A decade of changes in brain volume and cognition

Visual Impairment, Eye Conditions, and Diagnoses of Neurodegeneration and Dementia

Importance

Vision and eye conditions are associated with increased risk for Alzheimer disease and related dementias (ADRDs), but the nature of the association and the underlying biological pathways remain unclear. If causal, vision would be an important modifiable risk factor with viable population-level interventions.

Objective

To evaluate potentially causal associations between visual acuity, eye conditions (specifically cataracts and myopia), neuroimaging outcomes, and ADRDs.

Design, Setting, and Participants

A cohort and 2-sample bidirectional mendelian randomization (MR) study was conducted using UK Biobank participants and summary statistics from previously published genome-wide association studies on cataract, myopia, and AD. The participants included in the analysis were aged 55 to 70 years without dementia at baseline (calendar years 2006 to 2010), underwent genotyping, and reported on eye conditions; a subset completed visual acuity examinations (n = 69 852-71 429) or brain imaging (n = 36 591-36 855). Data were analyzed from August 15, 2022, through November 28, 2023.

Exposure

Self-reported cataracts, visual acuity, and myopia measured by refraction error.

Main Outcomes and Measures

ADRD, AD, and vascular dementia were identified from electronic medical records. Total and regional brain volumes were determined using magnetic resonance imaging.

Results

The sample included 304 953 participants (mean [SD] age, 62.1 (4.1) years; 163 825 women [53.72%]); 14 295 (4.69%) had cataracts and 2754 (3.86%) had worse than 20/40 vision. Cataracts (hazard ratio [HR], 1.18; 95% CI, 1.07-1.29) and myopia (HR, 1.35; 95% CI, 1.06-1.70) were associated with a higher hazard of ADRD. In MR analyses to estimate potential causal effects, cataracts were associated with increased risk of vascular dementia (inverse variance-weighted odds ratio [OR], 1.92; 95% CI, 1.26-2.92) but were not associated with increased dementia (OR, 1.21; 95% CI, 0.98-1.50). There were no associations between myopia and dementia. In MR for potential reverse causality, AD was not associated with cataracts (inverse variance-weighted OR, 0.99; 95% CI, 0.96-1.01). Genetic risk for cataracts was associated with smaller total brain (β = -597.43 mm3; 95% CI, -1077.87 to -117.00 mm3) and gray matter (β = -375.17 mm3; 95% CI, -680.10 to -70.24 mm3) volumes, but not other brain regions.

Conclusions and Relevance

In this cohort and MR study of UK Biobank participants, cataracts were associated with increased risk of dementia, especially vascular dementia, and reduced total brain volumes. These findings lend further support to the hypothesis that cataract extraction may reduce the risk for dementia.

Population clustering of structural brain aging and its association with brain development

Structural brain aging has demonstrated strong inter-individual heterogeneity and mirroring patterns with brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, most of the existing research focused on the cross-sectional changes of brain aging. In this investigation, we present a data-driven approach that incorporate both cross-sectional changes and longitudinal trajectories of structural brain aging and identified two brain aging patterns among 37,013 healthy participants from UK Biobank. Participants with accelerated brain aging also demonstrated accelerated biological aging, cognitive decline and increased genetic susceptibilities to major neuropsychiatric disorders. Further, by integrating longitudinal neuroimaging studies from a multi-center adolescent cohort, we validated the "last in, first out" mirroring hypothesis and identified brain regions with manifested mirroring patterns between brain aging and brain development. Genomic analyses revealed risk loci and genes contributing to accelerated brain aging and delayed brain development, providing molecular basis for elucidating the biological mechanisms underlying brain aging and related disorders.

Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter

Objective

Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships.

Methods

967 CN participants were included in this study. Subjects were classified into the middle-aged group (n = 302) and the old-aged group (n = 665) according to the age of 66. The data of 360 subjects from the Alzheimer's Disease Neuroimaging Initiative were used for training and internal test of the VBM-SBM and radiomics models, and the data of 607 subjects from the Australian Imaging, Biomarker and Lifestyle, the National Alzheimer's Coordinating Center, and the Parkinson's Progression Markers Initiative databases were used for the external tests. Logistics regression participated in the construction of both models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the two model performances. The DeLong test was used to compare the differences in AUCs between models. The Spearman correlation analysis was used to observe the correlations between age, VBM-SBM parameters, and radiomics features.

Results

The AUCs of the VBM-SBM model and radiomics model were 0.697 and 0.778 in the training set (p = 0.018), 0.640 and 0.789 in the internal test set (p = 0.007), 0.736 and 0.737 in the AIBL test set (p = 0.972), 0.746 and 0.838 in the NACC test set (p < 0.001), and 0.701 and 0.830 in the PPMI test set (p = 0.036). Weak correlations were observed between VBM-SBM parameters and radiomics features (p < 0.05).

Conclusion

The radiomics model achieved better performance than the VBM-SBM model. Radiomics provides a good option for researchers who prioritize performance and generalization, whereas VBM-SBM is more suitable for those who emphasize interpretability and clinical practice.

Research Advancements on the Correlation Between Spontaneous Intracerebral Hemorrhage of Different Etiologies and Imaging Markers of Cerebral Small Vessel Disease

Objective

The purpose of this review is to identify the correlation between ICH and CSVD imaging markers under SMASH-U classification by searching and analyzing a large number of literatures in recent years, laying a theoretical foundation for future clinical research. At the same time, by collecting clinical data to evaluate patient prognosis, analyzing whether there are differences or supplements between clinical trial conclusions and previous theories, and ultimately guiding clinical diagnosis and treatment through the analysis of imaging biomarkers.

Methods

In this review, by searching CNKI, Web of Science, PubMed, FMRS and other databases, the use of "spontaneous intracerebral hemorrhage", "hypertensive hemorrhagic cerebral small vessel disease", "cerebral small vessel disease imaging", "Based cerebral small vessel diseases", "SMASH the -u classification" and their Chinese equivalents for the main search term. We focused on reading and analyzing hundreds of relevant literatures in the last decade from August 2011 to April 2020, and also included some earlier literatures with conceptual data sources. After screening and ranking the degree of relevance to this study, sixty of them were cited for analysis and elaboration.

Results

In patients with ICH, the number of cerebral microbleeds in lobes, basal ganglia, and the deep brain is positively correlated with ICH volume and independently correlated with neurological functional outcomes; white matter hyperintensity severity is positively correlated with ICH recurrence risk; multiple lacunar infarction independently predict the risk of ICH; severe brain atrophy is an independent risk factor for a poor prognosis in the long term in patients diagnosed with ICH; and the number of enlarged perivascular spaces is correlated with ICH recurrence. However, small subcortical infarct and ICH are the subject of few studies. Higher CSVD scores are independently associated with functional outcomes at 90 days in patients diagnosed with ICH.

Structural brain changes in emotion recognition across the adult lifespan

Emotion recognition (ER) declines with increasing age, yet little is known whether this observation is based on structural brain changes conveyed by differential atrophy. To investigate whether age-related ER decline correlates with reduced grey matter (GM) volume in emotion-related brain regions, we conducted a voxel-based morphometry analysis using data of the Human Connectome Project-Aging (N = 238, aged 36-87) in which facial ER was tested. We expected to find brain regions that show an additive or super-additive age-related change in GM volume indicating atrophic processes that reduce ER in older adults. The data did not support our hypotheses after correction for multiple comparisons. Exploratory analyses with a threshold of P < 0.001 (uncorrected), however, suggested that relationships between GM volume and age-related general ER may be widely distributed across the cortex. Yet, small effect sizes imply that only a small fraction of the decline of ER in older adults can be attributed to local GM volume changes in single voxels or their multivariate patterns.

Cerebral volume is unaffected after pre-eclampsia

OBJECTIVES

Pre-eclampsia has been associated with cardiovascular, cerebrovascular and/or psychological complaints. Signs of altered brain morphology and more white-matter hyperintensities (WMHs) during and shortly after pre-eclampsia have been observed in some, but not all, studies. We compared volumes of cerebral structures and the number of WMHs between formerly pre-eclamptic women and those with normotensive gestational history and assessed the effect of age on brain volumes.

METHODS

Structural 7-Tesla magnetic resonance imaging of the brain was performed in 59 formerly pre-eclamptic women (aged 37 ± 6 years, 0.5-16 years postpartum) and 20 women with a history of normotensive pregnancy (aged 39 ± 5 years, 1-18 years postpartum). Fazekas scores were obtained to assess WMH load. Volumes of the whole brain, gray and white matter, brain lobes, and ventricular and pericortical cerebrospinal fluid (CSF) spaces were calculated after semiautomatic segmentation. Group differences were analyzed using ANCOVA and Bayes factors. Results were adjusted for age, educational attainment, presence of current hypertension and total intracranial volume. The effect of age on cerebral volumes was analyzed using linear regression analysis.

RESULTS

No changes in global and local brain volumes were observed between formerly pre-eclamptic and control women. Also, no difference in WMH load was observed. Independent of pre-eclamptic history, gray-matter volume significantly decreased with age, while ventricular and pericortical CSF space volumes significantly increased with age.

CONCLUSIONS

Volumetric changes of the cerebrum are age-related but are independent of pre-eclamptic history in the first two decades after childbirth. No evidence of greater WMH load after pre-eclampsia was found. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Comparison of the clinical value of MRI and plasma markers for cognitive impairment in patients aged ≥75 years: a retrospective study

Background

Dementia has become the main cause of disability in older adults aged ≥75 years. Cerebral small vessel disease (CSVD) is involved in cognitive impairment (CI) and dementia and is a cause of vascular CI (VCI), which is manageable and its onset and progression can be delayed. Simple and effective markers will be beneficial to the early detection and intervention of CI. The aim of this study is to investigate the clinical application value of plasma amyloid β1-42 (Aβ42), phosphorylated tau 181 (p-tau181) and conventional structural magnetic resonance imaging (MRI) parameters for cognitive impairment (CI) in patients aged ≥75 years.

Methods

We retrospectively selected patients who visited the Affiliated Hospital of Xuzhou Medical University and were clinically diagnosed with or without cognitive dysfunction between May 2018 and November 2021. Plasma indicators (Aβ42 and p-tau181) and conventional structural MRI parameters were collected and analyzed. Multivariate logistic regression and receiver operator characteristic (ROC) curve were used to evaluate the diagnostic value.

Results

One hundred and eighty-four subjects were included, including 54 cases in CI group and 130 cases in noncognitive impairment (NCI) groups, respectively. Univariate logistic regression analysis revealed that the percentages of Aβ42+_, P-tau 181+, and Aβ42+/P-tau181+ showed no significant difference between the groups of CI and NCI (all P > 0.05). Multivariate logistic regression analysis showed that moderate/severe periventricular WMH (PVWMH) (OR 2.857, (1.365-5.983), P = 0.005), lateral ventricle body index (LVBI) (OR 0.413, (0.243-0.700), P = 0.001), and cortical atrophy (OR 1.304, (1.079-1.575), P = 0.006) were factors associated with CI. The combined model including PVWMH, LVBI, and cortical atrophy to detect CI and NCI showed an area under the ROC curve (AUROC) is 0.782, with the sensitivity and specificity 68.5% and 78.5%, respectively.

Conclusion

For individuals ≥75 years, plasma Aβ42 and P-tau181 might not be associated with cognitive impairment, and MRI parameters, including PVWMH, LVBI and cortical atrophy, are related to CI. The cognitive statuses of people over 75 years old were used as the endpoint event in this study. Therefore, it can be considered that these MRI markers might have more important clinical significance for early assessment and dynamic observation, but more studies are still needed to verify this hypothesis.

Reevaluating the role of the hippocampus in memory: A meta-analysis of neurotoxic lesion studies in nonhuman primates

The hippocampus and perirhinal cortex are both broadly implicated in memory; nevertheless, their relative contributions to visual item recognition and location memory remain disputed. Neuropsychological studies in nonhuman primates that examine memory function after selective damage to medial temporal lobe structures report various levels of memory impairment-ranging from minor deficits to profound amnesia. The discrepancies in published findings have complicated efforts to determine the exact magnitude of visual item recognition and location memory impairments following damage to the hippocampus and/or perirhinal cortex. To provide the most accurate estimate to date of the overall effect size, we use meta-analytic techniques on data aggregated from 26 publications that assessed visual item recognition and/or location memory in nonhuman primates with and without selective neurotoxic lesions of the hippocampus or perirhinal cortex. We estimated the overall effect size, evaluated the relation between lesion extent and effect size, and investigated factors that may account for between-study variation. Grouping studies by lesion target and testing method, separate meta-analyses were conducted. One meta-analysis indicated that impairments on tests of visual item recognition were larger after lesions of perirhinal cortex than after lesions of the hippocampus. A separate meta-analysis showed that performance on tests of location memory was severely impaired by lesions of the hippocampus. For the most part, meta-regressions indicated that greater impairment corresponds with greater lesion extent; paradoxically, however, more extensive hippocampal lesions predicted smaller impairments on tests of visual item recognition. We conclude the perirhinal cortex makes a larger contribution than the hippocampus to visual item recognition, and the hippocampus predominately contributes to spatial navigation.

Neuroimaging and verbal memory assessment in healthy aging adults using a portable low-field MRI scanner and a web-based platform: results from a proof-of-concept population-based cross-section study

Consumer wearables and health monitors, internet-based health and cognitive assessments, and at-home biosample (e.g., saliva and capillary blood) collection kits are increasingly used by public health researchers for large population-based studies without requiring intensive in-person visits. Alongside reduced participant time burden, remote and virtual data collection allows the participation of individuals who live long distances from hospital or university research centers, or who lack access to transportation. Unfortunately, studies that include magnetic resonance neuroimaging are challenging to perform remotely given the infrastructure requirements of MRI scanners, and, as a result, they often omit socially, economically, and educationally disadvantaged individuals. Lower field strength systems (< 100 mT) offer the potential to perform neuroimaging at a participant's home, enabling more accessible and equitable research. Here we report the first use of a low-field MRI "scan van" with an online assessment of paired-associate learning (PAL) to examine associations between brain morphometry and verbal memory performance. In a sample of 67 individuals, 18-93 years of age, imaged at or near their home, we show expected white and gray matter volume trends with age and find significant (p < 0.05 FWE) associations between PAL performance and hippocampus, amygdala, caudate, and thalamic volumes. High-quality data were acquired in 93% of individuals, and at-home scanning was preferred by all individuals with prior MRI at a hospital or research setting. Results demonstrate the feasibility of remote neuroimaging and cognitive data collection, with important implications for engaging traditionally under-represented communities in neuroimaging research.

Changes in hippocampal volume during a preceding 10-year period do not correlate with cognitive performance and hippocampal blood‒brain barrier permeability in cognitively normal late-middle-aged men

Hippocampal blood-brain barrier (BBB) permeability may increase in normal healthy ageing and contribute to neurodegenerative disease. To examine this hypothesis, we investigated the correlation between blood-brain barrier (BBB) permeability, regional brain volume, memory functions and health and lifestyle factors in The Metropolit 1953 Danish Male Birth Cohort. We used dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with a gadolinium-based contrast agent to assess BBB permeability in 77 participants in the cohort. BBB permeability was measured as K_i values in the hippocampus, thalamus and white matter. Over a 10-year period, we observed progressive atrophy of both the left and right hippocampus (p = 0.001). There was no significant correlation between current BBB permeability and hippocampal volume, prior atrophy or cognition. The hippocampus volume ratio was associated with better visual and verbal memory scores (p < 0.01). Regional BBB differences revealed higher K_i values in the hippocampus and white matter than in the thalamus (p < 0.001). Participants diagnosed with type II diabetes had significantly higher BBB permeability in the white matter (p = 0.015) and thalamus (p = 0.016), which was associated with a higher Fazekas score (p = 0.024). We do not find evidence that BBB integrity is correlated with age-related hippocampal atrophy or cognitive functions. The association between diabetes, white matter hyperintensities and increased BBB permeability is consistent with the idea that cerebrovascular disease compromises BBB integrity. Our findings suggest that the hippocampus is particularly prone to age-related atrophy, which may explain some of the cognitive changes that accompany older age, but this prior atrophy is not correlated with current BBB permeability.

Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates

How well students learn and perform in academic contexts is a focus of interest for the students, their families, and the entire educational system. Although evidence has shown that several neurobiological factors are involved in scholastic achievement (SA), specific brain measures associated with academic outcomes and whether such associations are independent of other factors remain unclear. This study attempts to identify the relationship between brain structural parameters, and the Chilean national University Selection Test (PSU) results in high school graduates within a multidimensional approach that considers socio-economic, intellectual, nutritional, and demographic variables. To this end, the brain morphology of a sample of 102 students who took the PSU test was estimated using Magnetic Resonance Imaging. Anthropometric parameters, intellectual ability (IA), and socioeconomic status (SES) were also measured. The results revealed that, independently of sex, IA, gray matter volume, right inferior frontal gyrus thickness, and SES were significantly associated with SA. These findings highlight the role of nutrition, health, and socioeconomic variables in academic success.

Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics

BACKGROUND

Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable.

METHOD

We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains.

RESULTS

The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu.

CONCLUSION

For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.

Lobular Difference in Heritability of Brain Atrophy among Elderly Japanese: A Twin Study

Background and Objectives: Brain atrophy is related to cognitive decline. However, the heritability of brain atrophy has not been fully investigated in the Eastern Asian population. Materials and Methods: Brain imaging of 74 Japanese twins registered in the Osaka University Twin Registry was conducted with voxel-based morphometry SPM12 and was processed by individual voxel-based morphometry adjusting covariates (iVAC) toolbox. The atrophy of the measured lobes was obtained by comparing the focal volume to the average of healthy subjects. Classical twin analysis was used to measure the heritability of its z-scores. Results: The heritability of brain atrophy ranged from 0.23 to 0.97, depending upon the lobes. When adjusted to age, high heritability was reported in the frontal, frontal-temporal, and parietal lobes, but the heritability in other lobes was lower than 0.70. Conclusions: This study revealed a relatively lower heritability in brain atrophy compared to other ethnicities. This result suggests a significant environmental impact on the susceptibility of brain atrophy the Japanese. Therefore, environmental factors may have more influence on the Japanese than in other populations.

Association of Cerebral Small Vessel Disease With Gait and Balance Disorders

Cerebral small vessel disease (CSVD) is a common cerebrovascular disease and an important cause of gait and balance disorders. Gait and balance disorders can further lead to an increased risk of falls and a decreased quality of life. CSVD can damage gait and balance function by affecting cognitive function or directly disrupting motor pathways, and different CSVD imaging features have different characteristics of gait and balance impairment. In this article, the correlation between different imaging features of sporadic CSVD and gait and balance disorders has been reviewed as follows, which can provide beneficial help for standardized management of CSVD.

Longitudinal association between depressive symptoms and cognitive decline among middle-aged and elderly population

BACKGROUND

Depression is considered a risk factor for cognitive decline. The long-term impact of depressive symptoms on cognitive performance has not been established thus far.

OBJECTIVES

This study aimed to determine the longitudinal associations between depressive symptoms and cognitive performance among middle-aged and elderly population.

METHODS

We included 10,387 adults aged ≥45 years from the Health and Retirement Study (2004 to 2014) in this study. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CESD) scale. Participant's cognitive function was assessed via the telephone interview for cognitive status (TICS); the total cognitive score ranged from 0 to 35. We classified the participants into four clusters according to the quartile of the total cognitive score (TCS). We examined the change of depressive symptoms and cognitive performance by using the unconditional latent growth curve modeling (LGCM) method, and a parallel LGCM method was used to examine the longitudinal associations between depressive symptoms and cognitive performance among middle-aged and elderly adults in each cluster.

RESULTS

Participants with lower levels of cognitive performance were associated with a greater risk of high depressive symptoms. Results from unconditional LGCM showed a sustained decline in cognitive performance and an increasing trend in depressive symptoms per 2 years for each cluster of participants. The parallel LGCM indicated that baseline levels of depression showed a significant negative correlation with the cognitive performance at baseline (β [95% CI] of intercept_(Dep) predicting intercept_(TCS) were -0.33 [-0.41, -0.26], -0.03[-0.06, -0.00], -0.05 [-0.07, -0.02] and -0.64 [-0.70,-0.58], for clusters of Q1 to Q3 and the entire population, respectively). Further, a significant positive prospective association was observed between baseline levels of depression and changes in cognitive performance (intercept_(Dep) predicting slope_(TCS) were -0.05 [-0.08, -0.02], -0.09[-0.13, -0.05], -0.12 [-0.15, -0.08], -0.11 [-0.15, -0.06] and -0.04 [-0.06,-0.02] for clusters of Q1 to Q4 and the entire population, respectively). Moreover, for participants with the highest quartile of TCS, the rising trend of depressive symptoms accelerated the decline of cognitive performance during the follow-up period (Slope_(Dep) predicting Slope_(TCS): -0.44 [-0.86, -0.01]).

CONCLUSION

Our results suggest that depressive symptoms were associated with lower cognitive performance and larger subsequent decline during follow-up period. Adults with depression may require more medical attention, and early intervention is required to delay the development of cognitive impairment and dementia.

Brain CT can predict low lean mass in the elderly with cognitive impairment: a community-dwelling study

Background

The coexistence of sarcopenia and dementia in aging populations is not uncommon, and they may share common risk factors and pathophysiological pathways. This study aimed to evaluate the relationship between brain atrophy and low lean mass in the elderly with impaired cognitive function.

Methods

This cross-sectional study included 168 elderly patients who visited the multi-disciplinary dementia outpatient clinic at Kaohsiung Chang Gung Memorial Hospital for memory issues, between 2017 and 2019. The body composition was assessed by dual energy X-ray absorptiometry (DEXA) and CT based skeletal muscle index including L3 skeletal muscle index (L3SMI) and masseter muscle mass index (MSMI). The brain atrophy assessment was measured by CT based visual rating scale. Possible predictors of low lean mass in the elderly with cognitive impairement were identified by binary logistic regression. ROC curves were generated from binary logistic regression.

Results

Among the 81 participants, 43 (53%) remained at a normal appendicular skeletal muscle index (ASMI), whereas 38 (47%) showed low ASMI. Compared with the normal ASMI group, subjects with low ASMI exhibited significantly lower BMI, L3SMI, and MSMI (all p < 0.05), and showed significant brain atrophy as assessed by visual rating scale (p < 0.001). The accuracy of predictive models for low ASMI in the elderly with cognitive impairment were 0.875, (Area under curve (AUC) = 0.926, 95% confidence interval [CI] 0.844–0.972) in model 1 (combination of BMI, GCA and L3SMI) and 0.885, (Area under curve (AUC) = 0.931, [CI] 0.857–0.979) in model 2 (combination of BMI, GCA and MSMI).

Conclusions

Global cortical atrophy and body mass index combined with either L3 skeletal muscle index or masseter skeletal muscle index can predict low lean mass in the elderly with cognitive impairment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02626-8.

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes

Background. Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. Objective. In this work, we aimed to investigate whether machine learning techniques, when applied to a combination of baseline whole brain volumes and clinical data, can accurately predict individual motor outcome after stroke. Methods. Upper extremity Fugl-Meyer Assessments (FMA-UE) were conducted 1 week and 12 weeks, and structural MRI was performed 1 week, after onset in 56 patients with subcortical infarction. Proportional recovery model residuals were employed to assign patients to proportional and poor recovery groups (34 vs 22). A sophisticated machine learning scheme, consisting of conditional infomax feature extraction, synthetic minority over-sampling technique for nominal and continuous, and bagging classification, was employed to predict motor outcomes, with the input features being a combination of baseline whole brain volumes and clinical data (FMA-UE scores). Results. The proposed machine learning scheme yielded an overall balanced accuracy of 87.71% in predicting proportional vs poor recovery outcomes, a sensitivity of 93.77% in correctly identifying poor recovery outcomes, and a ROC AUC of 89.74%. Compared with only using clinical data, adding whole brain volumes can significantly improve the classification performance, especially in terms of the overall balanced accuracy (from 80.88% to 87.71%) and the sensitivity (from 92.23% to 93.77%). Conclusions. Experimental results suggest that a combination of baseline whole brain volumes and clinical data, when equipped with appropriate machine learning techniques, may provide valuable information for personalized rehabilitation planning after subcortical infarction.

White matter hyperintensities associate with cognitive slowing in patients with systemic lupus erythematosus and neuropsychiatric symptoms

Objective

To compare cognitive function between patients with different phenotypes of neuropsychiatric systemic lupus erythematosus (NPSLE) and assess its association with brain and white matter hyperintensity (WMH) volumes.

Methods

Patients attending the Leiden University Medical Centre NPSLE clinic between 2007 and 2015 without large brain infarcts were included (n=151; 42±13 years, 91% women). In a multidisciplinary consensus meeting, neuropsychiatric symptoms were attributed to systemic lupus erythematosus (SLE) (NPSLE, inflammatory (n=24) or ischaemic (n=12)) or to minor/non-NPSLE (n=115). Multiple regression analyses were performed to compare cognitive function between NPSLE phenotypes and to assess associations between brain and WMH volumes and cognitive function cross-sectionally.

Results

Global cognitive function was impaired in 5%, learning and memory (LM) in 46%, executive function and complex attention (EFCA) in 39% and psychomotor speed (PS) in 46% of all patients. Patients with inflammatory NPSLE showed the most cognitive impairment in all domains (p≤0.05).

Higher WMH volume associated with lower PS in the total group (B: −0.14 (95% CI −0.32 to −0.02)); especially in inflammatory NPSLE (B: −0.36 (95% CI −0.60 to −0.12). In the total group, lower total brain volume and grey matter volume associated with lower cognitive functioning in all domains (all: 0.00/0.01 (0.00;0.01)) and lower white matter volume associated with lower LM, EFCA and PS (all: 0.00/0.01 (0.00;0.01)).

Conclusion

We demonstrated that an association between brain and WMH volumes and cognitive function is present in patients with SLE, but differs between (NP)SLE phenotypes. WMHs associated with PS especially in inflammatory NPSLE, which suggests a different, potentially more severe underlying pathophysiological mechanism of cognitive impairment in this phenotype.

Comparing the effect of cognitive vs. exercise training on brain MRI outcomes in healthy older adults: A systematic review

Aging is associated with cognitive decline. Importantly cognition and cerebral health is enhanced with interventions like cognitive (CT) and exercise training (ET). However, effects of CT and ET interventions on brain magnetic resonance imaging outcomes have never been compared systematically. Here, the primary objective was to critically and systematically compare CT to ET in healthy older adults on brain MRI outcomes. A total of 38 studies were included in the final review. Although results were mixed, patterns were identified: CT showed improvements in white matter microstructure, while ET demonstrated macrostructural enhancements, and both demonstrated changes to task-based BOLD signal changes. Importantly, beneficial effects for cognitive and cerebral outcomes were observed by almost all, regardless of intervention type. Overall, it is suggested that future work include more than one MRI outcome, and report all results including null. To better understand the MRI changes associated with CT or ET, more studies explicitly comparing interventions within the same domain (i.e. resistance vs. aerobic) and between domains (i.e. CT vs. ET) are needed.

Severe Encephalatrophy and Related Disorders From Long-Term Ketamine Abuse: A Case Report and Literature Review

Ketamine is a glutamate N-methyl D-aspartate receptor antagonist and an anaesthetic agent that has been effectively used to treat depression. However, ketamine has also been increasingly used for recreational purposes. The dissociative side-effects of ketamine use, such as hallucinations, are the reason for abuse. Additionally, long-term ketamine abuse has been highly associated with liver-gallbladder and urinary symptoms. The present study reports the case of a 28-year-old young male adult with an 8-year history of daily inhalation of ketamine. We investigated the association between ketamine abuse and the mechanism of its adverse effects, particularly encephalatrophy, and attempted to find a link between these disorders. These results would help us to better understand ketamine usage, ketamine abuse effects and the addictive mechanism. To the best of our knowledge, the present case is the first report of severe brain atrophy related to ketamine abuse. Details of the patient are presented and the mechanism of the encephalatropy-associated ketamine abuse is discussed. Furthermore, organ dysfunction following chronic ketamine abuse may indicate that the side effects are the result of comprehensive action on multiple regions in the brain.

Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry

Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50

Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Contributions of cardiovascular risk and smoking to chronic obstructive pulmonary disease (COPD)-related changes in brain structure and function

Background

Brain damage and cardiovascular disease are extra-pulmonary manifestations of chronic obstructive pulmonary disease (COPD). Cardiovascular risk factors and smoking are contributors to neurodegeneration. This study investigates whether there is a specific, COPD-related deterioration in brain structure and function independent of cardiovascular risk factors and smoking.

Materials and methods

Neuroimaging and clinical markers of brain structure (micro- and macro-) and function (cognitive function and mood) were compared between 27 stable COPD patients (age: 63.0±9.1 years, 59.3% male, forced expiratory volume in 1 second [FEV1]: 58.1±18.0% pred.) and 23 non-COPD controls with >10 pack years smoking (age: 66.6±7.5 years, 52.2% male, FEV1: 100.6±19.1% pred.). Clinical relationships and group interactions with brain structure were also tested. All statistical analyses included correction for cardiovascular risk factors, smoking, and aortic stiffness.

Results

COPD patients had significantly worse cognitive function (p=0.011), lower mood (p=0.046), and greater gray matter atrophy (p=0.020). In COPD patients, lower mood was associated with markers of white matter (WM) microstructural damage (p<0.001), and lower lung function (FEV1/forced vital capacity and FEV1) with markers of both WM macro (p=0.047) and microstructural damage (p=0.028).

Conclusion

COPD is associated with both structural (gray matter atrophy) and functional (worse cognitive function and mood) brain changes that cannot be explained by measures of cardiovascular risk, aortic stiffness, or smoking history alone. These results have important implications to guide the development of new interventions to prevent or delay progression of neuropsychiatric comorbidities in COPD. Relationships found between mood and microstructural abnormalities suggest that in COPD, anxiety, and depression may occur secondary to WM damage. This could be used to better understand disabling symptoms such as breathlessness, improve health status, and reduce hospital admissions.

Cross-sectional and longitudinal characterization of SCD patients recruited from the community versus from a memory clinic: subjective cognitive decline, psychoaffective factors, cognitive performances, and atrophy progression over time

Background

Subjective cognitive decline (SCD) defines a heterogeneous population, part of which having Alzheimer’s disease (AD). We aimed at characterizing SCD populations according to whether or not they referred to a memory clinic, by assessing the factors associated with increased AD risk.

Methods

Seventy-eight cognitively unimpaired older adults from the IMAP+ study (Caen) were included, amongst which 28 healthy controls (HC) and 50 SCD recruited from the community (SCD-community; n = 23) or from a memory clinic (SCD-clinic; n = 27). Participants underwent cognitive, psychoaffective, structural MRI, FDG-PET, and amyloid-PET assessments. They were followed up over a mean period of 2.4 ± 0.8 years. The groups were compared in terms of baseline and follow-up levels of SCD (self- and informant-reported), cognition, subclinical anxiety and depression, and atrophy progression over time. We also investigated SCD substrates within each SCD group through the correlations between self-reported SCD and other psychometric and brain measures.

Results

Compared to HC, both SCD groups showed similar cognitive performances but higher informant-reported SCD and anxiety. Compared to SCD-community, SCD-clinic showed higher informant-reported SCD, depression score, and atrophy progression over time but similar brain amyloid load. A significant increase over time was found for depression in the SCD-community and for self-reported praxis-domestic activities SCD factor in the SCD-clinic. Higher self-reported SCD correlated with (i) lower grey matter volume and higher anxiety in SCD-community, (ii) greater informant-reported SCD in SCD-clinic, and (iii) lower glucose metabolism in both SCD groups.

Conclusions

Higher subclinical depression and informant-reported SCD specifically characterize the SCD group that refers to a memory clinic. The same group appears as a frailer population than SCD-community as they show greater atrophy progression over time. Yet, both the SCD groups were quite similar otherwise including for brain amyloid load and the SCD-community showed increased depression score over time. Altogether, our findings highlight the relevance of assessing psychoaffective factors and informant-reported SCD in SCD populations and point to both differences and similarities in SCD populations referring or not to a memory clinic.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0514-z) contains supplementary material, which is available to authorized users.

Lateral ventricle volume trajectories predict response inhibition in older age-A longitudinal brain imaging and machine learning approach

Objective

In a three-wave 6 yrs longitudinal study we investigated if the expansion of lateral ventricle (LV) volumes (regarded as a proxy for brain tissue loss) predicts third wave performance on a test of response inhibition (RI).

Participants and methods

Trajectories of left and right lateral ventricle volumes across the three waves were quantified using the longitudinal stream in Freesurfer. All participants (N = 74;48 females;mean age 66.0 yrs at the third wave) performed the Color-Word Interference Test (CWIT). Response time on the third condition of CWIT, divided into fast, medium and slow, was used as outcome measure in a machine learning framework. Initially, we performed a linear mixed-effect (LME) analysis to describe subject-specific trajectories of the left and right LV volumes (LVV). These features were input to a multinomial logistic regression classification procedure, predicting individual belongings to one of the three RI classes. To obtain results that might generalize, we evaluated the significance of a k-fold cross-validated f1-score with a permutation test, providing a p-value that approximates the probability that the score would be obtained by chance. We also calculated a corresponding confusion matrix.

Results

The LME-model showed an annual ∼ 3.0% LVV increase. Evaluation of a cross-validated score using 500 permutations gave an f1-score of 0.462 that was above chance level (p = 0.014). 56% of the fast performers were successfully classified. All these were females, and typically older than 65 yrs at inclusion. For the true slow performers, those being correctly classified had higher LVVs than those being misclassified, and their ages at inclusion were also higher.

Conclusion

Major contributions were: (i) a longitudinal design, (ii) advanced brain imaging and segmentation procedures with longitudinal data analysis, and (iii) a data driven machine learning approach including cross-validation and permutation testing to predict behaviour, solely from the individual’s brain “signatures” (LVV trajectories).

Music and Visual Art Training Modulate Brain Activity in Older Adults

Cognitive decline is an unavoidable aspect of aging that impacts important behavioral and cognitive skills. Training programs can improve cognition, yet precise characterization of the psychological and neural underpinnings supporting different training programs is lacking. Here, we assessed the effect and maintenance (3-month follow-up) of 3-month music and visual art training programs on neuroelectric brain activity in older adults using a partially randomized intervention design. During the pre-, post-, and follow-up test sessions, participants completed a brief neuropsychological assessment. High-density EEG was measured while participants were presented with auditory oddball paradigms (piano tones, vowels) and during a visual GoNoGo task. Neither training program significantly impacted psychometric measures, compared to a non-active control group. However, participants enrolled in the music and visual art training programs showed enhancement of auditory evoked responses to piano tones that persisted for up to 3 months after training ended, suggesting robust and long-lasting neuroplastic effects. Both music and visual art training also modulated visual processing during the GoNoGo task, although these training effects were relatively short-lived and disappeared by the 3-month follow-up. Notably, participants enrolled in the visual art training showed greater changes in visual evoked response (i.e., N1 wave) amplitude distribution than those from the music or control group. Conversely, those enrolled in music showed greater response associated with inhibitory control over the right frontal scalp areas than those in the visual art group. Our findings reveal a causal relationship between art training (music and visual art) and neuroplastic changes in sensory systems, with some of the neuroplastic changes being specific to the training regimen.

Cerebral small vessel disease and cognitive impairment

Cerebral small vessel disease (CSVD) is a pathophysiological process involving small arteries such as cerebellar arteries, arterioles, capillaries, and veinlets. Imaging features vary; they are mainly composed of recent subcortical infarcts, lacunes of presumed vascular origin, white matter hyperintensities (WMHs) of presumed vascular origin, cerebral microbleeds, enlarged perivascular spaces, and global and regional brain atrophy. CSVD is a common cause of vascular cognitive dysfunction, and in its end stage, dementia often develops. CSVD has been a major research hotspot; however, its causes are poorly understood. Neuroimaging markers of CSVD can be used as the basis for etiological analysis. This review highlights the relevance of neuroimaging markers and cognitive impairment, providing a new direction for the early recognition, treatment, and prevention of cognitive dysfunction in small cerebral angiopathy.