Figure
Differences between the gray matter (GM) and white matter (WM) associations with clinical variables. P -values are reported before and after FDR correction. BP = blood pressure.
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Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and res...
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... test whether the gray and white matter models showed differential associations with the clinical variables, Z tests for correlated samples ( Eq. (1) ) were run for each of the variables. The results showed stronger associations between blood pressure and white matter compared to gray matter, as shown in Table 9 . No differences in the associations of alcohol intake and stroke risk with these modalities were observed. ...Similar publications
The birth order literature emphasizes the role of parental investments in explaining why firstborns have higher human capital outcomes than their laterborn siblings. We use birth order as a proxy for investments and interact it with genetic endowments. Exploiting only within-family variation in both ensures they are exogenous as well as orthogonal...
Citations
... Recently, increasing attention has been paid to the estimate of brain age using MRI images via machine learning approaches [23,24]. The gap between brain age and chronological age (brainage gap) has been used to characterize brain aging. ...
... Patients with Alzheimer's disease (AD) and schizophrenia exhibit a greater brainage gap than healthy controls [25,26]. It has been shown that the brain-age gap is correlated with a broad range of cardiovascular and metabolic risk factors [23,24]. These findings suggested that the brain-age gap may be a good indicator of brain health. ...
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Metabolic syndrome (MetS) is characterized by a constellation of metabolic risk factors, including obesity, hypertriglyceridemia, low high-density lipoprotein (HDL) levels, hypertension, and hyperglycemia, and is associated with stroke and neurodegenerative diseases. This study capitalized on brain structural images and clinical data from the UK Biobank and explored the associations of brain morphology with MetS and brain aging due to MetS. Cortical surface area, thickness, and subcortical volumes were assessed using FreeSurfer. Linear regression was used to examine associations of brain morphology with five MetS components and the MetS severity in a metabolic aging group (N = 23,676, age 62.8 ± 7.5 years). Partial least squares (PLS) were employed to predict brain age using MetS-associated brain morphology. The five MetS components and MetS severity were associated with increased cortical surface area and decreased thickness, particularly in the frontal, temporal, and sensorimotor cortex, and reduced volumes in the basal ganglia. Obesity best explained the variation of brain morphology. Moreover, participants with the most severe MetS had brain age 1-year older than those without MetS. Brain age in patients with stroke (N = 1042), dementia (N = 83), Parkinson’s (N = 107), and multiple sclerosis (N = 235) was greater than that in the metabolic aging group. The obesity-related brain morphology had the leading discriminative power. Therefore, the MetS-related brain morphological model can be used for risk assessment of stroke and neurodegenerative diseases. Our findings suggested that prioritizing adjusting obesity among the five metabolic components may be more helpful for improving brain health in aging populations.
... Our t-SNE embedding of our latent space showed that there is separation between the classes of AD versus HC. There exists some entanglement between the classes, as the morphological changes to the brain displayed in older individuals bares a resemblance to neurodegeneration (Cole et al., 2019;De Lange et al., 2020;Cole et al., 2018). As such, it is expected that some of the older individual's in our cohort would (max age = 97) would be clustered close to AD individuals. ...
In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability of deep learning models by imbuing them with a similar style of reasoning. In this work, we apply this principle by classifying Alzheimer's Disease based on the similarity of images to training examples within the latent space. We use a contrastive loss combined with a diffusion autoencoder backbone, to produce a semantically meaningful latent space, such that neighbouring latents have similar image-level features. We achieve a classification accuracy comparable to black box approaches on a dataset of 2D MRI images, whilst producing human interpretable model explanations. Therefore, this work stands as a contribution to the pertinent development of accurate and interpretable deep learning within medical imaging.
... We used the XGBoost package in R [47,48] to build a ML model to predict age from a set of cortical thickness, cortical surface area, cortical volume, subcortical volume (i.e., 180/180/180/8 regions of interest for each hemisphere) and cortical summary measures from the training group, in total 1145 measures (similar to; [17,49,50] henceforth termed the 'full ML model'). In addition, we trained three separate ML models that included either measures of (i) cortical thickness (360 measures), (ii) surface area (360 measures) or (iii) subcortical volume measures (16 measures) only to predict age. ...
The 15q11.2 BP1-BP2 copy number variant (CNV) is associated with altered brain morphology and risk for atypical development, including increased risk for schizophrenia and learning difficulties for the deletion. However, it is still unclear whether differences in brain morphology are associated with neurodevelopmental or neurodegenerative processes. This study derived morphological brain MRI measures in 15q11.2 BP1-BP2 deletion (n = 124) and duplication carriers (n = 142), and matched deletion-controls (n = 496) and duplication-controls (n = 568) from the UK Biobank study to investigate the association with brain morphology and estimates of brain ageing. Further, we examined the ageing trajectory of age-affected measures (i.e., cortical thickness, surface area, subcortical volume, reaction time, hand grip strength, lung function, and blood pressure) in 15q11.2 BP1-BP2 CNV carriers compared to non-carriers. In this ageing population, the results from the machine learning models showed that the estimated brain age gaps did not differ between the 15q11.2 BP1-BP2 CNV carriers and non-carriers, despite deletion carriers displaying thicker cortex and lower subcortical volume compared to the deletion-controls and duplication carriers, and lower surface area compared to the deletion-controls. Likewise, the 15q11.2 BP1-BP2 CNV carriers did not deviate from the ageing trajectory on any of the age-affected measures examined compared to non-carriers. Despite altered brain morphology in 15q11.2 BP1-BP2 CNV carriers, the results did not show any clear signs of apparent altered ageing in brain structure, nor in motor, lung or heart function. The results do not indicate neurodegenerative effects in 15q11.2 BP1-BP2 CNV carriers.
... Conversely a positive BrainPAD is indicative of an older than "expected" brain. In the last decade, these tools have been able to predict age in both healthy adolescents (Franke et al., 2012) and older adult populations (Cole et al., 2018;de Lange et al., 2020) with a high degree of accuracy. Further, outputs from these algorithms show high test/retest reliability (Cole et al., 2017) and correctly identify higher baseline brain age cross-sectionally and accelerated brain aging longitudinally in clinical populations manifesting symptoms of cognitive decline including development of Alzheimers Disease (Franke et al., 2010), multiple sclerosis (Cole et al., 2019;Cole et al., 2020), stroke (Richard et al., 2020), dementia (Biondo et al., 2022) and traumatic brain injury (Cole et al., 2015). ...
Introduction
Changes in brain structure and function occur with aging. However, there is substantial heterogeneity both in terms of when these changes begin, and the rate at which they progress. Understanding the mechanisms and/or behaviors underlying this heterogeneity may allow us to act to target and slow negative changes associated with aging.
Methods
Using T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. We also assessed maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry. Associations were explored both linearly and logistically using categorical groupings.
Results
Visceral Adipose Tissue (VAT), Total Sleep Time (TST) and maximal aerobic capacity all showed significant associations with BrainPAD. Greater VAT was associated with higher (i.e,. older than chronological) BrainPAD (r = 0.149 p = 0.001)Greater TST was associated with higher BrainPAD (r = 0.087 p = 0.042) and greater aerobic capacity was associated with lower BrainPAD (r = −0.088 p = 0.040). With linear regression, both VAT and TST remained significant (p = 0.036 and 0.008 respectively). Each kg of VAT predicted a 0.741 year increase in BrainPAD, and each hour of increased TST predicted a 0.735 year increase in BrainPAD. Maximal aerobic capacity did not retain statistical significance in fully adjusted linear models.
Discussion
Accumulation of visceral adipose tissue and greater total sleep time, but not aerobic capacity, total daily physical activity, or sleep quantity and/or quality are associated with brains that are physiologically older than would be expected based upon chronological age alone (BrainPAD).
... 7,8 Moreover, other authors found an association between higher brain age and a greater risk of stroke, trapping stroke survivors in a vicious circle. 9 However, the clinical determinants of brain age in patients with stroke are currently unknown, warranting further imaging studies in stroke populations to identify potentially preventable risk factors. ...
... 4,24 We found that high RBA was associated with HTN, DM, and a history of smoking, in line with the results based on large cohorts such as UK Biobank and Whitehall II. 4,5,9,24 This adds to the body of evidence that cardiovascular health and brain health are intertwined and stresses the importance of preventative medicine. 25 Our results also showed that a history of prior stroke was the most influential clinical factor affecting RBA, with an effect size 3-fold larger than other clinical variables. ...
Background and Objectives
While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological “brain age”. We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of stroke patients will be associated with cardiovascular risk factors and worse functional outcomes.
Methods
We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison to chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs, and a logistic regression model of favorable functional outcomes taking RBA as input.
Results
We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age=62.8 years, 42.0% females). T2-FLAIR radiomics predicted chronological ages (mean absolute error=6.9 years, r=0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted Odds-Ratios: 0.58, 0.76, 0.48, 0.55; all p-values<0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.
Discussion
T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.
... For example, it has been found using data from the Alzheimer's disease neuroimaging initiative (18) and LIFE (19) that a higher estimate of biological age, relative to the true chronological age, predicts cognitive decline in pathological and normal ageing. In addition, higher brain age related to cardiovascular risk factors (20,21) and estimates changed even in correlation with pregnancy, childbirth (22) and oestradiol levels during menstrual cycle in women (23) . Notably, the obesity-associated higher gap in brain age further links to cognitive disadvantages in the LIFE cohort (16) , proposing brain age based on raw MRI a valuable tool for future precision medicine. ...
Most societies witness an ever increasing prevalence of both obesity and dementia, a scenario related to often underestimated individual and public health burden. Overnutrition and weight gain have been linked with abnormal functionality of homoeostasis brain networks and changes in higher cognitive functions such as reward evaluation, executive functions and learning and memory. In parallel, evidence has accumulated that modifiable factors such as obesity and diet impact the gut–brain axis and modulate brain health and cognition through various pathways. Using neuroimaging data from epidemiological studies and randomised clinical trials, we aim to shed light on the underlying mechanisms and to determine both determinants and consequences of obesity and diet at the level of human brain structure and function. We analysed multimodal 3T MRI of about 2600 randomly selected adults (47 % female, 18–80 years of age, BMI 18–47 kg/m ² ) of the LIFE-Adult study, a deeply phenotyped population-based cohort. In addition, brain MRI data of controlled intervention studies on weight loss and healthy diets acquired in lean, overweight and obese participants may help to understand the role of the gut–brain axis in food craving and cognitive ageing. We find that higher BMI and visceral fat accumulation correlate with accelerated brain age, microstructure of the hypothalamus, lower thickness and connectivity in default mode- and reward-related areas, as well as with subtle grey matter atrophy and white matter lesion load in non-demented individuals. Mediation analyses indicated that higher visceral fat affects brain tissue through systemic low-grade inflammation, and that obesity-related regional changes translate into cognitive disadvantages. Considering longitudinal studies, some, but not all data indicate beneficial effects of weight loss and healthy diets such as plant-based nutrients and dietary patterns on brain ageing and cognition. Confounding effects of concurrent changes in other lifestyle factors or false positives might help to explain these findings. Therefore a more holistic intervention approach, along with open science tools such as data and code sharing, in-depth pre-registration and pooling of data could help to overcome these limitations. In addition, as higher BMI relates to increased head micro-movements during MRI, and as head motion in turn systematically induces image artefacts, future studies need to rigorously control for head motion during MRI to enable valid neuroimaging results. In sum, our results support the view that overweight and obesity are intertwined with markers of brain health in the general population, and that weight loss and plant-based diets may help to promote brain plasticity. Meta-analyses and longitudinal cohort studies are underway to further differentiate causation from correlation in obesity- and nutrition-brain research.
... This was also true when investigating effects of known cardiovascular risk factors on brain ageing. 17 In addition, increased PAD scores correlated well with increased mortality or decreased survival in a range of different conditions. 15,18 To date, there are only few reports on classical 'motor' neurodegenerative disorders, such as Parkinson's disease and none on ALS. ...
Age is the most important single risk factor of sporadic amyotrophic lateral sclerosis. Neuroimaging together with machine-learning algorithms allows estimating individuals' brain age. Deviations from normal brain-ageing trajectories (so called predicted brain age difference) were reported for a number of neuropsychiatric disorders. While all of them showed increased predicted brain-age difference, there is surprisingly few data yet on it in motor neurodegenerative diseases. In this observational study, we made use of previously trained algorithms of 3377 healthy individuals and derived predicted brain age differences from volumetric MRI scans of 112 amyotrophic lateral sclerosis patients and 70 healthy controls. We correlated predicted brain age difference scores with vox-el-based morphometry data and multiple different motoric disease characteristics as well as cognitive/behavioural changes categorized according to Strong and Rascovsky. Against our primary hypothesis, there was no higher predicted brain-age difference in the amyo-trophic lateral sclerosis patients as a group. None of the motoric phenotypes/characteristics influenced predicted brain-age difference. However, cognitive/behavioural impairment led to significantly increased predicted brain-age difference, while slowly progressive as well as cognitive/behavioural normal amyotrophic lateral sclerosis patients had even younger brain ages than healthy controls. Of note, the cognitive/behavioural normal amyotrophic lateral sclerosis patients were identified to have increased cerebellar brain volume as potential resilience factor. Younger brain age was associated with longer survival. Our results raise the question whether younger brain age in amyotrophic lateral sclerosis with only motor impairment provides a cerebral reserve against cognitive and/or behavioural impairment and faster disease progression. This new conclusion needs to be tested in subsequent samples. In addition, it will be interesting to test whether a potential effect of cerebral reserve is specific for amyotrophic lateral sclerosis or can also be found in other neurodegenerative diseases with primary motor impairment. Abbreviations: ALS = amyotrophic lateral sclerosis; ALSFRS-R = ALS functional rating scale revised; ALScn = ALS without cognitive/behavioural impairments ('cognitive normal'); ALSci = ALS with cognitive impairment; ALSbi = ALS with behavioural impairment; ALScbi = ALS with cognitive and behavioural impairments; ALS-FTD = ALS with frontotemporal dementia; HCs = healthy controls; LMN ALS = lower motor neuron predominant ALS; MoCA = Montreal cognitive assessment; PAD = predicted brain age difference; PMA = progressive muscular atrophy; UMN ALS = upper motor neuron predominant ALS; VBM = voxel-based morphometry Graphical Abstract
... Brain age prediction has emerged as a useful tool for combining a rich variety of brain characteristics into single estimates per individual, providing a reliable proxy of brain integrity and health (Franke et al., 2010;Cole et al., 2019;Beck et al., 2021). Based on recent studies suggesting that tissue-specific age prediction can provide further detail De Lange et al., 2020a;Eavani et al., 2018;Voldsbekk et al., 2021), we estimated GM and WM brain age separately. WMH volume derived from T2 fluidattenuated inversion recovery (FLAIR) images was examined as an additional measure, as a number of studies indicate higher WMH prevalence in females compared to males (Alqarni et al., 2021;Jorgensen et al., 2018;Lohner et al., 2022;Sachdev et al., 2009;Than et al., 2021;Van Den Heuvel et al., 2004) and recent evidence points to sex-specific associations between cardiometabolic risk factors and WMH pathology (Alqarni et al., 2021). ...
The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
... Furthermore, different features and models could also have a dramatic effect on the final results. Several studies report the great potential of the multimodal features [80][81][82] and deep learning algorithms [83][84][85] [9] Mishra, S., Beheshti, I. & Khanna, P. A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions. IEEE Reviews in Biomedical ...
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies reveal that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a $+4.43$ years ($\text{$p$} < 0.0001$, $\text{Cohen's $d$} = 0.35$, $\text{95\% CI}:1.86 - 3.91$) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant $+2.09$ years ($\text{$p$} < 0.05$, $\text{Cohen's $d$} = 0.134483$) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.
... While individual variation in BAG reflects a combination of genetic and environmental factors (Elliott et al., 2019;Kaufmann et al., 2019;Vidal-Piñeiro et al., 2021), clinical studies indicate that an older "brain age" relative to what is expected for an individual's chronological age (i.e., positive BAG) may in part reflect accelerated neural aging processes (Cole, Raffel, et al., 2019;Han et al., 2020;Kaufmann et al., 2019;Kolenic et al., 2018;Rokicki et al., 2020;Tønnesen et al., 2020;van Gestel et al., 2019). Positive BAG values have also been associated with negative outcomes in population-based studies, including cardiovascular risk, cognitive impairments, and dementia risk (Biondo et al., 2021;de Lange, Anatürk, et al., 2020;Egorova et al., 2019;Franke & Gaser, 2012;Gaser et al., 2013;Kolbeinsson et al., 2020;Löwe et al., 2016;Wang et al., 2019). Previous studies have shown accurate age prediction based on diffusion-weighted imaging measures Cole, 2020;Richard et al., 2018;Voldsbekk et al., 2021), as well as associations between WM BAG and CMRs . ...
... Hence, future studies may aim to investigate regional and diffusion metric-specific estimates of brain aging in relation to APOE genotype and CMRs. Modality-specific BAG estimates are also relevant for identifying differences in brain tissue affected by a specific condition or disease Cole, 2020;de Lange, Anatürk, et al., 2020;Rokicki et al., 2020). For example, one of our previous studies found that BMI interacted with AD risk to influence gray-matter based BAG, such that females with greater AD risk benefited more from a higher BMI (Subramaniapillai et al., 2021). ...
Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.
