Predicted Brain Age After Stroke

Regional brain aging: premature aging of the domain general system predicts aphasia severity

Premature brain aging is associated with poorer cognitive reserve and lower resilience to injury. When there are focal brain lesions, brain regions may age at different rates within the same individual. Therefore, we hypothesize that reduced gray matter volume within specific brain systems commonly associated with language recovery may be important for long-term aphasia severity. Here we show that individuals with stroke aphasia have a premature brain aging in intact regions of the lesioned hemisphere. In left domain-general regions, premature brain aging, gray matter volume, lesion volume and age were all significant predictors of aphasia severity. Increased brain age following a stroke is driven by the lesioned hemisphere. The relationship between brain age in left domain-general regions and aphasia severity suggests that degradation is possible to specific brain regions and isolated aging matters for behavior.

Association of Blood Pressure With Brain Ages: A Cohort Study of Gray and White Matter Aging Discrepancy in Mid-to-Older Adults From UK Biobank

BACKGROUND

Gray matter (GM) and white matter (WM) impairments are both associated with raised blood pressure (BP), although whether elevated BP is differentially associated with the GM and WM aging process remains inadequately examined.

METHODS

We included 37 327 participants with diffusion-weighted imaging (DWI) and 39 630 participants with T1-weighted scans from UK Biobank. BP was classified into 4 categories: normal BP, high-normal BP, grade 1, and grade 2 hypertension. Brain age gaps (BAGs) for GM (BAGGM) and WM (BAGWM) were derived from diffusion-weighted imaging and T1 scans separately using 3-dimensional-convolutional neural network deep learning techniques.

RESULTS

There was an increase in both BAGGM and BAGWM with raised BP (P<0.05). BAGWM was significantly larger than BAGGM at high-normal BP (0.195 years older; P=0.006), grade 1 hypertension (0.174 years older; P=0.004), and grade 2 hypertension (0.510 years older; P<0.001), but not for normal BP. Mediation analysis revealed that the association between hypertension and cognitive decline was primarily mediated by WM impairment. Mendelian randomization analysis suggested a causal relationship between hypertension and WM aging acceleration (unstandardized B, 1.780; P=0.016) but not for GM (P>0.05). Sliding-window analysis indicated the association between hypertension and brain aging acceleration was moderated by chronological age, showing stronger correlations in midlife but weaker associations in the older age.

CONCLUSIONS

Compared with GM, WM was more vulnerable to raised BP. Our study provided compelling evidence that concerted efforts should be directed towards WM damage in individuals with hypertension in clinical practice.

Should individual timeline and serial CT/MRI panels of all patients be presented in acute brain insult cohorts? A pilot study of 45 patients with decompressive craniectomy after aneurysmal subarachnoid hemorrhage

PURPOSE

Our review of acute brain insult articles indicated that the patients' individual (i) timeline panels with the defined time points since the emergency call and (ii) serial brain CT/MRI slice panels through the neurointensive care until death or final brain tissue outcome at 12 months or later are not presented.

METHODS

We retrospectively constructed such panels for the 45 aneurysmal subarachnoid hemorrhage (aSAH) patients with a secondary decompressive craniectomy (DC) after the acute admission to neurointensive care at Kuopio University Hospital (KUH) from a defined population from 2005 to 2018. The patients were indicated by numbers (1.-45.) in the pseudonymized panels, tables, results, and discussion. The timelines contained up to ten defined time points on a logarithmic time axis until death ([Formula: see text]; 56%) or 3 years ([Formula: see text]; 44%). The brain CT/MRI panels contained a representative slice from the following time points: SAH diagnosis, after aneurysm closure, after DC, at about 12 months (20 survivors).

RESULTS

The timelines indicated re-bleeds and allowed to compare the times elapsed between any two time points, in terms of workflow swiftness. The serial CT/MRI slices illustrated the presence and course of intracerebral hemorrhage (ICH), perihematomal edema, intraventricular hemorrhage (IVH), hydrocephalus, delayed brain injury, and, in the 20 (44%) survivors, the brain tissue outcome.

CONCLUSIONS

The pseudonymized timeline panels and serial brain imaging panels, indicating the patients by numbers, allowed the presentation and comparison of individual clinical courses. An obvious application would be the quality control in acute or elective medicine for timely and equal access to clinical care.

Replication and Refinement of Brain Age Model for adolescent development

The discrepancy between chronological age and estimated brain age, known as the brain age gap, may serve as a biomarker to reveal brain development and neuropsychiatric problems. This has motivated many studies focusing on the accurate estimation of brain age using different features and models, of which the generalizability is yet to be tested. Our recent study has demonstrated that conventional machine learning models can achieve high accuracy on brain age prediction during development using only a small set of selected features from multimodal brain imaging data. In the current study, we tested the replicability of various brain age models on the Adolescent Brain Cognitive Development (ABCD) cohort. We proposed a new refined model to improve the robustness of brain age prediction. The direct replication test for existing brain age models derived from the age range of 8-22 years onto the ABCD participants at baseline (9 to 10 years old) and year-two follow-up (11 to 12 years old) indicate that pre-trained models could capture the overall mean age failed precisely estimating brain age variation within a narrow range. The refined model, which combined broad prediction of the pre-trained model and granular information with the narrow age range, achieved the best performance with a mean absolute error of 0.49 and 0.48 years on the baseline and year-two data, respectively. The brain age gap yielded by the refined model showed significant associations with the participants' information processing speed and verbal comprehension ability on baseline data.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke

BACKGROUND AND OBJECTIVES

Functional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes.

METHODS

We conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain structural MRIs and clinical measures from the ENIGMA Stroke Recovery. Brain age was calculated from 77 neuroanatomical features using a ridge regression model trained and validated on 4,314 healthy controls. We performed a 3-step mediation analysis with robust mixed-effects linear regression models to examine relationships between brain age, lesion damage, and stroke outcomes. We used propensity score matching and logistic regression to examine whether brain resilience predicts good vs poor outcomes in patients with matched lesion damage.

RESULTS

We examined 963 patients across 38 cohorts. Greater lesion damage was associated with older brain age (β = 0.21; 95% CI 0.04-0.38, p = 0.015), which in turn was associated with poorer outcomes, both in the sensorimotor domain (β = -0.28; 95% CI -0.41 to -0.15, p < 0.001) and across multiple domains of function (β = -0.14; 95% CI -0.22 to -0.06, p < 0.001). Brain age mediated 15% of the impact of lesion damage on sensorimotor performance (95% CI 3%-58%, p = 0.01). Greater brain resilience explained why people have better outcomes, given matched lesion damage (odds ratio 1.04, 95% CI 1.01-1.08, p = 0.004).

DISCUSSION

We provide evidence that younger brain age is associated with superior poststroke outcomes and modifies the impact of focal damage. The inclusion of imaging-based assessments of brain age and brain resilience may improve the prediction of poststroke outcomes compared with focal injury measures alone, opening new possibilities for potential therapeutic targets.

Advanced Brain Age and Chronic Poststroke Aphasia Severity

BACKGROUND AND OBJECTIVES

Chronic poststroke language impairment is typically worse in older individuals or those with large stroke lesions. However, there is unexplained variance that likely depends on intact tissue beyond the lesion. Brain age is an emerging concept, which is partially independent from chronologic age. Advanced brain age is associated with cognitive decline in healthy older adults; therefore, we aimed to investigate the relationship with stroke aphasia. We hypothesized that advanced brain age is a significant factor associated with chronic poststroke language impairments, above and beyond chronologic age, and lesion characteristics.

METHODS

This cohort study retrospectively evaluated participants from the Predicting Outcomes of Language Rehabilitation in Aphasia clinical trial (NCT03416738), recruited through local advertisement in South Carolina (US). Primary inclusion criteria were left hemisphere stroke and chronic aphasia (≥12 months after stroke). Participants completed baseline behavioral testing including the Western Aphasia Battery-Revised (WAB-R), Philadelphia Naming Test (PNT), Pyramids and Palm Trees Test (PPTT), and Wechsler Adult Intelligence Scale Matrices subtest, before completing 6 weeks of language therapy. The PNT was repeated 1 month after therapy. We leveraged modern neuroimaging techniques to estimate brain age and computed a proportional difference between chronologic age and estimated brain age. Multiple linear regression models were used to evaluate the relationship between proportional brain age difference (PBAD) and behavior.

RESULTS

Participants (N = 93, 58 males and 35 females, average age = 61 years) had estimated brain ages ranging from 14 years younger to 23 years older than chronologic age. Advanced brain age predicted performance on semantic tasks (PPTT) and language tasks (WAB-R). For participants with advanced brain aging (n = 47), treatment gains (improvement on the PNT) were independently predicted by PBAD (T = -2.0474, p = 0.0468, 9% of variance explained).

DISCUSSION

Through the application of modern neuroimaging techniques, advanced brain aging was associated with aphasia severity and performance on semantic tasks. Notably, therapy outcome scores were also associated with PBAD, albeit only among participants with advanced brain aging. These findings corroborate the importance of brain age as a determinant of poststroke recovery and underscore the importance of personalized health factors in determining recovery trajectories, which should be considered during the planning or implementation of therapeutic interventions.

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke

BACKGROUND AND OBJECTIVES

While chronological age is one of the most influential determinants of poststroke 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 patients with stroke 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 with 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% female patients). 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.

Retinal age gap as a predictive biomarker of stroke risk

BACKGROUND

The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke.

METHODS

A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk.

RESULTS

A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511).

CONCLUSIONS

We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

Brain age predicts long-term recovery in post-stroke aphasia

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

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.

Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation

Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.

Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals

Background:

There is considerable variability in the rate at which we age biologically, and the brain is particularly susceptible to the effects of aging.

Objective:

We examined the test-retest reliability of brain age at one- and three-year intervals and identified characteristics that predict the longitudinal change in brain-predicted age difference (brain-PAD, defined by deviations of brain age from chronological age).

Methods:

T1-weighted magnetic resonance images were acquired at three timepoints from 497 community-dwelling adults (73.8±3.5 years at baseline, 48% were female). Brain age was estimated from whole brain volume, using a publicly available algorithm trained on an independent dataset. Linear mixed models were used, adjusting for sex, age, and age².

Results:

Excellent retest reliability of brain age was observed over one and three years. We identified a significant sex difference in brain-PAD, where a faster rate of brain aging (worsening in brain age relative to chronological age) was observed in men, and this finding replicated in secondary analyses. The effect size, however, was relatively weak, equivalent to 0.16 years difference per year. A higher score in physical health related quality of life and verbal fluency were associated with a faster rate of brain aging, while depression was linked to a slower rate of brain aging, but these findings were not robust.

Conclusion:

Our study provides consistent evidence that older men have slightly faster brain atrophy than women. Given the sparsity of longitudinal research on brain age in older populations, future prospective studies are needed to confirm our findings.

Multimodal Brain Age Prediction with Feature Selection and Comparison

Brain age, an estimated biological age from anatomical and/or functional brain imaging data, and its deviation from the chronological age (brain age gap) have shown the potential to serve as biomarkers for characterizing typical brain development, the abnormal aging process, and early indicators of clinical neuropsychiatric problems. In this study, we leverage multimodal brain imaging data for brain age prediction. We studied and compared the performance of individual data modalities (gray matter density in components and regions of interest, cortical and subcortical anatomical features, resting-state functional connectivity) and different combinations of multiple data modalities using data collected from 1417 participants with age between 8 and 22 years. The result indicates that feature selection and multimodal imaging data can improve brain age prediction with linear support vector and partial least squares regression models. We have achieved a mean absolute error of 1.22 years on the test data with 188 features selected equally from all data sources, better than any individual source. After bias correction, the brain age gap was significantly associated with attention accuracy/speed and motor speed in addition to age. Our results conclude that traditional machine learning with proper feature selection can achieve similar if not better performance compared to complex deep learning neural network methods for the used sample size.

Neurodegeneration Over 3 Years Following Ischaemic Stroke: Findings From the Cognition and Neocortical Volume After Stroke Study

Background: Stroke survivors are at high risk of dementia, associated with increasing age and vascular burden and with pre-existing cognitive impairment, older age. Brain atrophy patterns are recognised as signatures of neurodegenerative conditions, but the natural history of brain atrophy after stroke remains poorly described. We sought to determine whether stroke survivors who were cognitively normal at time of stroke had greater total brain (TBV) and hippocampal volume (HV) loss over 3 years than controls. We examined whether stroke survivors who were cognitively impaired (CI) at 3 months following their stroke had greater brain volume loss than cognitively normal (CN) stroke participants over the next 3 years.

Methods: Cognition And Neocortical Volume After Stroke (CANVAS) study is a multi-centre cohort study of first-ever or recurrent adult ischaemic stroke participants compared to age- and sex-matched community controls. Participants were followed with MRI and cognitive assessments over 3 years and were free of a history of cognitive impairment or decline at inclusion. Our primary outcome measure was TBV change between 3 months and 3 years; secondary outcomes were TBV and HV change comparing CI and CN participants. We investigated associations between group status and brain volume change using a baseline-volume adjusted linear regression model with robust standard error.

Results: Ninety-three stroke (26 women, 66.7 ± 12 years) and 39 control participants (15 women, 68.7 ± 7 years) were available at 3 years. TBV loss in stroke patients was greater than controls: stroke mean (M) = 20.3 cm³ ± SD 14.8 cm³; controls M = 14.2 cm³ ± SD 13.2 cm³; [adjusted mean difference 7.88 95%CI (2.84, 12.91) p-value = 0.002]. TBV decline was greater in those stroke participants who were cognitively impaired (M = 30.7 cm³; SD = 14.2 cm³) at 3 months (M = 19.6 cm³; SD = 13.8 cm³); [adjusted mean difference 10.42; 95%CI (3.04, 17.80), p-value = 0.006]. No statistically significant differences in HV change were observed.

Conclusions: Ischaemic stroke survivors exhibit greater neurodegeneration compared to stroke-free controls. Brain atrophy is greater in stroke participants who were cognitively impaired early after their stroke. Early cognitive impairment was associated greater subsequent atrophy, reflecting the combined impacts of stroke and vascular brain burden. Atrophy rates could serve as a useful biomarker for trials testing interventions to reduce post-stroke secondary neurodegeneration.

Clinical Trail Registration:http://www.clinicaltrials.gov, identifier: NCT02205424.

Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P < 0.004). We tested subacute (≤90 days) and chronic (≥180 days) stroke subgroups separately, with exploratory analyses in early stroke (≤21 days) and across all time. Sub-analyses in chronic stroke were also performed based on class of sensorimotor deficits (impairment, activity limitations) and side of lesioned hemisphere. Worse sensorimotor behaviour was associated with a smaller ipsilesional thalamic volume in both early (n = 179; d = 0.68) and subacute (n = 274, d = 0.46) stroke. In chronic stroke (n = 404), worse sensorimotor behaviour was associated with smaller ipsilesional putamen (d = 0.52) and nucleus accumbens (d = 0.39) volumes, and a larger ipsilesional lateral ventricle (d = -0.42). Worse chronic sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n = 256) was associated with smaller ipsilesional putamen (d = 0.72) and larger lateral ventricle (d = -0.41) volumes, while several measures of activity limitations (n = 116) showed no significant relationships. In the full cohort across all time (n = 828), sensorimotor behaviour was associated with the volumes of the ipsilesional nucleus accumbens (d = 0.23), putamen (d = 0.33), thalamus (d = 0.33) and lateral ventricle (d = -0.23). We demonstrate significant relationships between post-stroke sensorimotor behaviour and reduced volumes of deep grey matter structures that were spared by stroke, which differ by time and class of sensorimotor measure. These findings provide additional insight into how different cortico-thalamo-striatal circuits support post-stroke sensorimotor outcomes.

Optimal Blood Pressure Keeps Our Brains Younger

Background: Elevated blood pressure (BP) is a major health risk factor and the leading global cause of premature death. Hypertension is also a risk factor for cognitive decline and dementia. However, when elevated blood pressure starts impacting cerebral health is less clear. We addressed this gap by estimating how a validated measure of brain health relates to changes in BP over a period of 12 years. Methods: Middle-age (44-46 years at baseline, n = 335, 52% female) and older-age (60-64 years, n = 351, 46% female) cognitively intact individuals underwent up to four brain scans. Brain health was assessed using a machine learning approach to produce an estimate of "observed" age (BrainAGE), which can be contrasted with chronological age. Longitudinal associations between blood pressures and BrainAGE were assessed with linear mixed-effects models. Results: A progressive increase in BP was observed over the follow up (MAP = 0.8 mmHg/year, SD = 0.92; SBP = 1.41 mmHg/year, SD = 1.49; DBP = 0.61 mmHg/year, SD = 0.78). In fully adjusted models, every additional 10 mmHg increase in blood pressure (above 90 for mean, 114 for systolic, and 74 for diastolic blood pressure) was associated with a higher BrainAGE by 65.7 days for mean, and 51.1 days for systolic/diastolic blood pressure. These effects occurred across the blood pressure range and were not exclusively driven by hypertension. Conclusion: Increasing blood pressure is associated with poorer brain health. Compared to a person becoming hypertensive, somebody with an ideal BP is predicted to have a brain that appears more than 6 months younger at midlife.

Factors associated with brain ageing - a systematic review

Background

Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing.

Methods

This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible.

Results

A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable.

Conclusion

This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets.

Trial registration

A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02331-4.

Validation of 10-Year Stroke Prediction Scores in a Community-Based Cohort of Chinese Older Adults

A stroke prediction model based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project was developed. We compared its predictive ability with the revised Framingham Stroke Risk Score (R-FSRS) for 5-year stroke incidence in a community cohort of Chinese adults, namely the Beijing Longitudinal Study of Aging (BLSA). Calibration, discrimination, and recalibration were used to compare the predictive ability between the two prediction models. Category-less net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values were also assessed. During a mean follow-up duration of 5.1 years, 106 incidents of fatal or non-fatal strokes occurred among 1,203 participants aged 55–84 years. The R-FSRS applied to our cohort underestimated the 5-year risk for stroke in men and women. China-PAR performed better than the R-FSRS in terms of calibration (men, R-FSRS: χ²-value 144.2 [P < 0.001], China-PAR: 10.4 [P = 0.238]; women, R-FSRS: 280.1 [P < 0.001], China-PAR: 12.5 [P = 0.129]). In terms of discrimination, R-FSRS and China-PAR models performed modestly in our cohort (C-statistic 0.603 [95% CI: 0.560–0.644] for men using China-PAR and 0.568 [95% CI: 0.524–0.610] using the R-FSRS; the corresponding numbers for women were 0.602 [95% CI: 0.564–0.639] and 0.575 [95% CI: 0.537–0.613). The recalibrated China-PAR model significantly improved the discrimination in C statistics and produced higher category-less NRI and IDI for stroke incidence than the R-FSRS. Although China-PAR fairly estimated stroke risk in our cohort, it did not sufficiently identify adults at high risk of stroke. Caution would be exercised by practitioners in applying the original China-PAR to Chinese older adults. Further studies are needed to develop an adequate prediction model based on the recalibrated China-PAR or to find new risk markers which could upgrade this model.