Preclinical Stages of Alzheimer's Disease Classification by a Rs-fMRI Study

Liu, Tiantian; Wang, Yonghao; Yan, Tianyi; Liu, Yunlei; Xu, Rong; Li, Jiancheng; Xie, Yunyan

doi:10.1109/cisp-bmei.2018.8633094

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Using multivariate patterns of activity, i.e., activity across multiple voxels, can increase sensitivity in differentiating between individuals or conditions [ 16 ] (but see Hebart & Baker [ 17 ] for a discussion on the benefits and pitfalls of MVPA as compared with classical univariate analyses). Regarding Alzheimer’s disease, some authors [ 18 ] applied MVPA to investigate the topologic alterations of resting-state functional connectivity in participants with subjective cognitive decline, MCI and AD compared with healthy individuals. They showed that by using MVPA, it was possible to predict whether a participant belonged to one of the three clinical groups or to the healthy control group, which indicated that patterns of resting-state data are already discriminant for cognitive decline and MCI due to AD.…”

Section: Introductionmentioning

confidence: 99%

Language Network Connectivity Increases in Early Alzheimer’s Disease

Pistono

Senoussi

Guerrier

et al. 2021

JAD

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Background: Language production deficits occur early in the course of Alzheimer’s disease (AD); however, only a few studies have focused on language network’s functional connectivity in mild cognitive impairment (MCI) due to AD. Objective: The current study aims to uncover the extent of language alteration at the MCI stage, at a behavioral and neural level, using univariate and multivariate analyses of structural MRI and resting-state fMRI. Methods: Twenty-four MCI due to AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI, and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; within the executive control networks, as well as the pattern of functional connectivity within the language network and within the executive control networks. Results: MCI due to AD participants had language impairment during standardized language tasks and connected-speech production. Regarding functional connectivity, univariate analyses were not able to discriminate participants, while multivariate pattern analyses could significantly predict participants’ group. Language network’s functional connectivity could discriminate MCI due to AD participants better than executive control networks. Most notably, they revealed an increased connectivity at the MCI stage, positively correlated with language performance. Conclusion: Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language.

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Section: Introductionmentioning

confidence: 99%

Language Network Connectivity Increases in Early Alzheimer’s Disease

Pistono

Senoussi

Guerrier

et al. 2021

JAD

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“…Four main frequency bands, namely delta (δ) [0.1-4] Hz, theta (θ) [4][5][6][7][8] Hz, alpha (α) [8][9][10][11][12][13] Hz and beta (β) [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz were extracted from each EEG signal using designed bandpass filters, and each related dataset was created. Furthermore, in order to assess which frequency band was the most distinctive in the classification of HC, SCD and MCI, we also filtered the signals in the entire range [0.1-30] Hz, and an additional dataset (all-band) was generated.…”

Section: Methodsmentioning

confidence: 99%

“…Although the task of classifying SCD and MCI subjects from HCs has been addressed in several studies [21,22], the discrimination between SCD and MCI conditions from a functional point of view is still poorly investigated in literature since anatomical and functional changes in brain between the two classes are subtler, making it a more challenging task to deal with [23]. Nevertheless, the intricacy of brain alterations in the early stages of AD makes it difficult to recognize patterns and develop accurate indicators for diagnosing and monitoring the development of AD on an individual basis [24,25].…”

Section: Introductionmentioning

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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