On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi, Ane; Aztiria, Asier; Basarab, Adrian

doi:10.1016/j.artmed.2016.06.003

Cited by 150 publications

(67 citation statements)

References 315 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The EEG provided promising results in MCI EEG analysis [7,11,12]. MCI condition prodromal to dementia is usually characterized by increased power at low frequencies, decreased power at high frequencies (slowing), complexity reduction and decreased synchronization between the different areas of the brain [2,7,13,14]. Moretti et al found a link between MCI prodromal to AD and a deficit of the temporoparietal cortex as well as a link between shrinkage of the temporoparietal zone and increased alpha3/alpha2 EEG power ratio [15].…”

Section: Introductionmentioning

confidence: 99%

“…An EEG-based diagnostic tool would be particularly suitable for this purpose since it is well tolerated, low-cost and widespread. The EEG provided promising results in MCI EEG analysis [7,11,12]. MCI condition prodromal to dementia is usually characterized by increased power at low frequencies, decreased power at high frequencies (slowing), complexity reduction and decreased synchronization between the different areas of the brain [2,7,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…It is still very difficult to make a diagnosis of MCI that degenerates into dementia, both because it is very difficult to keep MCI subjects actively involved in the follow-up program and because the currently available diagnostic tools are subjective and based on the medical history of the patient and on the administration of cognitive tests such as Montreal Cognitive Assessment (MoCA) [9]. Another diagnostic tool is Magnetic Resonance Imaging (MRI) [7]; however, many patients badly tolerate it and refuse to undergo it [10], except the first scanning that is mainly performed to rule out the presence of lesions or other causes of MCI. An EEG-based diagnostic tool would be particularly suitable for this purpose since it is well tolerated, low-cost and widespread.…”

Section: Introductionmentioning

confidence: 99%

“…Ahmadou and Adeli [18] introduced an enhanced probabilistic neural network classifier to discriminate between MCI and healthy subjects (98.4% accuracy). MCI condition, whenever it is the early symptom of a pathology that degenerates into dementia, appears associated with a progressive reduction in functional connectivity due to neuronal death [2,7,13,19,20]. An EEG-based model that quantifies brain electrical connectivity should be able to indirectly measure such changes.…”

Section: Introductionmentioning

confidence: 99%

“…MCI can be amnestic (aMCI) and non-amnestic [5], aMCI subjects are more likely to develop dementia due to Alzheimer's Disease (AD) [6]. MCI subjects should be monitored periodically, through specific follow-up programs, so that any necessary therapy can be undertaken as soon as the patient shows clear signs of progression towards dementia [7]; to this purpose, home patient care could help dramatically [8]. It is still very difficult to make a diagnosis of MCI that degenerates into dementia, both because it is very difficult to keep MCI subjects actively involved in the follow-up program and because the currently available diagnostic tools are subjective and based on the medical history of the patient and on the administration of cognitive tests such as Montreal Cognitive Assessment (MoCA) [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

Mammone

Salvo

Ieracitano

et al. 2017

Entropy

View full text Add to dashboard Cite

Abstract:In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI) condition, when caused by a disorder degenerating into dementia, affects the brain connectivity. Motivated by the promising results achieved through the recently developed descriptor of coupling strength between EEG signals, the Permutation Disalignment Index (PDI), the present paper introduces a novel PDI-based complex network model to evaluate the longitudinal variations in brain-electrical connectivity. A group of 33 amnestic MCI subjects was enrolled and followed-up with over four months. The results were compared to MoCA (Montreal Cognitive Assessment) tests, which scores the cognitive abilities of the patient. A significant negative correlation could be observed between MoCA variation and the characteristic path length (λ) variation (r = −0.56, p = 0.0006), whereas a significant positive correlation could be observed between MoCA variation and the variation of clustering coefficient (CC, r = 0.58, p = 0.0004), global efficiency (GE, r = 0.57, p = 0.0005) and small worldness (SW, r = 0.57, p = 0.0005). Cognitive decline thus seems to reflect an underlying cortical "disconnection" phenomenon: worsened subjects indeed showed an increased λ and decreased CC, GE and SW. The PDI-based connectivity model, proposed in the present work, could be a novel tool for the objective quantification of longitudinal brain-electrical connectivity changes in MCI subjects.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

Mammone

Salvo

Ieracitano

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

LANGaware: Leveraging machine learning on natural language for the early detection of neurodegenerative and psychiatric diseases

Rentoumi¹,

Vassiliou

Demiraj³

et al. 2021

Alzheimer's & Dementia

View full text Add to dashboard Cite

Background: Various neurodegenerative and psychiatric diseases are related with changes in spoken language [1], although they have seldom been investigated. We evaluate the effectiveness of our language-agnostic Machine Learning (ML) system to detect subtle changes in spoken language that manifest early signs of cognitive decline, thus assisting with its diagnosis. We evaluate our methodology using recordings of speech samples from multiple languages obtained from patient cohorts in early stages of cognitive decline and matched healthy controls.

show abstract

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

Amato,

Vergani,

Lassi

et al. 2024

Alz & Dem Diag Ass & Dis Mo

View full text Add to dashboard Cite

INTRODUCTIONEarly identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern.METHODSWe developed a cortical model of AD‐related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings.RESULTSOur model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50).DISCUSSIONPersonalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non‐invasive EEG recordings.Highlights Personalized cortical model estimating structural alterations from EEG recordings. Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%) Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%) Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y

show abstract

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Cited by 150 publications

References 315 publications

A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity

LANGaware: Leveraging machine learning on natural language for the early detection of neurodegenerative and psychiatric diseases

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings

Contact Info

Product

Resources

About