scholarly journals EEG Biomarker for Alzheimer’s Disease

2020 ◽  
Author(s):  
Demet Ilhan Algin ◽  
Demet Ozbabalık Adapinar ◽  
Oguz Osman Erdinc
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Cost Effective ◽  
Event Related Potential ◽  
Cortical Atrophy ◽  
Mri Findings ◽  
Magnetic Resonance Imaging Mri ◽  
Electroencephalogram Eeg

Alzheimer’s disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 50 million dementia cases estimated worldwide. There is no cure for AD. Currently, AD diagnosis is carried out using neuropsychological tests, neuroimaging scans, and laboratory tests. In the early stages of AD, brain computed tomography (CT) and magnetic resonance imaging (MRI) findings may be normal, but in late periods, diffuse cortical atrophy can be detected more prominently in the temporal and frontal regions. Electroencephalogram (EEG) is a test that records the electrical signals of the brain by using electrodes that directly reflects cortical neuronal functioning. In addition, EEG is noninvasive and widely available at low cost, has high resolution, and provides access to neuronal signals, unlike functional MR or PET which indirectly detects metabolic signals. Accurate, specific, and cost-effective biomarkers are needed to track the early diagnosis, progression, and treatment response of AD. The findings of EEG in AD are now identified as biomarkers. In this chapter, we reviewed studies that used EEG or event-related potential (ERP) indices as a biomarker of AD.

scholarly journals A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Author(s):  
FR Farina ◽  
DD Emek-Savaş ◽  
L Rueda-Delgado ◽  
R Boyle ◽  
H Kiiski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Healthy Aging ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Structural Mri ◽  
Lower Accuracy

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

scholarly journals Article Commentary: Alzheimer's Disease, Diagnosis and the Need for Biomarkers

2008 ◽  
Vol 3 ◽  
pp. BMI.S682 ◽  
Author(s):  
Claudie Hooper ◽  
Simon Lovestone ◽  
Ricardo Sainz-Fuertes
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Memory Loss ◽  
Cost Effective ◽  
Predictive Biomarker ◽  
Disease Diagnosis ◽  
Response To Therapy ◽  
Histopathological Analysis

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of aging that presents with memory loss, disorientation, confusion and a reduction in cognitive ability. Although a definite diagnosis of the disorder can only be made post-mortem by histopathological analysis, a number of methods are currently available for the in vivo assessment of AD including psycho-metric tests and neuro-imaging. However, these clinical assessments are relatively nonspecific and imaging is very costly. Genetic testing can be performed if familial AD is suspected, although such cases represent a very small minority of total AD cases. Apolipoprotein E genotype provides a measure for analysing the risk of developing AD, but does not act as an absolute predictive biomarker for AD. Therefore there is a need for an accurate, universal, specific and cost-effective biomarker to facilitate not only ante-mortem diagnosis of AD, but also to allow progression of the disease and response to therapy to be monitored. This is the ultimate goal that our group is pursuing through the pan-European AddNeuroMed project.

scholarly journals A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

2020 ◽  
Vol 9 (7) ◽  
pp. 2146
Author(s):  
Gopi Battineni ◽  
Nalini Chintalapudi ◽  
Francesco Amenta ◽  
Enea Traini
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Selection ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Support Vector ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

scholarly journals Survey on Identification of Alzheimer Disease Using Magnetic Resonance Imaging (MRI) Images

2019 ◽  
Vol 8 (11) ◽  
pp. 3564-3570
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Early Detection ◽  
Low Cost ◽  
Tau Proteins ◽  
Resonance Imaging ◽  
Human Errors ◽  
Magnetic Resonance Imaging Mri

Alzheimer’s disease (AD) is a neuro-degenerative disorder which is characterised functional and cognitive deficits that take place progressively. Early detection of the AD is important for the therapy to be early and this may slow down the disease and its progression. For the purpose of bringing about an improvement to the incidence of early detection of the AD, there may be certain Normal Controls (NC) that is based on the structural analysis of Magnetic Resonance Imaging (MRI). In fact, an early detection of the AD by means of using an MRI can help both patients, as well as physicians, to a great extent since it is of a low cost and is also a procedure that is non-invasive providing objective diagnosis by avoiding human errors. This has been connected to the accumulation of the amyloid and the tau proteins found in the brain which is probably the commonest cause for a case of dementia and also accounts for almost 70% of cases of dementia. The MRI is an extremely promising technique in terms of detection of functional or structural brain differences observed among both these patient populations. For the purpose of this work, there had been a new survey that had been made for the identification of a new case of Alzheimer’s disease by means of using the MRI images.

scholarly journals Robust EEG-Based Biomarkers to Detect Alzheimer’s Disease

2021 ◽  
Vol 11 (8) ◽  
pp. 1026
Author(s):  
Ali H. Al-Nuaimi ◽  
Marina Blūma ◽  
Shaymaa S. Al-Juboori ◽  
Chima S. Eke ◽  
Emmanuel Jammeh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Performance ◽  
Low Cost ◽  
Support Vector ◽  
Diagnostic Model ◽  
Large Numbers ◽  
Significant Performance ◽  
Eeg Connectivity ◽  
Electroencephalogram Eeg

Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy-to-use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity. and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (>=85% for sensitivity and 100% for specificity).

scholarly journals Evaluation of Cholinergic Deficiency in Preclinical Alzheimer’s Disease Using Pupillometry

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Shaun Frost ◽  
Liam Robinson ◽  
Christopher C. Rowe ◽  
David Ames ◽  
Colin L. Masters ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Low Cost ◽  
Cognitive Disorders ◽  
Maximum Velocity ◽  
Cost Effective ◽  
Maximum Acceleration ◽  
Noninvasive Technique ◽  
Preclinical Ad ◽  
Flash Response

Cortical cholinergic deficiency is prominent in Alzheimer’s disease (AD), and published findings of diminished pupil flash response in AD suggest that this deficiency may extend to the visual cortical areas and anterior eye. Pupillometry is a low-cost, noninvasive technique that may be useful for monitoring cholinergic deficits which generally lead to memory and cognitive disorders. The aim of the study was to evaluate pupillometry for early detection of AD by comparing the pupil flash response (PFR) in AD (N=14) and cognitively normal healthy control (HC,N=115) participants, with the HC group stratified according to high (N=38) and low (N=77) neocortical amyloid burden (NAB). Constriction phase PFR parameters were significantly reduced in AD compared to HC (maximum accelerationp<0.05, maximum velocityp<0.0005, average velocityp<0.005, and constriction amplitudep<0.00005). The high-NAB HC subgroup had reduced PFR response cross-sectionally, and also a greater decline longitudinally, compared to the low-NAB subgroup, suggesting changes to pupil response in preclinical AD. The results suggest that PFR changes may occur in the preclinical phase of AD. Hence, pupillometry has a potential as an adjunct for noninvasive, cost-effective screening for preclinical AD.

scholarly journals CSF Biomarker Profiles in CNS Infection Associated with HSV and VZV Mimic Pattern in Alzheimer’s Disease.

2020 ◽  
Author(s):  
Makiko Shinomoto ◽  
Takashi Kasai ◽  
Harutsugu Tatebe ◽  
Fukiko Kitani-Morii ◽  
Takuma Ohmichi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Axonal Injury ◽  
Cns Infection ◽  
Mri Findings ◽  
Cns Infections ◽  
Potential Biomarker ◽  
Hospitalization Period ◽  
Significant Difference

Abstract Alzheimer’s disease (AD) is the most common cause of dementia. Although AD was initially considered to be a cell autonomous neurodegenerative disorder, marked neuroinflammation is observed in the brains of patients with AD, alongside Aβ and tau pathology. Based on genetic and molecular biological findings, central nervous system (CNS) inflammatory processes have been postulated to be involved in the etiopathogenesis of AD, in which activated microglia play a key role. This has also been supported by the epidemiological observation that CNS infections were associated with the development of AD, and in particular the relationship between herpetic virus and AD has been well-investigated. For example, the presence of anti-herpes simplex virus (HSV) antibody was associated with an elevated risk of developing AD [1]. Moreover, anti-herpetic medication was associated with a reduced risk of dementia in a population-based study [2]. Similar results were also observed in the case of varicella zoster virus (VZV) infections [3]. Taking into consideration the reports above, we hypothesized that the biomarker signature representing AD might be observed in patients with herpetic viral CNS infections as a prognostic biomarker of AD development. In the current study, we aimed to determine whether or not the biomarkers related to AD and neurodegeneration were changed in patients with CNS infection by HSV and VZV compared with controls. This study focused on CSF levels of Aβ1-42, Aβ1-40, total-tau (t-tau), and tau phosphorylated at threonine 181 (p-tau) as molecules representing the AD signature; neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (p-NfH) as indicators of axonal injury; soluble triggering receptor expressed on myeloid cells 2 (sTREM2) as a potential biomarker for microglia activity; and glial fibrillary acidic protein (GFAP) as a biomarker for astrocytic damage. We also measured serum levels of NfL as a blood based biomarker for axonal injury. (For detailed methods, see Supplementary methods) The demographic characteristics, diagnosis, CSF profiles, results of viral detection, magnetic resonance imaging (MRI) findings, lowest score of the Glasgow coma scale (GCS) during the hospitalization period, and modified Rankin Scale (mRS) at discharge are summarized in Supplementary Table 1 and 2. There was no significant difference in age or sex among the HSV, VZV, and control groups.

Auditory Dysfunction in Alzheimer's Disease

2010 ◽  
Vol 15 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Sridhar Krishnamurti
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Health Care ◽  
Care Setting ◽  
Neurodegenerative Disorder ◽  
Disease Process ◽  
Clinical Protocols ◽  
Speech Language Pathologist ◽  
Auditory Dysfunction

Alzheimer's disease is neurodegenerative disorder which affects a growing number of older adults every year. With an understanding of auditory dysfunction in Alzheimer's disease, the speech-language pathologist working in the health care setting can provide better service to these individuals. The pathophysiology of the disease process in Alzheimer's disease increases the likelihood of specific types of auditory deficits as opposed to others. This article will discuss the auditory deficits in Alzheimer's disease, their implications, and the value of clinical protocols for individuals with this disease.

Acetylcholinesterase Inhibitory Potential and Antioxidant Activities of Five Cultivars of Rosa Damascena Mill. From Isparta, Turkey

2020 ◽  
Vol 18 (4) ◽  
pp. 354-359
Author(s):  
Shirin Tarbiat ◽  
Azize Simay Türütoğlu ◽  
Merve Ekingen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Free Radical ◽  
Neurodegenerative Disorder ◽  
Antioxidant Activities ◽  
Radical Scavenging ◽  
Acetylcholinesterase Activity ◽  
Rosa Damascena ◽  
Free Radical Damage

Alzheimer's disease is a neurodegenerative disorder characterized by memory loss and impairment of language. Alzheimer's disease is strongly associated with oxidative stress and impairment in the cholinergic pathway, which results in decreased levels of acetylcholine in certain areas of the brain. Hence, inhibition of acetylcholinesterase activity has been recognized as an acceptable treatment against Alzheimer's disease. Nature provides an array of bioactive compounds, which may protect against free radical damage and inhibit acetylcholinesterase activity. This study compares the in vitro antioxidant and anticholinesterase activities of hydroalcoholic extracts of five cultivars of Rosa Damascena Mill. petals (R. damascena 'Bulgarica', R. damascena 'Faik', R. damascena 'Iranica', R. damascena 'Complex-635' and R. damascena 'Complex-637') from Isparta, Turkey. The antioxidant activities of the hydroalcoholic extracts were tested for ferric ion reduction and DPPH radical scavenging activities. The anti-acetylcholinesterase activity was also evaluated. All rose cultivars showed a high potency for scavenging free radical and inhibiting acetylcholinesterase activity. There was a significant correlation between antioxidant and acetylcholinesterase inhibitory activity. Among cultivars, Complex-635 showed the highest inhibitory effect with an IC50 value of 3.92 µg/mL. Our results suggest that all these extracts may have the potential to treat Alzheimer's disease with Complex-635 showing more promise.

Repurposing Antihypertensive Drugs for the Management of Alzheimer’s Disease

2020 ◽  
Vol 27 ◽  
Author(s):  
Keng Yoon Yeong ◽  
Christine Law
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
Literature Review ◽  
Neurodegenerative Disorder ◽  
Antihypertensive Drugs ◽  
Future Prospects ◽  
Short Term

Alzheimer’s disease (AD) is a neurodegenerative disorder that has affected millions of people worldwide. However, currently there is no treatment to cure the disease. The AD drugs available in the market only manage the disease symptomatically and the effects are usually short-term. Thus, there is a need to look at alternatives AD therapies. Mid-life hypertension has not only been recognised as a risk factor for AD, but its relation with AD has also been well established. Thus, antihypertensives are postulated to be beneficial in managing AD. This literature review aims to shed some light on the potential of repurposing antihypertensives to treat AD, considering recent updates. Four classes of antihypertensives, as well as their potential limitations and future prospects in being utilised as AD therapeutics are discussed in this review.

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