Medial temporal lobe neuromagnetic hypoactivation and risk for developing cognitive decline in elderly population: A 2-year follow-up study

Functional Neurophysiological Biomarkers of Early-Stage Alzheimer's Disease: A Perspective of Network Hyperexcitability in Disease Progression

Network hyperexcitability (NH) has recently been suggested as a potential neurophysiological indicator of Alzheimer’s disease (AD), as new, more accurate biomarkers of AD are sought. NH has generated interest as a potential indicator of certain stages in the disease trajectory and even as a disease mechanism by which network dysfunction could be modulated. NH has been demonstrated in several animal models of AD pathology and multiple lines of evidence point to the existence of NH in patients with AD, strongly supporting the physiological and clinical relevance of this readout. Several hypotheses have been put forward to explain the prevalence of NH in animal models through neurophysiological, biochemical, and imaging techniques. However, some of these hypotheses have been built on animal models with limitations and caveats that may have derived NH through other mechanisms or mechanisms without translational validity to sporadic AD patients, potentially leading to an erroneous conclusion of the underlying cause of NH occurring in patients with AD. In this review, we discuss the substantiation for NH in animal models of AD pathology and in human patients, as well as some of the hypotheses considering recently developed animal models that challenge existing hypotheses and mechanisms of NH. In addition, we provide a preclinical perspective on how the development of animal models incorporating AD-specific NH could provide physiologically relevant translational experimental data that may potentially aid the discovery and development of novel therapies for AD.

Modulation of Brain Hyperexcitability: Potential New Therapeutic Approaches in Alzheimer's Disease

People with Alzheimer's disease (AD) have significantly higher rates of subclinical and overt epileptiform activity. In animal models, oligomeric Aβ amyloid is able to induce neuronal hyperexcitability even in the early phases of the disease. Such aberrant activity subsequently leads to downstream accumulation of toxic proteins, and ultimately to further neurodegeneration and neuronal silencing mediated by concomitant tau accumulation. Several neurotransmitters participate in the initial hyperexcitable state, with increased synaptic glutamatergic tone and decreased GABAergic inhibition. These changes appear to activate excitotoxic pathways and, ultimately, cause reduced long-term potentiation, increased long-term depression, and increased GABAergic inhibitory remodelling at the network level. Brain hyperexcitability has therefore been identified as a potential target for therapeutic interventions aimed at enhancing cognition, and, possibly, disease modification in the longer term. Clinical trials are ongoing to evaluate the potential efficacy in targeting hyperexcitability in AD, with levetiracetam showing some encouraging effects. Newer compounds and techniques, such as gene editing via viral vectors or brain stimulation, also show promise. Diagnostic challenges include identifying best biomarkers for measuring sub-clinical epileptiform discharges. Determining the timing of any intervention is critical and future trials will need to carefully stratify participants with respect to the phase of disease pathology.

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.

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.

Using Magnetoencephalography to Study Patterns of Brain Magnetic Activity in Alzheimer’s Disease

The use of magnetoencephalography to study neurophysiologic abnormalities associated with Alzheimer’s disease is reviewed. The most consistent observation is that Alzheimer’s disease patients exhibit an increase in focal slow-wave activity that covaried with cognitive performance. It is still unclear whether generation of focal slow-wave activity precedes or is a consequence of Alzheimer’s disease-related neuropathology. Also reviewed is the use of magnetoencephalography to identify early functional changes preceding the diagnosis of dementia. Magnetoencephalography detected neurophysiologic abnormalities associated with cognitive deficits before the diagnosis of mild cognitive impairment. This is supported by evidence presented suggesting that some patients with subjective cognitive complaints, without evidence of dementia, show an increase in focal slow-wave generators. Further research is needed to determine whether the outstanding spatial and temporal resolution of the magnetoencephalography technique could complement other neuroimaging techniques in identifying neurophysiologic abnormalities preceding the diagnosis of Alzheimer’s disease and mild cognitive impairment.

Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Purpose: Diabetes is a great risk factor for dementia and mild cognitive impairment (MCI). This study investigates whether complex network-derived features in resting state EEG (rsEEG) could be applied as a biomarker to distinguish amnestic mild cognitive impairment (aMCI) from normal cognitive function in subjects with type 2 diabetes (T2D).

Method: In this study, EEG was recorded in 28 patients with T2D (16 aMCI patients and 12 controls) during a no-task eyes-closed resting state. Pair-wise synchronization of rsEEG signals were assessed in six frequency bands (delta, theta, lower alpha, upper alpha, beta, and gamma) using phase lag index (PLI) and grouped into long distance (intra- and inter-hemispheric) and short distance interactions. PLI-weighted connectivity networks were also constructed, and characterized by mean clustering coefficient and path length. The correlation of these features and Montreal Cognitive Assessment (MoCA) scores was assessed.

Results: Main findings of this study were as follows: (1) In comparison with controls, patients with aMCI had a significant decrease of global mean PLI in lower alpha, upper alpha, and beta bands. Lower functional connection at short and long intra-hemispheric distance mainly appeared on the left hemisphere. (2) In the lower alpha band, clustering coefficient was significantly lower in aMCI group, and the path length significantly increased. (3) Cognitive status measured by MoCA had a significant positive correlation with cluster coefficient and negative correlation with path length in lower alpha band.

Conclusions: The brain network of aMCI patients displayed a disconnection syndrome and a loss of small-world architecture. The correlation between cognitive states and network characteristics suggested that the more in deterioration of the diabetes patients' cognitive state, the less optimal the network organization become. Hence, the complex network-derived biomarkers based on EEG could be employed to track cognitive function of diabetic patients and provide a new diagnosis tool for aMCI.

Potential utility of resting-state magnetoencephalography as a biomarker of CNS abnormality in HIV disease

There is a lack of a neuroimaging biomarker for HIV-Associated Neurocognitive Disorder. We report magnetoencephalography (MEG) data from patients with HIV disease and risk-group appropriate controls that were collected to determine the MEG frequency profile during the resting state, and the stability of the profile over 24 weeks. 17 individuals (10 HIV+, 7 HIV-) completed detailed neurobehavioral evaluations and 10min of resting-state MEG acquisition with a 306-channel whole-head system. The entire evaluation and MEG measurement were repeated 24 weeks later. Relative MEG power in the delta (0-4Hz), theta (4-7Hz), alpha (8-12Hz), beta (12-30Hz) and low gamma (30-50Hz) bands was computed for 8 predefined sensor groups. The median stability of resting-state relative power over 24 weeks of follow-up was .80 with eyes closed, and .72 with eyes open. The relative gamma power in the right occipital (t(15)=1.99, p<.06, r=-.46) and right frontal (t(15)=2.15, p<.05, r=-.48) regions was associated with serostatus. The effect of age on delta power was greater in the seropositive subjects (r(2)=.51) than in the seronegative subjects (r(2)=.11). Individuals with high theta-to-gamma ratios tended to have lower cognitive test performance, regardless of serostatus. The stability of the wide-band MEG frequency profiles over 24 weeks supports the utility of MEG as a biomarker. The links between the MEG profile, serostatus, and cognition suggest further research on its potential in HAND is needed.

Magnetoencephalography as a putative biomarker for Alzheimer's disease

Alzheimer's Disease (AD) is the most common dementia in the elderly and is estimated to affect tens of millions of people worldwide. AD is believed to have a prodromal stage lasting ten or more years. While amyloid deposits, tau filaments, and loss of brain cells are characteristics of the disease, the loss of dendritic spines and of synapses predate such changes. Popular preclinical detection strategies mainly involve cerebrospinal fluid biomarkers, magnetic resonance imaging, metabolic PET scans, and amyloid imaging. One strategy missing from this list involves neurophysiological measures, which might be more sensitive to detect alterations in brain function. The Magnetoencephalography International Consortium of Alzheimer's Disease arose out of the need to advance the use of Magnetoencephalography (MEG), as a tool in AD and pre-AD research. This paper presents a framework for using MEG in dementia research, and for short-term research priorities.

Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders

The pathophysiological mechanisms underlying clinical symptoms in neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD) are incompletely understood. Magnetoencephalography (MEG) is a relatively new functional neuroimaging technique, which allows the simultaneous recording of the brain's magnetic activity from large arrays of sensors covering the whole head. MEG studies in PD and AD have identified characteristic patterns of abnormal oscillatory activity in different frequency bands. Furthermore, MEG studies aimed at the characterization of distributed functional networks have demonstrated distinct patterns of abnormal connectivity in demented and non-demented PD, as well as in AD. In PD abnormal oscillatory activity and disturbed connectivity may respond differently to dopaminergic treatment. Further studies in this field could benefit from new technological developments such as ultra low field MRI and from the application of a well-defined theoretical framework such as graph theory to the study of disturbed brain networks.

Modulation of medial temporal lobe activity in epilepsy patients with hippocampal sclerosis during verbal working memory

It has been traditionally assumed that medial temporal lobe (MTL) is not required for working memory (WM). However, animal lesion and electrophysiological studies and human neuropsychological and neuroimaging studies have provided increasing evidences of a critical involvement of MTL in WM. Based on previous findings, the central aim of this study was to investigate the contribution of the MTL to verbal WM encoding. Here, we used magnetoencephalography (MEG) to compare the patterns of MTL activation of 9 epilepsy patients suffering from left hippocampal sclerosis with those of 10 healthy matched controls while they performed a verbal WM task. MEG recordings allow detailed tracking of the time course of MTL activation. We observed impaired WM performance associated with changes in the dynamics of MTL activity in epilepsy patients. Specifically, whereas patients showed decreased activity in damaged MTL, activity in the contralateral MTL was enhanced, an effect that became significant in the 600- to 700-ms interval after stimulus presentation. These findings strongly support the crucial contribution of MTL to verbal WM encoding and provide compelling evidence for the proposal that MTL contributes to both episodic memory and WM. Whether this pattern is signaling reorganization or a normal use of a damaged structure is discussed.

Event-related synchronization of alpha activity in early Alzheimer's disease and mild cognitive impairment: an MEG study combining beamformer and group comparison

In patients with Alzheimer's disease (AD), it is sometimes challenging to identify typical findings in electroencephalography (EEG) or magnetoencephalography (MEG) such as a slowing of the posterior dominant activity or an increase in slow activity. In this MEG study, we evaluated the event-related synchronization (ERS) of alpha activity after eye closing in patients with early AD and mild cognitive impairment (MCI) who presented no slow MEG pattern. Thirteen patients with probable AD and thirteen patients with MCI, who met NINCDS-ADRDA and Petersen's diagnostic criteria, respectively, were enrolled. We also selected fourteen age-matched normal control subjects. MEG activity was acquired during eye-open and eye-closed states. The ERS after eye closing within 8-15Hz frequency band was calculated and its cortical source was superimposed on the individual's MRI by using the beamformer implemented in Brain Electrical Source Analysis (BESA). The Source image was converted into a standardized image, and group comparisons across patients with AD, MCI and controls were performed using BrainVoyager QX. The averaged ERS was observed dominantly in posterior regions in all three groups. Significant difference in ERS was observed only for the comparison between AD patients and controls, with AD patients showing increased ERS in frontal regions. Frontal shift of posterior alpha activity was observed clearly in AD patients using the combination of beamformer and group comparison.

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.