Magnetic Resonance Imaging Markers Research Articles

Periodontitis and brain magnetic resonance imaging markers of Alzheimer's disease and cognitive aging.

We examined the association of clinical, microbiological, and host response features of periodontitis with MRI markers of atrophy/cerebrovascular disease in the Washington Heights Inwood Columbia Aging Project (WHICAP) Ancillary Study of Oral Health. We analyzed 468 participants with clinical periodontal data, microbial plaque and serum samples, and brain MRIs. We tested the association of periodontitis features with MRI features, after adjusting for multiple risk factors for Alzheimer's disease/Alzheimer's disease-related dementia (AD/ADRD). In fully adjusted models, having more teeth was associated with lower odds for infarcts, lower white matter hyperintensity (WMH) volume, higher entorhinal cortex volume, and higher cortical thickness. Higher extent of periodontitis was associated with lower entorhinal cortex volume and lower cortical thickness. Differential associations emerged between colonization by specific bacteria/serum antibacterial IgG responses and MRI outcomes. In an elderly cohort, clinical, microbiological, and serological features of periodontitis were associated with MRI findings related to ADRD risk. Further investigation of causal associations is warranted.

Quantitative Analysis of Retinal Perfusion in Patients with Frontotemporal Dementia Using Optical Coherence Tomography Angiography.

Optical coherence tomography angiography (OCT-A) provides detailed visualization of the perfusion of the vascular network of the eye. While in other forms of dementia, such as Alzheimer's disease and mild cognitive impairment, reduced retinal perfusion was frequently reported, data of patients with frontotemporal dementia (FTD) are lacking. Retinal and optic nerve head perfusion was evaluated in patients with FTD with OCT-A. Quantitative OCT-A metrics were analyzed and correlated with clinical markers and vascular cerebral lesions in FTD patients. OCT-A was performed in 18 eyes of 18 patients with FTD and 18 eyes of 18 healthy participants using RTVue XR Avanti with AngioVue. In addition, patients underwent a detailed ophthalmological, neurological, and neuropsychological examination, cerebral magnetic resonance imaging (MRI), and lumbar puncture. The flow density in the optic nerve head (ONH) and in the superficial capillary plexus (SCP) of the macula of patients was significantly lower compared to that of healthy controls (p < 0.001). Similarly, the VD in the deep capillary plexus (DCP) of the macula of patients was significantly lower compared to that of healthy controls (p < 0.001). There was no significant correlation between the flow density data, white matter lesions in brain MRI, cognitive deficits, and cerebrospinal fluid markers of dementia. Patients with FTD showed a reduced flow density in the ONH, and in the superficial and deep retinal capillary plexus of the macula, when compared with that of healthy controls. Quantitative analyses of retinal perfusion using OCT-A may therefore help in the diagnosis and monitoring of FTD. Larger and longitudinal studies are necessary to evaluate if OCT-A is a suitable biomarker for patients with FTD.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model.

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Cardiovascular magnetic resonance characterization of myocardial tissue injury in a miniature swine model of cancer therapy-related cardiovascular toxicity

BackgroundLeft ventricular ejection fraction (LVEF) is the most commonly clinically used imaging parameter for assessing cancer therapy-related cardiac dysfunction (CTRCD). However, LVEF declines may occur late, after substantial injury. This study sought to investigate cardiovascular magnetic resonance (CMR) imaging markers of subclinical cardiac injury in a miniature swine model. MethodsFemale Yucatan miniature swine (n=14) received doxorubicin (2mg/kg) every 3 weeks for 4 cycles. CMR, including cine, tissue characterization via T1 and T2 mapping, and late gadolinium enhancement (LGE) was performed on the same day as doxorubicin administration and three weeks after the final chemotherapy cycle. In addition, MR spectroscopy (MRS) was performed during the 3 weeks after the final chemotherapy in 7 pigs. A single CMR and MRS exam was also performed in three Yucatan miniature swine that were age- and weight-matched to the final imaging exam of the doxorubicin-treated swine to serve as controls. CTRCD was defined as histological early morphologic changes, including cytoplasmic vacuolization and myofibrillar loss of myocytes, based on post-mortem analysis of humanely euthanized pigs after the final CMR exam. ResultsOf 13 swine completing five serial CMR scans, 10 (77%) had histological evidence of CTRCD. Three animals had neither histological evidence nor changes in LVEF from baseline. No absolute LVEF <40% or LGE were observed. Native T1, extracellular volume (ECV), and T2 at 12 weeks were significantly higher in swine with CTRCD than those without CTRCD (1178 ms vs. 1134 ms, p=0.002, 27.4% vs. 24.5%, p=0.03, and 38.1 ms vs. 36.4 ms, p=0.02, respectively). There were no significant changes in strain parameters. The temporal trajectories in native T1, ECV, and T2 in swine with CTRCD showed similar and statistically significant increases. At the same time, there were no differences in their temporal changes between those with and without CTRCD. MRS myocardial triglyceride content substantially differed among controls, swine with and without CTRCD (0.89%, 0.30%, 0.54%, respectively, ANOVA, p=0.01), and associated with the severity of histological findings and incidence of vacuolated cardiomyocytes. ConclusionsSerial CMR imaging alone has a limited ability to detect histologic CTRCD beyond LVEF. Integrating MRS myocardial triglyceride content may be useful for detection of early potential CTRCD.

Magnetic Resonance Imaging-Negative Cerebral Amyloid Angiopathy: Cerebrospinal Fluid Amyloid-β42 over Amyloid Positron Emission Tomography.

Cerebral amyloid angiopathy (CAA) pathology is becoming increasingly important in Alzheimer's disease (AD) because of its potential link to amyloid-related imaging abnormalities, a critical side effect observed during AD immunotherapy. Identification of CAA without typical magnetic resonance imaging (MRI) markers (MRI-negative CAA) is challenging, and novel detection biomarkers are needed. We included 69 participants with high neuritic plaques (NP) burden, with and without CAA pathology (NP with CAA vs. NP without CAA) based on autopsy data from the Alzheimer's Disease Neuroimaging Initiative. Two participants with hemorrhagic CAA markers based on MRI were excluded and the final analysis involved 36 NP without CAA and 31 NP with CAA. A logistic regression model was used to compare the cerebrospinal fluid (CSF) amyloid-β42 (Aβ42), phosphorylated tau181, and total tau levels, the amyloid positron emission tomography (PET) standardized uptake ratio (SUVR), and cognitive profiles between NP with and without CAA. Regression models for CSF and PET were adjusted for age at death, sex, and the last assessed clinical dementia rating sum of boxes score. Models for cognitive performances was adjusted for age at death, sex, and education level. NP with CAA had significantly lower CSF Aβ42 levels when compared with those without CAA (110.5 pg/mL vs. 134.5 pg/mL, p-value = 0.002). Logistic regression analysis revealed that low CSF Aβ42 levels were significantly associated with NP with CAA (odds ratio [OR]: 0.957, 95% confidence interval [CI]: 0.928, 0.987, p-value = 0.005). However, amyloid PET SUVR did not differ between NP with CAA and those without CAA (1.39 vs. 1.48, p-value = 0.666). Logistic regression model analysis did not reveal an association between amyloid PET SUVR and NP with CAA (OR: 0.360, 95% CI: 0.007, 1.741, p-value = 0.606). CSF Aβ42 is more sensitive to predict MRI-negative CAA in high NP burden than amyloid PET.

Associations of Lipoprotein(a) Level with Cerebral Small Vessel Disease in Patients with Alzheimer's Disease.

This study aimed to examine the association of lipoprotein(a) [Lp(a)] level with the burden of cerebral small vessel disease (CSVD) in patients with Alzheimer's disease (AD). Data from 111 consecutive patients with AD admitted to Nanjing First Hospital from 2015 to 2022 were retrospectively analyzed in this study. Serum Lp(a) concentrations were grouped into tertiles (T1-T3). Brain magnetic resonance imaging (MRI) was rated for the presence of CSVD, including enlarged perivascular spaces (EPVS), lacunes, white-matter lesions, and cerebral microbleeds (CMBs). The CSVD burden was calculated by summing the scores of each MRI marker at baseline. A binary or ordinal logistic regression model was used to estimate the relationship of serum Lp(a) levels with CSVD burden and each MRI marker. Patients with higher tertiles of Lp(a) levels were less likely to have any CSVD (T1, 94.6%; T2, 78.4%; T3, 66.2%; p = 0.013). Multivariable analysis found that Lp(a) levels were inversely associated with the presence of CSVD (T2 vs. T1: adjusted odds ratio [aOR] 0.132, 95% confidence interval [CI] 0.018-0.946, p = 0.044; T3 vs. T1: aOR 0.109, 95% CI 0.016-0.737, p = 0.023) and CSVD burden (T3 vs. T1: aOR 0.576, 95% CI 0.362-0.915, p = 0.019). The independent relationship between Lp(a) levels and individual CSVD features was significant for moderate-to-severe EPVS in the centrum semiovale (T2 vs. T1: aOR 0.059, 95% CI 0.006-0.542, p = 0.012; T3 vs. T1: aOR 0.029, 95% CI 0.003-0.273, p = 0.002) and CMBs (T3 vs. T1: aOR 0.144, 95% CI 0.029-0.716, p = 0.018). In this study, serum Lp(a) level was inversely associated with CSVD in AD patients.

MRI volumetry and diffusion tensor imaging for diagnosis and follow-up of late post-traumatic injuries

BackgroundTraumatic Brain Injury (TBI) is a major cause of acquired disability and can cause devastating and progressive post-traumatic encephalopathy. TBI is a dynamic condition that continues to evolve over time. A better understanding of the pathophysiology of these late lesions is important for the development of new therapeutic strategies. ObjectivesThe primary objective was to compare the ability of fluid-attenuated reversion recovery (FLAIR) and diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) markers to identify participants with a Glasgow outcome scale extended (GOS-E) score of 7–8, up to 10 years after their original TBI. The secondary objective was to study the brain regionalization of DTI markers. Finally, we analyzed the evolution of late-developing brain lesions using repeated MRI images, also taken up to 10 years after the TBI. MethodsIn this retrospective study, participants were included from a cohort of people hospitalized following a severe TBI. Following their discharge, they were followed-up and clinically assessed, including a DTI-MRI scan, between 2012 and 2016. We performed a cross-sectional analysis on 97 participants at a median (IQR) of 5 years (3–6) post-TBI, and a further post-TBI longitudinal analysis over 10 years on a subpopulation (n = 17) of the cohort. ResultsAlthough the area under the curve (AUC) of FLAIR, fractional anisotropy (FA), and mean diffusivity (MD) were not significantly different, only the AUC of FA was statistically greater than 0.5. In addition, only the FA was correlated with clinical outcomes as assessed by GOS-E score (P<10−4). On the cross-sectional analysis, DTI markers allowed study post-TBI white matter lesions by region. In the longitudinal subpopulation analysis, the observed number of brain lesions increased for the first 5 years post-TBI, before stabilizing over the next 5 years. ConclusionsThis study has shown for the first time that post-TBI lesions can present in a two-phase evolution. These results must be confirmed in larger studies. French Data Protection Agency (Commission nationale de l'informatique et des libertés; CNIL) study registration no: 1934708v0.

The effect of pharyngeal structures on the severity of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is characterized by complete or partial cessation of breathing during sleep. The tongue is suggested as a possible anatomical site causing airway obstruction. However, the role of other pharyngeal structures in the development of OSA remains unclear. We designed a study using both the apnea-hypopnea index (AHI) and the oxygen saturation measurements to assess the severity of OSA. We aimed to identify critical anatomical structures of the upper airway that correlate with the severity of OSA and toevaluate the utility of magnetic resonance imaging (MRI) markers to detect possible OSA inpatients without overt symptoms. The study included participants referred to the neurology outpatient clinic from the check-up unit. Participants were grouped as controls, mild, moderate, or severe OSA according to the AHI. A cranial MRI with a field of view (FOV) encompassing the upper airway structures was obtained from all participants. The areas of the tongue and the uvula were measured on the sagittal images by drawing the boundaries of the tissues manually. The posterior air space (PAS) area was evaluated from regions of interest in five parallel planes. Of 105 participants, 30 were controls, 27 had mild, 25 had moderate, and 23 had severe OSA.The moderate and severe OSA groups did not differ in oxygen saturation levels during sleep. Therefore, patients withmoderate and severe OSA were combined into one group (moderate/severe OSA). The area of the tongue was significantly larger in the moderate/severe OSA group compared to the control group. Both the tongue and the uvula areas showed a significant positive correlation with the AHI. Our findings suggest that the tongue and uvula have prominent roles in the severity of OSAS. It may be useful to measure these structures with MRI to screen for at-risk individuals without overt OSA symptoms.

Probabilistic computer‐aided diagnosis of Alzheimer’s disease and frontotemporal dementia based on MRI and biochemical markers

AbstractBackgroundOne of the clinical problems for biomarkers' clinical use is the ability to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), and healthy subjects (CTR). This clearly challenges diagnosis and prognosis. We implemented a ML algorithm that provides individual probabilistic diagnoses for these dementias based on magnetic resonance imaging (MRI) and we correlated the results with biochemical markers.MethodWe studied 3T‐T1w MRI of 432 subjects. A subset of these participants had cerebrospinal fluid (CSF) and plasma biomarkers (Table 1). We obtained regional subcortical gray matter volumes and cortical thickness measures using Freesurfer. We implemented a calibrated classifier with a Support Vector Machine with only the MRI data. We tested paired‐wise classification and classification across the 3 groups. We obtained individual probabilities associated with group correspondence. We studied the correlation between these probabilities and CSF and plasma biomarkers. For this, we subdivided the groups into true‐group (subjects with classification according to clinical diagnosis) and false‐group (subjects which did not coincide with clinical diagnosis). Finally, we implemented a permutation test to find the importance of each region in the classification.ResultWe obtained accuracies of 90.7 ± 6.7% in the CTR vs AD classification, 88.6 ± 4.5% for CTR vs FTD, 79.3 ± 8.8% for AD vs FTD, and 79.9 ± 5.1% when discriminating the 3 groups. We obtained a significant positive correlation for plasma p‐tau181 for the false‐AD in the comparison AD vs CTR (Figure 1). The correlation of the false‐CTR was significantly positive for the CSF and plasma NfL. Finally, in the AD vs FTD, the true‐FTD had a significant negative correlation with CSF NfL. The other biochemical biomarkers did not provide additional information. The most important regions for classification are shown in Figure 2.ConclusionThe ML algorithm gave high accuracies. Within wrongly classified AD patients, probabilities correlated positively with plasma p‐tau181, suggesting hidden pathological processes associated with subjects clinically classified as CTR. Finally, the group probability within well‐classified FTD patients in comparison with AD negatively correlated with CSF NfL. We suggest that this approach can be used as a tool to try to develop personalized diagnoses.

Neurophysiological activity linked to Alzheimer’s disease pathology relates to longitudinal cognition and progression to mild cognitive impairment

AbstractBackgroundAlzheimer’s Disease (AD) is defined by the pathological accumulation of amyloid‐beta (Aß) and tau in the brain. Regional deposition of these proteins is associated with changes in frequency‐defined neurophysiological activity that can be detected using non‐invasive magnetoencephalography (MEG). Here we examined whether these neurophysiological alterations were associated with participants’ demographics and longitudinal changes in cognition. Additionally, we addressed the clinical utility of MEG to predict progression to mild cognitive impairment (MCI), as compared to other established AD imaging biomarkers.MethodWe used Positron Emission Tomography (PET) to measure the deposition of whole‐brain Aß ([18F]NAV4694) and tau ([18F] Flortaucipir), Magnetic Resonance Imaging (MRI) to measure hippocampal volume and resting‐state MEG to capture neurophysiological activity in a group of clinically unimpaired older adults with family history of AD (PREVENT‐AD cohort; n = 103). We implemented a multivariate partial least squares (PLS) analysis to test the association between imaging markers (i.e., neurophysiological activity and AD proteinopathy) and participants’ clinical profiles (i.e., demographic‐clinical variables and longitudinal cognition data). For the second analysis we used logistic regression models to assess the added value of neurophysiological activity to predict MCI progression compared to established MRI/PET imaging markers (n = 100; 14 MCI progressors).ResultThe PLS analysis identified significant latent variables that linked proteinopathy‐related neurophysiological activity slowing to older age, lower education, positive APOE e4 status and longitudinal deficits in cognition across multiple domains (Figure 1). The initial logistic regression model included demographic and clinical variables and had an AUC = 0.772, which increased to 0.794 after adding the MRI hippocampal volume. Incorporating MEG spectral power from temporal regions where tau accumulates resulted in a considerable increase in accuracy with an AUC = 0.873. The model including Aß and tau PET on top of MRI and MEG markers reached an AUC = 0.905 (Figure 2).ConclusionOur results show that the neurophysiological changes linked to AD pathology are associated with longitudinal cognitive impairment across multiple domains. MEG spectral power from early tau accumulating regions improved the accuracy for predicting MCI progression, contributing information beyond traditional demographic/clinical variables and structural MRI and representing a more accessible and less invasive alternative compared to PET imaging.

Gut microbiome‐related metabolites in plasma are associated with general cognition

AbstractBackgroundIncreasing evidence suggests a role of metabolism in neurological disorders including Alzheimer’s disease (AD). The human metabolome is determined by genes and modifiable factors such as gut microbiota, diet, medication, and other lifestyle choices. We hypothesized that metabolites related to endophenotypes of brain health maybe derived or influenced by these modifiable factors. Identifying the determinants of metabolites associated with AD‐related endophenotypes may help to discover new targets for drugs and prevention.MethodWe profiled plasma samples of 1082 participants of the Rotterdam study for 1387 metabolites using a non‐targeted metabolomics platform. After preprocessing and quality control, plasma levels of 991 metabolites were available for analysis of 1068 cognitively normal participants. We assessed the role of metabolites in general cognition (G‐factor) and magnetic resonance imaging (MRI) markers using linear regression. Replication analysis was performed for general cognition in an independent dataset. To identify the source of brain health‐associated metabolites, we estimated the explained variance (EV) of metabolites by genes, gut‐microbiome, medication use, and clinical features using gradient boosting decision tree (GBDT). Multiple testing correction was performed based on the false discovery rate (FDR) by Benjamini Hochberg (FDR&lt;0.05).ResultAmong the 991 analyzed metabolites, 14 showed significant associations with general cognition, 21 metabolites with total brain volume, and one with total white matter lesions. In the replication analysis of metabolites associated with cognition, 9 metabolites showed consistent association with G‐factor. While genetics explained more than 5% of the variance of all associated metabolites, for three cognition‐associated metabolites (ergothioneine, 3‐hydroxy‐2‐methylpyridine sulfate, 4‐vinylguaiacol sulfate), modifiable lifestyle factors, medication and microbiota additionally explained up to 15% of their variance, with lifestyle factors showing the largest influence. Among the metabolites associated with brain imaging phenotypes, clinical factors and medication explained more than 5% of the variance of four metabolites (3‐hydroxysebacate, 3‐hydroxybutyrylcarnitine, sphingomyelin (d18:2/24:2), unnamed metabolite).ConclusionOur study provides evidence for the association between circulating metabolites and cognition or brain imaging phenotypes in participants without cognitive impairment. The levels of these metabolites are influenced by genetics and in part by modifiable factors. Further, studies are warranted to elucidate the complex relationship between gut microbiota, diet, genome, metabolome, and cognition.

Cerebrovascular disease is associated with memory function in the Health and Aging in Africa Longitudinal Study of an INDEPTH community in rural South Africa

AbstractBackgroundHealth and Aging in Africa Longitudinal Study of an INDEPTH community in South Africa (HAALSI) is a sister study to the US‐based Health and Retirement Study. The purpose of HAALSI is to characterize the social, economic, biological, physical, and mental health factors in a population‐representative cohort of older adults in a rural community in Agincourt, South Africa. Beginning in 2020, as part of an Alzheimer’s disease and related disorders (ADRD) substudy, we conducted neuropsychological evaluations and structural magnetic resonance imaging (MRI) in a subset of HAALSI participants. Here, we report preliminary observations on the relationship of white matter hyperintensity (WMH) volume, a marker of small vessel cerebrovascular disease, and hippocampal volume, a marker of neurodegeneration, on memory function in a group of substudy participants.MethodNinety‐nine HAALSI ADRD participants with available MRI scans and neuropsychological outcomes at time of analysis (mean ± SD; age = 70.3 ± 10.9, 87% with no formal education or some primary education) were included in these analyses. Participants received research‐grade, harmonized 1.5T MRI scanning at a private hospital and were evaluated with a battery of neuropsychological tests. Using in‐house developed software and FreeSurfer, respectively, we derived total WMH volume and total hippocampal volume. We examined the association of these variables with memory function, operationalized as the delayed recall score of the Logical Memory Test, adapted for context.ResultAdjusting for education level, increased WMH volume was associated with poorer performance on the Logical Memory Test (standardized β = ‐0.21, p&lt;0.05) but hippocampal volume was not (standardized β = 0.09, p = 0.41). Associations of these MRI markers with memory function did not interact with education level.ConclusionIn these preliminary analyses of older adults in a rural South African community, markers of small vessel cerebrovascular disease but not neurodegeneration are associated with poorer cognitive outcomes. Future work will examine the pathways that give rise to cognitive impairment and dementia in this rural South African population and track cognitive trajectories over time.

Heart rate fragmentation and brain MRI markers of small vessel disease in MESA.

Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P=0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P=0.03). HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.

Higher MRI lesion load in multiple sclerosis is related to the N-glycosylation changes of cerebrospinal fluid immunoglobulin G.

Intrathecal clonal expansion of antibody-producing plasma cells in multiple sclerosis (MS) perpetuates central nervous system injury and is associated with active demyelination. Immunoglobulin G (IgG) effector functions are modulated by linked N-glycan structures. The aim of the study was to detect potential differences in N-glycosylation of IgG in serum and cerebrospinal fluid (CSF) and total sera proteins between people with MS and those in whom the diagnosis of MS was excluded. Furthermore, we investigated the association with standard laboratory biomarkers of intrathecal inflammation as well as clinical and neuroradiological disease activity. This cross-sectional study included patients with suspected demyelinating disease. MS diagnosis was based on the 2017 McDonald criteria and controls were patients with excluded MS diagnosis. N-glycans were compared with Expanded Disability Status Scale (EDSS), magnetic resonance imaging (MRI) markers of disease activity and biomarkers of intrathecal inflammation (cell count, CSF-IgG concentration, percentage of intrathecal IgG, oligoclonal bands (OCB), virus-specific antibody index (MRZH reaction)). Differences between groups were observed only in the CSF-IgG N-glycome. In MS, the presence of bisecting N-acetylglucosamine (Padj=2.63E-05) and monogalactosylation (Padj=1.49E-06) were more abundant and associated with positive OCBs. N-glycans monogalactosylated at the α6 arm FA2[6]G1 (r=0.56) and FA2[6]BG1 (r=0.45) correlated with percentage of intrathecal IgG, but not total CSF-IgG. This trait was also more abundant in MRZH positive people with MS who had higher MRI lesion load (P=0.018) but unrelated to active lesions or EDSS. More abundant monogalactosylation of intrathecally synthesized IgG is the most prominent trait in MS and is associated with higher MRI lesion load.

Multipredictor risk models for predicting individual risk of Alzheimer’s disease

BackgroundEarly prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.MethodsA total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction.ResultsDuring the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91).ConclusionsThe risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Association of cancer history with structural brain aging markers of Alzheimer's disease and related dementias risk.

Cancer survivors are less likely than comparably aged individuals without a cancer history to develop Alzheimer's disease and related dementias (ADRD). In the UK Biobank, we investigated associations between cancer history and five structural magnetic resonance imaging (MRI) markers for ADRD risk, using linear mixed-effects models to assess differences in mean values and quantile regression to examine whether associations varied across the distribution of MRI markers. Cancer history was associated with smaller mean hippocampal volume (b=-19 mm3 , 95% CI=-36, -1) and lower mean cortical thickness in the Alzheimer's disease signature region (b=-0.004 mm, 95% CI=-0.007, -0.000). Quantile regressions indicated individuals most vulnerable to ADRD were more affected by cancer history. Some brain MRI markers associated with ADRD risk were elevated in adults with a history of cancer. The magnitude of the adverse associations varied across quantiles of neuroimaging markers, and the pattern suggests possible harmful associations for individuals already at high ADRD risk. We found no evidence of an inverse association between cancer history and ADRD-related neurodegeneration. Cancer history was associated with smaller mean hippocampal volume and lower mean cortical thickness in the Alzheimer's disease signature region. Quantile regressions indicated individuals most vulnerable to ADRD were more affected by cancer history.

Ambient Air Pollution Exposure and Cerebral White Matter Hyperintensities in Older Adults: A Cross-Sectional Analysis in the Three-City Montpellier Study.

Growing epidemiological evidence suggests an adverse relationship between exposure to air pollutants and cognitive health, and this could be related to the effect of air pollution on vascular health. We aim to evaluate the association between air pollution exposure and a magnetic resonance imaging (MRI) marker of cerebral vascular burden, white matter hyperintensities (WMH). This cross-sectional analysis used data from the French Three-City Montpellier study. Randomly selected participants 65-80 years of age underwent an MRI examination to estimate their total and regional cerebral WMH volumes. Exposure to fine particulate matter (), nitrogen dioxide (), and black carbon (BC) at the participants' residential address during the 5 years before the MRI examination was estimated with land use regression models. Multinomial and binomial logistic regression assessed the associations between exposure to each of the three pollutants and categories of total and lobar WMH volumes. Participants' () median age at MRI was 70.7 years [interquartile range (IQR): 6.1], and 52% () were women. Median exposure to air pollution over the 5 years before MRI acquisition was 24.3 (IQR: 1.7) for , 48.9 (14.6) for , and 2.66 (0.60) for BC. We found no significant association between exposure to the three air pollutants and total WMH volume. We found that exposure was significantly associated with higher risk of temporal lobe WMH burden [odds ratio (OR) for an (95% confidence interval: 1.41, 2.36) for the second volume tercile, 2.04 (1.59, 2.61) for the third volume tercile, reference: first volume tercile]. Associations for other regional WMH volumes were inconsistent. In this population-based study in older adults, exposure was associated with increased risk of high WMH volume in the temporal lobe, strengthening the evidence on adverse effect on the brain. Further studies looking at different markers of cerebrovascular damage are still needed to document the potential vascular effects of air pollution. https://doi.org/10.1289/EHP12231.

Regional White Matter Hyperintensities Relate to Specific Cognitive Abilities in Older Adults Without Dementia.

White matter hyperintensities (WMHs) are magnetic resonance imaging markers of small vessel cerebrovascular disease that are associated with cognitive decline and clinical Alzheimer disease. Previous studies have often focused on global or total WMH; less is known about associations of regional WMHs and cognitive abilities among older adults without dementia. A total of 610 older adults with normal cognition (n=302) or mild cognitive impairment (n=308) from the Alzheimer's Disease Neuroimaging Initiative underwent neuropsychological testing and magnetic resonance imaging. Linear regression models examined associations between regional WMH volumes and cognition, adjusting for age, sex, education, apolipoprotein E ε4 allele frequency, and pulse pressure. Among all participants, greater regional WMH volume in all lobes was associated with poorer performance on memory and speed/executive functioning. Among participants with normal cognition, greater temporal and occipital WMH volumes were associated with poorer memory, whereas no regional WMH volumes were associated with speed/executive function. Results show that greater regional WMH volume relates to poorer cognitive functioning-even among those with normal cognition. Together with results from previous studies, our findings raise the possibility that WMH may be a useful therapeutic target and/or important effect modifier in treatment or prevention dementia trials.

Optical coherence tomography versus other biomarkers: Associations with physical and cognitive disability in multiple sclerosis

Background: Optical coherence tomography (OCT) is a biomarker of neuroaxonal loss in multiple sclerosis (MS). Objective: The objective was to assess the relative role of OCT, next to magnetic resonance imaging (MRI) and serum markers of disability in MS. Methods: A total of 100 patients and 52 controls underwent OCT to determine peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell-inner plexiform layers (GCIPL). Serum neurofilament light chain (sNfL), total lesion volume (TLV), and brain parenchymal fraction (BPF) were also assessed. The associations of OCT with disability were examined in linear regression models with correction for age, vision, and education. Results: In patients, pRNFL was associated with the Symbol Digit Modalities Test (SDMT; p = 0.030). In the multivariate analysis including sNfL and MRI measures, pRNFL (β = 0.19, p = 0.044) and TLV (β = −0.24, p = 0.023) were the only markers associated with the SDMT. pRNFL (p < 0.001) and GCIPL (p < 0.001) showed associations with the Expanded Disability Status Scale (EDSS). In the multivariate analysis, GCIPL showed the strongest association with the EDSS (β = −0.32, p < 0.001) followed by sNfL (β = 0.18, p = 0.024). Conclusion: The associations of OCT measures with cognitive and physical disability were independent of serum and brain MRI markers of neuroaxonal loss. OCT can be an important tool for stratification in MS, while longitudinal studies using combinations of biomarkers are warranted.

One-year regional brain volume changes as potential predictors of cognitive function in multiple sclerosis: a pilot study

BackgroundThe most reliable magnetic resonance imaging (MRI) marker of cognitive dysfunction in multiple sclerosis (MS) is brain atrophy. However, 1-year volumetric changes prior to cognitive assessment were never studied as potential predictors of cognition, which we aim to assess with this pilot work.MethodsTwenty-two MS patients were submitted to a baseline measure of 83 regional brain volumes with MRI and re-evaluated 1 year later; they were also tested with the Brief International Cognitive Assessment for MS (BICAMS): sustained attention and processing speed were examined with the Symbol Digit Modalities Test (SDMT), verbal and visuo-spatial learning and memory with the learning trials from the California Verbal Learning Test-II (CVLT) and the Brief Visuo-spatial Memory Test-revised (BVMT), respectively. Controlling for age, sex, and years of education, a multivariate linear regression model was created for each cognitive score at 1-year follow-up in a backward elimination manner, considering cross-sectional regional volumes and 1-year volume changes as potential predictors.ResultsDecreases in the volumes of the left amygdala and the right lateral orbitofrontal cortex in the year prior to assessment were identified as possible predictors of worse performance in verbal memory (P = 0.009) and visuo-spatial memory (P = 0.001), respectively, independently of cross-sectional brain regional volumes at time of testing.ConclusionOur work reveals novel 1-year regional brain volume changes as potential predictors of cognitive deficits in MS. This suggests a possible role of these regions in such deficits and might contribute to uncover cognitively deteriorating patients, whose detection is still unsatisfying in clinical practice.