Neuroimaging in Dementia: A Brief Review

qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis

Purpose

Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC).

Participants and Methods

The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA.

Results

Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state.

Conclusion

The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.

Cerebrospinal fluid microRNAs as potential biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is the leading form of dementia worldwide, but its early detection and diagnosis remain a challenge. MicroRNAs (miRNAs) are a group of small endogenous RNA molecules that regulate mRNA expression. Recent evidence suggests miRNAs play an important role in the five major hallmarks of AD pathophysiology: amyloidogenesis, tauopathy, neuroinflammation, synaptic dysfunction, and neuronal death. Compared to traditional biomarkers of AD, miRNAs display a greater degree of stability in cerebrospinal fluid. Moreover, aberrant changes in miRNA expression can be measured over time to monitor and guide patient treatment. Specific miRNA profiles and combinations may also be used to distinguish AD subjects from normal controls and other causes of dementia. Because of these properties, miRNAs are now being considered as promising and potential biomarkers of AD. This review comprehensively summarizes the diagnostic potential and regulatory roles miRNAs play in AD.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG

Purpose

Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR).

Participants and Methods

The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN).

Results

The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively.

Conclusion

The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.

A case report and literature review of cognitive malingering and psychopathology

Malingering of cognitive difficulties constitutes a major issue in psychiatric forensic settings. Here, we present a selective literature review related to the topic of cognitive malingering, psychopathology and their possible connections. Furthermore, we report a single case study of a 60-year-old man with a long and ongoing judicial history who exhibits a suspicious multi-domain neurocognitive disorder with significant reduction of autonomy in daily living, alongside a longtime history of depressive symptoms. Building on this, we suggest the importance of evaluating malingering conditions through both psychiatric and neuropsychological assessment tools. More specifically, the use of Performance Validity Tests (PVTs)-commonly but not quite correctly considered as tests of "malingering"-alongside the collection of clinical history and the use of routine psychometric testing, seems to be crucial in order to detect discrepancies between self-reported patient's symptoms, embedded validity indicators and psychometric results.

Healthy Lifestyle, Genetic Risk and Brain Health: A Gene-Environment Interaction Study in the UK Biobank

Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI < 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippocampal volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not <60 years. Healthy lifestyle was associated with higher total brain, grey matter and hippocampal volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.

The Role of 18F-Flortaucipir (AV-1451) in the Diagnosis of Neurodegenerative Disorders

Tau protein plays a vital role in maintaining the structural and functional integrity of the nervous system; however, hyperphosphorylation or abnormal phosphorylation of tau protein plays an essential role in the pathogenesis of several neurodegenerative disorders. The development of radioligand such as the 18F-flortaucipir (AV-1451) has provided us with the opportunity to assess the underlying tau pathology in various etiologies of dementia. For the purpose of this article, we aimed to evaluate the utility of 18F-AV-1451 in the differential diagnosis of various neurodegenerative disorders. We used PubMed to look for the latest, peer-reviewed, and informative articles. The scope of discussion included the role of 18F-AV-1451 positron emission tomography (PET) to aid in the diagnosis of Alzheimer’s disease (AD), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), and Parkinson’s disease with dementia (PDD). We also discussed if the tau burden identified by neuroimaging correlated well with the clinical severity and identified the various challenges of 18F-AV-1451 PET. We concluded that although the role of 18F-AV-1451 seems promising in the neuroimaging of AD, the benefit appears uncertain when it comes to the non-Alzheimer’s tauopathies. More research is required to identify the off-target binding sites of 18F-AV-1451 to determine its clinical utility in the future.

Neuroimaging in Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is one of the most common forms of dementia. It can present as neurocognitive decline, visual hallucinations, and concomitant symptoms of rapid eye movement (REM) sleep behavior disorder. Early diagnosis remains one of the cornerstones of managing this form of neurocognitive disorder but, often, making an early and accurate diagnosis can prove to be challenging. For this article, our goal was to review the utility of various neuroimaging modalities in making a swift and accurate diagnosis of DLB. We used PubMed to look for helpful, informative, and peer-reviewed articles. We discussed the role of a plethora of different imaging techniques, ranging from structural imaging like computed tomography (CT) and magnetic resonance imaging (MRI) to molecular imaging (single-photon emission computed tomography, positron emission to- tomography) as a diagnostic tool for DLB. We arrived at the conclusion that these novel neuroimaging modalities have already proven to be very helpful in ruling out differentials and making an early diagnosis of DLB. However, ongoing research is required to increase the diagnostic accuracy, leading to the early identification and treatment of DLB.