Kilian Hett

The parasagittal dural (PSD) space has been shown to harbor meningeal lymphatic vessels and may play a fundamental role in neurofluid egress and neuroimmune surveillance. Recent work has reported PSD hyperplasia associated with disease severity in Huntington disease (HD); however, beyond volumetric measures, imaging tools for probing PSD space composition non-invasively in vivo are still lacking. Here, we develop and apply a quantitative MRI relaxometry method to test the hypothesis that PSD T2 is lengthened in HD, consistent with neurofluid retention.A 1-mm isotropic, multi-echo turbo-spin-echo MRI sequence was implemented at 3-Tesla. The T2 relaxation time was fit voxel-wise, and a variational Bayesian-Gaussian-mixture clustering model was applied to assess relaxation-defined PSD compartments. In healthy adults, we assessed measurement reproducibility, the relationship between PSD T2 and venous oxygenation, and circadian variation. Additionally, participants were enrolled to evaluate the dependence of each compartment on group, age, and sex, using linear regression.Reproducibility assessments yielded excellent agreement (ICC=0.91; median PSD T2=131.2±27.9 ms). Unconclusive association with venous oxygenation or diurnal circadian oscillations were observed. Analysis indicated elevated relaxation time in both pre-manifest HD (+21.2 ms, p=0.01) and motor-manifest HD (+24.4 ms, p<0.01) compared to adult healthy controls. Compartment-level analyses localized this effect primarily to the higher-fluid PSD stromal compartment, where the largest effect size was noted (f²=0.24).Findings extend earlier volumetric work to provide evidence that PSD space T2 can be assessed reproducibly, and furthermore, that hyperplasia with neurofluid retention is present in the setting of HD.

Purpose: The choroid plexus (ChP) is the primary source of cerebrospinal fluid and an emerging marker of cerebral health, with enlargement and hypoperfusion reported in aging and neurodegeneration. However, frequent ChP calcifications can confound volumetric and perfusion measures. Although computed tomography (CT) is the gold standard for detecting calcification, it is rarely available in research MRI. Quantitative susceptibility mapping (QSM) offers an alternative sensitive to diamagnetic mineralization but lacks validated susceptibility thresholds. Method: Participants underwent CT and MRI within four weeks, including 3D T ₁ -weighted and a multi-echo gradient echo QSM MRI. ChP calcifications were identified on CT using standard diagnostic criteria. Using the Bayes decision boundary framework, we identified optimal susceptibility thresholds for detecting diamagnetic signals consistent with calcification and compared these thresholds with multiple density levels measured on gold standard CT images. Results: Across all participants (n=20; age=62.2±12.0 yrs), the optimal susceptibility threshold separating background ChP signal from calcifications was -0.10 ppm at 60 HU (low-density) and -0.15 ppm at 100 HU (high-density). Susceptibility values within calcified tissue exhibited a linear relationship with CT-derived tissue density. A significant positive association was observed between ChP volume and calcification volume among participants with detectable calcification ( β =2.26, p=0.047). Conclusion: This work should provide a practical framework for quantifying ChP calcifications routinely from MRI. The observed relationship between ChP volume and calcification volume highlights the importance of accounting for calcified tissue, particularly when calcification burden is substantial, when investigating ChP abnormalities in aging and neurodegenerative disease.

Nouveaux biomarqueurs multi-échelles et multi-modaux pour le diagnostic précoce de la maladie d’Alzheimer: La maladie d’Alzheimer est la première cause de démence chez les personnes âgées. Cette maladie est caractérisée par un déclin irréversible des fonctions cognitives. Les patients atteints par la maladie d’Alzheimer ont de sévères pertes de mémoire et ont de grandes difficultés à apprendre de nouvelles informations ce qui pose de gros problèmes dans leur vie quotidienne. À ce jour, cette maladie est diagnostiquée après que d’importantes altérations des structures du cerveaux apparaissent. De plus, aucune thérapie existe permettant de faire reculer ou de stopper la maladie. Le développement de nouvelles méthodes permettant la détection précoce de cette maladie est ainsi nécessaire. En effet, une détection précoce permettrait une meilleure prise en charge des patients atteints de cette maladie ainsi qu’une accélération de la recherche thérapeutique. Nos travaux de recherche portent sur l’utilisation de l’imagerie médicale, avec notamment l’imagerie par résonance magnétique (IRM) qui a démontrée ces dernières années son potentiel pour améliorer la détection et la prédiction de la maladie d’Alzheimer. Afin d’exploiter pleinement ce type d’imagerie, de nombreuses méthodes ont été proposées récemment. Au cours de nos recherches, nous nous sommes intéressés à un type de méthode en particulier qui est basé sur la correspondance de patchs dans de grandes bibliothèques d’images. Nous avons étudié ces méthodes à diverses échelles anatomiques c’est à dire, cerveaux entier, hippocampe, sous-champs de l’hippocampe) avec diverses modalités d’IRM (par exemple, IRM anatomique et imagerie de diffusion). Nous avons amélioré les performances de détection dans les stades les plus précoces avec l’imagerie par diffusion. Nous avons aussi proposé un nouveau schéma de fusion pour combiner IRM anatomique et imagerie de diffusion. De plus, nous avons montré que la correspondance de patchs était améliorée par l’utilisation de filtres dérivatifs. Enfin, nous avons proposé une méthode par graphe permettant de combiner les informations de similarité inter-sujet avec les informations apportées par la variabilité intra-sujet. Les résultats des expériences menées dans cette thèse ont montrées une amélioration des performances de diagnostique et de prognostique de la maladie d’Alzheimer comparé aux méthodes de l’état de l’art.

Objective: Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge. We aimed to evaluate structural and physiological changes in two key neurofluid components, the choroid plexus (ChP), which produces cerebrospinal fluid (CSF), and the parasagittal dural (PSD) space, a major CSF outflow pathway, across the HD spectrum and in relation to CSF flow dynamics. Methods: PSD and ChP volumes were assessed using a validated deep learning pipeline on 3‐Tesla T ₂ ‐weighted and FLAIR MRI. CSF flow at the cerebral aqueduct was measured with phase contrast MRI, and ChP perfusion was quantified using pseudo‐continuous arterial spin labeling MRI. Linear regression models assessed the relationships between PSD and ChP volume, CSF flow kinetics, ChP hemodynamics, disease severity, disease exposure, and disease presentation, adjusting for age, sex, and intracranial volume. Results: 80 HD participants and 65 age‐matched healthy controls were included. HD showed significantly larger ChP and PSD volumes ( p < 0.01) and reduced ChP perfusion ( p < 0.01). Greater CAG repeat expansion correlated with larger PSD and ChP volume and lower ChP perfusion ( p < 0.01). These alterations were associated with worse motor impairment ( p < 0.01). Interpretation: HD is associated with structural and functional alterations in neurofluid pathways. These findings suggest relevance for disease mechanisms and for optimizing CSF‐based therapeutic delivery, highlighting the need for further mechanistic studies.

Behavioural variant frontotemporal dementia is characterized by progressive changes to personality and behaviour, yet early detection and disease staging remain challenging. Current clinical neuroimaging relies on visual assessment of atrophy patterns, which may overlook subtle structural changes. While volumetric analysis has improved the ability to detect neurodegeneration and track disease progression, it may lack the sensitivity to identify microstructural alterations that precede frank atrophy. Texture analysis, a quantitative approach that evaluates the spatial regularity of grey matter density, has demonstrated promise in detecting early microstructural changes in Alzheimer’s disease and distinguishing frontotemporal dementia subtypes. However, its potential as a biomarker for early behavioural variant frontotemporal dementia detection and disease staging remain unexplored. This study evaluates the potential of autocorrelation-based texture features as biomarkers for detecting early-stage behavioural variant frontotemporal dementia and tracking disease progression, comparing their sensitivity and specificity to regional brain volume. We analysed structural MRI scans from behavioural variant frontotemporal dementia patients with mild (n = 21, 61.5 ± 8.5 years, 4.7% female) and moderate dementia (n = 11, 64.4 ± 9.3, 18.1% female) alongside healthy controls (n = 33, 63.1 ± 7.9 years; 36.3% female). Texture and volumetric measures were extracted from frontotemporal regions implicated in behavioural variant frontotemporal dementia pathology. First, we compared these features between healthy controls and patients with mild dementia to identify regions relevant for early diagnosis. Second, we compared patients with mild versus moderate dementia to identify regions linked to disease stage. Analyses were performed both within a composite frontotemporal region of interest and within 160 frontotemporal subregions. We applied false discovery rate correction for multiple comparisons. Microstructural abnormalities captured by texture analysis and volumetric reductions were significantly lower in patients with mild dementia compared to healthy controls within the composite frontotemporal and many subregions (PFDR < 0.05). In moderate dementia, texture features detected alterations in composite frontotemporal (PFDR < 0.05) and subregions, including the anterior cingulate, insula and orbitofrontal cortices (PFDR < 0.05) even when volumetric differences were absent. This study demonstrates that texture-based MRI metrics provide a sensitive measure of microstructural alterations in behavioural variant frontotemporal dementia, detecting some disease-related changes even in regions without detectable volumetric reductions. While volumetric measures are effective for identifying individuals with early-stage disease, texture analysis may offer increased sensitivity for tracking disease progression. Longitudinal studies are needed to validate the predictive value of texture features for clinical decline in behavioural variant frontotemporal dementia.

Objective: Reliable biomarkers are essential for tracking disease progression and advancing treatments for multiple system atrophy (MSA). In this study, we propose the MSA Atrophy Index (MSA‐AI), a novel composite volumetric measure to distinguish MSA from related disorders and monitor disease progression.Methods: Seventeen participants with an initial diagnosis of probable MSA were enrolled in the longitudinal bioMUSE study and underwent 3T MRI, biofluid analysis, and clinical assessments at baseline, 6, and 12 months. Final diagnoses were determined after 12 months using clinical progression, imaging, and fluid biomarkers. Ten participants retained an MSA diagnosis, while five were reclassified as either Parkinson disease (PD, n = 4) or dementia with Lewy bodies (DLB, n = 1). Cross‐sectional comparisons included additional MSA cases (n = 26), healthy controls (n = 23), pure autonomic failure (n = 23), PD (n = 56), and DLB (n = 8). Lentiform nucleus, cerebellum, and brainstem volumes were extracted using deep learning‐based segmentation. Z‐scores were computed using a normative dataset (n = 469) and integrated into the MSA‐AI. Group differences were tested with linear regression; longitudinal changes and clinical correlations were assessed using mixed‐effects models and Spearman correlations.Results: MSA patients exhibited significantly lower MSA‐AI scores compared to all other diagnostic groups (p < 0.001). The MSA‐AI effectively distinguished MSA from related synucleinopathies, correlated with baseline clinical severity (ρ = −0.57, p < 0.001), and predicted disease progression (ρ = −0.55, p = 0.03). Longitudinal reductions in MSA‐AI were associated with worsening clinical scores over 12 months (ρ = −0.61, p = 0.01).Interpretation: The MSA‐AI is a promising imaging biomarker for diagnosis and monitoring disease progression in MSA. These findings require validation in larger, independent cohorts.

Frontotemporal lobar degeneration (FTLD) includes different clinical syndromes with distinct patterns of symptoms and neuroanatomical locations of neurodegeneration. However, FTLD is clinically heterogeneous (with overlapping symptoms across several domains) and neuroanatomically heterogeneous (with brain atrophy in different locations in different patients). Traditional methods struggle to fully account for this heterogeneity. In this study, we use a relatively new neuroimaging approach, atrophy network mapping, to localize clinical symptoms in patients with FTLD to specific brain networks transdiagnostically. Data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative and 4-Repeat Tauopathy Neuroimaging Initiative. Inclusion required T1-weighted MRI and a diagnosis of behavioral-variant frontotemporal dementia (bvFTD), semantic-variant primary progressive aphasia (svPPA), nonfluent primary progressive aphasia (nfvPPA), progressive supranuclear palsy Richardson syndrome (PSP-rs), corticobasal syndrome (CBS), or normal cognition. Measures of social cognition (Interpersonal Reactivity Index, Revised Self-Monitoring Scale), language (Boston Naming Test, Animal Fluency), and motor function (Unified Parkinson Disease Rating Scale Part III, PSP Rating Scale) were correlated with neuroimaging measures, including cortical thickness, volume, and atrophy network mapping, a newer method that localizes regions connected to brain atrophy using a functional connectome from cognitively normal persons (n = 1,000). Fifty-seven patients with bvFTD (age 61.2 ± 6.8 years, 35% female), 41 PSP-rs (age 69.7 ± 7.4 years, 54% female), 39 CBS (age 66.2 ± 6.2 years, 51% female), 37 svPPA (age 63.0 ± 6.0 years, 46% female), and 36 nfvPPA (age 68.3 ± 7.3 years, 53% female) and 135 healthy age-matched controls (age 63.3 ± 7.4 years, 58% female) were included. Compared with atrophy alone, atrophy network mapping showed more consistent neuroimaging results across patients with the same clinical syndrome (Dice index mean 0.68 vs 0.11, paired t = 263.1, df = 4,452, p < 0.001), more strongly explained social cognition (F(1, 84) = 10.2, p = 0.002) and motor symptoms (F(1, 185) = 91.3, p < 0.001) across different syndromes, and showed novel neuroanatomical associations, with the temporal parietal junction relating to social cognition; Wernicke area relating to language symptoms; and association sensorimotor cortex, thalamus, and cerebellum relating to motor symptoms. Atrophy network mapping can improve understanding of brain-behavior relationships in clinically and neuroanatomically heterogeneous disorders such as FTLD.

Intravenous arachnoid granulations (AGs) are protrusions of the arachnoid membrane into the venous lumen and function as contributors to the cerebrospinal fluid (CSF) flow circuit. Patients with Parkinson disease (PD) often present with accumulation of alpha synuclein. Previous works have provided evidence for neurofluid circulation dysfunction in neurodegenerative diseases associated with changes in CSF egress, which may have implications regarding AG morphology. The present study aims to investigate group differences in AG volumetrics between healthy and PD participants, as well as relationships between AG characteristics and clinical assessments. Generalized linear models revealed significant increases in AG volumetrics and number in PD compared to healthy controls. Partial Spearman-rank correlation analyses demonstrated significant relationships between AG metrics and motor and cognitive assessments. Finally, AG volumetrics were positively correlated with objective actigraphy measures of sleep dysfunction, but not self-report sleep symptoms.

Objective: Investigations of cerebrospinal fluid (CSF) flow aberrations in Huntington's disease (HD) are of growing interest, as impaired CSF flow may contribute to mutant Huntington retention and observed heterogeneous responsiveness to intrathecally administered therapies.Method: We assessed net cerebral aqueduct CSF flow and velocity in 29 HD participants (17 premanifest and 12 manifest) and 51 age‐ and sex matched non‐HD control participants using 3‐Tesla magnetic resonance imaging methods. Regression models were applied to test hypotheses regarding: (i) net CSF flow and cohort, (ii) net CSF flow and disease severity (CAP‐score), and (iii) CSF volume after correcting for age and sex.Results: Group‐wise analyses support a decrease in net CSF flow in HD (mean 0.14 ± 0.27 mL/min) relative to control (mean 0.32 ± 0.20 mL/min) participants (p = 0.02), with lowest flow in the manifest HD cohort (mean 0.04 ± 0.25 mL/min). This finding was explained by hyperdynamic CSF movement, manifesting as higher caudal systolic CSF flow velocity and higher diastolic cranial CSF flow velocity across the cardiac cycle, in HD (caudal flow: 0.17 ± 0.07 mL/s, cranial flow: 0.14 ± 0.08 mL/s) compared to control (caudal flow: 0.13 ± 0.06 mL/s, cranial flow: 0.11 ± 0.04 mL/s) participants. A positive correlation between cranial diastolic flow and disease severity was observed (p = 0.02).Interpretations: Findings support aqueductal CSF flow dynamics changing with disease severity in HD. These accelerated changes are consistent with changes observed over the typical adult lifespan, and may have relevance to mutant Huntington retention and intrathecally administered therapeutics responsiveness. ANN NEUROL 2023;94:885–894

One of the pathological hallmarks of Alzheimer’s and related diseases is the increased accumulation of protein amyloid-β in the brain parenchyma. As such, recent studies have focused on characterizing protein and related clearance pathways involving perivascular flow of neurofluids, but human studies of these pathways are limited owing to limited methods for evaluating neurofluid circulation non-invasively in vivo. Here, we utilize non-invasive MRI methods to explore surrogate measures of CSF production, bulk flow, and egress in the context of independent PET measures of amyloid-β accumulation in older adults. Participants (N = 23) were scanned at 3.0T with 3D T₂-weighted turbo spin echo, 2D perfusion-weighted pseudo-continuous arterial spin labeling, and phase contrast angiography to quantify parasagittal dural space volume, choroid plexus perfusion, and net CSF flow through the aqueduct of Sylvius respectively. All participants also underwent dynamic PET imaging with amyloid-β tracer ¹¹C-Pittsburgh Compound B to quantify global cerebral amyloid-β accumulation. Spearman’s correlation analyses revealed a significant relationship between global amyloid-β accumulation and parasagittal dural space volume (rho = 0.529, p = 0.010), specifically in the frontal (rho = 0.527, p = 0.010), and parietal (rho = 0.616, p = 0.002) subsegments. No relationships were observed between amyloid-β and choroid plexus perfusion nor net CSF flow. Findings suggest that parasagittal dural space hypertrophy, and its possible role in CSF-mediated clearance, may be closely related to global amyloid-β accumulation. These findings are discussed in the context of our growing understanding of the physiological mechanisms of amyloid-β aggregation and clearance via neurofluids.