Brain and Ventricle Volume Alterations in Idiopathic Normal Pressure Hydrocephalus Determined by Artificial Intelligence-Based MRI Volumetry.

The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer's disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes. Additionally, correlations between the measured brain and ventricle volumes and two established semi-quantitative radiologic markers for iNPH were examined. An IRB-approved retrospective analysis was conducted on 123 age- and sex-matched subjects (41 iNPH, 41 AD, and 41 controls), with all of the iNPH patients undergoing routine clinical brain MRI prior to ventriculoperitoneal shunt implantation. Automated AI-based determination of different cortical and subcortical brain and ventricular volumes in mL, as well as calculation of population-based normalized percentiles according to an embedded database, was performed; the CE-certified software mdbrain v4.4.1 or above was used with a standardized T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence. Measured brain volumes and percentiles were analyzed for between-group differences and correlated with semi-quantitative measurements of the Evans' index and corpus callosal angle: iNPH patients exhibited ventricular enlargement and changes in gray and white matter compared to AD patients and controls, with the most significant differences observed in total ventricular volume (+67%) and the lateral (+68%), third (+38%), and fourth (+31%) ventricles compared to controls. Global ventriculomegaly and marked white matter reduction with concomitant preservation of gray matter compared to AD and CG were characteristic of iNPH, whereas global and frontoparietally accentuated gray matter reductions were characteristic of AD. Evans' index and corpus callosal angle differed significantly between the three groups and moderately correlated with the lateral ventricular volumes in iNPH patients [Evans' index (r > 0.83, p ≤ 0.001), corpus callosal angle (r < -0.74, p ≤ 0.001)]. AI-based MRI volumetry in iNPH patients revealed global ventricular enlargement and focal brain atrophy, which, in contrast to healthy controls and AD patients, primarily involved the supratentorial white matter and was marked temporomesially and in the midbrain, while largely preserving gray matter. Integrating AI volumetry in conjunction with traditional radiologic measures could enhance iNPH identification and differentiation, potentially improving patient management and therapy response assessment.

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors.

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized assessments is currently discouraged due to a series of technical and biological issues. In this work, we present a deep learning pipeline for segmentation-based brain atrophy quantification that improves upon the automated labels of the reference method from which it learns. This goal is achieved through tissue similarity regularization that exploits the a priori knowledge that scans from the same subject made within a short interval must have similar tissue volumes. To train the presented pipeline, we use unlabeled pairs of T1-weighted MRI scans having a tissue similarity prior, and generate the target brain tissue segmentations in a fully automated manner using the fsl_anat pipeline implemented in the FMRIB Software Library (FSL). Tissue similarity regularization is enforced during training through a weighted loss term that penalizes tissue volume differences between short-interval scan pairs from the same subject. In inference, the pipeline performs end-to-end skull stripping and brain tissue segmentation from a single T1-weighted MRI scan in its native space, i.e., without performing image interpolation. For longitudinal evaluation, each image is independently segmented first, and then measures of change are computed. We evaluate the presented pipeline in two different MRI datasets, MIRIAD and ADNI1, which have longitudinal and short-interval imaging from healthy controls (HC) and Alzheimer's disease (AD) subjects. In short-interval scan pairs, tissue similarity regularization reduces the quantification error and improves the consistency of measured tissue volumes. In the longitudinal case, the proposed pipeline shows reduced variability of atrophy measures and higher effect sizes of differences in annualized rates between HC and AD subjects. Our pipeline obtains a Cohen's d effect size of d=2.07 on the MIRIAD dataset, an increase from the reference pipeline used to train it (d=1.01), and higher than that of SIENA (d=1.73), a well-known state-of-the-art approach. In the ADNI1 dataset, the proposed pipeline improves its effect size (d=1.37) with respect to the reference pipeline (d=0.80) and surpasses SIENA (d=1.33). The proposed data-driven deep learning regularization reduces the biases and systematic errors learned from the reference segmentation method, which is used to generate the training targets. Improving the accuracy and reliability of atrophy quantification methods is essential to unlock brain atrophy as a diagnostic and prognostic marker in neurodegenerative pathologies.

Automated diffusion-weighted image analysis along the perivascular space index reveals glymphatic dysfunction in association with brain parenchymal lesions.

Brain glymphatic dysfunction is critical in neurodegenerative processes. While animal studies have provided substantial insights, understandings in humans remains limited. Recent attention has focused on the non-invasive evaluation of brain glymphatic function. However, its association with brain parenchymal lesions in large-scale population remains under-investigated. In this cross-sectional analysis of 1030 participants (57.14 ± 9.34 years, 37.18% males) from the Shunyi cohort, we developed an automated pipeline to calculate diffusion-weighted image analysis along the perivascular space (ALPS), with a lower ALPS value indicating worse glymphatic function. The automated ALPS showed high consistency with the manual calculation of this index (ICC = 0.81, 95% CI: 0.662-0.898). We found that those with older age and male sex had lower automated ALPS values (ß = -0.051, SE = 0.004, p < .001, per 10 years, and ß = -0.036, SE = 0.008, p < .001, respectively). White matter hyperintensity (ß = -2.458, SE = 0.175, p < .001) and presence of lacunes (OR = 0.004, 95% CI < 0.002-0.016, p < .001) were significantly correlated with decreased ALPS. The brain parenchymal and hippocampal fractions were significantly associated with decreased ALPS (ß = 0.067, SE = 0.007, p < .001 and ß = 0.040, SE = 0.014, p = .006, respectively) independent of white matter hyperintensity. Our research implies that the automated ALPS index is potentially a valuable imaging marker for the glymphatic system, deepening our understanding of glymphatic dysfunction.

Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology.

INTRODUCTION: Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. METHODS: We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RESULTS: RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below ± 3.95 CL for amyloid beta positivity cutoffs (CL < 24). DISCUSSION: The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. HIGHLIGHTS: We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.

Plasma levels of adipokines and insulin are associated with markers of brain atrophy and cognitive decline in the spectrum of Alzheimer's Disease.

The discovery that metabolic alterations often coexist with neurodegenerative conditions has sparked interest in the examination of metabolic regulatory factors as potential modulators of brain health. Here, we examined the role of adipokines (leptin, adiponectin, resistin, and IL6) and insulin on different markers of brain atrophy in participants on the spectrum of Alzheimer's Disease. We included 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with 1063 follow-up time points (average follow-up: one year); and examined the association between metabolic regulatory factors and volumetric MRI values, white matter hyperintensities, and measures of cognitive impairment. Higher leptin, resistin, IL6, and insulin were associated with markers of cerebral atrophy, such as lower total brain volume, or higher ventricular volume. Higher leptin and resistin were also associated with greater impairment in daily life activities. Higher adiponectin was associated with lower ventricle volume. There was no association between adipokines or insulin with white matter hyperintensities. Our findings indicate a co-occurrence between alterations in metabolic regulatory factors and in brain volume along the preclinical to clinical spectrum of Alzheimer's Disease. These results suggest that strategies aimed at promoting metabolic health may positively impact brain health.

Long-term cognitive and brain morphologic changes in chronic heart failure: Results of the Cognition.Matters-HF study.

AIMS: Cognitive impairment (CI) is a common, yet frequently unrecognized co-morbidity in chronic heart failure (HF). We quantified trajectories of cognitive performance, brain volume, and related clinical outcome over a time course of 6 years. METHODS AND RESULTS: The Cognition.Matters-HF cohort study recruited patients with stable HF of any aetiology and severity. Beyond cardiological assessment, the workup included cognitive testing and brain magnetic resonance imaging (MRI). Of 148 recruited patients, 70% exhibited CI at baseline. During the median follow-up time of 69 months (quartiles: 68, 70), indicators of HF severity remained essentially unaltered. CI was also stable, with the exception of intensity of attention, where age-adjusted t-scores decreased from 42 (38, 46) to 38 (34, 44; P < 0.001). Complete sets of four serial brain MRI scans were available in 47 patients (32% of total sample). Total brain volume shrank by 0.4% per year, from 1103 (1060, 1143) cm3 to 1078 (1027, 1117) cm3, which was within limits observed in non-diseased ageing individuals. During follow-up, 29 study participants (20%) died, and 26 (18%) were at least once hospitalized due to worsening HF. The presence of CI was not associated with overall (P = 0.290) or hospitalization-free (P = 0.450) survival. CONCLUSIONS: In patients with stable HF patients receiving guideline-directed pharmacologic treatment and regular medical care, the presence of CI did not affect overall and hospitalization-free 6-year survival. The loss of brain parenchyma observed in patients with stable HF did not exceed that of normal ageing.

The characteristics of brain atrophy prior to the onset of Alzheimer's disease: a longitudinal study.

Objective: We aimed to use the onset time of Alzheimer's disease (AD) as the reference time to longitudinally investigate the atrophic characteristics of brain structures prior to the onset of AD. Materials and methods: A total of 328 participants from the ADNI database with clear onset of AD and structural imaging data were included in our study. The time before the onset of AD (abbreviated as BAD) was calculated. We investigated the longitudinal brain changes in 97 regions using multivariate linear mixed effects regression models. Results: The average BAD was -28.15 months, with a range from -156 to 0 months. The 54 brain regions showed significant atrophy prior to the onset of AD, and these regions were mainly distributed in the frontal and temporal lobes. The parietal and occipital lobe exhibited relatively less atrophy than the other brain lobes. Sex, age, and magnetic field strength had greater direct impacts on structural indicators than APOE genotype and education. The analysis of interaction effects revealed that the APOE ε4 mutation carriers exhibited more severe structural changes in specific brain regions as the BAD increased. However, sex, age, and education had minimal regulatory influence on the structural changes associated with BAD. Conclusion: Longitudinal analysis, with the onset time point of AD as the reference, can accurately describe the features of structural changes preceding the onset of AD and provide a comprehensive understanding of AD development.

Quantifying chronic lesion expansion in multiple sclerosis: Exploring imaging markers for longitudinal assessment.

OBJECTIVES: Gradual expansion of multiple sclerosis lesions over time is known to have a significant impact on disease progression. However, accurately quantifying the volume changes in chronic lesions presents challenges due to their slow rate of progression and the need for longitudinal segmentation. Our study addresses this by estimating the expansion of chronic lesions using data collected over a 1-2 year period and exploring imaging markers that do not require longitudinal lesion segmentation. METHODS: Pre- and post-gadolinium 3D-T1, 3D FLAIR and diffusion tensor images were acquired from 42 patients with MS. Lesion expansion, stratified by the severity of tissue damage as measured by mean diffusivity change, was analysed between baseline and 48 months (Progressive Volume/Severity Index, PVSI). Central brain atrophy (CBA) and the degree of tissue loss inside chronic lesions (measured by the change of T1 intensity and mean diffusivity (MD)) were used as surrogate markers. RESULTS: CBA measured after 2 years of follow-up estimated lesion expansion at 4 years with a high degree of accuracy (r = 0.82, p < 0.001, ROC area under the curve 0.92, sensitivity of 94 %, specificity of 85 %). Increased MD within chronic lesions measured over 2 years was strongly associated with future expansion (r = 0.77, p < 0.001, ROC area under the curve 0.87, sensitivity of 81 % and specificity of 81 %). In contrast, change in lesion T1 hypointensity poorly explained future PVSI (best sensitivity and specificity 60 % and 59 % respectively). INTERPRETATION: CBA and, to a lesser extent, the change in MD within chronic MS lesions, measured over a period of 2 years, can provide a reliable and sensitive estimate of the extent and severity of chronic lesion expansion.

Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy.

BACKGROUND: Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR). METHODS: We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis. RESULTS: The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10- 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm2 per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10- 4) and thalamic volume (-9.507 cm3 per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10- 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10- 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm2 per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10- 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect. DISCUSSION: This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.

Additive Effect of Metabolic Syndrome on Brain Atrophy in People Living with HIV-Magnetic Resonance Volumetry Study.

With people living with HIV (PLWH) reaching the senium, the importance of aging-related comorbidities such as metabolic syndrome (MS) becomes increasingly important. This study aimed to determine the additive effect of MS on brain atrophy in PLWH. This prospective study included 43 PLWH, average age of 43.02 ± 10.93 years, and 24 healthy controls, average age of 36.87 ± 8.89 years. PLWH were divided into two subgroups: without MS and with MS, according to NCEP ATP III criteria. All patients underwent brain magnetic resonance imaging (MRI) on a 3T clinical scanner with MR volumetry, used for defining volumes of cerebrospinal fluid (CSF) spaces and white and grey matter structures, including basal ganglia. A Student's t-test was used to determine differences in brain volumes between subject subgroups. The binary classification was performed to determine the sensitivity and specificity of volumetry findings and cut-off values. Statistical significance was set at p < 0.05. PLWH presented with significantly lower volumes of gray matter, putamen, thalamus, globus pallidus, and nc. accumbens compared to healthy controls; cut-off values were: for gray matter 738.130 cm3, putamen 8.535 cm3, thalamus 11.895 cm3, globus pallidus 2.252 cm3, and nc. accumbens 0.715 cm3. The volumes of CSF and left lateral ventricles were found to be higher in PLWH with MS compared to those without MS, where, with a specificity of 0.310 and sensitivity of 0.714, it can be assumed that PLWH with a CSF volume exceeding 212.83 cm3 are likely to also have MS. This suggests that PLWH with metabolic syndrome may exhibit increased CSF volume above 212.83 cm3 as a consequence of brain atrophy. There seems to be an important connection between MS and brain volume reduction in PLWH with MS, which may add to the accurate identification of persons at risk of developing HIV-associated cognitive impairment.

New perspectives on the role of biological factors in anorexia nervosa: Brain volume reduction or oxidative stress, which came first?

Anorexia nervosa (AN) is an eating disorder (ED) that has seen an increase in its incidence in the last thirty years. Compared to other psychosomatic disorders, ED can be responsible for many major medical complications, moreover, in addition to the various systemic impairments, patients with AN undergo morphological and physiological changes affecting the cerebral cortex. Through immunohistochemical studies on portions of postmortem human brain of people affected by AN and healthy individuals, and western blot studies on leucocytes of young patients and healthy controls, this study investigated the role in the afore-mentioned processes of altered redox state. The results showed that the brain volume reduction in AN could be due to an increase in the rate of cell death, mainly by apoptosis, in which mitochondria, main cellular organelles affected by a decreased dietary intake, and a highly compromised intracellular redox balance, may play a pivotal role.

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.

Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118.

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.

Dynamic Mechanism of Cerebral Venous Disruption: Longitudinal Evidence From a Community-Based Cohort.

BACKGROUND: This study aims to investigate the temporal and spatial patterns of structural brain injury related to deep medullary veins (DMVs) damage. METHODS AND RESULTS: This is a longitudinal analysis of the population-based Shunyi cohort study. Baseline DMVs numbers were identified on susceptibility-weighted imaging. We assessed vertex-wise cortex maps and diffusion maps at both baseline and follow-up using FSL software and the longitudinal FreeSurfer analysis suite. We performed statistical analysis of global measurements and voxel/vertex-wise analysis to explore the relationship between DMVs number and brain structural measurements. A total of 977 participants were included in the baseline, of whom 544 completed the follow-up magnetic resonance imaging (age 54.97±7.83 years, 32% men, mean interval 5.56±0.47 years). A lower number of DMVs was associated with a faster disruption of white matter microstructural integrity, presented by increased mean diffusivity and radial diffusion (ß=0.0001 and SE=0.0001 for both, P=0.04 and 0.03, respectively), in extensive deep white matter (threshold-free cluster enhancement P<0.05, adjusted for age and sex). Of particular interest, we found a bidirectional trend association between DMVs number and change in brain volumes. Specifically, participants with mild DMVs disruption showed greater cortical enlargement, whereas those with severe disruption exhibited more significant brain atrophy, primarily involving clusters in the frontal and parietal lobes (multiple comparison corrected P<0.05, adjusted for age, sex, and total intracranial volume). CONCLUSIONS: Our findings posed the dynamic pattern of brain parenchymal lesions related to DMVs injury, shedding light on the interactions and chronological roles of various pathological mechanisms.

Berberine improves cognitive impairment by alleviating brain atrophy and promoting white matter reorganization in diabetic db/db mice: a magnetic resonance imaging-based study.

Diabetic cognitive impairment is a common complication in type 2 diabetes. Berberine (BBR) is an isoquinoline alkaloid that has been shown to have neuroprotective effects against diabetes. This study aimed to investigate the effect of BBR on the gray and white matter of the brain by using magnetic resonance imaging (MRI) and to explore the underlying mechanisms. The study used diabetic db/db mice and administered BBR (50 and 100 mg/kg) intragastrically for twelve weeks. Morris water maze was applied to examine cognitive function. T2-weighted imaging (T2WI) was performed to assess brain atrophy, and diffusion tensor imaging (DTI) combined with fiber tracking was conducted to monitor the structural integrity of the white matter, followed by histological immunostaining. Furthermore, the protein expressions of the phosphatidylinositol 3-kinase (PI3K)/ protein kinase B (AKT)/ glycogen synthase kinase-3ß (GSK-3ß) were detected. The results revealed that BBR significantly improved the spatial learning and memory of the db/db mice. T2WI exhibited ameliorated brain atrophy in the BBR-treated db/db mice, as evidenced by reduced ventricular volume accompanied by increased hippocampal volumes. DTI combined with fiber tracking revealed that BBR increased FA, fiber density and length in the corpus callosum/external capsule of the db/db mice. These imaging findings were confirmed by histological immunostaining. Notably, BBR significantly enhanced the protein levels of phosphorylated AKT at Ser473 and GSK-3ß at Ser9. Collectively, this study demonstrated that BBR significantly improved the cognitive function of the diabetic db/db mice through ameliorating brain atrophy and promoting white matter reorganization via AKT/GSK-3ß pathway.

No genetic causal associations between periodontitis and brain atrophy or cognitive impairment: evidence from a comprehensive bidirectional Mendelian randomization study.

BACKGROUND: Observational studies have explored the relationships of periodontitis with brain atrophy and cognitive impairment, but these findings are limited by reverse causation, confounders and have reported conflicting results. Our study aimed to investigate the causal associations of periodontitis with brain atrophy and cognitive impairment through a comprehensive bidirectional Mendelian randomization (MR) research. METHODS: We incorporated two distinct genome-wide association study (GWAS) summary datasets as an exploration cohort and a replication cohort for periodontitis. Four and eight metrics were selected for the insightful evaluation of brain atrophy and cognitive impairment, respectively. The former involved cortical thickness and surface area, left and right hippocampal volumes, with the latter covering assessments of cognitive performance, fluid intelligence scores, prospective memory, and reaction time for mild cognitive impairment to Alzheimer's disease (AD), Lewy body dementia, vascular dementia and frontotemporal dementia for severe situations. Furthermore, supplementary analyses were conducted to examine the associations between the longitudinal rates of change in brain atrophy and cognitive function metrics with periodontitis. The main analysis utilized the inverse variance weighting (IVW) method and evaluated the robustness of the results through a series of sensitivity analyses. For multiple tests, associations with p-values < 0.0021 were considered statistically significant, while p-values ≥ 0.0021 and < 0.05 were regarded as suggestive of significance. RESULTS: In the exploration cohort, forward and reverse MR results revealed no causal associations between periodontitis and brain atrophy or cognitive impairment, and only a potential causal association was found between AD and periodontitis (IVW: OR = 0.917, 95% CI from 0.845 to 0.995, P = 0.038). Results from the replication cohort similarly corroborated the absence of a causal relationship. In the supplementary analyses, the longitudinal rates of change in brain atrophy and cognitive function were also not found to have causal relationships with periodontitis. CONCLUSIONS: The MR analyses indicated a lack of substantial evidence for a causal connection between periodontitis and both brain atrophy and cognitive impairment.

Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis.

BACKGROUND: Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice. OBJECTIVE: We performed a systematic review and meta-analysis on the use of QSM in MS. METHODS: Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively. RESULTS: After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions. CONCLUSIONS: We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.

Alzheimer's and neurodegenerative disease biomarkers in blood predict brain atrophy and cognitive decline.

BACKGROUND: Although blood-based biomarkers have been identified as cost-effective and scalable alternatives to PET and CSF markers of neurodegenerative disease, little is known about how these biomarkers predict future brain atrophy and cognitive decline in cognitively unimpaired individuals. Using data from the Baltimore Longitudinal Study of Aging (BLSA), we examined whether plasma biomarkers of Alzheimer's disease (AD) pathology (amyloid-ß [Aß42/40], phosphorylated tau [pTau-181]), astrogliosis (glial fibrillary acidic protein [GFAP]), and neuronal injury (neurofilament light chain [NfL]) were associated with longitudinal brain volume loss and cognitive decline. Additionally, we determined whether sex, APOEε4 status, and plasma amyloid-ß status modified these associations. METHODS: Plasma biomarkers were measured using Quanterix SIMOA assays. Regional brain volumes were measured by 3T MRI, and a battery of neuropsychological tests assessed five cognitive domains. Linear mixed effects models adjusted for demographic factors, kidney function, and intracranial volume (MRI analyses) were completed to relate baseline plasma biomarkers to baseline and longitudinal brain volume and cognitive performance. RESULTS: Brain volume analyses included 622 participants (mean age ± SD: 70.9 ± 10.2) with an average of 3.3 MRI scans over 4.7 years. Cognitive performance analyses included 674 participants (mean age ± SD: 71.2 ± 10.0) with an average of 3.9 cognitive assessments over 5.7 years. Higher baseline pTau-181 was associated with steeper declines in total gray matter volume and steeper regional declines in several medial temporal regions, whereas higher baseline GFAP was associated with greater longitudinal increases in ventricular volume. Baseline Aß42/40 and NfL levels were not associated with changes in brain volume. Lower baseline Aß42/40 (higher Aß burden) was associated with a faster decline in verbal memory and visuospatial performance, whereas higher baseline GFAP was associated with a faster decline in verbal fluency. Results were generally consistent across sex and APOEε4 status. However, the associations of higher pTau-181 with increasing ventricular volume and memory declines were significantly stronger among individuals with higher Aß burden, as was the association of higher GFAP with memory decline. CONCLUSIONS: Among cognitively unimpaired older adults, plasma biomarkers of AD pathology (pTau-181) and astrogliosis (GFAP), but not neuronal injury (NfL), serve as markers of future brain atrophy and cognitive decline.

Cognitive Impairment and Brain Atrophy in Patients with Chronic Kidney Disease.

In Japan, the aging of the population is rapidly accelerating, with an increase in patients with chronic kidney disease (CKD) and those undergoing dialysis. As a result, the number of individuals with cognitive impairment (CI) is rising, and addressing this issue has become an urgent problem. A notable feature of dementia in CKD patients is the high frequency of vascular dementia, making its prevention through the management of classical risk factors such as hypertension, diabetes mellitus, dyslipidemia, smoking, etc., associated with atherosclerosis and arteriosclerosis. Other effective measures, including the use of renin-angiotensin system inhibitors, addressing anemia, exercise therapy, and lifestyle improvements, have been reported. The incidence and progression of CI may also be influenced by the type of kidney replacement therapy, with reports suggesting that long-duration dialysis, low-temperature hemodialysis, peritoneal dialysis, and kidney transplantation can have a preferable effect on the preservation of cognitive function. In conclusion, patients with CKD are at a higher risk of developing CI, with brain atrophy being a contributing factor. Despite the identification of various preventive measures, the evidence substantiating their efficacy remains limited across all studies. Future expectations lie in large-scale randomized controlled trials.

Bidirectional mediation of bone mineral density and brain atrophy on their associations with gait variability.

This mediation analysis aimed to investigate the associations among areal bone mineral density, mobility-related brain atrophy, and specific gait patterns. A total of 595 participants from the Taizhou Imaging Study, who underwent both gait and bone mineral density measurements, were included in this cross-sectional analysis. We used a wearable gait tracking device to collect quantitative gait parameters and then summarized them into independent gait domains with factor analysis. Bone mineral density was measured in the lumbar spine, femoral neck, and total hip using dual-energy X-ray absorptiometry. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of brain regions related to mobility were computed using FreeSurfer. Lower bone mineral density was found to be associated with higher gait variability, especially at the site of the lumbar spine (ß = 0.174, FDR = 0.001). Besides, higher gait variability was correlated with mobility-related brain atrophy, like the primary motor cortex (ß = 0.147, FDR = 0.006), sensorimotor cortex (ß = 0.153, FDR = 0.006), and entorhinal cortex (ß = 0.106, FDR = 0.043). Bidirectional mediation analysis revealed that regional brain atrophy contributed to higher gait variability through the low lumbar spine bone mineral density (for the primary motor cortex, P = 0.018; for the sensorimotor cortex, P = 0.010) and the low lumbar spine bone mineral density contributed to higher gait variability through the primary motor and sensorimotor cortices (P = 0.026 and 0.010, respectively).