207 research outputs found
Ten years of image analysis and machine learning competitions in dementia
Machine learning methods exploiting multi-parametric biomarkers, especially
based on neuroimaging, have huge potential to improve early diagnosis of
dementia and to predict which individuals are at-risk of developing dementia.
To benchmark algorithms in the field of machine learning and neuroimaging in
dementia and assess their potential for use in clinical practice and clinical
trials, seven grand challenges have been organized in the last decade.
The seven grand challenges addressed questions related to screening, clinical
status estimation, prediction and monitoring in (pre-clinical) dementia. There
was little overlap in clinical questions, tasks and performance metrics.
Whereas this aids providing insight on a broad range of questions, it also
limits the validation of results across challenges. The validation process
itself was mostly comparable between challenges, using similar methods for
ensuring objective comparison, uncertainty estimation and statistical testing.
In general, winning algorithms performed rigorous data preprocessing and
combined a wide range of input features.
Despite high state-of-the-art performances, most of the methods evaluated by
the challenges are not clinically used. To increase impact, future challenges
could pay more attention to statistical analysis of which factors relate to
higher performance, to clinical questions beyond Alzheimer's disease, and to
using testing data beyond the Alzheimer's Disease Neuroimaging Initiative.
Grand challenges would be an ideal venue for assessing the generalizability of
algorithm performance to unseen data of other cohorts. Key for increasing
impact in this way are larger testing data sizes, which could be reached by
sharing algorithms rather than data to exploit data that cannot be shared.Comment: 12 pages, 4 table
Trajectories and Determinants of Quality of Life in Dementia with Lewy Bodies and Alzheimer's Disease
Background: Quality of Life (QoL) is an important outcome measure in dementia, particularly in the context of interventions.
Research investigating longitudinal QoL in dementia with Lewy bodies (DLB) is currently lacking.
Objective: To investigate determinants and trajectories of QoL in DLB compared to Alzheimer’s disease (AD) and controls.
Methods: QoL was assessed annually in 138 individuals, using the EQ5D-utility-score (0–100) and the health-related Visual
Analogue Scale (VAS, 0–100). Twenty-nine DLB patients (age 69 ± 6), 68 AD patients (age 70 ± 6), and 41 controls (age
70 ± 5) were selected from the Dutch Parelsnoer Institute-Neurodegenerative diseases and Amsterdam Dementia Cohort. We
examined clinical work-up over time as determinants of QoL, including cognitive tests, neuropsychiatric inventory, Geriatric
Depression Scale (GDS), and disability assessment of dementia (DAD).
Results: Mixed models showed lower baseline VAS-scores in DLB compared to AD and controls (AD: ±SE = -7.6 ± 2.8,
controls: ±SE = -7.9 ± 3.0, p < 0.05). An interaction between diagnosis and time since diagnosis indicated steeper decline
on VAS-scores for AD patients compared to DLB patients (±SE = 2.9 ± 1.5, p < 0.1). EQ5D-utility-scores over time did not
differ between groups. Higher GDS and lower DAD-scores were independently associated with lower QoL in dementia patients
(GDS: VAS ±SE = -1.8 ± 0.3, EQ5D-utility ±SE = -3.7 ± 0.4; DAD: VAS = 0.1 ± 0.0, EQ5D-utility ±SE = 0.1 ± 0.1,
p < 0.05). No associations between cognitive tests and QoL remained in the multivariate model.
Conclusion: QoL is lower in DLB, while in AD QoL shows steepe
Diversity in Alzheimer\u27s Disease Drug Trials: The Importance of Eligibility Criteria
INTRODUCTION: To generalize safety and efficacy findings, it is essential that diverse populations are well represented in Alzheimer\u27s disease (AD) drug trials. In this review, we aimed to investigate participant diversity in disease-modifying AD trials over time, and the frequencies of participant eligibility criteria.
METHODS: A systematic review was performed using Medline, Embase, the Cochrane Library, and Clinicaltrials.gov, identifying 2247 records.
RESULTS: In the 101 included AD trials, participants were predominantly White (median percentage: 94.7%, interquartile range: 81.0-96.7%); and this percentage showed no significant increase or decrease over time (2001-2019). Eligibility criteria such as exclusion of persons with psychiatric illness (78.2%), cardiovascular disease (71.3%) and cerebrovascular disease (68.3%), obligated caregiver attendance (80.2%), and specific Mini-Mental State Examination scores (90.1%; no significant increase/decrease over time) may have led to a disproportionate exclusion of ethnoracially diverse individuals.
DISCUSSION: Ethnoracially diverse participants continue to be underrepresented in AD clinical trials. Several recommendations are provided to broaden eligibility criteria
Small vessel disease burden and functional brain connectivity in mild cognitive impairment
Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease
Machine learning methods have shown large potential for the automatic early
diagnosis of Alzheimer's Disease (AD). However, some machine learning methods
based on imaging data have poor interpretability because it is usually unclear
how they make their decisions. Explainable Boosting Machines (EBMs) are
interpretable machine learning models based on the statistical framework of
generalized additive modeling, but have so far only been used for tabular data.
Therefore, we propose a framework that combines the strength of EBM with
high-dimensional imaging data using deep learning-based feature extraction. The
proposed framework is interpretable because it provides the importance of each
feature. We validated the proposed framework on the Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and
area-under-the-curve (AUC) of 0.970 on AD and control classification.
Furthermore, we validated the proposed framework on an external testing set,
achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive
decline (SCD) classification. The proposed framework significantly outperformed
an EBM model using volume biomarkers instead of deep learning-based features,
as well as an end-to-end convolutional neural network (CNN) with optimized
architecture.Comment: 11 pages, 5 figure
Small vessel disease burden and functional brain connectivity in mild cognitive impairment
Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing. Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox. Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group. Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning.</p
Small vessel disease burden and functional brain connectivity in mild cognitive impairment
Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing. Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox. Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group. Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning.</p
Small vessel disease burden and functional brain connectivity in mild cognitive impairment
Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning
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