MEG biomarker of Alzheimer's disease: Absence of a prefrontal generator during auditory sensory gating

Abstract

Magnetoencephalography (MEG), a direct measure of neuronal activity, is an underexplored tool in the search for biomarkers of Alzheimer’s disease (AD). In this study we used MEG source estimates of auditory gating generators, non-linear correlations with neuropsychological results, and multivariate analyses to examine the sensitivity and specificity of gating topology modulation to detect AD. Our results demonstrated the use of MEG localization of a medial prefrontal (mPFC) gating generator as a discrete (binary) detector of AD at the individual level and resulted in re-categorizing the participant categories in: 1) controls with mPFC generator localized in response to both the standard and deviant tones; 2) a possible preclinical stage of AD participants (a lower functioning group of controls) in which mPFC activation was localized to the deviant tone only; and 3) symptomatic AD in which mPFC activation was not localized to either the deviant or standard tones. This approach showed a large effect size (0.9) and high accuracy, sensitivity and specificity (100%) in identifying symptomatic AD patients within a limited research sample. The present results demonstrate high potential of mPFC activation as a non-invasive biomarker of AD pathology during putative preclinical and clinical stages.

Neuroimaging Reveals Heterogeneous Neural Correlates of Reading Deficit in Individuals with Dyslexia Consistent with a Multiple Deficit Model

“…One important branch of neuroimaging is the search for a biomarker in neurological, neuropsychiatric and neurodevelopmental disorders (including dyslexia). For instance, promising strides here have been made using various neuroimaging techniques in Alzheimer's disease (MRI [10], fMRI [11], PET [12] and MEG [13]), schizophrenia (PET [14], EEG [15] and MEG [16]), attention deficit hyperactivity disorder (ADHD) (MEG and structural MRI [17], DKI [18], MRI and MFC [19]) and dyslexia (MEG and structural MRI [17], structural MRI [20], ERPs [21,22], MEG [23] and fMRI [24]). It should be noted that some of the above cited papers explicitly claim the search for neuroimaging biomarkers, while others do not, but the results reported can be considered as potential candidates for neuroimaging biomarkers.…”

Supervised Sparse Components Analysis with Application to Brain Imaging Data

“…The basic R code for this method is as follows: tuneparams = optparasearch(X=X, Z=Z, search.method="ncomp1st", maxpct4ncomp=0.5, muX=0. 5) where the argument maxpct4ncomp = 0.5 means that 0:5 λ max is used as the regularized parameter when the number of components is searched and where λ max is the maximum of the regularized parameters among the possible candidates. In order to obtain the final fit result with optimized parameters, the following code should be implemented: fit1 = msma(X=X, Z=Z, comp=tuneparams$optncomp, lambdaX=tuneparams $optlambdaX, lambdaY=tuneparams$optlambdaY, muX = 0.5) For more details, please see the package manual.…”

“…[4] examined sMRI and cerebrospinal fluid (CSF) markers. Magnetoencephalography (MEG) is also useful as AD biomarker, and its localization using sMRI has high accuracy [5]. Schizophrenia is the second most studied disorder after dementia.…”

Dysfunction of Inferior Parietal Lobule During Sensory Gating in Patients With Amnestic Mild Cognitive Impairment

“…There have been some studies examining the SG function in neurodegenerative diseases, including dementia (Jessen et al, 2001;Cancelli et al, 2006;Thomas et al, 2010;Cheng et al, 2012;Josef Golubic et al, 2017). For example, Thomas and colleagues, recruiting 19 patients with probable AD and 17 healthy older adults, have revealed a significant P50 SG deficit in AD patients than in control subjects (Thomas et al, 2010).…”