Regional rather than global brain age mediates cognitive function in cerebral small vessel disease

Lee, Pei-Lin; Kuo, Chen Yuan; Wang, Pei Ning; Chen, Liang Kung; Lin, Ching Po; Chou, Kun Hsien; Chung, Chih‐Ping

doi:10.1093/braincomms/fcac233

Cited by 9 publications

(4 citation statements)

References 59 publications

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“…To deepen our understanding of how migraines influence brain health, we examined the role of brain volume in linking the frequency of headaches in migraine patients to the Brain Age Gap. This type of analysis, known as mediation analysis, is a standard method in the realm of neuroimaging [ 48 ]. Utilizing a single-tier, three-variable mediation model, we sought to discover if segmented brain volume could serve as a mediator (M) between headache frequency (independent variable, X) and the Brain Age Gap (dependent variable, Y).…”

Section: Methodsmentioning

confidence: 99%

Increased MRI-based Brain Age in chronic migraine patients

Navarro-González,

García-Azorín,

Guerrero-Peral

et al. 2023

J Headache Pain

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Introduction Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants. Methods We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74). Results CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine. Conclusion The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

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Section: Methodsmentioning

confidence: 99%

Increased MRI-based Brain Age in chronic migraine patients

Navarro-González,

García-Azorín,

Guerrero-Peral

et al. 2023

J Headache Pain

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“…The extent to which each subject deviates from healthy brain‐aging trajectories, expressed as the difference between predicted and chronological age (the brain‐predicted age difference, brain‐PAD), has been proposed as an index of structural brain health, sensitive to pathology in a wide spectrum of neurological and psychiatric disorders (Kaufmann et al, 2019 ). As a relevant example, brain‐age predictions are sensitive to white matter hyperintensities (WMH) and brain volumes, imaging features that are both manifestations of cerebral small vessel disease (Lee et al, 2022 ; Shi et al, 2022 ; Wagen et al, 2022 ), which is thought to be the main pathogenetic mechanisms through which FD impacts brain health (Kolodny et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm

Montella,

Tranfa,

Scaravilli

et al. 2024

Human Brain Mapping

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While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain‐age paradigm to assess whether FD patients' brains appear older than normal and to validate brain‐predicted age difference (brain‐PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1‐weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4–86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain‐age predictions in the internal cohort. Within a linear modeling framework, brain‐PAD was tested for age/sex‐adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel‐level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain‐age model achieved accurate out‐of‐sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain‐PAD than HC (estimated marginal means: 3.1 vs. −0.1, p = .01). Brain‐PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = −153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain‐PAD correlates with FD‐related multi‐organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

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“…(5) The extent to which each subject deviates from healthy brain-aging trajectories, expressed as the difference between predicted and chronological age (the brain-predicted age difference, brain-PAD), has been proposed as an index of structural brain health, sensitive to pathology in a wide spectrum of neurological and psychiatric disorders. (6) As a relevant example, brain-age predictions are sensitive to white matter hyperintensities (WMH) and brain volumes, imaging features that are both manifestations of cerebral small vessel disease, (7)(8)(9) which is thought to be the main pathogenetic mechanisms through which FD impacts brain health.…”

Section: Introductionmentioning

confidence: 99%

Assessing Brain Involvement in Fabry Disease with Deep Learning and the Brain-Age Paradigm

Montella

Tranfa

Scaravilli

et al. 2023

Preprint

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Background While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. Methods MRI scans of FD patients and healthy controls (HC) from a single Institution were retrospectively studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from 8 publicly available sources (N=2160; mean age=33y[range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs HC), FASTEX score, and both global and voxel-level neuroimaging measures. Results We studied 52 FD patients (mean age: 40.6 years, SD: 12.6; 28F) and 58 HC (mean age: 38.4 years, SD: 13.4; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error=4.01y, R2=0.90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs -0.1, p=0.01). Brain-PAD was associated with FASTEX score (B=0.10, p=0.02), brain parenchymal fraction (B=-153.50, p=0.001), white matter hyperintensities load (B=0.85, p=0.01), and tissue volume reduction throughout the brain. Conclusions We demonstrated that the brains of FD patients appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

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Regional rather than global brain age mediates cognitive function in cerebral small vessel disease

Cited by 9 publications

References 59 publications

Increased MRI-based Brain Age in chronic migraine patients

Increased MRI-based Brain Age in chronic migraine patients

Assessing brain involvement in Fabry disease with deep learning and the brain‐age paradigm

Assessing Brain Involvement in Fabry Disease with Deep Learning and the Brain-Age Paradigm

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