Hovagim Bakardjian’s research while affiliated with French Institute of Health and Medical Research and other places

Episodic memory (EM) alterations are a hallmark of Alzheimer’s disease (AD). We assessed EM longitudinally in cognitively normal elders at-risk for AD (with subjective memory complaints), as a function of amyloid-β (Aβ) burden, neurodegeneration (N), and progression to prodromal AD. We stratified 264 INSIGHT-preAD study subjects in controls (Aβ-/N−), stable/N− or N + (Aβ +), and progressors/N− or N + (Aβ +) groups (progressors were included only until AD-diagnosis). We used linear mixed-effect models with Aβ and N status, or progression to AD as factors, to analyze behavioral performance in an old/new word-recognition task based on the free and cued selective reminding test (FCSRT). The controls and stable/N− groups showed near-ceiling accuracy and RT improvement across follow-up. The stable/N + group showed accuracy reduction and no RT improvement, i.e., Aβ + /N + cumulative effect. The progressors showed a marked performance decline. EM alterations may constitute early preclinical markers of progression to prodromal AD, while individuals are cognitively normal according to neuropsychological standards.

Background Alzheimer’s disease (AD) includes progressive symptoms spread along a continuum of preclinical and clinical stages. Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks in elderly cognitively-healthy memory complainers at risk of AD for carrying pathophysiological biomarkers (amyloidopathy and tauopathy). Objective We analyzed resting-state electroencephalography (EEG) of 318 cognitively-healthy subjective memory complainers from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24). Methods Using ¹⁸F-florbetapir PET-scanner, subjects were stratified between amyloid negative (A–; n = 230) and positive (A+; n = 88) groups. Differences between A+ and A– were estimated at source-level in each band-power of the EEG spectrum. Results At M0, we found an increase of theta power in the mid-frontal cortex in A+ compared to A–. No significant association was found between mid-frontal theta and the individuals’ cognitive performance. At M24, theta power increased in A+ relative to A– individuals in the posterior cingulate cortex and the pre-cuneus. Alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A– group only at M24. Theta power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in the A+ individuals and a non-linear longitudinal progression at M24. Conclusion We provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of elderly individuals at-risk for AD.

Alzheimer's Disease (AD) includes progressive symptoms spread along a continuum of pre-clinical (pre-AD) and clinical stages. Pre-AD refers to cognitively healthy individuals with presence of positive pathophysiological biomarkers of AD (i.e., markers of amyloidopathy and tauopathy). Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks of pre-AD. To address this issue, we analysed resting-state EEG data of 318 cognitively healthy individuals with subjective memory complains from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24). Using 18F-florbetapir PET-scanner, subjects were stratified between amyloid positive (A-; n=230) and amyloid negative (A+; n=88) groups. Differences between A+ and A- individuals were estimated at source level in each band of the EEG power spectrum. At M0, we found an increase of theta-band power in the mid-frontal cortex in A+ compared to A-. No significant association was found between mid-frontal theta power and the individuals' cognitive performance. While the very same effect was not replicated at M24, theta-band power increased in A+ relative to A- individuals in the posterior cingulate cortex and the pre-cuneus. Furthermore, alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A- group only at M24. These results provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of pre-AD. Theta-band power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in individuals at-risk of AD and a non-linear longitudinal progression of the AD-spectrum.

Background The cognitive reserve (CR) moderate the effect of brain pathophysiology on cognitive deficits in Alzheimer's Disease (AD) continuum. In a previous study on individuals with subjective memory complaint (SMCs), a condition at risk for AD, from the INSIGHT‐preAD cohort, we found that CR altered the association of amyloid load with neurophysiological mechanisms generating posterior electroencephalographic (EEG) alpha rhythms in quiet wakefulness. We used educational attainment (Edu) as an indicator of CR. Method In the present work we tested the hypothesis of the interaction of Edu and cerebral amyloid‐b load with the association between posterior alpha rhythms, as revealed by resting‐state EEG (rsEEG) activity, and functional connectivity, as revealed by functional magnetic resonance imaging (fMRI, 3‐Tesla Verio system), among cholinergic basal forebrain (BF), thalamus, and posterior cortical areas. Resting‐state EEG and fMRI data were acquired in 318 cognitively intact individuals (age between 70 and 85 years) with SMC. Participants were stratified into two groups of amyloid‐positive (SMCpos) and ‐negative (SMCneg), using the standard diagnostic markers of Alzheimer's neuropathology based on cortical‐to‐cerebellum standardized uptake value ratio in the PET imaging. Then, the education attainment level was used to stratify the SMC participants in those with a low‐to‐moderate education level (SMC Edu‐) and those with a high education level (SMC Edu+). Result Results showed a significant positive association between temporal alpha rhythms and above‐mentioned functional connectivity in the SMC cases with low brain amyloid accumulation and education attainment (SMCneg Edu‐). In contrast, the SMCneg Edu+ seniors showed the amplest posterior alpha rhythms, but not a positive association between temporal alpha rhythms and fMRI connectivity among cholinergic BF, thalamus, and posterior cerebral cortex, possibly due to a (annulation) of high CR. Of note, the SMCpos Edu+ seniors showed this positive association, possibly due to the (neutralization) of CR and brain amyloidosis (Figure 1). Conclusion The present results suggest that, in SMC seniors, high CR affects cortical neural synchronization mechanisms generating posterior rsEEG alpha rhythms, but not through the ascending cholinergic system to the posterior cortex. Unfortunately, these CR effects may be annulled at earlier stages of Alzheimer's amyloid pathophysiology.

There is substantial experimental evidence for dysregulation of several microRNA (miRNA) expression levels in Alzheimer’s disease (AD). MiRNAs modulate critical brain intracellular signaling pathways and are associated with AD core pathophysiological mechanisms. First, we conducted a real-time quantitative PCR-based pilot study to identify a set of brain-enriched miRNAs in a monocentric cohort of cognitively normal individuals with subjective memory complaints, a condition associated with increased risk of AD. Second, we investigated the impact of age, sex, and the Apolipoprotein E ε4 (APOE ε4) allele, on the identified miRNA plasma concentrations. In addition, we explored the cross-sectional and longitudinal association of the miRNAs plasma concentrations with regional brain metabolic uptake using amyloid-β (Aβ)-positron emission tomography (Aβ-PET) and 18F-fluorodeoxyglucose-PET (18F-FDG-PET). We identified a set of six brain-enriched miRNAs—miRNA-125b, miRNA-146a, miRNA-15b, miRNA-148a, miRNA-26b, and miRNA-100. Age, sex, and APOE ε4 allele were not associated with individual miRNA abundance. MiRNA-15b concentrations were significantly lower in the Aβ-PET-positive compared to Aβ-PET-negative individuals. Furthermore, we found a positive effect of the miRNA-15b*time interaction on regional metabolic 18F-FDG-PET uptake in the left hippocampus. Plasma miRNA-125b concentrations, as well as the miRNA-125b*time interaction (over a 2-year follow-up), were negatively associated with regional Aβ-PET standard uptake value ratio in the right anterior cingulate cortex. At baseline, we found a significantly negative association between plasma miRNA-125b concentrations and 18F-FDG-PET uptake in specific brain regions. In an asymptomatic at-risk population for AD, we show significant associations between plasma concentrations of miRNA-125b and miRNA-15b with core neuroimaging biomarkers of AD pathophysiology. Our results, coupled with existing experimental evidence, suggest a potential protective anti-Aβ effect of miRNA-15b and a biological link between miRNA-125b and Aβ-independent neurotoxic pathways.

Background Cognitive reserve (CR) is present in Alzheimer’s disease (AD) seniors with high education attainment making them clinically resilient to extended brain neuropathology and neurodegeneration. Here we tested whether subjective memory complaint (SMC) seniors with AD neuropathology and high education attainment may present abnormal eyes‐closed resting state posterior electroencephalographic (rsEEG) rhythms around individual alpha frequency peak (IAFp), typically altered in AD patients with mild cognitive impairment and dementia. Method We selected all individual baseline data of the prospective INSIGHT‐preAD cohort (Paris) including mini mental state examination score ≥ 28, artifact‐free markers of 18F‐florbetapir positron emission tomography (amyPET), structural magnetic resonance imaging, and rsEEG rhythms (e.g., 172 SMC seniors). Delta, theta, alpha1, alpha2, and alpha3 bands were determined on individual basis based on IAFp, while beta1 (14‐20 Hz), beta2 (20‐30 Hz), and gamma (30‐40 Hz) were standard fixed bands. Result The SMC participants negative to amyPET AD markers (SMCneg) with high (over low‐moderate) education level showed higher posterior alpha 2 power density (possibly “neuroprotective”). Furthermore, amyPET‐positive SMC (SMCpos) participants with high (over low‐moderate) education level showed higher temporal alpha 3 power density (possibly “neuroprotective”) and lower posterior alpha 2 power density (possibly “compensatory”; Figure 1). This effect may reflect CR as no differences in brain gray‐white matter and cognitive functions were observed between these SMCpos/SMCneg sub‐groups. Conclusion Preclinical Alzheimer’s neuropathology may interact with education attainment and neurophysiological mechanisms generating cortical alpha rhythms around IAFp (i.e., alpha 2 and 3) in quiet wakefulness.

Neuroinflammation, a key early pathomechanistic alteration of Alzheimer's disease, may represent either a detrimental or a compensatory mechanism or both (according to the disease stage). YKL-40, a glycoprotein highly expressed in differentiated glial cells, is a candidate biomarker for in vivo tracking neuroinflammation in humans. We performed a longitudinal study in a monocentric cohort of cognitively healthy individuals at risk for Alzheimer's disease exploring whether age, sex, and the apolipoprotein E ε4 allele affect plasma YKL-40 concentrations. We investigated whether YKL-40 is associated with brain amyloid-β (Aβ) deposition, neuronal activity, and neurodegeneration as assessed via neuroimaging biomarkers. Finally, we investigated whether YKL-40 may predict cognitive performance. We found an age-associated increase of YKL-40 and observed that men display higher concentrations than women, indicating a potential sexual dimorphism. Moreover, YKL-40 was positively associated with memory performance and negatively associated with brain Aβ deposition (but not with metabolic signal). Consistent with translational studies, our results suggest a potentially protective effect of glia on incipient brain Aβ accumulation and neuronal homeostasis.

Background Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease (AD) (Jack et al. , 2018). Electroencephalography (EEG) is a non‐invasive and cheap technique that would be an interesting screening tool for the preclinical stage of AD. Method We included participants from the INSIGHT‐preAD cohort, which is an ongoing single‐center multimodal observational study designed to identify risk factors and markers of progression to clinical AD in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint (Dubois et al. , 2018). We divided the subjects into four groups, according to their amyloid status (on ¹⁸ F‐florbetapir PET) and neurodegeneration status (on ¹⁸F‐fluorodeoxyglucose PET). We analysed 314 baseline 256‐channel high‐density eyes‐closed 1‐minute resting‐state EEG recordings. We extracted 10 quantitative EEG biomarkers, including spectral measures, algorithmic complexity and functional connectivity metrics. We evaluated three different classifiers (random forest, decision tree and logistic regression) to predict amyloid and neurodegeneration status from multimodal data, combining EEG, ApoE4 genotype, demographic, neuropsychological and MRI data. We divided the dataset into a training set (n=258) and validation set (n=46). We used a 5‐fold cross‐validation on the training set to identify the optimal combination of features. Result As recently published (Gaubert et al. , 2019), the most prominent effects of neurodegeneration on EEG metrics were localized in fronto‐central regions with an increase in high‐frequency oscillations (higher beta and gamma power) and a decrease in low‐frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted Symbolic Mutual Information in theta band. In the machine learning analysis, among the different features, EEG was the most strongly predictive of neurodegeneration. Similar predictive performance was obtained when reducing the number of electrodes from 256 to 8. EEG biomarkers combined with hippocampal volumetry had a high negative predictive value (91%) and high sensitivity (82%) to predict neurodegeneration. The combination of ApoE4 genotype with demographic data was most strongly predictive of amyloid status. Conclusion EEG could be a valuable tool to screen for or rule out neurodegeneration in elderly memory complainers. This procedure has been patented under the following PCT number: PCT/EP2019/086629.