A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

Gómez-Ramírez, Jaime; Quilis-Sancho, Javier; Fernández-Blázquez, Miguel A.

doi:10.1007/s12021-021-09520-z

Cited by 9 publications

(9 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate the relationship between sex and Brain2ICV, we built the probabilistic Bayesian model expressed in Equations (2a)–(2d): B i ∼ N( µ i , σ) µ i = γ X[i] γ j ∼ N (0, 1), for j = 1, 2 σ ∼ Hal f Normal(1) where B i denotes the variable Brain2ICV or the ratio between the brain volume at an old age and the maximum brain volume at a young age for Subject i , and this is normally distributed with a mean µ i and a standard deviation σ . The index variable for sex γ j , with the index j = 1, 2, represents the average of Brain2ICV for male ( j = 1) and female ( j = 2) subjects (no order implied), which are normally distributed using the same prior, N (0, 1), for both male and female subjects [ 52 ]. The prior σ is assumed to be normally distributed, half-normally to be exact, with a standard deviation equal to 1 (a half-normal distribution can be directly sampled from a normal distribution by taking the absolute value of each sampled value).…”

Section: Methodsmentioning

confidence: 99%

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

Gómez-Ramírez

Fernández-Blázquez

González-Rosa

2022

Life

Self Cite

View full text Add to dashboard Cite

We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires—at least in principle—two MRI scans, one performed at a young age and one at an old age. There is, however, a way to address this problem when having only one MRI scan obtained at an old age. We computed the total brain losses of elderly subjects as a ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 years were assessed. A causal analysis of factors affecting brain atrophy was performed using probabilistic Bayesian modelling and the mathematics of causal inference. We found that both age and sex were causally related to brain atrophy, with women reaching an elderly age with a 1% larger brain volume relative to their intracranial volume than men. How the brain ages and the rationale for sex differences in brain volume losses during the adult lifespan are questions that need to be addressed with causal inference and empirical data. The graphical causal modelling presented here can be instrumental in understanding a puzzling scientific area of study—the biological aging of the brain.

show abstract

Section: Methodsmentioning

confidence: 99%

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

Gómez-Ramírez

Fernández-Blázquez

González-Rosa

2022

Life

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dataset used in this study comes from a single-center, observational cohort study [19,39,40]. The participants are home-dwelling elderly volunteers, 69-88 years of age, and without relevant psychiatric, neurological, or systemic disorders.…”

Section: Study Participantsmentioning

confidence: 99%

“…Hippocampal volume loss is also a well-recognized biomarker for the diagnosis of Alzheimer's disease in cross-sectional [14], longitudinal [15], and meta-analysis [16]. The variation in hippocampal volume as a function of age studied in the UK Biobank Imaging dataset (N = 19,793) shows an acceleration in hippocampal volume reduction in middle age (starting at around 50 years of age) [17]. Age-related atrophy in other subcortical structures varies, with a more rapid decline in the thalamus and putamen compared to caudate and amygdala [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

2022

Self Cite

View full text Add to dashboard Cite

Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.

show abstract

“…Furthermore, the identification of regional brain atrophy in PD has been of interest as a possible marker of certain symptoms of PD and of the progression of PD [2]. However, studies conducted in non-PD populations have shown that estimates of regional volume [5,6] and of cortical thickness vary depending on the software toolbox [7,8]. Overall, a range of factors matter in the replicability of neuroimaging findings, including computational environments [9,10], analysis tools and versions [11,12], statistical models [13],…”

Section: Introductionmentioning

confidence: 99%

Longitudinal brain structure changes in Parkinson’s disease: a replication study

Sokołowski

Bhagwat

Chatelain

et al. 2023

Preprint

View full text Add to dashboard Cite

Context: An existing major challenge in Parkinson's disease (PD) research is the identification of biomarkers of disease progression. While Magnetic Resonance Imaging (MRI) is a potential source of PD biomarkers, none of the MRI measures of PD are robust enough to warrant their adoption in clinical research. This study is part of a project that aims to replicate 11 PD studies reviewed in a recent survey (JAMA neurology, 78(10) 2021) to investigate the robustness of PD neuroimaging findings to data and analytical variations. Objective. This study attempts to replicate the results in Hanganu et al. (Brain, 137(4) 2014) using data from the Parkinson's Progression Markers Initiative (PPMI). Methods. Using 25 PD subjects and 18 healthy controls, we analyzed the rate of change of cortical thickness and of the volume of subcortical structures, and we measured the relationship between MRI structural changes and cognitive decline. We compared our findings to the results in the original study. Results. (1) Similarly to the original study, PD patients with mild cognitive impairment (MCI) exhibited increased cortical thinning over time compared to patients without MCI in the right middle temporal gyrus, insula, and precuneus. (2) The rate of cortical thinning in the left inferior temporal and precentral gyri in PD patients correlated with the change in cognitive performance. (3) There were no group differences in the change of subcortical volumes. (4) We did not find a relationship between the change in subcortical volumes and the change in cognitive performance. Conclusion. Despite important differences in the dataset used in this replication study, and despite differences in sample size, we were able to partially replicate the original results. We produced a publicly available reproducible notebook allowing researchers to further investigate the reproducibility of the results in Hanganu et al. (2014) when more data becomes available in PPMI.

show abstract

A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

Cited by 9 publications

References 58 publications

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

A Causal Analysis of the Effect of Age and Sex Differences on Brain Atrophy in the Elderly Brain

Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

Longitudinal brain structure changes in Parkinson’s disease: a replication study

Contact Info

Product

Resources

About