Exploratory Data Analysis in a Six-Year Longitudinal Study in Healthy Brain Aging

Gómez-Ramírez, Jaime; Villanueva, Marina Avila; Payo, Belén Frades; Quijano, Teodoro del Ser; Valentí-Soler, Meritxell; Sevilla, María Ascensión Zea; Fernández-Blázquez, Miguel A.

doi:10.1101/674853

Cited by 6 publications

(7 citation statements)

References 97 publications

(77 reference statements)

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“…The dataset for this work was collected by a longitudinal study about healthy aging based in Madrid, Spain [Gómez-Ramírez et al, 2019], [Fernández-Blázquez et al, 2020] conceived to investigate the effect of aging on the human brain. The dataset is part of single-center, observational, longitudinal cohort study that started in 2011 and it is still ongoing.…”

Section: Participantsmentioning

confidence: 99%

A comparative analysis of automated MRI brain segmentation in a large longitudinal dataset: Freesurfer vs. FSL

Quilis-Sancho¹,

Fernández-Blázquez²,

Gómez-Ramírez³

2020

Preprint

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The study of brain volumetry and morphology of the different brain structures can determine the diagnosis of an existing disease, quantify its prognosis or even help to identify an early detection of dementia. Manual segmentation is an extremely time consuming task and automated methods are thus, gaining importance as clinical tool for diagnosis. In the last few years, AI-based segmentation has delivered, in some cases, superior results than manual segmentation, in both time and accuracy. In this study we aim at performing a comparative analysis of automated brain segmentation. In order to test the performance of automated segmentation methods, the two most commonly used software libraries for brain segmentation Freesurfer and FSL, were put to work in each of the 4028 MRIs available in the study. We find a lack of linear correlation between the segmentation results obtained from Freesurfer and FSL. On the other hand. Freesurfer volume estimates of subcortical brain structures tends to be larger than FSL estimates of same areas. The study builds on an uniquely large, longitudinal dataset of over 4,000 MRIs, all performed with identical equipment to help researchers understand what to expect from fully automated segmentation procedures.

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Section: Participantsmentioning

confidence: 99%

A comparative analysis of automated MRI brain segmentation in a large longitudinal dataset: Freesurfer vs. FSL

Quilis-Sancho¹,

Fernández-Blázquez²,

Gómez-Ramírez³

2020

Preprint

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“…The total number of features considered to study conversion to MCI was 91 (Supplementary Materials Table S1). A complete description of the dataset is provided in 15 .…”

Section: Resultsmentioning

confidence: 99%

Selecting the most important self-assessed features for predicting conversion to Mild Cognitive Impairment with Random Forest and Permutation-based methods

Gómez-Ramírez

Ávila-Villanueva

Fernández-Blázquez

2019

Preprint

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Alzheimer's Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. are called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study we leverage on The Vallecas Project, a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques. 4/185/18 6/18

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“…The Vallecas Project is an ongoing single-center, observational, longitudinal cohort study 45,46 . The participants, originally recruited between 2011 and 2013 in Madrid, Spain, are home-dwelling volunteers, aged 70 to 85, without relevant psychiatric, neurological, or systemic disorders.…”

Section: Methodsmentioning

confidence: 99%

“…The total number of features considered to study conversion to MCI was 91 (Supplementary Materials Table S1). A complete description of the dataset is provided in 46 .…”

Section: Methodsmentioning

confidence: 99%

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

Gómez-Ramírez

Ávila-Villanueva

Fernández-Blázquez

2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Alzheimer’s Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

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Exploratory Data Analysis in a Six-Year Longitudinal Study in Healthy Brain Aging

Cited by 6 publications

References 97 publications

A comparative analysis of automated MRI brain segmentation in a large longitudinal dataset: Freesurfer vs. FSL

A comparative analysis of automated MRI brain segmentation in a large longitudinal dataset: Freesurfer vs. FSL

Selecting the most important self-assessed features for predicting conversion to Mild Cognitive Impairment with Random Forest and Permutation-based methods

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

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