BACKGROUND: Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas, and they present the advantage of being extracted from previously acquired clinical magnetic resonance imaging (MRI) scans. SCNs have shown pathophysiological changes in many brain disorders, including multiple sclerosis. OBJECTIVE: To investigate alterations of SCNs at the individual level in patients with clinically isolated syndrome (CIS), thereby assessing their clinical relevance. METHODS: We analyzed baseline data collected in a prospective multicenter (MAGNIMS) study. CIS patients (n = 60) and healthy controls (n = 38) underwent high-resolution 3T MRI. Measures of disability and cognitive processing were obtained for patients. Single-subject SCNs were extracted from brain 3D-T1 weighted sequences; global and local network parameters were computed. RESULTS: Compared to healthy controls, CIS patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed. Alterations of centrality measures and clustering of connections were observed in specific cortical areas in CIS patients when compared with healthy controls. CONCLUSION: Our study indicates that SCNs can be used to demonstrate clinically relevant alterations of connectivity in CIS.

• MeSH
• demyelinizační nemoci * diagnostické zobrazování   MeSH
• kognice MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mozek diagnostické zobrazování   MeSH
• nervové dráhy diagnostické zobrazování   MeSH
• prospektivní studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH

BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

• MeSH
• deep learning * MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• magnetická rezonanční tomografie * MeSH
• mozek * diagnostické zobrazování   patologie   MeSH
• neurodegenerativní nemoci diagnostické zobrazování   MeSH
• průřezové studie MeSH
• retrospektivní studie MeSH
• roztroušená skleróza * diagnostické zobrazování   patologie   MeSH
• stárnutí * patologie   fyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH

We provide here normative values of yearly percentage brain volume change (PBVC/y) as obtained with Structural Imaging Evaluation, using Normalization, of Atrophy, a widely used open-source software, developing a PBVC/y calculator for assessing the deviation from the expected PBVC/y in patients with neurological disorders. We assessed multicenter (34 centers, 11 acquisition protocols) magnetic resonance imaging data of 720 healthy participants covering the whole adult lifespan (16-90 years). Data of 421 participants with a follow-up > 6 months were used to obtain the normative values for PBVC/y and data of 392 participants with a follow-up <1 month were selected to assess the intrasubject variability of the brain volume measurement. A mixed model evaluated PBVC/y dependence on age, sex, and magnetic resonance imaging parameters (scan vendor and magnetic field strength). PBVC/y was associated with age (p < 0.001), with 60- to 70-year-old participants showing twice more volume decrease than participants aged 30-40 years. PBVC/y was also associated with magnetic field strength, with higher decreases when measured by 1.5T than 3T scanners (p < 0.001). The variability of PBVC/y normative percentiles was narrower as the interscan interval was longer (e.g., 80th normative percentile was 50% smaller for participants with 2-year than with 1-year follow-up). The use of these normative data, eased by the freely available calculator, might help in better discriminating pathological from physiological conditions in the clinical setting.

• MeSH
• atrofie MeSH
• difuzní magnetická rezonance MeSH
• dlouhověkost * MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mozek diagnostické zobrazování   patologie   MeSH
• multicentrické studie jako téma MeSH
• retrospektivní studie MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• stárnutí patologie   MeSH
• velikost orgánu MeSH
• zobrazování trojrozměrné MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Sclerosis multiplex (SM) je najčastejším získaným, netraumatickým ochorením centrálneho nervového systému (CNS) mladých dospelých. Magnetická rezonancia (MR) má v diagnostike SM dôležitú úlohu. Aj vďaka kvantitatívnym MR technikám (používaným vo výskumných štúdiách) máme dnes podrobnejšie informácie o patofyziológii ochorenia i štrukturálnych zmenách mozgového tkaniva, ku ktorým v priebehu ochorenia dochádza. Na diagnostiku ochorenia sa v klinickej praxi používajú konvenčné MR techniky zobrazenia. Práve o nálezy dostupné týmto typom zobrazenia sa opierajú diagnostické McDonaldove kritériá SM z roku 2001, ktoré boli v roku 2005 upravené. Vzhľadom na stále stúpajúci význam MR vyšetrenia v diagnostickom procese SM, autorky v článku informujú o nových MR kritériách diagnostiky SM, vypracovaných a odporúčaných európskou skupinou MR expertov - MAGNIMS (Magnetic Resonance Network in Multiple Sclerosis).

Multiple sclerosis (MS) is the most common acquired, non-traumatic disease of the central nervous system (CNS) in young adults. Magnetic resonance imaging (MRI) plays an important role in MS diagnostic. Thanks quantitative MRI techniques (used in research studies) we have more detailed information on the pathophysiology of the disease and the structural changes of brain tissue, which occurs in the course of the disease. To diagnose disease in clinical practise, the conventional MRI techniques are used. Currently available findings on this type of imaging is adjusted based McDonald diagnostic criteria from 2001, which were revised in 2005 and clearly define the MRI dissemination in space and time. Given the ever increasing importance of MRI in the diagnostic process of MS, authors of the article inform about new diagnostic MRI criteria for MS, developed and recommended by the MRI group of experts - MAGNIMS (Magnetic Resonance Network in Multiple Sclerosis).

• MeSH
• časové faktory MeSH
• diagnostické techniky neurologické využití   MeSH
• kontrastní látky MeSH
• lidé MeSH
• magnetická rezonanční spektroskopie metody   MeSH
• magnetická rezonanční tomografie metody   využití   MeSH
• mezinárodní klasifikace nemocí MeSH
• progrese nemoci MeSH
• referenční standardy MeSH
• roztroušená skleróza diagnóza   MeSH
• směrnice pro lékařskou praxi jako téma MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• přehledy MeSH

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

• MeSH
• atrofie patologie   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• mladý dospělý MeSH
• mozek diagnostické zobrazování   patologie   MeSH
• prognóza MeSH
• progrese nemoci MeSH
• relabující-remitující roztroušená skleróza * diagnostické zobrazování   patologie   MeSH
• roztroušená skleróza * diagnostické zobrazování   patologie   MeSH
• šedá hmota diagnostické zobrazování   patologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH