Brain Parenchymal Fraction Research Articles

Lipid and brain volumetric measures in multiple sclerosis patients: findings from a large observational study.

This study aimed to investigate relationships between cholesterol profile, brain volumetric MRI, and clinical measures in a large observational cohort of multiple sclerosis (MS) patients. We included 1.505 patients with 4.966 time points including complete lipid, clinical, and imaging data. The time among lipid, brain MRI and clinical measures was under 90 days. Cross-sectional statistical analysis at baseline was performed using an adjusted linear regression and analysis of longitudinal lipid and MRI measures data was performed using adjusted linear mixed models. We found associations between higher high-density lipoprotein cholesterol (HDL-C) and lower brain parenchymal fraction (BPF) at cross-sectional analysis at baseline (B = -0.43, CI 95%: -0.73, -0.12, p = 0.005), as well as in longitudinal analysis over follow-up (B = -0.32 ± 0.072, χ2 = 36.6; p = < 0.001). Higher HDL-C was also associated with higher T2-lesion volume in longitudinal analysis (B = 0.11 ± 0.023; χ2 = 23.04; p = < 0.001). We observed a weak negative association between low-density lipoprotein cholesterol (LDL-C) levels and BPF at baseline (B = -0.26, CI 95%: -0.4, -0.11, p = < 0.001) as well as in longitudinal analysis (B = -0.06 ± 0.03, χ2 = 4.46; p = 0.03). T2-LV did not show an association with LDL-C. We did not find any association between lipid measures and disability. The effect of lipid levels on MRI measures and disability was minimal (Cohen f2 < 0.02). Our results contradict the previously described exclusively positive effect of HDL-C on brain atrophy in patients with MS. Higher LDL-C was weakly associated with higher brain atrophy but not with higher lesion burden.

Quantifying brain health in acute ischemic stroke through effective reserve.

To quantify brain health using a measure of reserve that incorporates pre-existing pathology. We analyzed two retrospective ischemic stroke cohorts (GASROS and SALVO) with neuroimaging and 90-day modified Rankin Scores (mRS) available. White matter hyperintensity (WMHv), brain, and intracranial volumes were automatically extracted, and brain parenchymal fraction (BPF) calculated. The latent variable effective reserve (eR) was modeled using age, WMHv, and BPF or brain volume in GASROS. Models were compared using Bayes Information Criterion (BIC). The best model's eR estimates were categorized into quartiles and evaluated in SALVO. GASROS included 476 (median age: 65.8; 65.3% male) and SALVO included 43 (median age: 69.2; 62.8% male) patients. Inverse associations between eR and mRS was seen in both models, with brain volume outperforming BPF (path coefficients: -0.67, -0.48, respectively; p < 0.001; |ΔBIC| = 362). Quartile-based eR stratification in SALVO showed a similar inverse trend, with worse outcomes in the low reserve group (mRS≤2 - highest vs lowest quartile: 85/90% vs 59/45% for GASROS/SALVO). Expanding the concept of eR, highlights its clinical translational potential. The strong link between higher eR and better outcomes underscores its value as a protective brain health metric.

Glymphatic dysfunction in multiple sclerosis and its association with disease pathology and disability.

The role of the glymphatic system in multiple sclerosis (MS)-related disability remains underexplored. Diffusion-tensor image analysis along the perivascular space (DTI-ALPS) offers a non-invasive method to assess glymphatic function. To evaluate glymphatic function in MS patients with lower and higher disability. This study included 118 MS patients who underwent structural, diffusion-weighted imaging, and clinical assessment. The participants were divided into lower (MS-L, n = 57) and higher disability (MS-H, n = 61) subgroups. Brain parenchymal fraction (BPF), lesion load (LL), and DTI-ALPS index were measured. Subgroup differences and correlations between DTI-ALPS index and other measures were explored. Logistic regression was performed to evaluate BPF, LL, and DTI-ALPS index in classifying lower and higher disability patients. Significant differences in DTI-ALPS index between MS-H and MS-L (d = -0.71, false discovery rate-corrected p-value (p-FDR) = 0.001) were found. The DTI-ALPS index correlated significantly with disease duration (rp = -0.29, p-FDR = 0.002) and EDSS (rsp = -0.35, p-FDR = 0.0002). It also showed significant correlations with BPF and LL. DTI-ALPS index and LL were significant predictors of disability subgroup (DTI-ALPS: odds ratio (OR) = 1.77, p = 0.04, LL: OR = 0.94, p = 0.02). Our findings highlight DTI-ALPS index as an imaging biomarker in MS, suggesting the involvement of glymphatic impairment in MS pathology, although further research is needed to elucidate its role in contributing to MS-related disability.

The effect of alemtuzumab on neurodegeneration in relapsing-remitting multiple sclerosis: A five-year prospective mono-center study

BackgroundRelapsing-remitting multiple sclerosis (RRMS) is an inflammatory and neurodegenerative disease. After two or more short courses of alemtuzumab (ALZ), an immune reconstitution is achieved, which long-term results in reduced disease activity. We aimed to investigate the effect of ALZ on measures of neurodegeneration (i.e., brain atrophy, and retinal layer thinning). MethodsWe designed an observational prospective mono-center study in RRMS patients initiating ALZ treatment. Patients were assessed at baseline (month 0) and thereafter annually for five years with clinical measures, synthetic magnetic resonance imaging (SyMRI) and optical coherence tomography (OCT), with a re-baseline SyMRI scan and an OCT exam 24 months after initiating ALZ. Persons with neurological symptoms but without evidence of neurological disease served as symptomatic controls (SCs, n = 27). ResultsForty-nine RRMS patients were included. Baseline median expanded disability status scale [2.0 (IQR 1.5)] was unchanged during follow-up, 71 % were progression-free, 33 % achieved no evidence of disease activity-3 (NEDA-3). Between baseline and month 60, SyMRI showed a reduction of brain parenchymal fraction (BPF) and grey matter (GM) volume in patients. The BPF reduction was greater in RRMS patients than in SCs (p < 0.05), and more pronounced in patients with high pre-baseline disease activity than in those without (p < 0.01). OCT showed significant thinning of macular ganglion cell and inner plexiform layers (mGCIPL) and in peripapillary retinal nerve fiber layer (pRNFL) in patients. In contrast, absolute values of white matter (WM) volume and myelin content (MyC) quantified by SyMRI, were stable or increased after re-baseline (month 24) and up to month 60, and this increase appeared limited to patients without high pre-baseline disease activity and to patients with NEDA-3 or disability worsening during follow-up. A strong positive correlation between WM volume and GM volume at baseline was lost after ALZ intervention for their delta values, i.e., change from re-baseline (month 24) to month 60. While the positive baseline correlation between WM volume and MyC increased for their delta values, the positive baseline correlation between GM volume and MyC changed to negative for their delta values. ConclusionWe showed that neurodegeneration continued in RRMS patients under ALZ treatment, but it appeared to be limited to BPF and GM, and more pronounced in patients with disease activity. Our data suggest that patients who respond to ALZ treatment show signs of remyelination. OCT and SyMRI have potential to quantify measures of neurodegeneration that is affected by treatment intervention in RRMS.

A 2-stage model of heterogenous treatment effects for brain atrophy in multiple sclerosis utilizing the MS PATHS research network

BackgroundTwo-stage models of heterogenous treatment effects (HTE) may advance personalized medicine in multiple sclerosis (MS). Brain atrophy is a relatively objective outcome measure that has strong relationships to MS prognosis and treatment effects and is enabled by standardized MRI. ObjectivesTo predict brain atrophy outcomes for patients initiating disease-modifying therapies (DMT) with different efficacies, considering the patients’ baseline brain atrophy risk measured via brain parenchymal fraction (BPF). MethodsAnalyses included patients enrolled in the Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) network who started DMT and had complete baseline data and ≥ 6-month brain MRI follow-up. All brain MRIs were acquired using standardized acquisition sequences on Siemens 3T scanners. BPF change risk was derived by linear mixed effects models using baseline covariates. Model performance was assessed by predicted versus actual BPF change R2. Propensity score (PS) weighting was used to balance covariates between groups defined by DMT efficacy (high: natalizumab, alemtuzumab, ocrelizumab, and rituximab; moderate: dimethyl fumarate, fingolimod, and cladribine; low: teriflunomide, interferons, and glatiramer acetate). HTE models predicting 1 year change in BPF were built using a weighted linear mixed effects model with low-efficacy DMT as the reference. ResultsAnalyses included 581 high-, 183 moderate-, and 106 low-efficacy DMT-treated patients. The mean and median number of brain MRI observations per treatment period were 2.9 and 3.0, respectively. Risk model performance R2=0.55. After PS weighting, covariate standardized mean differences were <10 %, indicating excellent balance across measured variables. Changes in BPF between baseline and follow-up were found to be statistically significant (p < 0.001), suggesting a pathological change. Patients with low brain atrophy risk had a similar outcome regardless of DMT selection. In patients with high brain atrophy risk, high- and moderate-efficacy DMTs performed similarly, while a 2-fold worse BPF change was predicted for patients selecting low-efficacy DMTs (p < 0.001). Similar results were observed in a sensitivity analysis adjusting for pseudoatrophy effects in a sub-population of patients treated with natalizumab. ConclusionsThe relative benefit of selecting higher efficacy treatments may vary depending on patients’ baseline brain atrophy risk. Poor outcomes are predicted in individuals with high baseline risk who are treated with low-efficacy DMTs.

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 (β = -0.051, SE = 0.004, p < .001, per 10 years, and β = -0.036, SE = 0.008, p < .001, respectively). White matter hyperintensity (β = -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 (β = 0.067, SE = 0.007, p < .001 and β = 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.

The pattern of cortical thickness associated with executive dysfunction in MCI and SCC: The MEMENTO cohort

BackgroundThe association between the pattern of cortical thickness (CT) and executive dysfunction (ED) in mild cognitive impairment (MCI) and subjective cognitive complaints (SCC) is still poorly understood. We aimed to investigate the association between CT and ED in a large French cohort (MEMENTO) of 2323 participants with MCI or SCC. MethodsAll participants with available CT and executive function data (verbal fluency and Trail Making Test [TMT]) were selected (n=1924). Linear regressions were performed to determine relationships between executive performance and the brain parenchymal fraction (BPF) and CT using FreeSurfer. ResultsThe global executive function score was related to the BPF (sß: 0.091, P<0.001) and CT in the right supramarginal (sß: 0.060, P=0.041) and right isthmus cingulate (sß: 0.062, P=0.011) regions. Literal verbal fluency was related to the BPF (sß: 0.125, P<0.001) and CT in the left parsorbitalis region (sß: 0.045, P=0.045). Semantic verbal fluency was related to the BPF (sß: 0.101, P<0.001) and CT in the right supramarginal region (sß: 0.061, P=0.042). The time difference between the TMT parts B and A was related to the BPF (sß: 0.048, P=0.045) and CT in the right precuneus (sß: 0.073, P=0.019) and right isthmus cingulate region (sß: 0.054, P=0.032). ConclusionsIn a large clinically based cohort of participants presenting with either MCI or SCC (a potential early stage of Alzheimer's disease [AD]), ED was related to the BPF and CT in the left pars orbitalis, right precuneus, right supramarginal, and right isthmus cingulate regions. This pattern of lesions adds knowledge to the conventional anatomy of ED and could contribute to the early diagnosis of AD.

Association of NOTCH3 Variant Risk Category With 2-Year Clinical and Radiologic Small Vessel Disease Progression in Patients With CADASIL.

Pathogenic variants in NOTCH3 are the main cause of hereditary cerebral small vessel disease (SVD). SVD-associated NOTCH3 variants have recently been categorized into high risk (HR), moderate risk (MR), or low risk (LR) for developing early-onset severe SVD. The most severe NOTCH3-associated SVD phenotype is also known as cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). We aimed to investigate whether NOTCH3 variant risk category is associated with 2-year progression rate of SVD clinical and neuroimaging outcomes in CADASIL. A single-center prospective 2-year follow-up study was performed of patients with CADASIL. Clinical outcomes were incident stroke, disability (modified Rankin Scale), and executive function (Trail Making Test B given A t-scores). Neuroimaging outcomes were mean skeletonized mean diffusivity (MSMD), normalized white matter hyperintensity volume (nWMHv), normalized lacune volume (nLV), and brain parenchymal fraction (BPF). Cox regression and mixed-effect models, adjusted for age, sex, and cardiovascular risk factors, were used to study 2-year changes in outcomes and differences in disease progression between patients with HR-NOTCH3 and MR-NOTCH3 variants. One hundred sixty-two patients with HR (n = 90), MR (n = 67), and LR (n = 5) NOTCH3 variants were included. For the entire cohort, there was 2-year mean progression for MSMD (β = 0.20, 95% CI 0.17-0.23, p = 7.0 × 10-24), nLV (β = 0.13, 95% CI 0.080-0.19, p = 2.1 × 10-6), nWMHv (β = 0.092, 95% CI 0.075-0.11, p = 8.8 × 10-20), and BPF (β = -0.22, 95% CI -0.26 to -0.19, p = 3.2 × 10-22), as well as an increase in disability (p = 0.002) and decline of executive function (β = -0.15, 95% CI -0.30 to -3.4 × 10-5, p = 0.05). The HR-NOTCH3 group had a higher probability of 2-year incident stroke (hazard ratio 4.3, 95% CI 1.4-13.5, p = 0.011), and a higher increase in MSMD (β = 0.074, 95% CI 0.013-0.14, p = 0.017) and nLV (β = 0.14, 95% CI 0.034-0.24, p = 0.0089) than the MR-NOTCH3 group. Subgroup analyses showed significant 2-year progression of MSMD in young (n = 17, β = 0.014, 95% CI 0.0093-0.019, p = 1.4 × 10-5) and premanifest (n = 24, β = 0.012, 95% CI 0.0082-0.016, p = 1.1 × 10-6) individuals. In a trial-sensitive time span of 2 years, we found that patients with HR-NOTCH3 variants have a significantly faster progression of major clinical and neuroimaging outcomes, compared with patients with MR-NOTCH3 variants. This has important implications for clinical trial design and disease prediction and monitoring in the clinic. Moreover, we show that MSMD is a promising outcome measure for trials enrolling premanifest individuals.

Orthostatic hypotension, cognition and structural brain imaging in hemodynamically impaired patients

BackgroundOrthostatic hypotension (OH) is associated with an increased risk of dementia, potentially attributable to cerebral hypoperfusion. We investigated which patterns and characteristics of OH are related to cognition or to potentially underlying structural brain injury in hemodynamically impaired patients and healthy reference participants. MethodsParticipants with carotid occlusive disease or heart failure, and reference participants from the Heart-Brain Connection Study underwent OH measurements, neuropsychological assessment and brain MRI. We analyzed the association between OH, global cognitive functioning, white matter hyperintensity (WMH) volume and brain parenchymal fraction with linear regression. We stratified by participant group, severity and duration of OH, chronotropic incompetence and presence of orthostatic symptoms. ResultsOf 337 participants (mean age 67.3 ± 8.8 years, 118 (35.0%) women), 113 (33.5%) had OH. Overall, presence of OH was not associated with cognitive functioning (β: −0.12 [−0.24–0.00]), but we did observe worse cognitive functioning in those with severe OH (≥ 30/15 mmHg; β: −0.18 [−0.34 to −0.02]) and clinically manifest OH (β: −0.30 [−0.52 to −0.08]). These associations did not differ significantly by OH duration or chronotropic incompetence, and were similar between patient groups and reference participants. Similarly, both severe OH and clinically manifest OH were associated with a lower brain parenchymal fraction, and severe OH also with a somewhat higher WMH volume. ConclusionsSevere OH and clinically manifest OH are associated with worse cognitive functioning. This supports the notion that specific patterns and characteristics of OH determine its impact on brain health.

Structural MRI measures are associated with fatigue severity and persistence in a large, real-world cohort of people with multiple sclerosis

Background: Results of research on radiological hallmarks of multiple sclerosis (MS) fatigue have been conflicting. Objective: To investigate the associations of lesion and brain compartment volumes with fatigue severity and persistence in people with multiple sclerosis (PwMS). Methods: The Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) network collects standardized data during routine care of PwMS from 10 healthcare institutions. Magnetic resonance imaging (MRI) predictors included baseline brain parenchymal (BPF) and gray matter fractions (GMF) and T2 lesion volume (T2LV). The Quality of Life in Neurological Disorders (Neuro-QOL) fatigue subscore was analyzed linearly and categorically using T-score cutpoints, with a period of elevated symptoms defined as T-score ⩾ mean + 0.5 SD over follow-up. Results: At baseline, of 4012 participants (average age: 45.6 ± 11.8 years; 73% female; 31% progressive MS), 2058 (51%) had no fatigue, 629 (16%) had mild fatigue, and 1325 (33%) had moderate-to-severe fatigue. One SD greater baseline BPF and GMF were associated with 0.83 (p < 0.001) and 0.38 (p = 0.02) lower values in the baseline Neuro-QOL fatigue T-score. A 1 SD lower log of total T2LV was associated with a 0.49 (p < 0.001) lower baseline fatigue T-score. Higher BPF and lower T2LV at baseline were associated with lower odds of subsequent periods of elevated fatigue. Conclusion: Baseline lesion burden and lower generalized whole-brain volumes were associated with MS fatigue in cross-sectional and longitudinal analyses in a large, real-world cohort of PwMS.

Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

Assessment of brain structure and volume reveals neurodevelopmental abnormalities in preterm infants with low-grade intraventricular hemorrhage

There is increasing evidence of abnormal neurodevelopmental outcomes in preterm infants with low-grade intraventricular hemorrhage (IVH). The purpose of the study was to explore whether brain microstructure and volume are associated with neuro-behavioral outcomes at 40 weeks corrected gestational age in preterm infants with low-grade IVH. MR imaging at term-equivalent age (TEA) was performed in 25 preterm infants with mild IVH (Papile grading I/II) and 40 control subjects without IVH. These subjects all had neonatal behavioral neurological assessment (NBNA) at 40 weeks’ corrected age. Microstructure and volume evaluation of the brain were performed by using diffusion kurtosis imaging (DKI) and Synthetic MRI. Correlations among microstructure parameters, volume, and developmental outcomes were explored by using Spearman's correlation. In preterm infants with low-grade IVH, the volume of brain parenchymal fraction (BPF) was reduced. In addition, mean kurtosis (MK), fractional anisotropy (FA), radial kurtosis (RK), axial kurtosis (AK) in several major brain regions were reduced, while mean diffusivity (MD) was increased (P < 0.05). BPF, RK in the cerebellum, MK in the genu of the corpus callosum, and MK in the thalamus of preterm infants with low-grade IVH were associated with lower NBNA scores (r = 0.831, 0.836, 0.728, 0.772, P < 0.05). DKI and Synthetic MRI can quantitatively evaluate the microstructure alterations and brain volumes in preterm infants with low-grade IVH, which provides clinicians with a more comprehensive and accurate neurobehavioral assessment of preterm infants with low-grade IVH.

Enlarged perivascular spaces are associated with white matter injury, cognition and inflammation in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy.

Enlarged perivascular spaces have been previously reported in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, but their significance and pathophysiology remains unclear. We investigated associations of white matter enlarged perivascular spaces with classical imaging measures, cognitive measures and plasma proteins to better understand what enlarged perivascular spaces represent in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and whether radiographic measures of enlarged perivascular spaces would be of value in future therapeutic discovery studies for cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Twenty-four individuals with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and 24 age- and sex-matched controls were included. Disease status was determined based on the presence of NOTCH3 mutation. Brain imaging measures of white matter hyperintensity, brain parenchymal fraction, white matter enlarged perivascular space volumes, clinical and cognitive measures as well as plasma proteomics were used in models. White matter enlarged perivascular space volumes were calculated via a novel, semiautomated pipeline, and levels of 7363 proteins were quantified in plasma using the SomaScan assay. The relationship of enlarged perivascular spaces with global burden of white matter hyperintensity, brain atrophy, functional status, neurocognitive measures and plasma proteins was modelled with linear regression models. Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and control groups did not exhibit differences in mean enlarged perivascular space volumes. However, increased enlarged perivascular space volumes in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy were associated with increased white matter hyperintensity volume (β = 0.57, P = 0.05), Clinical Dementia Rating Sum-of-Boxes score (β = 0.49, P = 0.04) and marginally with decreased brain parenchymal fraction (β = -0.03, P = 0.10). In interaction term models, the interaction term between cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy disease status and enlarged perivascular space volume was associated with increased white matter hyperintensity volume (β = 0.57, P = 0.02), Clinical Dementia Rating Sum-of-Boxes score (β = 0.52, P = 0.02), Mini-Mental State Examination score (β = -1.49, P = 0.03) and marginally with decreased brain parenchymal fraction (β = -0.03, P = 0.07). Proteins positively associated with enlarged perivascular space volumes were found to be related to leukocyte migration and inflammation, while negatively associated proteins were related to lipid metabolism. Two central hub proteins were identified in protein networks associated with enlarged perivascular space volumes: CXC motif chemokine ligand 8/interleukin-8 and C-C motif chemokine ligand 2/monocyte chemoattractant protein 1. The levels of CXC motif chemokine ligand 8/interleukin-8 were also associated with increased white matter hyperintensity volume (β = 42.86, P = 0.03), and levels of C-C motif chemokine ligand 2/monocyte chemoattractant protein 1 were further associated with decreased brain parenchymal fraction (β = -0.0007, P < 0.01) and Mini-Mental State Examination score (β = -0.02, P < 0.01) and increased Trail Making Test B completion time (β = 0.76, P < 0.01). No proteins were associated with all three studied imaging measures of pathology (brain parenchymal fraction, enlarged perivascular spaces, white matter hyperintensity). Based on associations uncovered between enlarged perivascular space volumes and cognitive functions, imaging and plasma proteins, we conclude that white matter enlarged perivascular space volumes may capture pathologies contributing to chronic brain dysfunction and degeneration in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy.

Age-stratified Assessment of Brain Volumetric Segmentation on the Indian Population Using Quantitative Magnetic Resonance Imaging.

Automated methods for quantifying brain tissue volumes have gained clinical interest for their objective assessment of neurological diseases. This study aimed to establish reference curves for brain volumes and fractions in the Indian population using Synthetic MRI (SyMRI), aquantitative imaging technique providing multiple contrast-weighted images through fast postprocessing. The study included acohort of 314 healthy individuals aged 15-65years from multiple hospitals/centers across India. The SyMRI-quantified brain volumes and fractions, including brain parenchymal fraction (BPF), gray matter fraction (GMF), white matter fraction (WMF), and myelin. Normative age-stratified quantification curves were created based on the obtained data. The results showed significant differences in brain volumes between the sexes, but not after normalization by intracranial volume. The findings provide normative data for the Indian population and can be used for comparative analysis of brain structure values. Furthermore, our data indicate that the use of fractions rather than absolute volumes in normative curves, such as BPF, GMF, and WMF, can mitigate sex and population differences as they account for individual differences in head size or brain volume.

Discontinuity of deep medullary veins in SWI is associated with deep white matter hyperintensity volume and cognitive impairment in cerebral small vessel disease

BackgroundDiscontinuation of the deep medullary veins (DMVs) may be an early imaging marker for identifying cognitive impairment caused by cerebral small vessel disease (CSVD). However, this method lacks mechanistic exploration. We aimed to investigate whether the DMV score is related to CSVD imaging markers and cognitive impairment in patients with CSVD. MethodsThis retrospective study included patients with CSVD who completed DMV score and cognition (e.g., MMSE, MoCA) assessments, and underwent MRI scanning (T2-FLAIR for white matter hyperintensities (WMH) volume, T1-weighted MRI for brain parenchymal fractions (BPF) analysis, and SWI for assessment of DMV score). The CSVD imaging markers were quantitatively assessed using the AccuBrain® system. We assessed the diagnostic value of neuroimaging biomarkers for detecting CSVD-related cognitive impairment. In addition, we explored the relationship between the DMV score, CSVD imaging markers, and cognition using mediation analysis. ResultsNinety-four patients with CSVD were divided into a cognitive impairment group (n = 39) and a non-cognitive impairment group (n = 55). Higher DMV scores, larger WMH volumes, and smaller BPF were observed in the cognitive impairment group than those in the non-cognitive impairment group. Receiver operating characteristics (ROC) analysis revealed that the discovery value of the integration of patient age, BPF, whole WMH volume, and DMV score for cognitive impairment was 0.742, with a sensitivity and specificity of 79.5 % and 61.5 %, respectively. Mediation analysis showed mediation by WMH and BPF in the relationship between DMV score and cognitive impairment (all P < 0.05). LimitationsThis study did not evaluate the DMV score in subregions according to DMV anatomy. ConclusionsThe DMV score is significantly associated with cognitive impairment in patients with CSVD, and this association is mediated through WMH and BPF.

Understanding the relationship between type-2 diabetes, MRI markers of neurodegeneration and small vessel disease, and dementia risk: a mediation analysis.

To explore to which extent neurodegeneration and cerebral small vessel disease (SVD) could mediate the association between type-2 diabetes and higher dementia risk.The analytical sample consisted in 2228 participants, out of the Three-City study, aged 65 and older, free of dementia at baseline who underwent brain MRI. Diabetes was defined by medication intake or fasting or non-fasting elevated glucose levels. Dementia status was assessed every 2 to 3 years, during up to 12 years of follow-up. Brain parenchymal fraction (BPF) and white matter hyperintensities volume (WMHV) were selected as markers of neurodegeneration and cerebral SVD respectively. We performed a mediation analysis of the effect of baseline BPF and WMHV (mediators) on the association between diabetes and dementia risk using linear and Cox models adjusted for age, sex, education level, hypertension, hypercholesterolemia, BMI, smoking and alcohol drinking status, APOE-ε4 status, and study site.At baseline, 8.8% of the participants had diabetes. Diabetes (yes vs. no) was associated with higher WMHV (βdiab = 0.193, 95% CI 0.040; 0.346) and lower BPF (βdiab=-0.342, 95% CI -0.474; -0.210), as well as with an increased risk of dementia over 12 years of follow-up (HRdiab=1.65, 95% CI 1.04; 2.60). The association between diabetes status and dementia risk was statistically mediated by higher WMHV (HRdiab=1.05, 95% CI 1.01; 1.11, mediated part = 10.8%) and lower BPF (HRdiab=1.12, 95% CI 1.05; 1.20, mediated part = 22.9%).This study showed that both neurodegeneration and cerebral SVD statistically explained almost 30% of the association between diabetes and dementia.

Subcortical volumes and cognition in CADASIL – A pilot study

BackgroundCerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) represents the most common heritable cause of vascular dementia. Subcortical volumes might be proxies of brain reserve capacity and reflective of cognitive function. We explored the impact of subcortical volumes on cognition in CADASIL patients. MethodsWe included 90 patients with pathogenic NOTCH3 variants from the Taiwan Associated Genetic and Nongenetic Small Vessel Disease cohort. They underwent MRI sessions at baseline. The volumes of the putamen, caudate, pallidum, thalamus, cerebellum, cortical gray matter and brain parenchymal fraction (BPF) were calculated by using FreeSurfer. We tested the association of the subcortical volumes, cortical gray matter volume and BPF with scores of the Mini-mental state examination (MMSE), cognitive domains, and the diagnosis of vascular cognitive impairment (VCI) which was defined as MMSE score <24. ResultsThe thalamus and putamen were consistently associated with the MMSE (thalamus adjusted beta per SD decrement -1.41 [95 % CI -2.68–(-0.14)], p = 0.03; R²=0.25; putamen -1.93 [95 % CI -2.99–(-0.86)], p < 0.001; R²=0.36) and VCI (thalamus OR per SD-decrement 3.66 [95 % CI 1.38–9.72], p = 0.009), putamen (OR 3.06 [95 % CI 1.21–7.73], p = 0.02). A larger thalamus volume was also associated with better executive function and visuospatial perception. The cortical gray matter volume and the BPF showed associations with various cognitive outcomes in all analyses. ConclusionAlthough cortical gray matter volume and the BPF still appear to be robust markers of cognitive performance in CADASIL, the volumes of the thalamus and the putamen might also be promising regions of interest for future research.

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

The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS. A multi-center dataset consisting of 5,294 T1-weighted magnetic resonance images of the brain from healthy individuals aged between 19 and 89 years was used to train a convolutional neural network (CNN) for biological brain age prediction. The trained model was then used to calculate the brain age gap in 195 patients with relapsing remitting MS (20-60 years). Additionally, saliency maps were generated for healthy subjects and patients with MS to identify brain regions that were deemed important for the brain age prediction task by the CNN. Overall, the application of the CNN revealed accelerated brain aging with a larger brain age gap for patients with MS with a mean of 6.98 ± 7.18 years in comparison to healthy test set subjects (0.23 ± 4.64 years). The brain age gap for MS patients was weakly to moderately correlated with age at disease onset (ρ = -0.299, p < 0.0001), EDSS score (ρ = 0.206, p = 0.004), disease duration (ρ = 0.162, p = 0.024), lesion volume (ρ = 0.630, p < 0.0001), and brain parenchymal fraction (ρ = -0.718, p < 0.0001). The saliency maps indicated significant differences in the lateral ventricle (p < 0.0001), insula (p < 0.0001), third ventricle (p < 0.0001), and fourth ventricle (p = 0.0001) in the right hemisphere. In the left hemisphere, the inferior lateral ventricle (p < 0.0001) and the third ventricle (p < 0.0001) showed significant differences. Furthermore, the Dice similarity coefficient showed the highest overlap of salient regions between the MS patients and the oldest healthy subjects, indicating that neurodegeneration is accelerated in this patient cohort. In conclusion, the results of this study show that the brain age gap is a valuable surrogate biomarker to measure disease progression in patients with multiple sclerosis.

Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing

IntroductionMultiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI). MethodsWe trained the StyleGAN model unsupervised using T1-weighted GRE MR images and diffusion-based ADC maps of MS patients and healthy controls. We then used the trained model to resample MR images from real input data and modified them by manipulations in the latent space to simulate MS progression. We analyzed the resulting simulation-related patterns mimicking disease progression by comparing the intensity profiles of the original and manipulated images and determined the brain parenchymal fraction (BPF). ResultsOur results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T1-weighted and ADC maps and increasing lesion extent on ADC maps. ConclusionOverall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.

Impaired cerebral interstitial fluid dynamics in cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy, caused by cysteine-altering variants in NOTCH3, is the most prevalent inherited cerebral small vessel disease. Impaired cerebral interstitial fluid dynamics has been proposed as one of the potential culprits of neurodegeneration and may play a critical role in the initiation and progression of cerebral small vessel disease. In the present study, we aimed to explore the cerebral interstitial fluid dynamics in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy and to evaluate its association with clinical features, imaging biomarkers and disease severity of cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy. Eighty-one participants carrying a cysteine-altering variant in NOTCH3, including 44 symptomatic cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy patients and 37 preclinical carriers, and 21 age- and sex-matched healthy control individuals were recruited. All participants underwent brain MRI studies and neuropsychological evaluations. Cerebral interstitial fluid dynamics was investigated by using the non-invasive diffusion tensor image analysis along the perivascular space method. We found that cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy patients exhibited significantly lower values of diffusion tensor image analysis along the perivascular space index comparing to preclinical carriers and healthy controls. For the 81 subjects carrying NOTCH3 variants, older age and presence of hypertension were independently associated with decreased diffusion tensor image analysis along the perivascular space index. The degree of cerebral interstitial fluid dynamics was strongly related to the severity of cerebral small vessel disease imaging markers, with a positive correlation between diffusion tensor image analysis along the perivascular space index and brain parenchymal fraction and negative correlations between diffusion tensor image analysis along the perivascular space index and total volume of white matter hyperintensity, peak width of skeletonized mean diffusivity, lacune numbers and cerebral microbleed counts. In addition, diffusion tensor image analysis along the perivascular space index was a significant risk factor associated with the development of clinical symptoms of stroke or cognitive dysfunction in individuals carrying NOTCH3 variants. In cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy patients, diffusion tensor image analysis along the perivascular space index was significantly associated with Mini-Mental State Examination scores. Mediation analysis showed that compromised cerebral interstitial fluid dynamics was not only directly associated with cognitive dysfunction but also had an indirect effect on cognition by influencing brain atrophy, white matter disruption, lacunar lesions and cerebral microbleeds. In conclusion, cerebral interstitial fluid dynamics is impaired in cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy and its disruption may play an important role in the pathogenesis of cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy. Diffusion tensor image analysis along the perivascular space index may serve as a biomarker of disease severity for cerebral autosomal dominant arteriopathy with subcortical infarcts and leucoencephalopathy.