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

Abstract

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.

Tau pathology in early Alzheimer's disease is linked to selective disruptions in neurophysiological network dynamics

“…For example, magnetoencephalography (MEG) distinguishes Alzheimer's disease pathology from frontotemporal lobar degeneration by their spectral signatures, while retaining functional anatomical concordance with the clinical syndromes ( Sami et al., 2018 ). The brain's evoked and induced responses as measured by MEG and electroencephalography (EEG) distinguish Alzheimer's disease from controls, in advanced disease ( Sitnikova et al., 2018 ), mild cognitive impairment stage ( Dauwels et al., 2010 , Hughes et al., 2019 ), and even presymptomatically in carriers of autosomal dominant mutations ( Ochoa et al., 2017 , Suarez-Revelo et al., 2016 ). The spectral features of noninvasive clinical studies recapitulate invasive and ex vivo recordings of transgenic model systems ( Koss et al., 2016 , Kurudenkandy et al., 2014 , Sami et al., 2018 ).…”

Functional Requirements of Small- and Large-Scale Neural Circuitry Connectome Models

“…In a rapidly evolving paradigm, variations on this technique are utilized with older methods to offer the modeler a menu of possible calibration/validation datasets (Tables 1, 2, 3, and 4). Disease "signatures" (biomarkers) in the new paradigms can be used to calibrate models of diseased vs. healthy states and measure the effects of neuromodulation, electroceutical, or pharmaceutical techniques to restore the healthy state [24,[49][50][51][52].…”

Classification of evoked responses to inverted faces reveals both spatial and temporal cortical response abnormalities in Autism spectrum disorder

“…Here, we chose to combine the inverted versus upright processing line of research with a machine learning multivarate approach. Previous studies used machine learning decoding in visual perception ( King et al, 2016 ), object recognition ( Cichy et al, 2016 ), face processing ( Van de Nieuwenhuijzen et al, 2013 ) and timing of face perception ( Dobs et al, 2019 ), and to detect participants with mild cognitive impairment ( Hughes et al, 2019 ), neurological or brain injuries ( Aoe et al, 2019 , Claassen et al, 2019 ), and schizophrenia ( Shim et al, 2016 ), to mention a few. In this study we use machine learning-based brain signal decoding to investigate the spatial and temporal characterisitics of the evoked response to neutral upright faces and inverted faces, in ASD versus typically developing (TD) participants, using whole head magnetoencephalography (MEG).…”