Normative volumes and relaxation times at 3T during brain development.

While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care.

Neurometabolism and brain morphometry in an adolescent female with an extra-hepatic congenital portosystemic shunt.

Background: Chronic hepatic encephalopathy (CHE) has been reported both in patients with congenital porto-systemic shunts (CPSS) and chronic liver disease. CHE is difficult to recognize in children as there is no clear definition and its manifestations are highly variable. CHE is associated with variations in brain volumes and metabolites that have already been demonstrated using 1.5-3T MRI systems. However, the in-depth study of brain metabolism requires the high spectral resolution of high magnetic fields. Objectives and Methods: We analyzed the neurometabolic profile, brain volumes and T1 relaxation times of a child with a CPSS using high field proton magnetic resonance spectroscopy (1H MRS, 7T) combined with MRI and compared it to an age-matched control group. We also evaluated the impact of shunt closure on neurocognitive symptoms using adapted neuropsychological tests. Results: 7T MRS revealed a significant increase in glutamine compared to controls, a decrease in brain osmolytes, and a slight elevation in NAA concentrations. 7T MRI scans showed morphological abnormalities but no changes in the signal intensity of the globus pallidus. Neurocognitive testing revealed attention deficit disorder, language difficulties, and mild intellectual disability. Most of these areas improved after shunt closure. Conclusions: In this paediatric case of type B HE with normal fasting ammonia, neurometabolic profile was compatible with what has been previously shown in chronic liver disease, while also demonstrating an isolated glutamine peak. In addition, neurocognitive function partially improved after shunt closure, arguing strongly for shunt closure in both presymptomatic and symptomatic patients.

Evaluation of a nnU-Net type automated clinical volumetric tumor segmentation tool for diffuse low-grade glioma follow-up.

BACKGROUND AND PURPOSE: Diffuse low-grade gliomas (DLGG) are characterized by a slow and continuous growth and always evolve towards an aggressive grade. Accurate prediction of the malignant transformation is essential as it requires immediate therapeutic intervention. One of its most precise predictors is the velocity of diameter expansion (VDE). Currently, the VDE is estimated either by linear measurements or by manual delineation of the DLGG on T2 FLAIR acquisitions. However, because of the DLGG's infiltrative nature and its blurred contours, manual measures are challenging and variable, even for experts. Therefore we propose an automated segmentation algorithm using a 2D nnU-Net, to 1) gain time and 2) standardize VDE assessment. MATERIALS AND METHODS: The 2D nnU-Net was trained on 318 acquisitions (T2 FLAIR & 3DT1 longitudinal follow-up of 30 patients, including pre- & post-surgery acquisitions, different scanners, vendors, imaging parameters…). Automated vs. manual segmentation performance was evaluated on 167 acquisitions, and its clinical interest was validated by quantifying the amount of manual correction required after automated segmentation of 98 novel acquisitions. RESULTS: Automated segmentation showed a good performance with a mean Dice Similarity Coefficient (DSC) of 0.82±0.13 with manual segmentation and a substantial concordance between VDE calculations. Major manual corrections (i.e., DSC<0.7) were necessary only in 3/98 cases and 81% of the cases had a DSC>0.9. CONCLUSION: The proposed automated segmentation algorithm can successfully segment DLGG on highly variable MRI data. Although manual corrections are sometimes necessary, it provides a reliable, standardized and time-winning support for VDE extraction to asses DLGG growth.

Tract-wise microstructural analysis informs on current and future disability in early multiple sclerosis.

OBJECTIVES: Microstructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize microstructural brain tissue changes both in lesions and normal-appearing brain tissue. However, the impact of the location of microstructural alterations in terms of neuronal pathways has not been thoroughly explored so far. Here, we study the extent and the location of tissue changes probed using quantitative MRI along white matter (WM) tracts extracted from a connectivity atlas. METHODS: We quantified voxel-wise T1 tissue alterations compared to normative values in a cohort of 99 MS patients. For each WM tract, we extracted metrics reflecting tissue alterations both in lesions and normal-appearing WM and correlated these with cross-sectional disability and disability evolution after 2 years. RESULTS: In early MS patients, T1 alterations in normal-appearing WM correlated better with disability evolution compared to cross-sectional disability. Further, the presence of lesions in supratentorial tracts was more strongly associated with cross-sectional disability, while microstructural alterations in infratentorial pathways yielded higher correlations with disability evolution. In progressive patients, all major WM pathways contributed similarly to explaining disability, and correlations with disability evolution were generally poor. CONCLUSIONS: We showed that microstructural changes evaluated in specific WM pathways contribute to explaining future disability in early MS, hence highlighting the potential of tract-wise analyses in monitoring disease progression. Further, the proposed technique allows to estimate WM tract-specific microstructural characteristics in clinically compatible acquisition times, without the need for advanced diffusion imaging.

Interrelation between cardiac and brain small-vessel disease: a pilot quantitative PET and MRI study.

BACKGROUND: Small-vessel disease (SVD) plays a crucial role in cardiac and brain ischemia, but little is known about potential interrelation between both. We retrospectively evaluated 370 patients, aiming at assessing the interrelation between cardiac and brain SVD by using quantitative 82Rb cardiac PET/CT and brain MRI. RESULTS: In our population of 370 patients, 176 had normal myocardial perfusion, 38 had pure cardiac SVD and 156 had obstructive coronary artery disease. All underwent both a cardiac 82Rb PET/CT and a brain 1.5T or 3T MRI. Left-ventricle myocardial blood flow (LV-MBF) and flow reserve (LV-MFR) were recorded from 82Rb PET/CT, while Fazekas score, white matter lesion (WMab) volume, deep gray matter lesion (GMab) volume, and brain morphometry (for z-score calculation) using the MorphoBox research application were derived from MRI. Groups were compared with Kruskal-Wallis test, and the potential interrelation between heart and brain SVD markers was assessed using Pearson's correlation coefficient. Patients with cardiac SVD had lower stress LV-MBF and MFR (P < 0.001) than patients with normal myocardial perfusion; Fazekas scores and WMab volumes were similar in those two groups (P > 0.45). In patients with cardiac SVD only, higher rest LV-MBF was associated with a lower left-putamen (rho = - 0.62, P = 0.033), right-thalamus (rho = 0.64, P = 0.026), and right-pallidum (rho = 0.60, P = 0.039) z-scores and with a higher GMab volume. Lower stress LV-MBF was associated with lower left-caudate z-score (rho = 0.69, P = 0.014), while lower LV-MFR was associated with lower left (rho = 0.75, P = 0.005)- and right (rho = 0.59, P = 0.045)-putamen z-scores, as well as higher right-thalamus GMab volume (rho = - 0.72, P = 0.009). CONCLUSION: Significant interrelations between cardiac and cerebral SVD markers were found, especially regarding deep gray matter alterations, which supports the hypothesis of SVD as a systemic disease.

Diagnostic delay of multiple sclerosis: prevalence, determinants and consequences.

BACKGROUND: Early diagnosis and treatment of patients with multiple sclerosis (MS) are associated with better outcomes; however, diagnostic delays remain a major problem. OBJECTIVE: Describe the prevalence, determinants and consequences of delayed diagnoses. METHODS: This single-centre ambispective study analysed 146 adult relapsing-remitting MS patients (2016-2021) for frequency and determinants of diagnostic delays and their associations with clinical, cognitive, imaging and biochemical measures. RESULTS: Diagnostic delays were identified in 77 patients (52.7%), including 42 (28.7%) physician-dependent cases and 35 (24.0%) patient-dependent cases. Diagnosis was delayed in 22 (15.1%) patients because of misdiagnosis by a neurologist. A longer diagnostic delay was associated with trends towards greater Expanded Disability Status Scale (EDSS) scores (B = 0.03; p = 0.034) and greater z-score of the blood neurofilament light chain (B = 0.35; p = 0.031) at the time of diagnosis. Compared with patients diagnosed at their first clinical relapse, patients with a history of >1 relapse at diagnosis (n = 63; 43.2%) had a trend towards greater EDSS scores (B = 0.06; p = 0.006) and number of total (B = 0.13; p = 0.040) and periventricular (B = 0.06; p = 0.039) brain lesions. CONCLUSION: Diagnostic delays in MS are common, often determined by early misdiagnosis and associated with greater disease burden.

Corpus callosum in children with neurodevelopmental delay: MRI standard qualitative assessment versus automatic quantitative analysis.

BACKGROUND: The corpus callosum (CC) is a key brain structure. In children with neurodevelopmental delay, we compared standard qualitative radiological assessments with an automatic quantitative tool. METHODS: We prospectively enrolled 73 children (46 males, 63.0%) with neurodevelopmental delay at single university hospital between September 2020 and September 2022. All of them underwent 1.5-T brain magnetic resonance imaging (MRI) including a magnetization-prepared 2 rapid acquisition gradient echoes - MP2RAGE sequence. Two radiologists blindly reviewed the images to classify qualitatively the CC into normal, hypoplasic, hyperplasic, and/or dysgenetic classes. An automatic tool (QuantiFIRE) was used to provide brain volumetry and T1 relaxometry automatically as well as deviations of those parameters compared with a healthy age-matched cohort. The MRI reference standard for CC volumetry was based on the Garel et al. study. Cohen κ statistics was used for interrater agreement. The radiologists and QuantiFIRE's diagnostic accuracy were compared with the reference standard using the Delong test. RESULTS: The CC was normal in 42 cases (57.5%), hypoplastic in 20 cases (27.4%), and hypertrophic in 11 cases (15.1%). T1 relaxometry values were abnormal in 26 children (35.6%); either abnormally high (18 cases, 24.6%) or low (8 cases, 11.0%). The interrater Cohen κ coefficient was 0.91. The diagnostic accuracy of the QuantiFIRE prototype was higher than that of the radiologists for hypoplastic and normal CC (p = 0.003 for both subgroups, Delong test). CONCLUSIONS: An automated volumetric and relaxometric assessment can assist the evaluation of brain structure such as the CC, particularly in the case of subtle abnormalities. RELEVANCE STATEMENT: Automated brain MRI segmentation combined with statistical comparison to normal volume and T1 relaxometry values can be a useful diagnostic support tool for radiologists. KEY POINTS: • Corpus callosum abnormality detection is challenging but clinically relevant. • Automated quantitative volumetric analysis had a higher diagnostic accuracy than that of visual appreciation of radiologists. • Quantitative T1 relaxometric analysis might help characterizing corpus callosum better.

High field brain proton magnetic resonance spectroscopy and volumetry in children with chronic, compensated liver disease - A pilot study.

BACKGROUND: There is increasing evidence that children or young adults having acquired liver disease in childhood display neurocognitive impairment which may become more apparent as they grow older. The molecular, cellular and morphological underpinnings of this clinical problem are incompletely understood. AIM: Therefore, we used the advantages of highly-resolved proton magnetic resonance spectroscopy at ultra-high magnetic field to analyze the neurometabolic profile and brain morphometry of children with chronic, compensated liver disease, hypothesizing that with high field spectroscopy we would identify early evidence of rising brain glutamine and decreased myoinositol, such as has been described both in animals and humans with more significant liver disease. METHODS: Patients (n = 5) and age-matched controls (n = 19) underwent 7T MR scans and short echo time 1H MR spectra were acquired using the semi-adiabatic SPECIAL sequence in two voxels located in gray and white matter dominated prefrontal cortex, respectively. A 3D MP2RAGE sequence was also acquired for brain volumetry and T1 mapping. Liver disease had to have developed at least 6 months before entering the study. Subjects underwent routine blood analysis and neurocognitive testing using validated methods within 3 months of MRI and MRS. RESULTS: Five children aged 8-16 years with liver disease acquired in childhood were included. Baseline biological characteristics were similar among patients. There were no statistically significant differences between subjects and controls in brain metabolite levels or brain volumetry. Finally, there were minor neurocognitive fluctuations including attention deficit in one child, but none fell in the statistically significant range. CONCLUSION: Children with chronic, compensated liver disease did not display an abnormal neurometabolic profile, neurocognitive abnormalities, or signal intensity changes in the globus pallidus. Despite the absence of neurometabolic changes, it is an opportunity to emphasize that it is only by developing the use of 1H MRS at high field in the clinical arena that we will understand the significance and generalizability of these findings in children with CLD. Healthy children displayed neurometabolic regional differences as previously reported in adult subjects.

Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients.

OBJECTIVES AND AIMS: Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS: We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS: The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, ß = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, ß = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS: We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.

A Multicenter Longitudinal MRI Study Assessing LeMan-PV Software Accuracy in the Detection of White Matter Lesions in Multiple Sclerosis Patients.

BACKGROUND: Detecting new and enlarged lesions in multiple sclerosis (MS) patients is needed to determine their disease activity. LeMan-PV is a software embedded in the scanner reconstruction system of one vendor, which automatically assesses new and enlarged white matter lesions (NELs) in the follow-up of MS patients; however, multicenter validation studies are lacking. PURPOSE: To assess the accuracy of LeMan-PV for the longitudinal detection NEL white-matter MS lesions in a multicenter clinical setting. STUDY TYPE: Retrospective, longitudinal. SUBJECTS: A total of 206 patients with a definitive MS diagnosis and at least two follow-up MRI studies from five centers participating in the Swiss Multiple Sclerosis Cohort study. Mean age at first follow-up = 45.2 years (range: 36.9-52.8 years); 70 males. FIELD STRENGTH/SEQUENCE: Fluid attenuated inversion recovery (FLAIR) and T1-weighted magnetization prepared rapid gradient echo (T1-MPRAGE) sequences at 1.5 T and 3 T. ASSESSMENT: The study included 313 MRI pairs of datasets. Data were analyzed with LeMan-PV and compared with a manual "reference standard" provided by a neuroradiologist. A second rater (neurologist) performed the same analysis in a subset of MRI pairs to evaluate the rating-accuracy. The Sensitivity (Se), Specificity (Sp), Accuracy (Acc), F1-score, lesion-wise False-Positive-Rate (aFPR), and other measures were used to assess LeMan-PV performance for the detection of NEL at 1.5 T and 3 T. The performance was also evaluated in the subgroup of 123 MRI pairs at 3 T. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) and Cohen's kappa (CK) were used to evaluate the agreement between readers. RESULTS: The interreader agreement was high for detecting new lesions (ICC = 0.97, Pvalue < 10-20 , CK = 0.82, P value = 0) and good (ICC = 0.75, P value < 10-12 , CK = 0.68, P value = 0) for detecting enlarged lesions. Across all centers, scanner field strengths (1.5 T, 3 T), and for NEL, LeMan-PV achieved: Acc = 61%, Se = 65%, Sp = 60%, F1-score = 0.44, aFPR = 1.31. When both follow-ups were acquired at 3 T, LeMan-PV accuracy was higher (Acc = 66%, Se = 66%, Sp = 66%, F1-score = 0.28, aFPR = 3.03). DATA CONCLUSION: In this multicenter study using clinical data settings acquired at 1.5 T and 3 T, and variations in MRI protocols, LeMan-PV showed similar sensitivity in detecting NEL with respect to other recent 3 T multicentric studies based on neural networks. While LeMan-PV performance is not optimal, its main advantage is that it provides automated clinical decision support integrated into the radiological-routine flow. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Submillimeter T1 atlas for subject-specific abnormality detection at 7T.

PURPOSE: Studies at 3T have shown that T1 relaxometry enables characterization of brain tissues at the single-subject level by comparing individual physical properties to a normative atlas. In this work, an atlas of normative T1 values at 7T is introduced with 0.6 mm isotropic resolution and its clinical potential is explored in comparison to 3T. METHODS: T1 maps were acquired in two separate healthy cohorts scanned at 3T and 7T. Using transfer learning, a template-based brain segmentation algorithm was adapted to ultra-high field imaging data. After segmenting brain tissues, volumes were normalized into a common space, and an atlas of normative T1 values was established by modeling the T1 inter-subject variability. A method for single-subject comparisons restricted to white matter and subcortical structures was developed by computing Z-scores. The comparison was applied to eight patients scanned at both field strengths for proof of concept. RESULTS: The proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results. CONCLUSION: A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.

Cerebrospinal Fluid Cortisol and Dehydroepiandrosterone Sulfate, Alzheimer's Disease Pathology, and Cognitive Decline.

Introduction: Elevated cortisol levels have been reported in Alzheimer's disease (AD) and may accelerate the development of brain pathology and cognitive decline. Dehydroepiandrosterone sulfate (DHEAS) has anti-glucocorticoid effects and it may be involved in the AD pathophysiology. Objectives: To investigate associations of cerebrospinal fluid (CSF) cortisol and DHEAS levels with (1) cognitive performance at baseline; (2) CSF biomarkers of amyloid pathology (as assessed by CSF Aß levels), neuronal injury (as assessed by CSF tau), and tau hyperphosphorylation (as assessed by CSF p-tau); (3) regional brain volumes; and (4) clinical disease progression. Materials and Methods: Individuals between 49 and 88 years (n = 145) with mild cognitive impairment or dementia or with normal cognition were included. Clinical scores, AD biomarkers, brain MRI volumetry along with CSF cortisol and DHEAS were obtained at baseline. Cognitive and functional performance was re-assessed at 18 and 36 months from baseline. We also assessed the following covariates: apolipoprotein E (APOE) genotype, BMI, and education. We used linear regression and mixed models to address associations of interest. Results: Higher CSF cortisol was associated with poorer global cognitive performance and higher disease severity at baseline. Cortisol and cortisol/DHEAS ratio were positively associated with tau and p-tau CSF levels, and negatively associated with the amygdala and insula volumes at baseline. Higher CSF cortisol predicted more pronounced cognitive decline and clinical disease progression over 36 months. Higher CSF DHEAS predicted more pronounced disease progression over 36 months. Conclusion: Increased cortisol in the CNS is associated with tau pathology and neurodegeneration, and with decreased insula and amygdala volume. Both CSF cortisol and DHEAS levels predict faster clinical disease progression. These results have implications for the identification of patients at risk of rapid decline as well as for the development of interventions targeting both neurodegeneration and clinical manifestations of AD.

Brainstem lesions are associated with diffuse spinal cord involvement in early multiple sclerosis.

BACKGROUND: Early infratentorial and focal spinal cord lesions on magnetic resonance imaging (MRI) are associated with a higher risk of long-term disability in patients with multiple sclerosis (MS). The role of diffuse spinal cord lesions remains less understood. The purpose of this study was to evaluate focal and especially diffuse spinal cord lesions in patients with early relapsing-remitting MS and their association with intracranial lesion topography, global and regional brain volume, and spinal cord volume. METHODS: We investigated 58 MS patients with short disease duration (< 5 years) from a large academic MS center and 58 healthy controls matched for age and sex. Brain, spinal cord, and intracranial lesion volumes were compared among patients with- and without diffuse spinal cord lesions and controls. Binary logistic regression models were used to analyse the association between the volume and topology of intracranial lesions and the presence of focal and diffuse spinal cord lesions. RESULTS: We found spinal cord involvement in 75% of the patients (43/58), including diffuse changes in 41.4% (24/58). Patients with diffuse spinal cord changes exhibited higher volumes of brainstem lesion volume (p = 0.008). The presence of at least one brainstem lesion was associated with a higher probability of the presence of diffuse spinal cord lesions (odds ratio 47.1; 95% confidence interval 6.9-321.6 p < 0.001) as opposed to focal spinal cord lesions (odds ratio 0.22; p = 0.320). Patients with diffuse spinal cord lesions had a lower thalamus volume compared to patients without diffuse spinal cord lesions (p = 0.007) or healthy controls (p = 0.002). CONCLUSIONS: Diffuse spinal cord lesions are associated with the presence of brainstem lesions and with a lower volume of the thalamus. This association was not found in patients with focal spinal cord lesions. If confirmed, thalamic atrophy in patients with diffuse lesions could increase our knowledge on the worse prognosis in patients with infratentorial and SC lesions.

Systemic and central nervous system neuroinflammatory signatures of neuropsychiatric symptoms and related cognitive decline in older people.

BACKGROUND: Neuroinflammation may contribute to psychiatric symptoms in older people, in particular in the context of Alzheimer's disease (AD). We sought to identify systemic and central nervous system (CNS) inflammatory alterations associated with neuropsychiatric symptoms (NPS); and to investigate their relationships with AD pathology and clinical disease progression. METHODS: We quantified a panel of 38 neuroinflammation and vascular injury markers in paired serum and cerebrospinal fluid (CSF) samples in a cohort of cognitively normal and impaired older subjects. We performed neuropsychiatric and cognitive evaluations and measured CSF biomarkers of AD pathology. Multivariate analysis determined serum and CSF neuroinflammatory alterations associated with NPS, considering cognitive status, AD pathology, and cognitive decline at follow-up visits. RESULTS: NPS were associated with distinct inflammatory profiles in serum, involving eotaxin-3, interleukin (IL)-6 and C-reactive protein (CRP); and in CSF, including soluble intracellular cell adhesion molecule-1 (sICAM-1), IL-8, 10-kDa interferon-γ-induced protein, and CRP. AD pathology interacted with CSF sICAM-1 in association with NPS. Presenting NPS was associated with subsequent cognitive decline which was mediated by CSF sICAM-1. CONCLUSIONS: Distinct systemic and CNS inflammatory processes are involved in the pathophysiology of NPS in older people. Neuroinflammation may explain the link between NPS and more rapid clinical disease progression.

Periventricular gradient of T1 tissue alterations in multiple sclerosis.

OBJECTIVE: Pathology in multiple sclerosis is not homogenously distributed. Recently, it has been shown that structures adjacent to CSF are more severely affected. A gradient of brain tissue involvement was shown with more severe pathology in periventricular areas and in proximity to brain surfaces such as the subarachnoid spaces and ependyma, and hence termed the "surface-in" gradient. Here, we study whether (i) the surface-in gradient of periventricular tissue alteration measured by T1 relaxometry is already present in early multiple sclerosis patients, (ii) how it differs between early and progressive multiple sclerosis patients, and (iii) whether the gradient-derived metrics in normal-appearing white matter and lesions correlate better with physical disability than conventional MRI-based metrics. METHODS: Forty-seven patients with early multiple sclerosis, 52 with progressive multiple sclerosis, and 92 healthy controls were included in the study. Isotropic 3D T1 relaxometry maps were obtained using the Magnetization-Prepared 2 Rapid Acquisition Gradient Echoes sequence at 3 T. After spatially normalizing the T1 maps into a study-specific common space, T1 inter-subject variability within the healthy cohort was modelled voxel-wise, yielding a normative T1 atlas. Individual comparisons of each multiple sclerosis patient against the atlas were performed by computing z-scores. Equidistant bands of voxels were defined around the ventricles in the supratentorial white matter; the z-scores in these bands were analysed and compared between the early and progressive multiple sclerosis cohorts. Correlations between both conventional and z-score-gradient-derived MRI metrics and the Expanded Disability Status Scale were assessed. RESULTS: Patients with early and progressive multiple sclerosis demonstrated a periventricular gradient of T1 relaxation time z-scores. In progressive multiple sclerosis, z-score-derived metrics reflecting the gradient of tissue abnormality in normal-appearing white matter were more strongly correlated with disability (maximal rho = 0.374) than the conventional lesion volume and count (maximal rho = 0.189 and 0.21 respectively). In early multiple sclerosis, the gradient of normal-appearing white matter volume with z-scores > 2 at baseline correlated with clinical disability assessed at two years follow-up. CONCLUSION: Our results suggest that the surface-in white matter gradient of tissue alteration is detectable with T1 relaxometry and is already present at clinical disease onset. The periventricular gradients correlate with clinical disability. The periventricular gradient in normal-appearing white matter may thus qualify as a promising biomarker for monitoring of disease activity from an early stage in all phenotypes of multiple sclerosis.

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study.

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision. Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Clinical correlates of white matter lesions in Parkinson's disease using automated multi-modal segmentation measures.

BACKGROUND: Age-related white matter lesions (WML) are common, impact neuronal connectivity, and affect motor function and cognition. In addition to pathological nigrostriatal losses, WML are also common co-morbidities in Parkinson's disease (PD) that affect postural stability and gait. Automated brain volume measures are increasingly incorporated into the clinical reporting workflow to facilitate precision in medicine. Recently, multi-modal segmentation algorithms have been developed to overcome challenges with WML quantification based on single-modality input. OBJECTIVE: We evaluated WML volumes and their distribution in a case-control cohort of PD patients to predict the domain-specific clinical severity using a fully automated multi-modal segmentation algorithm. METHODS: Fifty-five subjects comprising of twenty PD patients and thirty-five age- and gender-matched control subjects underwent standardized motor/gait and cognitive assessments and brain MRI. Spatially differentiated WML obtained using automated segmentation algorithms on multi-modal MPRAGE and FLAIR images were used to predict domain-specific clinical severity. Preliminary statistical analysis focused on describing the relationship between WML and clinical scores, and the distribution of WML by brain regions. Subsequent stepwise regressions were performed to predict each clinical score using WML volumes in different brain regions, while controlling for age. RESULTS: WML volume strongly correlates with both motor and cognitive dysfunctions in PD patients (p < 0.05), with differential impact in the frontal lobe and periventricular regions on cognitive domains (p < 0.01) and severity of motor deficits (p < 0.01), respectively. CONCLUSION: Automated multi-modal segmentation algorithms may facilitate precision medicine through regional WML load quantification, which show potential as imaging biomarkers for predicting domain-specific disease severity in PD.

Quantitative comparison of subcortical and ventricular volumetry derived from MPRAGE and MP2RAGE images using different brain morphometry software.

OBJECTIVE: In brain volume assessment with MR imaging, it is of interest to know the effects of the pulse sequence and software used, to determine whether they provide equivalent data. The aim of this study was to compare cross-sectional volumes of subcortical and ventricular structures and their repeatability derived from MP2RAGE and MPRAGE images using MorphoBox, and FIRST or ALVIN. MATERIALS AND METHODS: MPRAGE and MP2RAGE T1-weighted images were obtained from 24 healthy volunteers. Back-to-back scans were performed in 12 of them. Volumes, coefficients of variation, concordance, and correlations were determined. RESULTS: Significant differences were found for volumes derived from MorphoBox and FIRST. Ventricular volumes determined by MorphoBox and ALVIN were similar. Differences between volumes obtained using MPRAGE and MP2RAGE were significant for a few regions. Coefficients of variation, ranged from 0.2 to 9.1%, showed a significant inverse correlation with the mean volume. There was a correlation between volume measures, but agreement was rated as poor for most regions. CONCLUSION: MP2RAGE sequences and MorphoBox are valid options for assessing subcortical and ventricular volumes, in the same way as MPRAGE and FIRST or ALVIN, accepted tools for clinical research. However, caution is needed when comparing volumes obtained with different tools.

Plasma neurofilament light and phosphorylated tau 181 as biomarkers of Alzheimer's disease pathology and clinical disease progression.

BACKGROUND: To assess the performance of plasma neurofilament light (NfL) and phosphorylated tau 181 (p-tau181) to inform about cerebral Alzheimer's disease (AD) pathology and predict clinical progression in a memory clinic setting. METHODS: Plasma NfL and p-tau181, along with established cerebrospinal fluid (CSF) biomarkers of AD pathology, were measured in participants with normal cognition (CN) and memory clinic patients with cognitive impairment (mild cognitive impairment and dementia, CI). Clinical and neuropsychological assessments were performed at inclusion and follow-up visits at 18 and 36 months. Multivariate analysis assessed associations of plasma NfL and p-tau181 levels with AD, single CSF biomarkers, hippocampal volume, and clinical measures of disease progression. RESULTS: Plasma NfL levels were higher in CN participants with an AD CSF profile (defined by a CSF p-tau181/Aß1-42 > 0.0779) as compared with CN non-AD, while p-tau181 plasma levels were higher in CI patients with AD. Plasma NfL levels correlated with CSF tau and p-tau181 in CN, and with CSF tau in CI patients. Plasma p-tau181 correlated with CSF p-tau181 in CN and with CSF tau, p-tau181, Aß1-42, and Aß1-42/Aß1-40 in CI participants. Compared with a reference model, adding plasma p-tau181 improved the prediction of AD in CI patients while adding NfL did not. Adding p-tau181, but not NfL levels, to a reference model improved prediction of cognitive decline in CI participants. CONCLUSION: Plasma NfL indicates neurodegeneration while plasma p-tau181 levels can serve as a biomarker of cerebral AD pathology and cognitive decline. Their predictive performance depends on the presence of cognitive impairment.

Improving diagnosis accuracy of brain volume abnormalities during childhood with an automated MP2RAGE-based MRI brain segmentation.

BACKGROUND AND PURPOSE: It can be challenging to depict brain volume abnormalities in the pediatric population on magnetic resonance imaging (MRI). The aim of the study was to evaluate the inter-radiologist reliability in brain MRI interpretation, including brain volume assessment and the efficiency of an automated brain segmentation. MATERIALS AND METHODS: We performed a single-center prospective study including 44 patients aged six months to five years recruited from the University Hospital, having a 1.5T brain MRI using a MP2RAGE sequence. All MRI were randomly and blindly reviewed by one junior and two senior pediatric radiologists. Inter-observer agreements were assessed using Fleiss' kappa coefficient. Brain volumetry (total intracranial volume (TIV), brain parenchyma, and cerebrospinal fluid volumes) was estimated using the MorphoBox prototype. Clinical head circumference (HC) and z scores were reported. A Pearson correlation coefficient was calculated between brain volumes with HC. RESULTS: Twenty-four brain MRI examinations were normal and twenty were pathological. Brain volume abnormalities were poorly detected by junior and senior radiologists: sensitivities 16.67% [confidence interval 4.7-44.8], 33.33% [13-60] and 30.7% [12-58] and specificities 93.75% [79-98], 84.38% [68-93] and 77% [60-88], respectively. Brain volume apart, interobserver kappa coefficients were 0.93 between junior and seniors as well as between seniors. Brain volumes were significantly correlated with HC (P<0.0001). In patients with normal MRI, brain parenchyma volumes increased regularly with age. Low brain volume was easier to identify with automated quantification. CONCLUSION: Brain volume was poorly appreciated by radiologists. The fully automated brain segmentation used can provide quantitative data to better diagnose, describe, and follow-up brain volume abnormalities.