MRI of healthy brain aging: A review

Different Grey Matter Microstructural Patterns in Cognitively Healthy Versus Typical Ageing Healthy Versus Typical Brain Ageing

Ageing is a complex phenomenon affecting a wide range of coexisting biological processes. The homogeneity of the studied population is an essential parameter for valid interpretations of outcomes. The presented study capitalises on the MRI data available in the Human Connectome Project-Aging (HCP-A) and, within individuals over 55 years of age who passed the HCP-A section criteria, compares a subgroup of 37 apparently neurocognitively healthy individuals selected based on stringent criteria with 37 age and sex-matched individuals still representative of typical ageing but who did not pass the stringent definition of neurocognitively healthy. Specifically, structural scans, diffusion weighted imaging and T1w/T2w ratio were utilised. Furthermore, data of 26 HCP-A participants older than 90 years as notional 'super-agers' were analysed. The relationship of age and several microstructural MRI metrics (T1w/T2w ratio, mean diffusivity, intracellular volume fraction and free water volume fraction) differed significantly between typical and healthy ageing cohort in areas highly relevant for ageing such as hippocampus, prefrontal and temporal cortex and cerebellum. However, the trajectories of the healthy ageing population did not show substantially better overlap with the findings in people older than 90 than those of the typical population. Therefore, caution must be exercised in the choice of adequate study group characteristics relevant for respective ageing-related hypotheses. Contrary to typical ageing group, the healthy ageing cohort may show generally stable levels of several MRI metrics of interest.

Aging, brain plasticity, and motor learning

Motor skill learning, the process of acquiring new motor skills, is critically important across the lifespan, from early development through adulthood and into older age, as well as in pathological conditions (i.e., rehabilitation). Extensive research has demonstrated that motor skill acquisition in young adults is accompanied by significant neuroplastic changes, including alterations in brain structure (gray and white matter), function (i.e., activity and connectivity), and neurochemistry (i.e., levels of neurotransmitters). In the aging population, motor performance typically declines, characterized by slower and less accurate movements. However, despite these age-related changes, older adults maintain the capacity for skill improvement through training. In this review, we explore the extent to which the aging brain retains the ability to adapt in response to motor learning, specifically whether skill acquisition is accompanied by neural changes. Furthermore, we discuss the associations between inter-individual variability in brain structure and function and the potential for future learning in older adults. Finally, we consider the use of non-invasive brain stimulation techniques aimed at optimizing motor learning in this population. Our review provides insights into the neurobiological underpinnings of motor learning in older adults and emphasizes strategies to enhance their motor skill acquisition.

Structural brain correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study

Sustained attention is important for maintaining cognitive function and autonomy during ageing, yet older people often show reductions in this domain. The role of the underlying neurobiology is not yet well understood, with most neuroimaging studies primarily focused on fMRI. Here, we utilise sMRI to investigate the relationships between age, structural brain volumes and sustained attention performance. Eighty-nine healthy older adults (50-84 years, Mage 65.5 (SD=8.4) years, 74 f) underwent MRI brain scanning and completed two sustained attention tasks: a rapid visual information processing (RVP) task and sustained attention to response task (SART). Independent hierarchical linear regressions demonstrated that greater volumes of white matter hyperintensities (WMH) were associated with worse RVP_A' performance, whereas greater grey matter volumes were associated with better RVP_A' performance. Further, greater cerebral white matter volumes were associated with better SART_d' performance. Importantly, mediation analyses revealed that both grey and white matter volumes completely mediated the relationship between ageing and sustained attention. These results explain disparate attentional findings in older adults, highlighting the intervening role of brain structure.

Neuronal Cell Rearrangement During Aging: Antioxidant Compounds as a Potential Therapeutic Approach

Aging is a natural process that leads to time-related changes and a decrease in cognitive abilities, executive functions, and attention. In neuronal aging, brain cells struggle to respond to oxidative stress. The structure, function, and survival of neurons can be mediated by different pathways that are sensitive to oxidative stress and age-related low-energy states. Mitochondrial impairment is one of the most noticeable signs of brain aging. Damaged mitochondria are thought to be one of the main causes that feed the inflammation related to aging. Also, protein turnover is involved in age-related impairments. The brain, due to its high oxygen usage, is particularly susceptible to oxidative damage. This review explores the mechanisms underlying neuronal cell rearrangement during aging, focusing on morphological changes that contribute to cognitive decline and increased susceptibility to neurodegenerative diseases. Potential therapeutic approaches are discussed, including the use of antioxidants (e.g., Vitamin C, Vitamin E, glutathione, carotenoids, quercetin, resveratrol, and curcumin) to mitigate oxidative damage, enhance mitochondrial function, and maintain protein homeostasis. This comprehensive overview aims to provide insights into the cellular and molecular processes of neuronal aging and highlight promising therapeutic avenues to counteract age-related neuronal deterioration.

Age-related volume decrease in subcortical gray matter is a part of healthy brain aging in men

BACKGROUND

With the global population aging, the number of individuals over 60 is expected to double by 2050. Brain volume increases until age 13, stabilizes between 18 and 35, then declines by 0.2% annually. Magnetic resonance imaging (MRI) studies highlight significant gray matter atrophy, necessitating differentiation between normal aging and neurodegeneration.

AIMS

This study assessed the impact of aging on subcortical gray matter in healthy males to identify biomarkers of physiological aging.

METHODS

A retrospective study of 106 healthy males who underwent brain MRI from 2012 to 2016, divided into two age groups: younger and older than 35 years. MRI scans were performed using a 3 T machine, and volumetric analysis was conducted with VolBrain software. Subcortical gray matter volumes were compared between groups. The Shapiro-Wilk test evaluated normality. Student's t-test and Mann-Whitney U test were used for statistical analysis, with significance defined as p < 0.05.

RESULTS

Total intracranial volume was comparable between age groups (p = 0.527). Significant volume reductions (p < 0.05) were observed in subcortical gray matter structures, including the nucleus accumbens, caudate nucleus, globus pallidus, putamen, thalamus, and ventral diencephalon, particularly on the right side in the elderly group.

CONCLUSIONS

Subcortical gray matter volume in healthy males shows significant differences between older and younger individuals (p < 0.05), with asymmetrical reduction and certain structures on the right aging more rapidly. These findings are significant for distinguishing healthy aging from neurodegeneration.

The impact of body position on neurofluid dynamics: present insights and advancements in imaging

The intricate neurofluid dynamics and balance is essential in preserving the structural and functional integrity of the brain. Key among these forces are: hemodynamics, such as heartbeat-driven arterial and venous blood flow, and hydrodynamics, such as cerebrospinal fluid (CSF) circulation. The delicate interplay between these dynamics is crucial for maintaining optimal homeostasis within the brain. Currently, the widely accepted framework for understanding brain functions is the Monro-Kellie's doctrine, which posits a constant sum of intracranial CSF, blood flow and brain tissue volumes. However, in recent decades, there has been a growing interest in exploring the dynamic interplay between these elements and the impact of external factors, such as daily changes in body position. CSF circulation in particular plays a crucial role in the context of neurodegeneration and dementia, since its dysfunction has been associated with impaired clearance mechanisms and accumulation of toxic substances. Despite the implementation of various invasive and non-invasive imaging techniques to investigate the intracranial hemodynamic or hydrodynamic properties, a comprehensive understanding of how all these elements interact and are influenced by body position remains wanted. Establishing a comprehensive overview of this topic is therefore crucial and could pave the way for alternative care approaches. In this review, we aim to summarize the existing understanding of intracranial hemodynamic and hydrodynamic properties, fundamental for brain homeostasis, along with factors known to influence their equilibrium. Special attention will be devoted to elucidating the effects of body position shifts, given their significance and remaining ambiguities. Furthermore, we will explore recent advancements in imaging techniques utilized for real time and non-invasive measurements of dynamic body fluid properties in-vivo.

Frontoparietal atrophy trajectories in cognitively unimpaired elderly individuals using longitudinal Bayesian clustering

INTRODUCTION

Frontal and/or parietal atrophy has been reported during aging. To disentangle the heterogeneity previously observed, this study aimed to uncover different clusters of grey matter profiles and trajectories within cognitively unimpaired individuals.

METHODS

Structural magnetic resonance imaging (MRI) data of 307 Aβ-negative cognitively unimpaired individuals were modelled between ages 60-85 from three cohorts worldwide. We applied unsupervised clustering using a novel longitudinal Bayesian approach and characterized the clusters' cerebrovascular and cognitive profiles.

RESULTS

Four clusters were identified with different grey matter profiles and atrophy trajectories. Differences were mainly observed in frontal and parietal brain regions. These distinct frontoparietal grey matter profiles and longitudinal trajectories were differently associated with cerebrovascular burden and cognitive decline.

DISCUSSION

Our findings suggest a conciliation of the frontal and parietal theories of aging, uncovering coexisting frontoparietal GM patterns. This could have important future implications for better stratification and identification of at-risk individuals.

Analysis and visualization of the effect of multiple sclerosis on biological brain age

Introduction

The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS.

Methods

A multi-center dataset consisting of 5,294 T1-weighted magnetic resonance images of the brain from healthy individuals aged between 19 and 89 years was used to train a convolutional neural network (CNN) for biological brain age prediction. The trained model was then used to calculate the brain age gap in 195 patients with relapsing remitting MS (20-60 years). Additionally, saliency maps were generated for healthy subjects and patients with MS to identify brain regions that were deemed important for the brain age prediction task by the CNN.

Results

Overall, the application of the CNN revealed accelerated brain aging with a larger brain age gap for patients with MS with a mean of 6.98 ± 7.18 years in comparison to healthy test set subjects (0.23 ± 4.64 years). The brain age gap for MS patients was weakly to moderately correlated with age at disease onset (ρ = -0.299, p < 0.0001), EDSS score (ρ = 0.206, p = 0.004), disease duration (ρ = 0.162, p = 0.024), lesion volume (ρ = 0.630, p < 0.0001), and brain parenchymal fraction (ρ = -0.718, p < 0.0001). The saliency maps indicated significant differences in the lateral ventricle (p < 0.0001), insula (p < 0.0001), third ventricle (p < 0.0001), and fourth ventricle (p = 0.0001) in the right hemisphere. In the left hemisphere, the inferior lateral ventricle (p < 0.0001) and the third ventricle (p < 0.0001) showed significant differences. Furthermore, the Dice similarity coefficient showed the highest overlap of salient regions between the MS patients and the oldest healthy subjects, indicating that neurodegeneration is accelerated in this patient cohort.

Discussion

In conclusion, the results of this study show that the brain age gap is a valuable surrogate biomarker to measure disease progression in patients with multiple sclerosis.

Subcortical volume in middle-aged adults with fetal alcohol spectrum disorders

Studies of youth and young adults with prenatal alcohol exposure (PAE) have most consistently reported reduced volumes of the corpus callosum, cerebellum and subcortical structures. However, it is unknown whether this continues into middle adulthood or if individuals with PAE may experience premature volumetric decline with aging. Forty-eight individuals with fetal alcohol spectrum disorders (FASD) and 28 healthy comparison participants aged 30 to 65 participated in a 3T MRI session that resulted in usable T1-weighted and T2-weighted structural images. Primary analyses included volumetric measurements of the caudate, putamen, pallidum, cerebellum and corpus callosum using FreeSurfer software. Analyses were conducted examining both raw volumetric measurements and subcortical volumes adjusted for overall intracranial volume (ICV). Models tested for main effects of age, sex and group, as well as interactions of group with age and group with sex. We found the main effects for group; all regions were significantly smaller in participants with FASD for models using raw volumes (P's < 0.001) as well as for models using volumes adjusted for ICV (P's < 0.046). Although there were no significant interactions of group with age, females with FASD had smaller corpus callosum volumes relative to both healthy comparison females and males with FASD (P's < 0.001). As seen in children and adolescents, adults aged 30 to 65 with FASD showed reduced volumes of subcortical structures relative to healthy comparison adults, suggesting persistent impact of PAE. Moreover, the observed volumetric reduction of the corpus callosum in females with FASD could suggest more rapid degeneration, which may have implications for cognition as these individuals continue to age.

Impact of repetitive negative thinking on subjective cognitive decline: insights into cognition and brain structure

Introduction

Individuals with subjective cognitive decline (SCD) express concern about self-perceived cognitive decline despite no objective impairment and are at higher risk of developing Alzheimer's disease. Despite documented links between SCD and repetitive negative thinking (RNT), the specific impact of RNT on brain integrity and cognition in exacerbating the SCD condition remains unclear. We aimed to investigate the influence of RNT on global cognition and brain integrity, and their interrelationships among healthy middle-aged and older adults experiencing SCD.

Methods

Out of 616 individuals with neuroimaging and neuropsychological data available, 89 (mean age = 56.18 years; 68.54% females) met SCD criteria. Eighty-nine non-SCD individuals matched by age, sex, and education were also selected and represented the control group (mean age = 56.09 years; 68.54% females). Global cognition was measured using the preclinical Alzheimer's cognitive composite (PACC5), which includes dementia screening, episodic memory, processing speed, and category fluency tests. RNT was calculated through three questionnaires assessing intrusive thoughts, persistent worry, and rumination. We generated cortical thickness (CTh) maps and quantified the volume of white matter lesions (WML) in the whole brain, as grey and white matter integrity measures, respectively.

Results

SCD individuals exhibited higher RNT scores, and thinner right temporal cortex compared to controls. No differences were observed in PACC5 and WML burden between groups. Only the SCD group demonstrated positive associations in the CTh-PACC5, CTh-RNT, and WML-RNT relationships.

Discussion

In this cross-sectional study, RNT was exclusively associated with brain integrity in SCD. Even though our findings align with the broader importance of investigating treatable psychological factors in SCD, further research may reveal a modulatory effect of RNT on the relationship between cognition and brain integrity in SCD.

Effects of sleep deprivation on brain atrophy in individuals with mild cognitive impairment and Alzheimer's disease

Dementia, a prevalent condition in the United States, affecting millions of individuals and their families, underscores the importance of healthy cognitive ageing, which involves maintaining cognitive function and mental wellness as individuals grow older, promoting overall well-being and quality of life. Our original research study investigates the correlation between lifestyle factors and brain atrophy in individuals with mild cognitive impairment (MCI) or Alzheimer's disease (AD), as well as healthy older adults. Conducted over six months in West Texas, the research involved 20 participants aged 62-87. Findings reveal that sleep deprivation in MCI subjects and AD patients correlate with posterior cingulate cortex, hippocampal atrophy and total brain volume, while both groups exhibit age-related hippocampal volume reduction. Notably, fruit/vegetable intake negatively correlates with certain brain regions' volume, emphasizing the importance of diet. Lack of exercise is associated with reduced brain volume and hippocampal atrophy, underlining the cognitive benefits of physical activity. The study underscores lifestyle's significant impact on cognitive health, advocating interventions to promote brain health and disease prevention, particularly in MCI/AD cases. While blood profile data showed no significant results regarding cognitive decline, the study underscores the importance of lifestyle modifications in preserving cognitive function.

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.

Does resistance training in older adults lead to structural brain changes associated with a lower risk of Alzheimer's dementia? A narrative review

Dementia, particularly Alzheimer's Disease (AD), has links to several modifiable risk factors, especially physical inactivity. When considering the relationship between physcial activity and dementia risk, cognitive benefits are generally attributed to aerobic exercise, with resistance exercise (RE) receiving less attention. This review aims to address this gap by evaluating the impact of RE on brain structures and cognitive deficits associated with AD. Drawing insights from randomized controlled trials (RCTs) utilizing structural neuroimaging, the specific influence of RE on AD-affected brain structures and their correlation with cognitive function are discussed. Preliminary findings suggest that RE induces structural brain changes in older adults that could reduce the risk of AD or mitigate AD progression. Importantly, the impacts of RE appear to follow a dose-response effect, reversing pathological structural changes and improving associated cognitive functions if performed at least twice per week for at least six months, with greatest effects in those already experiencing some element of cognitive decline. While more research is eagerly awaited, this review contributes insights into the potential benefits of RE for cognitive health in the context of AD-related changes in brain structure and function.

Mapping the impact of age and APOE risk factors for late onset Alzheimer's disease on long range brain connections through multiscale bundle analysis

Alzheimer's disease currently has no cure and is usually detected too late for interventions to be effective. In this study we have focused on cognitively normal subjects to study the impact of risk factors on their long-range brain connections. To detect vulnerable connections, we devised a multiscale, hierarchical method for spatial clustering of the whole brain tractogram and examined the impact of age and APOE allelic variation on cognitive abilities and bundle properties including texture e.g., mean fractional anisotropy, variability, and geometric properties including streamline length, volume, and shape, as well as asymmetry. We found that the third level subdivision in the bundle hierarchy provided the most sensitive ability to detect age and genotype differences associated with risk factors. Our results indicate that frontal bundles were a major age predictor, while the occipital cortex and cerebellar connections were important risk predictors that were heavily genotype dependent, and showed accelerated decline in fractional anisotropy, shape similarity, and increased asymmetry. Cognitive metrics related to olfactory memory were mapped to bundles, providing possible early markers of neurodegeneration. In addition, physiological metrics such as diastolic blood pressure were associated with changes in white matter tracts. Our novel method for a data driven analysis of sensitive changes in tractography may differentiate populations at risk for AD and isolate specific vulnerable networks.

Alcohol Use Disorder and Dementia: A Review

PURPOSE

By 2040, 21.6% of Americans will be over age 65, and the population of those older than age 85 is estimated to reach 14.4 million. Although not causative, older age is a risk factor for dementia: every 5 years beyond age 65, the risk doubles; approximately one-third of those older than age 85 are diagnosed with dementia. As current alcohol consumption among older adults is significantly higher compared to previous generations, a pressing question is whether drinking alcohol increases the risk for Alzheimer's disease or other forms of dementia.

SEARCH METHODS

Databases explored included PubMed, Web of Science, and ScienceDirect. To accomplish this narrative review on the effects of alcohol consumption on dementia risk, the literature covered included clinical diagnoses, epidemiology, neuropsychology, postmortem pathology, neuroimaging and other biomarkers, and translational studies. Searches conducted between January 12 and August 1, 2023, included the following terms and combinations: "aging," "alcoholism," "alcohol use disorder (AUD)," "brain," "CNS," "dementia," "Wernicke," "Korsakoff," "Alzheimer," "vascular," "frontotemporal," "Lewy body," "clinical," "diagnosis," "epidemiology," "pathology," "autopsy," "postmortem," "histology," "cognitive," "motor," "neuropsychological," "magnetic resonance," "imaging," "PET," "ligand," "degeneration," "atrophy," "translational," "rodent," "rat," "mouse," "model," "amyloid," "neurofibrillary tangles," "α-synuclein," or "presenilin." When relevant, "species" (i.e., "humans" or "other animals") was selected as an additional filter. Review articles were avoided when possible.

SEARCH RESULTS

The two terms "alcoholism" and "aging" retrieved about 1,350 papers; adding phrases-for example, "postmortem" or "magnetic resonance"-limited the number to fewer than 100 papers. Using the traditional term, "alcoholism" with "dementia" resulted in 876 citations, but using the currently accepted term "alcohol use disorder (AUD)" with "dementia" produced only 87 papers. Similarly, whereas the terms "Alzheimer's" and "alcoholism" yielded 318 results, "Alzheimer's" and "alcohol use disorder (AUD)" returned only 40 citations. As pertinent postmortem pathology papers were published in the 1950s and recent animal models of Alzheimer's disease were created in the early 2000s, articles referenced span the years 1957 to 2024. In total, more than 5,000 articles were considered; about 400 are herein referenced.

DISCUSSION AND CONCLUSIONS

Chronic alcohol misuse accelerates brain aging and contributes to cognitive impairments, including those in the mnemonic domain. The consensus among studies from multiple disciplines, however, is that alcohol misuse can increase the risk for dementia, but not necessarily Alzheimer's disease. Key issues to consider include the reversibility of brain damage following abstinence from chronic alcohol misuse compared to the degenerative and progressive course of Alzheimer's disease, and the characteristic presence of protein inclusions in the brains of people with Alzheimer's disease, which are absent in the brains of those with AUD.

Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

Neuroanatomical correlates of distracted straight driving performance: a driving simulator MRI study across the lifespan

Background

Driving is the preferred mode of transportation for adults across the healthy age span. However, motor vehicle crashes are among the leading causes of injury and death, especially for older adults, and under distracted driving conditions. Understanding the neuroanatomical basis of driving may inform interventions that minimize crashes. This exploratory study examined the neuroanatomical correlates of undistracted and distracted simulated straight driving.

Methods

One-hundred-and-thirty-eight participants (40.6% female) aged 17-85 years old (mean and SD = 58.1 ± 19.9 years) performed a simulated driving task involving straight driving and turns at intersections in a city environment using a steering wheel and foot pedals. During some straight driving segments, participants responded to auditory questions to simulate distracted driving. Anatomical T1-weighted MRI was used to quantify grey matter volume and cortical thickness for five brain regions: the middle frontal gyrus (MFG), precentral gyrus (PG), superior temporal cortex (STC), posterior parietal cortex (PPC), and cerebellum. Partial correlations controlling for age and sex were used to explore relationships between neuroanatomical measures and straight driving behavior, including speed, acceleration, lane position, heading angle, and time speeding or off-center. Effects of interest were noted at an unadjusted p-value threshold of 0.05.

Results

Distracted driving was associated with changes in most measures of straight driving performance. Greater volume and cortical thickness in the PPC and cerebellum were associated with reduced variability in lane position and heading angle during distracted straight driving. Cortical thickness of the MFG, PG, PPC, and STC were associated with speed and acceleration, often in an age-dependent manner.

Conclusion

Posterior regions were correlated with lane maintenance whereas anterior and posterior regions were correlated with speed and acceleration, especially during distracted driving. The regions involved and their role in straight driving may change with age, particularly during distracted driving as observed in older adults. Further studies should investigate the relationship between distracted driving and the aging brain to inform driving interventions.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.

Tackling cognitive decline in late adulthood: Cognitive interventions

Affordable and easy-to-administer interventions such as cognitive training, cognitively stimulating everyday leisure activities, and non-invasive brain stimulation techniques, are promising avenues to counteract age-related cognitive decline and support people in maintaining cognitive health into late adulthood. However, the same pattern of findings emerges across all three fields of cognitive intervention research: whereas improvements within the intervention context are large and often reliable, generalisation to other cognitive abilities and contexts are severely limited. These findings suggest that while cognitive interventions can enhance the efficiency with which people use their existing cognitive capacity, these interventions are unlikely to expand existing capacity limits. Therefore, future research investigating generalisation of enhanced efficiency constitutes a promising avenue for developing reliably effective cognitive interventions.

Age-associated cortical similarity networks correlate with cell type-specific transcriptional signatures

Human brain structure shows heterogeneous patterns of change across adults aging and is associated with cognition. However, the relationship between cortical structural changes during aging and gene transcription signatures remains unclear. Here, using structural magnetic resonance imaging data of two separate cohorts of healthy participants from the Cambridge Centre for Aging and Neuroscience (n = 454, 18-87 years) and Dallas Lifespan Brain Study (n = 304, 20-89 years) and a transcriptome dataset, we investigated the link between cortical morphometric similarity network and brain-wide gene transcription. In two cohorts, we found reproducible morphometric similarity network change patterns of decreased morphological similarity with age in cognitive related areas (mainly located in superior frontal and temporal cortices), and increased morphological similarity in sensorimotor related areas (postcentral and lateral occipital cortices). Changes in morphometric similarity network showed significant spatial correlation with the expression of age-related genes that enriched to synaptic-related biological processes, synaptic abnormalities likely accounting for cognitive decline. Transcription changes in astrocytes, microglia, and neuronal cells interpreted most of the age-related morphometric similarity network changes, which suggest potential intervention and therapeutic targets for cognitive decline. Taken together, by linking gene transcription signatures to cortical morphometric similarity network, our findings might provide molecular and cellular substrates for cortical structural changes related to cognitive decline across adults aging.

The Catastrophe of Intracerebral Hemorrhage Drives the Capillary-Hemorrhage Dementias, Including Alzheimer's Disease

This review advances an understanding of several dementias, based on four premises. One is that capillary hemorrhage is prominent in the pathogenesis of the dementias considered (dementia pugilistica, chronic traumatic encephalopathy, traumatic brain damage, Alzheimer's disease). The second premise is that hemorrhage introduces four neurotoxic factors into brain tissue: hypoxia of the tissue that has lost its blood supply, hemoglobin and its breakdown products, excitotoxic levels of glutamate, and opportunistic pathogens that can infect brain cells and induce a cytotoxic immune response. The third premise is that where organisms evolve molecules that are toxic to itself, like the neurotoxicity ascribed to hemoglobin, amyloid- (A), and glutamate, there must be some role for the molecule that gives the organism a selection advantage. The fourth is the known survival-advantage roles of hemoglobin (oxygen transport), of A (neurotrophic, synaptotrophic, detoxification of heme, protective against pathogens) and of glutamate (a major neurotransmitter). From these premises, we propose 1) that the brain has evolved a multi-factor response to intracerebral hemorrhage, which includes the expression of several protective molecules, including haptoglobin, hemopexin and A; and 2) that it is logical, given these premises, to posit that the four neurotoxic factors set out above, which are introduced into the brain by hemorrhage, drive the progression of the capillary-hemorrhage dementias. In this view, A expressed at the loci of neuronal death in these dementias functions not as a toxin but as a first responder, mitigating the toxicity of hemoglobin and the infection of the brain by opportunistic pathogens.

Quantifying the effect of cerebral atrophy on head injury risk in elderly individuals: Insights from computational biomechanics and experimental analysis of bridging veins

The objective of this study was to quantitatively investigate the relationship between cerebral atrophy and the risk of injury in elderly individuals. To achieve this, a sophisticated computational biomechanics approach utilizing finite element analysis was employed to simulate the mechanical behavior of the brain and skull under various conditions. In addition, particular emphasis was placed on understanding the role of cerebral bridging veins (BVs) and their mechanical properties at different ages in the occurrence of head injuries. Head models representing healthy brains and five atrophy models were developed based on imaging data. After validation, the models underwent the identical impact loading conditions to enable the simulation of brain damage. The resulting outcomes of the models with brain atrophy were then compared to the results obtained from the healthy model, allowing for a comparative analysis. Simulations showed increased relative displacement with worsening brain atrophy, particularly in the frontal and occipital regions. Compared to the healthy brain model, relative displacement increased by 2.36 %-9.21 % in the atrophy models, indicating an elevated risk of injury. In severe brain atrophy (FEM 6), the strain reached 83.59 % in local model simulations, leading to damage and rupture of cerebral BVs in both young and elderly individuals. Mechanical tests on cerebral BVs demonstrated a negative correlation between age and ultimate force, stress, and strain, suggesting increased susceptibility to damage with age. An observed sharp decline of approximately 50 % in ultimate stress and 35 % in ultimate strain was noted as age increased. We implemented a 50 % reduction in the intensity of head impact forces; nevertheless, vascular damage continues to manifest in the elderly population. To establish a truly safe zone, it is imperative to further decrease the intensity of the impact. This investigation represents a significant step forward in our understanding of the complex interplay between cerebral atrophy, the mechanical properties of BVs at different age, and the risk of head injury in the elderly. Through continued research in this field, we can strive to improve the quality of care, enhance prevention strategies, and ultimately enhance the well-being and safety of the elderly population.

Social isolation from childhood to mid-adulthood: is there an association with older brain age?

BACKGROUND

Older brain age - as estimated from structural MRI data - is known to be associated with detrimental mental and physical health outcomes in older adults. Social isolation, which has similar detrimental effects on health, may be associated with accelerated brain aging though little is known about how different trajectories of social isolation across the life course moderate this association. We examined the associations between social isolation trajectories from age 5 to age 38 and brain age assessed at age 45.

METHODS

We previously created a typology of social isolation based on onset during the life course and persistence into adulthood, using group-based trajectory analysis of longitudinal data from a New Zealand birth cohort. The typology comprises four groups: 'never-isolated', 'adult-only', 'child-only', and persistent 'child-adult' isolation. A brain age gap estimate (brainAGE) - the difference between predicted age from structural MRI date and chronological age - was derived at age 45. We undertook analyses of brainAGE with trajectory group as the predictor, adjusting for sex, family socio-economic status, and a range of familial and child-behavioral factors.

RESULTS

Older brain age in mid-adulthood was associated with trajectories of social isolation after adjustment for family and child confounders, particularly for the 'adult-only' group compared to the 'never-isolated' group.

CONCLUSIONS

Although our findings are associational, they indicate that preventing social isolation, particularly in mid-adulthood, may help to avert accelerated brain aging associated with negative health outcomes later in life.

Healthy brain aging and delayed dementia in Texas rural elderly

Healthy aging is the process of preserving and enhancing one's independence, physical and mental well-being, and overall quality of life. It involves the mental, emotional, and cognitive wellness. Although biological and genetic factors have a significant influence on the process of aging gracefully, other adjustable factors also play a crucial role. Adopting positive behaviors such as maintaining a nutritious and balanced diet, engaging in regular physical activity, effectively managing stress and anxiety, ensuring sufficient sleep, nurturing spiritual coping mechanisms, and prioritizing overall well-being from an early stage can collectively influence both lifespan and the quality of health during advanced years. We aim to explore the potential impacts of biological, psychosocial, and environmental factors on the process of healthy cognitive aging in individuals who exhibit healthy aging. Additionally, we plan to present initial findings that demonstrate how maintaining good cognitive health during aging could potentially postpone the emergence of neurodegenerative disorders. We hypothesize that there will be strong associations between biological, environmental, and social factors that cause some elderly to be superior in cognitive health than others. For preliminary data collection, we recruited 25 cognitively healthy individuals and 5 individuals with MCI/AD between the ages of 60-90 years. We conducted anthropometric measurements, and blood biomarker testing, administered surveys, and obtained structural brain magnetic resonance imaging (MRI) scans. The Montreal Cognitive Assessment (MoCA) scores and sub-scores for the healthy group were also reported. We found that at baseline, individuals exhibiting healthy cognitive aging, and those with MCI/AD had comparable measures of anthropometrics and blood biomarkers. The healthy group exhibited lower signs of brain volume loss and the ones observed were age-related. Moreover, within the healthy group, there was a significant correlation (p = 0.003) between age and MoCA scores. Conversely, within the individuals with MCI/AD, the MRI scans showed disease signs of grey and white matter and loss of cerebral volume. Healthy brain aging is a scientific area that remains under-explored. Our current study findings support our hypothesis. Future studies are required in diverse populations to determine the various biological, psychological, environmental, lifestyle, and social factors that contribute to it.

Changes of gray matter volumes of subcortical regions across the lifespan: a Human Connectome Project study

We assessed changes in gray matter volume (GMV) of nine subcortical regions (accumbens, amygdala, brainstem, caudate, cerebellar cortex, pallidum, putamen, thalamus, and ventral diencephalon) across the lifespan in a large sample of participants in the Human Connectome Project (n = 2,458, 5-90 yr old, 1,113 males and 1,345 females). 3T MRI data were acquired using a harmonized protocol and were processed in an identical way for all brains. GMVs of individual regions were adjusted for estimated total intracranial volume and regressed against age. We found highly statistically significant changes in GMV with age (P < 0.001) that were distinct among areas and mostly consistent between sexes, as follows. 1) The GMVs of accumbens, caudate, putamen, and cerebellum decreased with age in a linear fashion. The rate of decrease was steeper in males than in females for all regions. 2) The GMVs of the amygdala, pallidum, thalamus, ventral diencephalon, and brainstem changed with age in a quadratic fashion, i.e., increasing first and decreasing afterward. The estimated age at the peak (vertex) of the parabola was 51.8 yr for the brainstem and 28.0-37.9 yr for the other regions. The peak occurred earlier in males than in females, by an average of 8 yr, with the exception of the brainstem, where the age at the peak was very similar in both sexes. These results confirm previous findings and offer new insights into region-specific age-related changes in subcortical brain GMVs.NEW & NOTEWORTHY We report mixed effects of age on subcortical grey matter volume (GMV) during lifespan (n = 2458, 5-90 yr old, 1113 male, 1345 female). Striatal and cerebellar GMVs decreased linearly with age, more steeply in males. In contrast, GMVs of the amygdala, pallidum, thalamus, ventral diencephalon, and brainstem changed in a quadratic fashion, increasing first and decreasing afterward, with males peaking earlier than females in all regions but the brainstem where they peaked at nearly the same time.

Exposure to Polystyrene Nanoplastics Led to Learning and Memory Deficits in Zebrafish by Inducing Oxidative Damage and Aggravating Brain Aging

Nanoplastics (NPs) may pass through the blood-brain barrier, giving rise to serious concerns about their potential toxicity to the brain. In this study, the effects of NPs exposure on learning and memory, the primary cognitive functions of the brain, are assessed in zebrafish with classic T-maze exploration tasks. Additionally, to reveal potential affecting mechanisms, the impacts of NPs exposure on brain aging, oxidative damage, energy provision, and the cell cycle are evaluated. The results demonstrate that NP-exposed zebrafish takes significantly longer for their first entry and spends markedly less time in the reward zone in the T-maze task, indicating the occurrence of learning and memory deficits. Moreover, higher levels of aging markers (β-galactosidase and lipofuscin) are detected in the brains of NP-exposed fish. Along with the accumulation of reactive free radicals, NP-exposed zebrafish suffer significant levels of brain oxidative damage. Furthermore, lower levels of Adenosine triphosphate (ATP) and cyclin-dependent kinase 2 and higher levels of p53 are observed in the brains of NP-exposed zebrafish, suggesting that NPs exposure also results in a shortage of energy supply and an arrestment of the cell cycle. These findings suggest that NPs exposure may pose a severe threat to brain health, which deserves closer attention.

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels' tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects' three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels' diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model's age predictions in patients with intracranial vessel diseases.

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

Increased MRI-based Brain Age in chronic migraine patients

INTRODUCTION

Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.

METHODS

We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).

RESULTS

CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.

CONCLUSION

The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

Atypical Hierarchical Connectivity Revealed by Stepwise Functional Connectivity in Aging

Hierarchical functional structure plays a crucial role in brain function. We aimed to investigate how aging affects hierarchical functional structure and to evaluate the relationship between such effects and molecular, microvascular, and cognitive features. We used resting-state functional magnetic resonance imaging (fMRI) data from 95 older adults (66.94 ± 7.23 years) and 44 younger adults (21.8 ± 2.53 years) and employed an innovative graph-theory-based analysis (stepwise functional connectivity (SFC)) to reveal the effects of aging on hierarchical functional structure in the brain. In the older group, an SFC pattern converged on the primary sensory-motor network (PSN) rather than the default mode network (DMN). Moreover, SFC decreased in the DMN and increased in the PSN at longer link-steps in aging, indicating a reconfiguration of brain hub systems during aging. Subsequent correlation analyses were performed between SFC values and molecular, microvascular features, and behavioral performance. Altered SFC patterns were associated with dopamine and serotonin, suggesting that altered hierarchical functional structure in aging is linked to the molecular fundament with dopamine and serotonin. Furthermore, increased SFC in the PSN, decreased SFC in the DMN, and accelerated convergence rate were all linked to poorer microvascular features and lower executive function. Finally, a mediation analysis among SFC features, microvascular features, and behavioral performance indicated that the microvascular state may influence executive function through SFC features, highlighting the interactive effects of SFC features and microvascular state on cognition.

Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans

BACKGROUND

Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses.

NEW METHOD

The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2 * , which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2 * , and MTsat, computed from multiple sets of images.

RESULTS

QUIQI restores homoscedasticity in analyses of quantitative MRI data computed from multiple scans. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy.

COMPARISON WITH EXISTING METHODS

QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis.

CONCLUSION

QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain

The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.

Neuropsychological Evaluation and Functional Magnetic Resonance Imaging Tasks in the Preoperative Assessment of Patients with Brain Tumors: A Systematic Review

BACKGROUND

Current surgical treatment of gliomas relies on a function-preserving, maximally safe resection approach. Functional Magnetic Resonance Imaging (fMRI) is a widely employed technology for this purpose. A preoperative neuropsychological evaluation should accompany this exam. However, only a few studies have reported both neuropsychological tests and fMRI tasks for preoperative planning-the current study aimed to systematically review the scientific literature on the topic.

METHODS

PRISMA guidelines were followed. We included studies that reported both neuropsychological tests and fMRI. Exclusion criteria were: no brain tumors, underage patients, no preoperative assessment, resting-state fMRI only, or healthy sample population/preclinical studies.

RESULTS

We identified 123 papers, but only 15 articles were included. Eight articles focused on language; three evaluated cognitive performance; single papers studied sensorimotor cortex, prefrontal functions, insular cortex, and cerebellar activation. Two qualitative studies focused on visuomotor function and language. According to some authors, there was a strong correlation between performance in presurgical neuropsychological tests and fMRI. Several papers suggested that selecting well-adjusted and individualized neuropsychological tasks may enable the development of personalized and more efficient protocols. The fMRI findings may also help identify plasticity phenomena to avoid unintentional damage during neurosurgery.

CONCLUSIONS

Most studies have focused on language, the most commonly evaluated cognitive function. The correlation between neuropsychological and fMRI results suggests that altered functions during the neuropsychological assessment may help identify patients who could benefit from an fMRI and, possibly, functions that should be tested. Neuropsychological evaluation and fMRI have complementary roles in the preoperative assessment.

Changes in levels of the antioxidant glutathione in brain and blood across the age span of healthy adults: A systematic review

Aging is characterized by a gradual decline of the body's biological functions, which can lead to increased production of reactive oxygen species (ROS). Antioxidants neutralize ROS and maintain balance between oxidation and reduction. If ROS production exceeds the ability of antioxidant systems to neutralize, a damaging state of oxidative stress (OS) may exist. The reduced form of glutathione (GSH) is the most abundant antioxidant, and decline of GSH is considered a marker of OS. Our review summarizes the literature on GSH variations with age in healthy adults in brain (in vivo, ex vivo) and blood (plasma, serum), and reliability of in vivo magnetic resonance spectroscopy (MRS) measurement of GSH. A systematic PubMed search identified 35 studies. All in vivo MRS studies (N = 13) reported good to excellent reproducibility of GSH measures. In brain, 3 out of 4 MRS studies reported decreased GSH with age, measured in precuneus, cingulate, and occipital regions, while 1 study reported increased GSH with age in frontal and sensorimotor regions. In post-mortem brain, out of 3 studies, 2 reported decreased GSH with age in hippocampal and frontal regions, while 1 study reported increased GSH with age in a frontal region. Oxidized glutathione disulfide (GSSG) was reported to be increased in caudate with age in 1 study, suggesting OS. Although findings in the brain lacked a clear consensus, the majority of studies suggested a decline of GSH with age. The low number of studies (particularly ex vivo) and potential regional differences may have contributed to variability in the findings in brain. In blood, in contrast, GSH levels predominately were reported to decrease with advancing age (except in the oldest-old, who may represent a select group of particularly successful agers), while GSSG findings lacked consensus. The larger number of studies assessing age-specific GSH level changes in blood (N = 16) allowed for more robust consensus across studies than in brain. Overall, the literature suggests that aging is associated with increased OS in brain and body, but the timing and regional distribution of changes in the brain require further study. The contribution of brain OS to brain aging, and the effect of interventions to raise brain GSH levels on decline of brain function, remain understudied. Given that reliable tools to measure brain GSH exist, we hope this paper will serve as a catalyst to stimulate more work in this field.

The influence of bilingualism on gray matter volume in the course of aging: a longitudinal study

Background

Bilingualism is associated with higher gray matter volume (GMV) as a form of brain reserve in brain regions such as the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL). A recent cross-sectional study reported the age-related GMV decline in the left IFG and IPL to be steeper for bilinguals than for monolinguals. The present study aimed at supporting this finding for the first time with longitudinal data.

Methods

In the current study, 200 participants aged 19 to 79 years (87 monolinguals, 113 sequential bilinguals, mostly native German speakers with variable second language background) were included. Trajectories of GMV decline in the bilateral IFG and IPL were analyzed in mono- and bilinguals over two time points (mean time interval: 3.6 years). For four regions of interest (left/right IFG and left/right IPL), mixed Analyses of Covariance were conducted to assess (i) GMV changes over time, (ii) GMV differences for language groups (monolinguals/bilinguals), and (iii) the interaction between time point and language group. Corresponding analyses were conducted for the two factors of GMV, surface area (SA) and cortical thickness (CT).

Results

There was higher GMV in bilinguals compared to monolinguals in the IPL, but not IFG. While the left and right IFG and the right IPL displayed a similar GMV change in mono- and bilinguals, GMV decline within the left IPL was significantly steeper in bilinguals. There was greater SA in bilinguals in the bilateral IPL and a steeper CT decline in bilinguals within in the left IPL.

Conclusion

The cross-sectional observations of a steeper GMV decline in bilinguals could be confirmed for the left IPL. Additionally, the higher GMV in bilinguals in the bilateral IPL may indicate that bilingualism contributes to brain reserve especially in posterior brain regions. SA appeared to contribute to bilinguals' higher GMV in the bilateral IPL, while CT seemed to account for the steeper structural decline in bilinguals in the left IPL. The present findings demonstrate the importance of time as an additional factor when assessing the neuroprotective effects of bilingualism on structural features of the human brain.

Emotional Regulation and Adolescent Concussion: Overview and Role of Neuroimaging

Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.

Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping

BACKGROUND

Microstructural changes in deep gray matter (DGM) nuclei are related to physiological behavior, cognition, and memory. Therefore, it is critical to study age-dependent trajectories of biomarkers in DGM nuclei for understanding brain development and aging, as well as predicting cognitive or neurodegenerative diseases.

OBJECTIVES

We aimed to (1) characterize age-dependent trajectories of mean susceptibility, adjusted volume, and total iron content simultaneously in DGM nuclei using quantitative susceptibility mapping (QSM); (2) examine potential contributions of sex related effects to the different age-dependence trajectories of volume and iron deposition; and (3) evaluate the ability of brain age prediction by combining mean magnetic susceptibility and volume of DGM nuclei.

METHODS

Magnetic susceptibilities and volumetric values of DGM nuclei were obtained from 220 healthy participants (aged 10-70 years) scanned on a 3T MRI system. Regions of interest (ROIs) were drawn manually on the QSM images. Univariate regression analysis between age and each of the MRI measurements in a single ROI was performed. Pearson correlation coefficients were calculated between magnetic susceptibility and adjusted volume in a single ROI. The statistical significance of sex differences in age-dependent trajectories of magnetic susceptibilities and adjusted volumes were determined using one-way ANCOVA. Multiple regression analysis was used to evaluate the ability to estimate brain age using a combination of the mean susceptibilities and adjusted volumes in multiple DGM nuclei.

RESULTS

Mean susceptibility and total iron content increased linearly, quadratically, or exponentially with age in all six DGM nuclei. Negative linear correlation was observed between adjusted volume and age in the head of the caudate nucleus (CN; R² = 0.196, p < 0.001). Quadratic relationships were found between adjusted volume and age in the putamen (PUT; R² = 0.335, p < 0.001), globus pallidus (GP; R² = 0.062, p = 0.001), and dentate nucleus (DN; R² = 0.077, p < 0.001). Males had higher mean magnetic susceptibility than females in the PUT (p = 0.001), red nucleus (RN; p = 0.002), and substantia nigra (SN; p < 0.001). Adjusted volumes of the CN (p < 0.001), PUT (p = 0.030), GP (p = 0.007), SN (p = 0.021), and DN (p < 0.001) were higher in females than those in males throughout the entire age range (10-70 years old). The total iron content of females was higher than that of males in the CN (p < 0.001), but lower than that of males in the PUT (p = 0.014) and RN (p = 0.043) throughout the entire age range (10-70 years old). Multiple regression analyses revealed that the combination of the mean susceptibility value of the PUT, and the volumes of the CN and PUT had the strongest associations with brain age (R² = 0.586).

CONCLUSIONS

QSM can be used to simultaneously investigate age- and sex- dependent changes in magnetic susceptibility and volume of DGM nuclei, thus enabling a comprehensive understanding of the developmental trajectories of iron accumulation and volume in DGM nuclei during brain development and aging.

Fast and functionally specific cortical thickness changes induced by visual stimulation

Structural characteristics of the human brain serve as important markers of brain development, aging, disease progression, and neural plasticity. They are considered stable properties, changing slowly over time. Multiple recent studies reported that structural brain changes measured with magnetic resonance imaging (MRI) may occur much faster than previously thought, within hours or even minutes. The mechanisms behind such fast changes remain unclear, with hemodynamics as one possible explanation. Here we investigated the functional specificity of cortical thickness changes induced by a flickering checkerboard and compared them to blood oxygenation level-dependent (BOLD) functional MRI activity. We found that checkerboard stimulation led to a significant thickness increase, which was driven by an expansion at the gray-white matter boundary, functionally specific to V1, confined to the retinotopic representation of the checkerboard stimulus, and amounted to 1.3% or 0.022 mm. Although functional specificity and the effect size of these changes were comparable to those of the BOLD signal in V1, thickness effects were substantially weaker in V3. Furthermore, a comparison of predicted and measured thickness changes for different stimulus timings suggested a slow increase of thickness over time, speaking against a hemodynamic explanation. Altogether, our findings suggest that visual stimulation can induce structural gray matter enlargement measurable with MRI.

R1ρ dispersion in white matter correlates with quantitative metrics of cognitive impairment

Much previous neuroimaging research in Alzheimer's disease has focused on the roles of amyloid and tau proteins, but recent studies have implicated microvascular changes in white matter as early indicators of damage related to later dementia. We used MRI to derive novel, non-invasive measurements of R1ρ dispersion using different locking fields to characterize variations of microvascular structure and integrity in brain tissues. We developed a non-invasive 3D R1ρ dispersion imaging technique using different locking fields at 3T. We acquired MR images and cognitive assessments of participants with mild cognitive impairment (MCI) and compared them to age-matched healthy controls in a cross-sectional study. After providing informed consent, 40 adults aged 62 to 82 years (n = 17 MCI) were included in this study. White matter ΔR1ρ-fraction measured by R1ρ dispersion imaging showed a strong correlation with the cognitive status of older adults (βstd = -0.4, p-value < 0.01) independent of age, in contrast to other conventional MRI markers such as T2, R1ρ, and white matter hyperintense lesion volume (WMHs) measured with T2-FLAIR. The correlation of WMHs with cognitive status was no longer significant after adjusting for age and sex in linear regression analysis, and the size of the regression coefficient was substantially decreased (53% lower). This work establishes a new non-invasive method that potentially characterizes impairment of the microvascular structure of white matter in MCI patients compared to healthy controls. The application of this method in longitudinal studies would improve our fundamental understanding of the pathophysiologic changes that accompany abnormal cognitive decline with aging and help identify potential targets for treatment of Alzheimer's disease.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

Is musical engagement enough to keep the brain young?

Music-making and engagement in music-related activities have shown procognitive benefits for healthy and pathological populations, suggesting reductions in brain aging. A previous brain aging study, using Brain Age Gap Estimation (BrainAGE), showed that professional and amateur-musicians had younger appearing brains than non-musicians. Our study sought to replicate those findings and analyze if musical training or active musical engagement was necessary to produce an age-decelerating effect in a cohort of healthy individuals. We scanned 125 healthy controls and investigated if musician status, and if musical behaviors, namely active engagement (AE) and musical training (MT) [as measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)], had effects on brain aging. Our findings suggest that musician status is not related to BrainAGE score, although involvement in current physical activity is. Although neither MT nor AE subscales of the Gold-MSI are predictive for BrainAGE scores, dispositional resilience, namely the ability to deal with challenge, is related to both musical behaviors and sensitivity to musical pleasure. While the study failed to replicate the findings in a previous brain aging study, musical training and active musical engagement are related to the resilience factor of challenge. This finding may reveal how such musical behaviors can potentially strengthen the brain's resilience to age, which may tap into a type of neurocognitive reserve.

Association between social comparison orientation and hippocampal properties in older adults: A multimodal MRI study

Social comparison orientation (SCO) refers to the tendency to compare oneself with others and has two distinct dimensions: one about opinions and the other about abilities. Although dissociable neural mechanisms underlying the two dimensions of social comparison can be assumed, little is known about how each dimension of SCO is associated with cognitive and brain health among older adults. To investigate this, we analyzed the SCO scale questionnaire data, neuropsychological assessment data, and multimodal MRI data collected from 90 community-dwelling older adults. We found that global cognitive performance was positively correlated with the score of the opinion subscale but not with the score of the ability subscale and the total score. Similarly, hippocampal volume was positively correlated with opinion score alone. Additionally, the resting-state functional connectivity between the hippocampal seed and the default mode network showed a positive correlation only with the opinion score. Moreover, fractional anisotropy in the hippocampal cingulum was positively correlated with opinion score only. These findings suggest that global cognition and hippocampal properties in older age are associated with the SCO of opinion, which could reflect a regular habit of performing the types of cognitively demanding activities involved in evaluation of self and other opinions.

Diagnosing vascular cognitive impairment: Current challenges and future perspectives

Cerebrovascular disease is a major cause of cognitive decline and dementia. This is referred to as vascular cognitive impairment (VCI). Diagnosing VCI is important, among others to optimize treatment to prevent further vascular injury. This narrative review addresses challenges in current diagnostic approaches to VCI and potential future developments. First we summarize how diagnostic criteria for VCI evolved over time. We then highlight challenges in diagnosing VCI in clinical practice: assessment of severity of vascular brain injury on brain imaging is often imprecise and the relation between vascular lesion burden and cognitive functioning shows high intersubject variability. This can make it difficult to establish causality in individual patients. Moreover, because VCI is essentially an umbrella term, it lacks specificity on disease mechanisms, prognosis, and treatment. We see the need for a fundamentally different approach to diagnosing VCI, which should be more dimensional, including multimodal quantitative assessment of injury, with more accurate estimation of cognitive impact, and include biological definitions of disease that can support further development of targeted treatment. Recent developments in the field that can form the basis of such an approach are discussed.

Interrelating differences in structural and functional connectivity in the older adult's brain

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.

Can probiotics mitigate age-related neuroinflammation leading to improved cognitive outcomes?

Changes in brain structure and cognitive function are a natural part of aging; however, in some cases these changes are more severe resulting in mild cognitive impairment (MCI) or Alzheimer's disease (AD). Evidence is mounting to show that neuroinflammation is an underlying risk factor for neurodegenerative disease progression. Age-related neuroinflammation does not appear to occur in isolation and is part of increased systemic inflammation, which may in turn be triggered by changes in the gut associated with aging. These include an increase in gut permeability, which allows immune triggering compounds into the body, and alterations in gut microbiota composition leading to dysbiosis. It therefore follows that, treatments that can maintain healthy gut function may reduce inflammation and protect against, or improve, symptoms of age-associated neurodegeneration. The aim of this mini review was to evaluate whether probiotics could be used for this purpose. The analysis concluded that there is preliminary evidence to suggest that specific probiotics may improve cognitive function, particularly in those with MCI; however, this is not yet convincing and larger, multilocation, studies focus on the effects of probiotics alone are required. In addition, studies that combine assessment of cognition alongside analysis of inflammatory biomarkers and gut function are needed. Immense gains could be made to the quality of life of the aging population should the hypothesis be proven to be correct.

Heritability of Subcortical Grey Matter Structures

Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson's correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45-0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72-0.85). A strong correlation between CAT12 and volBrain (r = 0.74-0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases.

Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging

Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.

Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning

BACKGROUND AND PURPOSE

Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework.

MATERIALS AND METHODS

BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients' pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT.

RESULTS

The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54-3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction.

CONCLUSION

We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy.

Imaging of Normal Brain Aging

Understanding normal brain aging physiology is essential to improving healthy human longevity, differentiation, and early detection of diseases, such as neurodegenerative diseases, which are an enormous social and economic burden. Functional decline, such as reduced physical activity and cognitive abilities, is typically associated with brain aging. The authors summarize the aging brain mechanism and effects of aging on the brain observed by brain structural MR imaging and advanced neuroimaging techniques, such as diffusion tensor imaging and functional MR imaging.

Dissecting motor skill acquisition: Spatial coordinates take precedence

Practicing a previously unknown motor sequence often leads to the consolidation of motor chunks, which enable its accurate execution at increasing speeds. Recent imaging studies suggest the function of these structures to be more related to the encoding, storage, and retrieval of sequences rather than their sole execution. We found that optimal motor skill acquisition prioritizes the storage of the spatial features of the sequence in memory over its rapid execution early in training, as proposed by Hikosaka in 1999. This process, seemingly diminished in older adults, was partially restored by anodal transcranial direct current stimulation over the motor cortex, as shown by a sharp improvement in accuracy and an earlier yet gradual emergence of motor chunks. These results suggest that the emergence of motor chunks is preceded by the storage of the sequence in memory but is not its direct consequence; rather, these structures depend on, and result from, motor practice.