Brain regions show different metabolic and protein arginine methylation phenotypes in frontotemporal dementias and Alzheimer’s disease

Zhang, Fangrong; Rakhimbekova, Anastasia; Lashley, Tammaryn

doi:10.1016/j.pneurobio.2022.102400

Cited by 9 publications

(7 citation statements)

References 96 publications

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“…This often causes FTD to be misdiagnosed as AD in many patients. [61]. The similarity between neuronal degeneration patterns of AD and FTD in their advanced stages can also be partly attributed to the variable patterns of neurodegeneration in FTD, which impact different individuals in distinct ways [61].…”

Section: Discussionmentioning

confidence: 99%

“…[61]. The similarity between neuronal degeneration patterns of AD and FTD in their advanced stages can also be partly attributed to the variable patterns of neurodegeneration in FTD, which impact different individuals in distinct ways [61]. This level of variability and the possibility of misdiagnosis of FTD as AD necessitates the development of a technique that can accurately differentiate between AD and FTD while effectively identifying the affected brain regions…”

Section: Discussionmentioning

confidence: 99%

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Leveraging SVD Entropy and Explainable Machine Learning for Alzheimer's and Frontotemporal Dementia Detection using EEG

Lal,

Chikkankod,

Longo

2023

Preprint

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<p>Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) represent formidable neurodegenerative challenges. Existing research into optimal feature-extraction techniques for discerning pertinent AD/FTD biomarkers from Electroencephalography (EEG) data presents room for enhancement. Addressing this, our study undertakes a comprehensive evaluation of diverse feature-extraction methodologies, encompassing Higuchi's Fractal Dimension, Singular Value Decomposition Entropy, Zero Crossing Rate, Detrended Fluctuation Analysis, and Hjorth parameters. Our results highlight SVD Entropy as the superior measure, a finding not deeply explored in previous research with respect to AD/FTD. Furthermore, we aim to design an optimal machine learning pipeline incorporating sliding window segmentation, feature-extraction, and a supervised learning algorithm for discriminating AD/FTD patients from healthy controls. To amplify the interpretability of this study, we harnessed Explainable AI to generate feature-importance topographic brain plots. These plots emphasized the midbrain region's pronounced feature importance in Parkinson's Disease detection, which is consistent with current research, underscoring its efficacy in accurately discerning spatial information from EEG. The final results highlight a model incorporating SVD Entropy, KNN Classifier, and 90% overlapped sliding windows as the best performer, yielding a mean F1-score of 93% with an Accuracy of 91% for AD and HC discrimination, an average F1-score of 92.5% with an Accuracy of 93% for FTD and HC differentiation, and F1-score of 91.5% with an Accuracy of 91% for FTD and AD distinction. This research presents a novel architectural pipeline for detecting AD and FTD from EEG data, addressing the crucial need for accurate differentiation between AD and FTD, especially in the early stages where misdiagnosis is common, while also providing a comparative analysis of various feature-extraction measures.</p>

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Leveraging SVD Entropy and Explainable Machine Learning for Alzheimer's and Frontotemporal Dementia Detection using EEG

Lal,

Chikkankod,

Longo

2023

Preprint

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“…However, this aligns with the involvement of the occipital lobe in advanced FTD stages, where it shares degeneration patterns with AD [53,54]. This overlap and variability in the FTD presentation underscores the need for accurate differential diagnosis between these diseases [54].…”

mentioning

confidence: 87%

“…While topographic maps highlight the occipital region in distinguishing FTD from HC, this may seem unexpected given FTD's primary impact on frontal and temporal regions [51,52]. However, this aligns with the involvement of the occipital lobe in advanced FTD stages, where it shares degeneration patterns with AD [53,54]. This overlap and variability in the FTD presentation underscores the need for accurate differential diagnosis between these diseases [54].…”

mentioning

confidence: 99%

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

Lal,

Chikkankod,

Longo

2024

Brain Sciences

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Early-stage Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.

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“…However, it has now been discovered that genetic and epigenetic mechanisms also play a significant role in the disease’s pathophysiology. Recent studies have demonstrated that PRMTs (and specifically PRMT4, PRMT5 and PRMT8) are implicated in the disease and are involved in the modulation of various signaling pathways, which eventually cause accumulation of beta-amyloid, tau phosphorylation, neuroinflammation and cell death [ 45 ].…”

Section: Role Of Prmts In Alzheimer’s Diseasementioning

confidence: 99%

Functional Implications of Protein Arginine Methyltransferases (PRMTs) in Neurodegenerative Diseases

Angelopoulou,

Pyrgelis,

Ahire

et al. 2023

Biology

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During the aging of the global population, the prevalence of neurodegenerative diseases will be continuously growing. Although each disorder is characterized by disease-specific protein accumulations, several common pathophysiological mechanisms encompassing both genetic and environmental factors have been detected. Among them, protein arginine methyltransferases (PRMTs), which catalyze the methylation of arginine of various substrates, have been revealed to regulate several cellular mechanisms, including neuronal cell survival and excitability, axonal transport, synaptic maturation, and myelination. Emerging evidence highlights their critical involvement in the pathophysiology of neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia–amyotrophic lateral sclerosis (FTD-ALS) spectrum, Huntington’s disease (HD), spinal muscular atrophy (SMA) and spinal and bulbar muscular atrophy (SBMA). Underlying mechanisms include the regulation of gene transcription and RNA splicing, as well as their implication in various signaling pathways related to oxidative stress responses, apoptosis, neuroinflammation, vacuole degeneration, abnormal protein accumulation and neurotransmission. The targeting of PRMTs is a therapeutic approach initially developed against various forms of cancer but currently presents a novel potential strategy for neurodegenerative diseases. In this review, we discuss the accumulating evidence on the role of PRMTs in the pathophysiology of neurodegenerative diseases, enlightening their pathogenesis and stimulating future research.

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Brain regions show different metabolic and protein arginine methylation phenotypes in frontotemporal dementias and Alzheimer’s disease

Cited by 9 publications

References 96 publications

Leveraging SVD Entropy and Explainable Machine Learning for Alzheimer's and Frontotemporal Dementia Detection using EEG

Leveraging SVD Entropy and Explainable Machine Learning for Alzheimer's and Frontotemporal Dementia Detection using EEG

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

Functional Implications of Protein Arginine Methyltransferases (PRMTs) in Neurodegenerative Diseases

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