Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction

Antiageing strategy for neurodegenerative diseases: from mechanisms to clinical advances

In the context of global ageing, the prevalence of neurodegenerative diseases and dementia, such as Alzheimer's disease (AD), is increasing. However, the current symptomatic and disease-modifying therapies have achieved limited benefits for neurodegenerative diseases in clinical settings. Halting the progress of neurodegeneration and cognitive decline or even improving impaired cognition and function are the clinically meaningful goals of treatments for neurodegenerative diseases. Ageing is the primary risk factor for neurodegenerative diseases and their associated comorbidities, such as vascular pathologies, in elderly individuals. Thus, we aim to elucidate the role of ageing in neurodegenerative diseases from the perspective of a complex system, in which the brain is the core and peripheral organs and tissues form a holistic network to support brain functions. During ageing, the progressive deterioration of the structure and function of the entire body hampers its active and adaptive responses to various stimuli, thereby rendering individuals more vulnerable to neurodegenerative diseases. Consequently, we propose that the prevention and treatment of neurodegenerative diseases should be grounded in holistic antiageing and rejuvenation means complemented by interventions targeting disease-specific pathogenic events. This integrated approach is a promising strategy to effectively prevent, pause or slow down the progression of neurodegenerative diseases.

Compensatory increase in oxygen extraction fraction is associated with age-related cerebrovascular disease

Cerebrovascular disease is an important contributor to dementia with reductions in cerebral blood flow (CBF) potentially compromising oxygen supply. In early stages, reduced CBF may be associated with a compensatory increase in oxygen extraction fraction (OEF) to maintain the cerebral metabolic rate of oxygen consumption (CMRO2). We used a simultaneous PET-MRI protocol to measure OEF, CBF, CMRO2, and arterial transit time (ATT) in elderly people (n = 24, age 69.6 ± 5.3 years) with a range of vascular disease risk (QRisk 18.7 ± 10.8 %) and cognitive abilities (MoCA scores 26.7 ± 3.4) to determine if a) vascular disease risk (parameterised with QRisk2 score) is associated with altered CBF, ATT, OEF and CMRO2, b) if impaired blood supply and increasing transit times are associated with elevated OEF and c) if these physiological measures are associated with impaired cognition. ATT rose by 132 ms per 10 point increase in QRisk and there was a trend for reduced CBF. Compensatory increases in OEF occurred in association with modified ATT and CBF, preserving CMRO2. There was no regional variation to these relationships. Cognitive impairment was associated with prolonged ATT. These findings demonstrate the potential use of multi-delay time ASL and Quantitative Susceptibility Mapping for the early detection of cerebrovascular changes and provide evidence for compensatory increases in oxygen extraction in the presence of reduced blood flow.

Choroid plexus volumes and auditory verbal learning scores are associated with conversion from mild cognitive impairment to Alzheimer's disease

PURPOSE

Mild cognitive impairment (MCI) can be the prodromal phase of Alzheimer's disease (AD) where appropriate intervention might prevent or delay conversion to AD. Given this, there has been increasing interest in using magnetic resonance imaging (MRI) and neuropsychological testing to predict conversion from MCI to AD. Recent evidence suggests that the choroid plexus (ChP), neural substrates implicated in brain clearance, undergo volumetric changes in MCI and AD. Whether the ChP is involved in memory changes observed in MCI and can be used to predict conversion from MCI to AD has not been explored.

METHOD

The current study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate whether later progression from MCI to AD (progressive MCI [pMCI], n = 115) or stable MCI (sMCI, n = 338) was associated with memory scores using the Rey Auditory Verbal Learning Test (RAVLT) and ChP volumes as calculated from MRI. Classification analyses identifying pMCI or sMCI group membership were performed to compare the predictive ability of the RAVLT and ChP volumes.

FINDING

The results indicated a significant difference between pMCI and sMCI groups for right ChP volume, with the pMCI group showing significantly larger right ChP volume (p = .01, 95% confidence interval [-.116, -.015]). A significant linear relationship between the RAVLT scores and right ChP volume was found across all participants, but not for the two groups separately. Classification analyses showed that a combination of left ChP volume and auditory verbal learning scores resulted in the most accurate classification performance, with group membership accurately predicted for 72% of the testing data.

CONCLUSION

These results suggest that volumetric ChP changes appear to occur before the onset of AD and might provide value in predicting conversion from MCI to AD.

Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer

The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.

Correspondence between BOLD fMRI task response and cerebrovascular reactivity across the cerebral cortex

BOLD sensitivity to baseline perfusion and blood volume is a well-acknowledged fMRI confound. Vascular correction techniques based on cerebrovascular reactivity (CVR) might reduce variance due to baseline cerebral blood volume, however this is predicated on an invariant linear relationship between CVR and BOLD signal magnitude. Cognitive paradigms have relatively low signal, high variance and involve spatially heterogenous cortical regions; it is therefore unclear whether the BOLD response magnitude to complex paradigms can be predicted by CVR. The feasibility of predicting BOLD signal magnitude from CVR was explored in the present work across two experiments using different CVR approaches. The first utilized a large database containing breath-hold BOLD responses and 3 different cognitive tasks. The second experiment, in an independent sample, calculated CVR using the delivery of a fixed concentration of carbon dioxide and a different cognitive task. An atlas-based regression approach was implemented for both experiments to evaluate the shared variance between task-invoked BOLD responses and CVR across the cerebral cortex. Both experiments found significant relationships between CVR and task-based BOLD magnitude, with activation in the right cuneus (R ² = 0.64) and paracentral gyrus (R ² = 0.71), and the left pars opercularis (R ² = 0.67), superior frontal gyrus (R ² = 0.62) and inferior parietal cortex (R ² = 0.63) strongly predicted by CVR. The parietal regions bilaterally were highly consistent, with linear regressions significant in these regions for all four tasks. Group analyses showed that CVR correction increased BOLD sensitivity. Overall, this work suggests that BOLD signal response magnitudes to cognitive tasks are predicted by CVR across different regions of the cerebral cortex, providing support for the use of correction based on baseline vascular physiology.

The value of arterial spin labelling perfusion MRI in brain age prediction

Current structural MRI-based brain age estimates and their difference from chronological age-the brain age gap (BAG)-are limited to late-stage pathological brain-tissue changes. The addition of physiological MRI features may detect early-stage pathological brain alterations and improve brain age prediction. This study investigated the optimal combination of structural and physiological arterial spin labelling (ASL) image features and algorithms. Healthy participants (n = 341, age 59.7 ± 14.8 years) were scanned at baseline and after 1.7 ± 0.5 years follow-up (n = 248, mean age 62.4 ± 13.3 years). From 3 T MRI, structural (T1w and FLAIR) volumetric ROI and physiological (ASL) cerebral blood flow (CBF) and spatial coefficient of variation ROI features were constructed. Multiple combinations of features and machine learning algorithms were evaluated using the Mean Absolute Error (MAE). From the best model, longitudinal BAG repeatability and feature importance were assessed. The ElasticNetCV algorithm using T1w + FLAIR+ASL performed best (MAE = 5.0 ± 0.3 years), and better compared with using T1w + FLAIR (MAE = 6.0 ± 0.4 years, p < .01). The three most important features were, in descending order, GM CBF, GM/ICV, and WM CBF. Average baseline and follow-up BAGs were similar (-1.5 ± 6.3 and - 1.1 ± 6.4 years respectively, ICC = 0.85, 95% CI: 0.8-0.9, p = .16). The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most, and performed best in a cross-sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from the blood oxygen level-dependent (BOLD) signal time series is not possible. This study proposes a deep learning-based method for reconstruction of respiratory variation (RV) waveforms directly from BOLD fMRI data in pediatric participants (aged 5 to 21 years old), and does not require any respiratory measurement device. To do this, the Lifespan Human Connectome Project in Development (HCP-D) dataset, which includes respiratory measurements, was used to both train a convolutional neural network (CNN) and evaluate its performance. Results show that a CNN can capture informative features from the BOLD signal time course and reconstruct accurate RV timeseries, especially when the subject has a prominent respiratory event. This work advances the use of direct estimation of physiological parameters from fMRI, which will eventually lead to reduced complexity and decrease the burden on participants because they may not be required to wear a respiratory bellows.

Cortical thinning in chemotherapy-treated older long-term breast cancer survivors

Cognitive decline is an increasing issue for cancer survivors, especially for older adults, as chemotherapy affects brain structure and function. The purpose of this single center study was to evaluate alterations in cortical thickness and cognition in older long-term survivors of breast cancer who had been treated with chemotherapy years ago. In this prospective cohort study, we enrolled 3 groups of women aged ≥ 65 years with a history of stage I-III breast cancer who had received adjuvant chemotherapy 5 to 15 years ago (chemotherapy group, C +), age-matched women with breast cancer but no chemotherapy (no-chemotherapy group, C-) and healthy controls (HC). All participants underwent brain magnetic resonance imaging and neuropsychological testing with the NIH Toolbox Cognition Battery at time point 1 (TP1) and again at 2 years after enrollment (time point 2 (TP2)). At TP1, there were no significant differences in cortical thickness among the 3 groups. Longitudinally, the C + group showed cortical thinning in the fusiform gyrus (p = 0.006, effect size (d) = -0.60 [ -1.86, -0.66]), pars triangularis (p = 0.026, effect size (d) = -0.43 [-1.68, -0.82]), and inferior temporal lobe (p = 0.026, effect size (d) = -0.38 [-1.62, -0.31]) of the left hemisphere. The C + group also showed decreases in neuropsychological scores such as the total composite score (p = 0.01, effect size (d) = -3.9726 [-0.9656, -6.9796], fluid composite score (p = 0.03, effect size (d) = -4.438 [-0.406, -8.47], and picture vocabulary score (p = 0.04, effect size (d) = -3.7499 [-0.0617, -7.438]. Our results showed that cortical thickness could be a candidate neuroimaging biomarker for cancer-related cognitive impairment and accelerated aging in older long-term cancer survivors.

Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly

Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.

Cortical Thinning in High-Grade Asymptomatic Carotid Stenosis

BACKGROUND AND PURPOSE

High-grade carotid artery stenosis may alter hemodynamics in the ipsilateral hemisphere, but consequences of this effect are poorly understood. Cortical thinning is associated with cognitive impairment in dementia, head trauma, demyelination, and stroke. We hypothesized that hemodynamic impairment, as represented by a relative time-to-peak (TTP) delay on MRI in the hemisphere ipsilateral to the stenosis, would be associated with relative cortical thinning in that hemisphere.

METHODS

We used baseline MRI data from the NINDS-funded Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis-Hemodynamics (CREST-H) study. Dynamic contrast susceptibility MR perfusion-weighted images were post-processed with quantitative perfusion maps using deconvolution of tissue and arterial signals. The protocol derived a hemispheric TTP delay, calculated by subtraction of voxel values in the hemisphere ipsilateral minus those contralateral to the stenosis.

RESULTS

Among 110 consecutive patients enrolled in CREST-H to date, 45 (41%) had TTP delay of at least 0.5 seconds and 9 (8.3%) subjects had TTP delay of at least 2.0 seconds, the maximum delay measured. For every 0.25-second increase in TTP delay above 0.5 seconds, there was a 0.006-mm (6 micron) increase in cortical thickness asymmetry. Across the range of hemodynamic impairment, TTP delay independently predicted relative cortical thinning on the side of stenosis, adjusting for age, sex, hypertension, hemisphere, smoking history, low-density lipoprotein cholesterol, and preexisting infarction (P=0.032).

CONCLUSIONS

Our findings suggest that hemodynamic impairment from high-grade asymptomatic carotid stenosis may structurally alter the cortex supplied by the stenotic carotid artery.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors

Introduction

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG.

Methods

T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction.

Results

The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers.

Discussion

In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.

Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions

Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.

MRI of healthy brain aging: A review

We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T₁ , T₂ , T₂ *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.

Spiritual Fitness: A New Dimension in Alzheimer's Disease Prevention

BACKGROUND

Religious and spiritual interventions may have an effect on Alzheimer's disease prevention. Kirtan Kriya meditation has been shown to mitigate the deleterious effects of chronic stress on cognition, reverse memory loss, and create psychological and spiritual wellbeing, which may reduce multiple drivers of Alzheimer's disease risk.

OBJECTIVE

To detail a new concept in medicine called Spiritual Fitness, a merging of stress reduction, basic wellbeing, and psycho/spiritual wellbeing to prevent Alzheimer's disease.

METHODS

The literature on the topics mentioned above is described, including an in-depth discussion on why and how each are critical to advancing the future of Alzheimer's disease prevention. The many negative effects of chronic stress, and the benefits of Kirtan Kriya, are reviewed. The four pillars of basic wellbeing, six practical aspects of psychological wellbeing, and the four new non-sectarian features of spiritual fitness are then disclosed. Moreover, instructions on practicing Kirtan Kriya are offered in the Supplementary Material.

CONCLUSION

Religious and spiritual practices, including Kirtan Kriya, are crucial components in the development of enhanced cognition and well-being, which may help prevent and, in some cases, reverse cognitive decline. The key point of this review is that making a commitment to live a brain longevity lifestyle including spiritual fitness is a critically important way for aging Alzheimer's disease free. We hope that this article will inspire scientists, clinicians, and patients to embrace this new concept of spiritual fitness and make it a part of every multidomain program for the prevention of cognitive disability.