Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC.

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Brain-Derived Neurotrophic Factor Val66Met is Associated with Variation in Cortical Structure in Healthy Aging Subjects.

Aging is associated with progressive brain atrophy and declines in learning and memory, often attributed to hippocampal or cortical deterioration. The role of brain-derived neurotrophic factor (BDNF) in modulating the structural and functional changes in the brain and visual system, particularly in relation to BDNF Val66Met polymorphism, remains underexplored. In this present cross-sectional observational study, we aimed to assess the effects of BDNF polymorphism on brain structural integrity, cognitive function, and visual pathway alterations. A total of 108 older individuals with no evidence of dementia and a mean (SD) age of 67.3 (9.1) years were recruited from the Optic Nerve Decline and Cognitive Change (ONDCC) study cohort. The BDNF Met allele carriage had a significant association with lower entorhinal cortex volume (6.7% lower compared to the Val/Val genotype, P = 0.02) and posterior cingulate volume (3.2% lower than the Val/Val group, P = 0.03), after adjusting for confounding factors including age, sex and estimated total intracranial volumes (eTIV). No significant associations were identified between the BDNF Val66Met genotype and other brain volumetric or diffusion measures, cognitive performances, or vision parameters except for temporal retinal nerve fibre layer thickness. Small but significant correlations were found between visual structural and functional, cognitive, and brain morphological metrics. Our findings suggest that carriage of BDNF Val66Met polymorphism is associated with lower entorhinal cortex and posterior cingulate volumes and may be involved in modulating the cortical morphology along the aging process.

The protocol for an observational Australian cohort study of CADASIL: The AusCADASIL study.

Introduction: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a rare genetic condition with a broad phenotypic presentation. This study aims to establish the first Australian cohort of individuals affected by CADASIL (AusCADASIL) and examine its clinical features and longitudinal course, and to investigate neuroimaging and blood biomarkers to assist in early diagnosis and identify disease progression. Methods: Participants will be recruited from six study centres across Australia for an observational study of CADASIL. We aim to recruit 150 participants with diagnosed CADASIL, family history of CADASIL or suspected CADASIL symptoms, and 150 cognitively normal NOTCH3 negative individuals as controls. Participants will complete: 1) online questionnaires on medical and family history, mental health, and wellbeing; 2) neuropsychological evaluation; 3) neurological examination and brain MRI; 4) ocular examination and 5) blood sample donation. Participants will have annual follow-up for 4 years to assess their progression and will be asked to invite a study partner to corroborate their self-reported cognitive and functional abilities.Primary outcomes include cognitive function and neuroimaging abnormalities. Secondary outcomes include investigation of genetics and blood and ocular biomarkers. Data from the cohort will contribute to an international consortium, and cohort participants will be invited to access future treatment/health intervention trials. Discussion: AusCADASIL will be the first study of an Australian cohort of individuals with CADASIL. The study will identify common pathogenic variants in this cohort, and characterise the pattern of clinical presentation and longitudinal progression, including imaging features, blood and ocular biomarkers and cognitive profile.

Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study.

Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.

Brain structural covariances in the ageing brain in the UK Biobank.

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

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: The COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMHs) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well documented in populations of different ethnicities and/or from different geographical regions. METHODS: We investigated how WMHs were associated with vascular risk factors and cognition in both Whites and Asians, using data from five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1946). WMH volumes (whole brain, periventricular, and deep) were quantified with UBO Detector and harmonized using the ComBat model. We also harmonized various vascular risk factors and scores for global cognition and individual cognitive domains. RESULTS: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake, and insufficient physical activity. Hypertension and stroke had stronger associations with WMH volumes in Whites than in Asians. No associations between WMH volumes and cognitive performance were found after correction for multiple testing. CONCLUSION: The current study highlights ethnic differences in the contributions of vascular risk factors to WMHs.

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

BACKGROUND: Gray matter (GM) and white matter (WM) impairments are both associated with raised blood pressure (BP), although whether elevated BP is differentially associated with the GM and WM aging process remains inadequately examined. METHODS: We included 37 327 participants with diffusion-weighted imaging (DWI) and 39 630 participants with T1-weighted scans from UK Biobank. BP was classified into 4 categories: normal BP, high-normal BP, grade 1, and grade 2 hypertension. Brain age gaps (BAGs) for GM (BAGGM) and WM (BAGWM) were derived from diffusion-weighted imaging and T1 scans separately using 3-dimensional-convolutional neural network deep learning techniques. RESULTS: There was an increase in both BAGGM and BAGWM with raised BP (P<0.05). BAGWM was significantly larger than BAGGM at high-normal BP (0.195 years older; P=0.006), grade 1 hypertension (0.174 years older; P=0.004), and grade 2 hypertension (0.510 years older; P<0.001), but not for normal BP. Mediation analysis revealed that the association between hypertension and cognitive decline was primarily mediated by WM impairment. Mendelian randomization analysis suggested a causal relationship between hypertension and WM aging acceleration (unstandardized B, 1.780; P=0.016) but not for GM (P>0.05). Sliding-window analysis indicated the association between hypertension and brain aging acceleration was moderated by chronological age, showing stronger correlations in midlife but weaker associations in the older age. CONCLUSIONS: Compared with GM, WM was more vulnerable to raised BP. Our study provided compelling evidence that concerted efforts should be directed towards WM damage in individuals with hypertension in clinical practice.

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.

Metagenomic analysis of oral and intestinal microbiome of patients during the initial stage of orthodontic treatment.

INTRODUCTION: This prospective study analyzed changes in the oral and intestinal microbiomes in patients before and after fixed orthodontic treatment, elucidating the impacts of fixed orthodontic treatment on patient health and metabolism. METHODS: Metagenomic analysis was conducted on stool, dental plaque, and saliva samples from 10 fixed orthodontic patients. All the samples were sequenced with Illumina NovaSeq 6000 with a paired-end sequencing length of 150 bp. Identification of taxa in metagenomes and functional annotation of genes of the microbiota were performed using the data after quality control. Clinical periodontal parameters, including the gingiva index, plaque index, and pocket probing depth, were examined at each time point in triplicates. Patients also received a table to record their oral hygiene habits of brushing, flossing, and dessert consumption frequency over 1 month. RESULTS: The brushing and flossing times per day of patients were significantly increased after treatment compared with baseline. The number of times a patient ate dessert daily was also fewer after treatment than at baseline. In addition, the plaque index decreased significantly, whereas the pH value of saliva, gingiva index, and pocket probing depth did not change. No significant differences were observed between the participants before and after orthodontic treatment regarding alpha-diversity analysis of the gut, dental plaque, or saliva microbiota. However, on closer analysis, periodontal disease-associated bacteria levels in the oral cavity remain elevated. Alterations in gut microbiota were also observed after orthodontic treatment. CONCLUSIONS: The richness and diversity of the microbiome did not change significantly during the initial stage of fixed orthodontic treatment. However, the levels of periodontal disease-associated bacteria increased.

Metagenomic analysis reveals distinct changes in the gut microbiome of obese Chinese children.

BACKGROUND: The prevalence of obese children in China is increasing, which poses a great challenge to public health. Gut microbes play an important role in human gut health, and changes in gut status are closely related to obesity. However, how gut microbes contribute to obesity in children remains unclear. In our study, we performed shotgun metagenomic sequencing of feces from 23 obese children, 8 overweight children and 22 control children in Chengdu, Sichuan, China. RESULTS: We observed a distinct difference in the gut microbiome of obese children and that of controls. Compared with the controls, bacterial pathogen Campylobacter rectus was significantly more abundant in obese children. In addition, functional annotation of microbial genes revealed that there might be gut inflammation in obese children. The guts of overweight children might belong to the transition state between obese and control children due to a gradient in relative abundance of differentially abundant species. Finally, we compared the gut metagenomes of obese Chinese children and obese Mexican children and found that Trichuris trichiura was significantly more abundant in the guts of obese Mexican children. CONCLUSIONS: Our results contribute to understanding the changes in the species and function of intestinal microbes in obese Chinese children.

The Association of APOE ε4 Allele with Retinal Layer Thickness and Microvasculature in Older Adults: Optic Nerve Decline and Cognitive Change Study.

PURPOSE: To investigate the relationship between the apolipoprotein E (APOE) ε4 allele and retinal structural and vascular characteristics in older adult participants from several research studies. We also studied the relationship between these structural and vascular characteristics with multifocal visual evoked potential (mfVEP) indices, neuropsychological parameters and MRI brain volumes in these participants. METHODS: In this study, 109 participants with a mean (SD) age of 67.1 (9.0) years were recruited. Participants were classified as APOE ε4 carriers or non-carriers based on the presence or absence of the ε4 allele. Baseline measurements included peripapillary retinal nerve fibre layer optical coherence tomography (RNFL OCT), and OCT-angiography (OCT-A) for evaluation of the retinal layer thickness and vessel density (VD) parameters. A multifocal visual evoked potential (mfVEP) test, including amplitude and latency, was used to assess the visual pathway function. Finally, cognitive function was evaluated using a battery of neuropsychological tests. OCT-A images were analysed in ImageJ to quantify VD in the superficial and deep vascular plexus and the size of the foveal avascular zone (FAZ). The relationship between carriers of APOE ε4 allele and these ocular parameters was analysed using generalised estimating equation (GEE) models and data adjusted for age, sex and inter-eye differences as within-subject variables (p < 0.05). RESULTS: Twenty-four participants were APOE ε4 carriers. Temporal RNFL thickness was decreased in APOE ε4 carriers (p < 0.01). Vessel density between carriers and non-carriers was not significantly different at either the superficial or deep level. The FAZ area was significantly smaller in ε4 carriers in both superficial (p < 0.01) and deep layers (p < 0.003). CONCLUSIONS: Retinal abnormalities were present in participants with increased genetic risk of dementia due to presence of the ε4 allele. These findings provide preliminary evidence for their potential role in the diagnosis of dementia.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMH) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well-documented in populations of different ethnicities and/or from different geographical regions. METHOD: Magnetic resonance imaging data of five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1,946) were examined for WMH and their associations with vascular risk factors and cognition. RESULT: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake and insufficient physical activity. Participants with moderate or higher physical activity had less WMH than those who never exercised, but the former two groups did not differ. Hypertension and stroke had stronger associations with WMH volumes in the White, compared to Asian subsample. DISCUSSION: The current study highlighted the ethnic differences in the contributions of vascular risk factors to WMH.

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease.

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.

Increased Resting-State Functional Connectivity as a Compensatory Mechanism for Reduced Brain Volume in Prediabetes and Type 2 Diabetes.

OBJECTIVE: To investigate the contribution of alterations in brain structure and function to cognitive function and their interactions in individuals with diabetes and patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS: This population-based study included 2,483 participants who underwent structural MRI (n = 569 with normal glucose metabolism [NGM], n = 1,353 with prediabetes, and n = 561 with T2DM) and cognitive testing. Of these, 2145 participants also underwent functional MRI (n = 496 NGM, n = 1,170 prediabetes, and n = 479 T2DM). Multivariate linear regression models were used to assess the association of brain volume and functional connectivity with cognition, as well as the association of brain volume and functional connectivity. RESULTS: Compared with NGM participants, those with T2DM had lower brain volume in a wide range of brain regions and stronger functional connectivity between the bilateral thalamus and brain functional network (visual network and default mode network), and those with prediabetes had lower brain volume in specific local regions (subcortical gray matter volume and subcortical subregions [bilateral thalamus, bilateral nucleus accumbens, and right putamen]) and stronger functional connectivity between the right thalamus and visual network. Cognition was associated with greater right thalamus volume and lower functional connectivity between the right thalamus and visual network. Functional connectivity between the right thalamus and visual network was associated with lower right thalamus volume. CONCLUSIONS: Cognition was associated with greater brain volume and lower functional connectivity in T2DM. Increased functional connectivity may indicate a compensatory mechanism for reduced brain volume that begins in the prediabetic phase.

Hormonal factors moderate the associations between vascular risk factors and white matter hyperintensities.

To examine the moderation effects of hormonal factors on the associations between vascular risk factors and white matter hyperintensities in men and women, separately. White matter hyperintensities were automatically segmented and quantified in the UK Biobank dataset (N = 18,294). Generalised linear models were applied to examine (1) the main effects of vascular and hormonal factors on white matter hyperintensities, and (2) the moderation effects of hormonal factors on the relationship between vascular risk factors and white matter hyperintensities volumes. In men with testosterone levels one standard deviation higher than the mean value, smoking was associated with 27.8% higher white matter hyperintensities volumes in the whole brain. In women with a shorter post-menopause duration (one standard deviation below the mean), diabetes and higher pulse wave velocity were associated with 28.8% and 2.0% more deep white matter hyperintensities, respectively. These findings highlighted the importance of considering hormonal risk factors in the prevention and management of white matter hyperintensities.

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI.

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

An accelerated degeneration of white matter microstructure and networks in the nondemented old-old.

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review.

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.

Cognitive decline is associated with frequency-specific resting state functional changes in normal aging.

Resting state low-frequency brain activity may aid in our understanding of the mechanisms of aging-related cognitive decline. Our purpose was to explore the characteristics of the amplitude of low-frequency fluctuations (ALFF) in different frequency bands of fMRI to better understand cognitive aging. Thirty-seven cognitively normal older individuals underwent a battery of neuropsychological tests and MRI scans at baseline and four years later. ALFF from five different frequency bands (typical band, slow-5, slow-4, slow-3, and slow-2) were calculated and analyzed. A two-way ANOVA was used to explore the interaction effects in voxel-wise whole brain ALFF of the time and frequency bands. Paired-sample t-test was used to explore within-group changes over four years. Partial correlation analysis was performed to assess associations between the altered ALFF and cognitive function. Significant interaction effects of time × frequency were distributed over inferior frontal gyrus, superior frontal gyrus, right rolandic operculum, left thalamus, and right putamen. Significant ALFF reductions in all five frequency bands were mainly found in the right hemisphere and the posterior cerebellum; whereas localization of the significantly increased ALFF were mainly found in the cerebellum at typical band, slow-5 and slow-4 bands, and left hemisphere and the cerebellum at slow-3, slow-2 bands. In addition, ALFF changes showed frequency-specific correlations with changes in cognition. These results suggest that changes of local brain activity in cognitively normal aging should be investigated in multiple frequency bands. The association between ALFF changes and cognitive function can potentially aid better understanding of the mechanisms underlying normal cognitive aging.