Individualized diagnosis of preclinical Alzheimer’s Disease using deep neural networks

Park, Jinhee; Jang, Sehyeon; Gwak, Jeonghwan; Kim, Byeong C.; Lee, Jang Jae; Choi, Kyu Yeong; Lee, Kun Ho; Jun, Sung Chan; Jang, Gil-Jin; Ahn, Sangtae

doi:10.1016/j.eswa.2022.118511

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…These architectures were mainly developed for MIbased EEG signals decoding, as well as for the classification and interpretation of EEG-based BCIs. Park et al employed them in the field of the identification of preclinical AD from EEG to overcome the limitation of high inter-subject variability, which affects the possibility of extracting robust handcrafted features [82]. However, to our knowledge, they have never been used for the specific task of discriminating SCD from MCI.…”

Section: Performance Comparison With Cnn-based Modelsmentioning

confidence: 99%

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

et al. 2023

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Objective. This study aims to design and implement the first Deep Learning (DL) model to classify subjects in the prodromic states of Alzheimer’s Disease (AD) based on resting-state electroencephalographic signals. Approach. EEG recordings of 17 Healthy Controls (HC), 56 Subjective Cognitive Decline (SCD) and 45 Mild Cognitive Impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting Delta, Theta, Alpha, Beta and Delta-to-Theta frequency bands using bandpass filters. To classify SCD vs MCI and HC vs SCD vs MCI, we propose a framework based on the Transformer architecture, which uses Multi-Head Attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10-second epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the Transformer were assessed for both epochs and subjects and compared with other DL models. Main results. Results showed that the Delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an AUC of 0.807, while the highest results for the HC vs SCD vs MCI classification were obtained on Alpha and Theta with a micro-AUC higher than 0.74. Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD.

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Section: Performance Comparison With Cnn-based Modelsmentioning

confidence: 99%

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

et al. 2023

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“…In particular, resting-state EEG (rsEEG) has been confirmed as a biomarker related to disease progression [12]- [14] which can be used to discriminate early stages of AD [15]. In this context, rsEEG-based DL models have also shown remarkable results in the identification of MCI [16], [17] and preclinical AD [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG

Sibilano,

Buongiorno,

Lassi

et al. 2024

IEEE J. Biomed. Health Inform.

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The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

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“…ANNs have recently been utilized to improve disease diagnosis and classification. Deep neural networks using resting state EEG data of elderly individuals is proposed as a diagnosis tool for preclinical Alzheimer’s disease ( Park et al, 2022 ). Attention deficit and hyperactivity disorder (ADHD) classification with EEG and ANNs is studied in Martínez González et al (2022) .…”

Section: Introductionmentioning

confidence: 99%

Topological data analysis for revealing dynamic brain reconfiguration in MEG data

Duman

Tatar

2023

PeerJ

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In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.

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Individualized diagnosis of preclinical Alzheimer’s Disease using deep neural networks

Cited by 6 publications

References 47 publications

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Understanding the Role of Self-Attention in a Transformer Model for the Discrimination of SCD From MCI Using Resting-State EEG

Topological data analysis for revealing dynamic brain reconfiguration in MEG data

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