Brain structural covariances in the ageing brain in the UK Biobank

The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank (N = 42,075, aged 45-83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations.

Exploring role of natural compounds in molecular alterations associated with brain ageing: A perspective towards nutrition for ageing brain

Aging refers to complete deterioration of physiological integrity and function. By midcentury, adults over 60 years of age and children under 15 years will begin to outnumber people in working age. This shift will bring multiple global challenges for economy, health, and society. Eventually, aging is a natural process playing a vital function in growth and development during pediatric stage, maturation during adult stage, and functional depletion. Tissues experience negative consequences with enhanced genomic instability, deregulated nutrient sensing, mitochondrial dysfunction, and decline in performance on cognitive tasks. As brain ages, its volume decreases, neurons & glia get inflamed, vasculature becomes less developed, blood pressure increases with a risk of stroke, ischemia, and cognitive deficits. Diminished cellular functions leads to progressive reduction in functional and emotional capacity with higher possibility of disease and finally death. This review overviews cellular as well as molecular aspects of aging, biological pathway related to accelerated brain aging, and strategies minimizing cognitive aging. Age-related changes include altered bioenergetics, decreased neuroplasticity and flexibility, aberrant neural activity, deregulated Ca2+ homeostasis in neurons, buildup of reactive oxygen species, and neuro-inflammation. Unprecedented progress has been achieved in recent studies, particularly in terms of how herbal or natural substances affect genetic pathways and biological functions that have been preserved through evolution. Herein, the present work provides an overview of ageing and age-related disorders and explore the molecular mechanisms that underlie therapeutic effects of herbal and natural chemicals on neuropathological signs of brain aging.

The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing

Ageing is a crucial risk factor for Alzheimer's disease (AD) and is characterised by systemic changes in both intracellular and extracellular microenvironments that affect the entire body instead of a single organ. Understanding the specific mechanisms underlying the role of ageing in disease development can facilitate the treatment of ageing-related diseases, such as AD. Signs of brain ageing have been observed in both AD patients and animal models. Alleviating the pathological changes caused by brain ageing can dramatically ameliorate the amyloid beta- and tau-induced neuropathological and memory impairments, indicating that ageing plays a crucial role in the pathophysiological process of AD. In this review, we summarize the impact of several age-related factors on AD and propose that preventing pathological changes caused by brain ageing is a promising strategy for improving cognitive health.

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria

Modelling population reference curves or normative modelling is increasingly used with the advent of large neuroimaging studies. In this paper we assess the performance of fitting methods from the perspective of clinical applications and investigate the influence of the sample size. Further, we evaluate linear and non-linear models for percentile curve estimation and highlight how the bias-variance trade-off manifests in typical neuroimaging data. We created plausible ground truth distributions of hippocampal volumes in the age range of 45 to 80 years, as an example application. Based on these distributions we repeatedly simulated samples for sizes between 50 and 50,000 data points, and for each simulated sample we fitted a range of normative models. We compared the fitted models and their variability across repetitions to the ground truth, with specific focus on the outer percentiles (1st, 5th, 10th) as these are the most clinically relevant. Our results quantify the expected decreasing trend in variance of the volume estimates with increasing sample size. However, bias in the volume estimates only decreases a modest amount, without much improvement at large sample sizes. The uncertainty of model performance is substantial for what would often be considered large samples in a neuroimaging context and rises dramatically at the ends of the age range, where fewer data points exist. Flexible models perform better across sample sizes, especially for non-linear ground truth. Surprisingly large samples of several thousand data points are needed to accurately capture outlying percentiles across the age range for applications in research and clinical settings. Performance evaluation methods should assess both bias and variance. Furthermore, caution is needed when attempting to go near the ends of the age range captured by the source data set and, as is a well known general principle, extrapolation beyond the age range should always be avoided. To help with such evaluations of normative models we have made our code available to guide researchers developing or utilising normative models.

Alpha lipoic acid treatment in late middle age improves cognitive function: Proteomic analysis of the protective mechanisms in the hippocampus

Alpha lipoic acid (ALA), a powerful antioxidant, has the potential to relieve age-related cognitive impairment and neurodegenerative disease. Clinical randomized controlled studies have demonstrated the cognitive improvement effects of lipoic acid in Alzheimer's disease (AD). In the present study, we examined the effects of ALA on cognitive function in ageing mice and its protective mechanisms. Eighteen-month-old male C57BL6/J mice received ALA or normal saline for 2 months. The Morris water maze test revealed improved cognitive function in animals that received ALA. Furthermore, tandem Mass Tags (TMT) based liquid chromotography with mass spectrometry/mass spectrometry (LC-MS/MS) was established to identify the target proteins. The results showed that 10 proteins were changed significantly. Gene Ontology (GO) analysis indicated that the upregulated proteins were enriched in terminal bouton, synaptic transmission and lipid transporter activity while the down-regulated proteins were involved in nuclear transcription factor-κB binding, apoptosis and mitogen-activated protein kinase binding. Based on the GO results, two upregulated proteins oxysterol-binding protein-related protein 10 (OSBPL10) and oligophrenin 1 (OPHN1), and one downregulated protein, CDK5 regulatory subunit-associated protein 3 (CDK5rap3), were validated through Western blotting. The results were consistent with the proteomic results. Modulation of synaptic transmission, lipid transporter activity and neuroinflammation appears to be the mechanisms of ALA in the aged brain.

Brain ageing and neurodegeneration in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is a psychiatric condition characterized by alternating episodes of mania and depression frequently associated with cognitive impairments. BD is associated with brain alterations in fronto-temporal and limbic networks. Recent conceptualizations view BD as a neurodegenerative disorder characterized by progressive deterioration of grey and white matter (GM, WM) volumes and accelerated brain ageing. Therefore, we conducted a review gathering neuroimaging evidence about neurodegenerative processes in BD.

METHODS

A literature search was conducted on the PubMed, Scopus and Web of Science databases in September 2021. After title and abstract screening of the retrieved records, 19 studies that met our inclusion criteria were included in the review.

RESULTS

The available evidence suggests the presence of a progressive reduction of GM volumes at the whole-brain level and in the amygdala, prefrontal regions and the anterior cingulate cortex. Conversely, WM lesions and alterations seem to emerge only in the early phases of the condition masking the effects of normal ageing. Lastly, machine learning models indicate that the gap between predicted and chronological brain age differs considerably between healthy controls and BD patients, as the latter are characterized by larger gaps.

LIMITATIONS

The included studies had cross-sectional study design, small sample sizes and heterogeneous methodology, and lack of control for pharmacological treatment.

CONCLUSIONS

BD seems to be associated with generalized age-related structural GM volumes reductions and functional brain alterations thus suggesting the presence of neurodegenerative processes. Future systematic reviews and meta-analyses should be conducted to quantify the magnitude of brain ageing-related effects in BD.

Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment

Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.

Subclinical cognitive deficits are associated with reduced cerebrovascular response to visual stimulation in mid-sixties men

Reduced cerebrovascular response to neuronal activation is observed in patients with neurodegenerative disease. In the present study, we examined the correlation between reduced cerebrovascular response to visual activation (ΔCBF_Vis.Act) and subclinical cognitive deficits in a human population of mid-sixties individuals without neurodegenerative disease. Such a correlation would suggest that impaired cerebrovascular function occurs before overt neurodegenerative disease. A total of 187 subjects (age 64-67 years) of the Metropolit Danish Male Birth Cohort participated in the study. ΔCBF_Vis.Act was measured using arterial spin labelling (ASL) MRI. ΔCBF_Vis.Act correlated positively with cognitive performance in: Global cognition (p = 0.046), paired associative memory (p = 0.025), spatial recognition (p = 0.026), planning (p = 0.016), simple processing speed (p < 0.01), and with highly significant correlations with current intelligence (p < 10^-5), and more complex processing speed (p < 10^-3), the latter two explaining approximately 11-13% of the variance. Reduced ΔCBF_Vis.Act was independent of brain atrophy. Our findings suggest that inhibited cerebrovascular response to neuronal activation is an early deficit in the ageing brain and associated with subclinical cognitive deficits. Cerebrovascular dysfunction could be an early sign of a trajectory pointing towards the development of neurodegenerative disease. Future efforts should elucidate if maintenance of a healthy cerebrovascular function can protect against the development of dementia.

Brain and cognitive ageing: The present, and some predictions (…about the future)

Experiencing decline in one's cognitive abilities is among the most feared aspects of growing old [53]. Age-related cognitive decline carries a huge personal, societal, and financial cost both in pathological ageing (such as dementias) and also within the non-clinical majority of the population. A projected 152 million people worldwide will suffer from dementia by 2050 [3]. The early stages of cognitive decline are much more prevalent than dementia, and can still impose serious limitations of performance on everyday activities, independence, and quality of life in older age [5], [60], [80]. Cognitive decline also predicts poorer health, adherence to medical regimens, and financial decision-making, and can herald dementia, illness, and death [6], [40]. Of course, when seeking to understand why some people experience more severe cognitive ageing than others, researchers have turned to the organ of thinking for clues about the nature, possible mechanisms, and determinants that might underpin more and less successful cognitive agers. However, that organ is relatively inaccessible, a limitation partly alleviated by advances in neuroimaging. Here we discuss lessons for cognitive and brain ageing that have come from neuroimaging research (especially structural brain imaging), what neuroimaging still has left to teach us, and our views on possible ways forward in this multidisciplinary field.

MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention

The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats (p=0.015 for the interaction term). Cox regression showed that older BrainAGE at 5 months was associated with higher mortality risk (p=0.03). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.

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.

Learning to synthesise the ageing brain without longitudinal data

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.

Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis

Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

Conditioning learning in an attentional task relates to age and ventricular expansion in a nonhuman primate (Microcebus murinus)

The grey mouse lemur (Microcebus murinus) is a promising nonhuman primate model for brain ageing and neurodegenerative diseases. Age-related cognitive decline in this model is well described, however, data on possible relations between attention and age, as they are known from humans, are missing. We tested 10 mouse lemurs in a touchscreen-based version of the 5-choice-serial-reaction-time-task (5CSRTT) on visuo-spatial attention: subjects had to interact with a briefly presented stimulus occurring unpredictably in one out of five locations on the touchscreen. Animals were trained to an 80 % performance at a four seconds stimulus presentation duration (SPD) and subsequently challenged by a SPD of two seconds. Additionally, ventricular expansion was assessed using structural magnetic resonance imaging. Trials to the 80 % criterion at four seconds SPD correlated significantly with age and with ventricular expansion, especially around the occipital lobe. Once criterion performance was reached, two seconds challenge performance was independent of age. In four subjects that were additionally challenged with 1.5, 1.0, 0.8, or 0.6 s SPDs or variable delays preceding stimulus presentation, performance linearly declined with decreasing SPD, i.e. increasing attentional demand. In conclusion, this is the first report of 5CSRTT data in mouse lemurs and demonstrates the general applicability of this task of visuo-spatial attention to this nonhuman primate model. Results further demonstrate age-related deficits in learning during acquisition of the 5CSRTT and suggest that both may be linked through age-related atrophy of occipital structures and a resulting deficit in central visual processes.

Personality factors and cerebral glucose metabolism in community-dwelling older adults

Personality factors have been associated with Alzheimer's disease (AD) and dementia, but they have not been examined against markers of regional brain glucose metabolism (a primary measure of brain functioning) in older adults without clinically diagnosed cognitive impairment. The relationship between personality factors derived from the five-factor model and cerebral glucose metabolism determined using positron emission tomography (PET) with [18F]-2-fluoro-2-deoxy-D-glucose (18F-FDG-PET) was examined in a cohort of 237 non-demented, community-dwelling older adults aged 60-89 years (M ± SD = 73.76 ± 6.73). Higher neuroticism and lower scores on extraversion and conscientiousness were significantly associated with decreased glucose metabolism in brain regions typically affected by AD neuropathological processes, including the hippocampus and entorhinal cortex. Furthermore, while there were significant differences between apolipoprotein E (APOE) ε4 allele carriers and non-carriers on ¹⁸F-FDG-PET results in the neocortex and other brain regions (p < 0.05), there was no significant difference between carriers and non-carriers on personality factors and no significant interactions were found between APOE ε4 carriage and personality factors on brain glucose metabolism. In conclusion, we found significant relationships between personality factors and glucose metabolism in neural regions more susceptible to AD neuropathology in older adults without clinically significant cognitive impairment. These findings support the need for longitudinal research into the potential mechanisms underlying the relationship between personality and dementia risk, including measurement of change in other AD biomarkers (amyloid and tau imaging) and how they correspond to change in personality factors. Future research is also warranted to determine whether timely psychological interventions aimed at personality facets (specific aspects or characteristics of personality factors) can affect imaging or other biomarkers of AD resulting in delay or ideally preventing the onset of the cognitive impairment.

Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank

Measurement of hippocampal volume has proven useful to diagnose and track progression in several brain disorders, most notably in Alzheimer's disease (AD). For example, an objective evaluation of a patient's hippocampal volume status may provide important information that can assist diagnosis or risk stratification of AD. However, clinicians and researchers require access to age-related normative percentiles to reliably categorise a patient's hippocampal volume as being pathologically small. Here we analysed effects of age, sex, and hemisphere on the hippocampus and neighbouring temporal lobe volumes, in 19,793 generally healthy participants in the UK Biobank. A key finding of the current study is a significant acceleration in the rate of hippocampal volume loss in middle age, more pronounced in females than in males. In this report, we provide normative values for hippocampal and total grey matter volume as a function of age for reference in clinical and research settings. These normative values may be used in combination with our online, automated percentile estimation tool to provide a rapid, objective evaluation of an individual's hippocampal volume status. The data provide a large-scale normative database to facilitate easy age-adjusted determination of where an individual hippocampal and temporal lobe volume lies within the normal distribution.

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Largest normative database for hippocampal volume across age to date.

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Hippocampal volume loss accelerates in middle age.

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Acceleration of hippocampal volume loss is more pronounced in women than in men.

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With the online tool the database provides a useful resource for research and clinical studies.

MRI-based evaluation of structural degeneration in the ageing brain: Pathophysiology and assessment

Advances in MRI technology have significantly contributed to our ability to understand the process of brain ageing, allowing us to track and assess changes that occur during normal ageing and neurological conditions. This paper focuses on reviewing structural changes of the ageing brain that are commonly seen using MRI, summarizing the pathophysiology, prevalence, and neuroanatomical distribution of changes including atrophy, lacunes, white matter lesions, and dilated perivascular spaces. We also review the clinically accessible methodology for assessing these MRI-based changes, covering visual rating scales, as well computer-aided and fully automated methods. Subsequently, we consider novel assessment methods designed to evaluate changes across the whole brain, and finally discuss new directions in this field of research.

eQTL of KCNK2 regionally influences the brain sulcal widening: evidence from 15,597 UK Biobank participants with neuroimaging data

The grey and white matter volumes are known to reduce with age. This cortical shrinkage is visible on magnetic resonance images and is conveniently identified by the increased volume of cerebrospinal fluid in the sulci between two gyri. Here, we replicated this finding using the UK Biobank dataset and studied the genetic influence on these cortical features of aging. We divided all individuals genetically confirmed of British ancestry into two sub-cohorts (12,162 and 3435 subjects for discovery and replication samples, respectively). We found that the heritability of the sulcal opening ranges from 15 to 45% (SE = 4.8%). We identified 4 new loci that contribute to this opening, including one that also affects the sulci grey matter thickness. We identified the most significant variant (rs864736) on this locus as being an expression quantitative trait locus (eQTL) for the KCNK2 gene. This gene regulates the immune-cell into the central nervous system (CNS) and controls the CNS inflammation, which is implicated in cortical atrophy and cognitive decline. These results expand our knowledge of the genetic contribution to cortical shrinking and promote further investigation into these variants and genes in pathological context such as Alzheimer’s disease in which brain shrinkage is a key biomarker.

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of 'brain-predicted age' as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain-predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test-retest and multi-centre reliability of brain-predicted age using two samples (within-scanner N = 20; between-scanner N = 11). CNN brain-predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain-predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain-predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain-predicted age was a heritable phenotype for all models and input data (h² ≥ 0.5). Brain-predicted age showed high test-retest reliability (intraclass correlation coefficient [ICC] = 0.90-0.99). Multi-centre reliability was more variable within high ICCs for GM (0.83-0.96) and poor-moderate levels for WM and raw data (0.51-0.77). Brain-predicted age represents an accurate, highly reliable and genetically-influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1-MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real-time information on brain health in clinical settings.

Structural brain changes and all-cause mortality in the elderly population-the mediating role of inflammation

While MRI brain changes have been related to mortality during ageing, the role of inflammation in this relationship remains poorly understood. Hence, this study aimed to investigate the impact of MRI changes on all-cause mortality and the mediating role of cytokines. All-cause mortality was evaluated in 268 community dwelling elderly (age 65-83 years) in the MEMO study (Memory and Morbidity in Augsburg elderly). MRI markers of brain atrophy and cerebral small vessel disease (SVD), C-reactive protein (CRP) and a panel of cytokines in serum were assessed. Cox proportional hazard models were used to estimate the association of MRI changes with survival over 9 years. Regression models were used to assess the hypothesis that inflammation is mediating the relationship between MRI-brain changes and mortality. In total, 77 (29 %) deaths occurred during a mean follow up of 9 years. After adjusting for confounders, the degree of global cortical atrophy and the level of the cytokines CRP, TNF-α and IL-8 were of higher significance in study participants who had died at follow-up in comparison to survivors. In Cox proportional hazard models, higher degrees of global cortical atrophy (HR 1.56, p = 0.003) and regional atrophy of the temporal lobe (HR 1.38, p = 0.011) were associated with a significantly increased risk of mortality. Mediation analyses revealed a partial mediation by IL-6 and IL-8 of the effects of global cortical atrophy on mortality. Global cortical brain atrophy is a significant indicator of survival in the elderly. Our study supports a possible role for inflammation in the atrophy pathogenesis. If replicated in other samples, IL-6 and IL-8 level assessment may improve risk prognosis for mortality.