Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment

Li, Aojie; Yue, Ling; Xiao, Shifu; Liu, Manhua

doi:10.1007/s11682-021-00545-1

Cited by 16 publications

(11 citation statements)

References 41 publications

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“…In order to draw inferences on the relevance of amygdala ROIs for cognitive decline, longitudinal analyses are essential. Previous studies showed that smaller whole amygdala volumes can predict a relevant longitudinal change in cognitive performance (den Heijer et al, 2006;Kovacevic et al, 2009;Li et al, 2022). Kovacevic et al (2009) showed that smaller whole amygdala volumes predicted a decline in MMSE scores after 6 months in an MCI cohort, with associations that were at least as strong as those seen for hippocampus volume.…”

Section: Discussionmentioning

confidence: 92%

7T Amygdala and Hippocampus Subfields in Volumetry-Based Associations with Memory: A 3-Year Follow-Up Study of Early Alzheimer's Disease

Göschel

Kurz

Dell’Orco

et al. 2023

Preprint

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Section: Discussionmentioning

confidence: 92%

7T Amygdala and Hippocampus Subfields in Volumetry-Based Associations with Memory: A 3-Year Follow-Up Study of Early Alzheimer's Disease

Göschel

Kurz

Dell’Orco

et al. 2023

Preprint

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“…In particular, the boundary between SCD and MCI, which is the first clinically diagnosed stage of the disease, is very blurry [4]. Although studies based on neuroimaging methods have investigated the possibility of identifying SCD and MCI in the progression of AD, reporting interesting results, they still require expensive and time-consuming diagnostic tools [21,45].…”

Section: Discussionmentioning

confidence: 99%

“…Mini-Mental State Examination (MMSE) or Montreal Cognitive Assessment, of HC, SCD and MCI subjects, respectively. Results showed that imaging volumetric features of the brain were more correlated with the scores of cognitive tests than individual features extracted from brain subregions, such as the hippocampal area [45]. Such neuroimaging-based studies, although allow to characterize SCD and MCI effectively, still require time-consuming and expensive techniques to acquire data and thus are not easily replicable.…”

Section: Related Workmentioning

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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“…Total CFI scores range 0-14, with higher scores indicating higher levels of subjective decline. In prior studies, individuals endorsing similar levels of SCD (CFI total ≥ 4) have demonstrated increased risk for beta amyloid positivity, increased risk for progression to symptomatic disease, and impaired performance on cognitive testing using the rater-administered BAC (51)(52)(53)(54)(55)37).…”

Section: Cognitive Function Instrumentmentioning

confidence: 99%

Remote self-administration of digital cognitive tests using the Brief Assessment of Cognition: Feasibility, reliability, and sensitivity to subjective cognitive decline

et al. 2022

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Cognitive impairment is a common and pervasive feature of etiologically diverse disorders of the central nervous system, and a target indication for a growing number of symptomatic and disease modifying drugs. Remotely acquired digital endpoints have been recognized for their potential in providing frequent, real-time monitoring of cognition, but their ultimate value will be determined by the reliability and sensitivity of measurement in the populations of interest. To this end, we describe initial validation of remote self-administration of cognitive tests within a regulatorily compliant tablet-based platform. Participants were 61 older adults (age 55+), including 20 individuals with subjective cognitive decline (SCD). To allow comparison between remote (in-home) and site-based testing, participants completed 2 testing sessions 1 week apart. Results for three of four cognitive domains assessed demonstrated equivalence between remote and site-based tests, with high cross-modality ICCs (absolute agreement) for Symbol Coding (ICC = 0.75), Visuospatial Working Memory (ICC = 0.70) and Verbal Fluency (ICC > 0.73). Group differences in these domains were significant and reflected sensitivity to objective cognitive impairment in the SCD group for both remote and site-based testing (p < 0.05). In contrast, performance on tests of verbal episodic memory suggested inflated performance during unmonitored testing and indicate reliable use of remote cognitive assessments may depend on the construct, as well as the population being tested.

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Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment

Cited by 16 publications

References 41 publications

7T Amygdala and Hippocampus Subfields in Volumetry-Based Associations with Memory: A 3-Year Follow-Up Study of Early Alzheimer's Disease

7T Amygdala and Hippocampus Subfields in Volumetry-Based Associations with Memory: A 3-Year Follow-Up Study of Early Alzheimer's Disease

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

Remote self-administration of digital cognitive tests using the Brief Assessment of Cognition: Feasibility, reliability, and sensitivity to subjective cognitive decline

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