG data, dividing the full-band signal into the well-known delta, theta, alpha, beta and gamma bands. Both ERP and ERD analyses were followed by cluster analysis with correction for multicomparisons whenever significant differences were found between groups.

RESULTS

Regarding ERP (full-band analysis), our findings have shown both patient groups (MCI and AD) with reduced P450 amplitude (compared to HE controls) in the execution of the non-match 1-back task at many scalp electrodes, chiefly at parietal and centro-parietal areas. However, no significant differences were found between MCI and AD in ERP analysis whatever was the task. As for sub-band analyses, ERD/ERS measures revealed that HE subjects elicited consistently greater alpha ERD responses than MCI and AD patients during the 1-back task in the match condition, with all differences located at frontal, central and occipital regions. Moreover, in the non-match condition, it was possible to distinguish between MCI and AD patients when they were performing the 0-back task, with MCI presenting more desynchronization than AD on the theta band at temporal and fronto-temporal areas. In summary, ERD analyses have revealed themselves more valuable than ERP, since they showed significant differences in all three group comparisons: HE vs. MCI, HE vs. AD, and MCI vs. AD.

CONCLUSIONS

Based on these findings, we conclude that ERD responses to working memory (N-back) tasks could be useful not only for early MCI diagnosis or for improved AD diagnosis, but probably also for assessing the likelihood of MCI progression to AD, after further validated by a longitudinal study.

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.

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Directional information flow in patients with Alzheimer's disease. A source-space resting-state MEG study

In a recent magnetoencephalography (MEG) study, we found posterior-to-anterior information flow over the cortex in higher frequency bands in healthy subjects, with a reversed pattern in the theta band. A disruption of information flow may underlie clinical symptoms in Alzheimer's disease (AD). In AD, highly connected regions (hubs) in posterior areas are mostly disrupted. We therefore hypothesized that in AD the information flow from these hub regions would be disturbed. We used resting-state MEG recordings from 27 early-onset AD patients and 26 healthy controls. Using beamformer-based virtual electrodes, we estimated neuronal oscillatory activity for 78 cortical regions of interest (ROIs) and 12 subcortical ROIs of the AAL atlas, and calculated the directed phase transfer entropy (dPTE) as a measure of information flow between these ROIs. Group differences were evaluated using permutation tests and, for the AD group, associations between dPTE and general cognition or CSF biomarkers were determined using Spearman correlation coefficients. We confirmed the previously reported posterior-to-anterior information flow in the higher frequency bands in the healthy controls, and found it to be disturbed in the beta band in AD. Most prominently, the information flow from the precuneus and the visual cortex, towards frontal and subcortical structures, was decreased in AD. These disruptions did not correlate with cognitive impairment or CSF biomarkers. We conclude that AD pathology may affect the flow of information between brain regions, particularly from posterior hub regions, and that changes in the information flow in the beta band indicate an aspect of the pathophysiological process in AD.