Browsing by Author "Radaelli, Marta (25947736800)"

Objectives: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients. Patients and methods: We analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like manifestations (idiopathic recurrent optic neuritis and myelitis, n = 37), who were recruited from February 2010 to December 2019. Seventy-three percent of aquaporin-4-seronegative patients with NMOSD-like manifestations also had a clinical follow-up (median duration of 4 years). The deep-learning neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans of aquaporin-4-seropositive NMOSD and MS patients and was then applied to aquaporin-4-seronegative NMOSD and NMOSD-like manifestations. Assignment of unclassified aquaporin-4-seronegative patients was compared with their clinical follow-up. Results: The final algorithm differentiated aquaporin-4-seropositive NMOSD and MS patients with an accuracy of 0.95. All aquaporin-4-seronegative NMOSD and 36/37 aquaporin-4-seronegative patients with NMOSD-like manifestations were classified as NMOSD. Anti-MOG patients had a similar probability of being NMOSD or MS. At clinical follow-up, one unclassified aquaporin-4-seronegative patient evolved to MS, three developed NMOSD, and the others did not change phenotype. Conclusions: Our findings support the inclusion of aquaporin4-seronegative patients into NMOSD and suggest a possible expansion to aquaporin-4-seronegative unclassified patients with NMOSD-like manifestations. Anti-MOG patients are likely to have intermediate brain features between NMOSD and MS. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

Objectives: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients. Patients and methods: We analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like manifestations (idiopathic recurrent optic neuritis and myelitis, n = 37), who were recruited from February 2010 to December 2019. Seventy-three percent of aquaporin-4-seronegative patients with NMOSD-like manifestations also had a clinical follow-up (median duration of 4 years). The deep-learning neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans of aquaporin-4-seropositive NMOSD and MS patients and was then applied to aquaporin-4-seronegative NMOSD and NMOSD-like manifestations. Assignment of unclassified aquaporin-4-seronegative patients was compared with their clinical follow-up. Results: The final algorithm differentiated aquaporin-4-seropositive NMOSD and MS patients with an accuracy of 0.95. All aquaporin-4-seronegative NMOSD and 36/37 aquaporin-4-seronegative patients with NMOSD-like manifestations were classified as NMOSD. Anti-MOG patients had a similar probability of being NMOSD or MS. At clinical follow-up, one unclassified aquaporin-4-seronegative patient evolved to MS, three developed NMOSD, and the others did not change phenotype. Conclusions: Our findings support the inclusion of aquaporin4-seronegative patients into NMOSD and suggest a possible expansion to aquaporin-4-seronegative unclassified patients with NMOSD-like manifestations. Anti-MOG patients are likely to have intermediate brain features between NMOSD and MS. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

Objectives: To validate imaging features able to discriminate neuromyelitis optica spectrum disorders from multiple sclerosis with conventional magnetic resonance imaging (MRI). Methods: In this cross-sectional study, brain and spinal cord scans were evaluated from 116 neuromyelitis optica spectrum disorder patients (98 seropositive and 18 seronegative) in chronic disease phase and 65 age-, sex-, and disease duration–matched multiple sclerosis patients. To identify independent predictors of neuromyelitis optica diagnosis, after assessing the prevalence of typical/atypical findings, the original cohort was 2:1 randomized in a training sample (where a multivariate logistic regression analysis was run) and a validation sample (where the performance of the selected variables was tested and validated). Results: Typical brain lesions occurred in 50.9% of neuromyelitis optica patients (18.1% brainstem periventricular/periaqueductal, 32.7% periependymal along lateral ventricles, 3.4% large hemispheric, 6.0% diencephalic, 4.3% corticospinal tract), 72.2% had spinal cord lesions (46.3% long transverse myelitis, 36.1% short transverse myelitis), 37.1% satisfied 2010 McDonald criteria, and none had cortical lesions. Fulfillment of at least 2 of 5 of absence of juxtacortical/cortical lesions, absence of periventricular lesions, absence of Dawson fingers, presence of long transverse myelitis, and presence of periependymal lesions along lateral ventricles discriminated neuromyelitis optica patients in both training (sensitivity = 0.92, 95% confidence interval [CI] = 0.84–0.97; specificity = 0.91, 95% CI = 0.78–0.97) and validation samples (sensitivity = 0.82, 95% CI = 0.66–0.92; specificity = 0.91, 95% CI = 0.71–0.99). MRI findings and criteria performance were similar irrespective of serostatus. Interpretation: Although up to 50% of neuromyelitis optica patients have no typical lesions and a relatively high percentage of them satisfy multiple sclerosis criteria, several easily applicable imaging features can help to distinguish neuromyelitis optica from multiple sclerosis. ANN NEUROL 2019;85:371–384. © 2019 American Neurological Association

Objectives: To validate imaging features able to discriminate neuromyelitis optica spectrum disorders from multiple sclerosis with conventional magnetic resonance imaging (MRI). Methods: In this cross-sectional study, brain and spinal cord scans were evaluated from 116 neuromyelitis optica spectrum disorder patients (98 seropositive and 18 seronegative) in chronic disease phase and 65 age-, sex-, and disease duration–matched multiple sclerosis patients. To identify independent predictors of neuromyelitis optica diagnosis, after assessing the prevalence of typical/atypical findings, the original cohort was 2:1 randomized in a training sample (where a multivariate logistic regression analysis was run) and a validation sample (where the performance of the selected variables was tested and validated). Results: Typical brain lesions occurred in 50.9% of neuromyelitis optica patients (18.1% brainstem periventricular/periaqueductal, 32.7% periependymal along lateral ventricles, 3.4% large hemispheric, 6.0% diencephalic, 4.3% corticospinal tract), 72.2% had spinal cord lesions (46.3% long transverse myelitis, 36.1% short transverse myelitis), 37.1% satisfied 2010 McDonald criteria, and none had cortical lesions. Fulfillment of at least 2 of 5 of absence of juxtacortical/cortical lesions, absence of periventricular lesions, absence of Dawson fingers, presence of long transverse myelitis, and presence of periependymal lesions along lateral ventricles discriminated neuromyelitis optica patients in both training (sensitivity = 0.92, 95% confidence interval [CI] = 0.84–0.97; specificity = 0.91, 95% CI = 0.78–0.97) and validation samples (sensitivity = 0.82, 95% CI = 0.66–0.92; specificity = 0.91, 95% CI = 0.71–0.99). MRI findings and criteria performance were similar irrespective of serostatus. Interpretation: Although up to 50% of neuromyelitis optica patients have no typical lesions and a relatively high percentage of them satisfy multiple sclerosis criteria, several easily applicable imaging features can help to distinguish neuromyelitis optica from multiple sclerosis. ANN NEUROL 2019;85:371–384. © 2019 American Neurological Association

Background: Functional magnetic resonance imaging (fMRI) correlates of cognitive deficits have not been thoroughly studied in patients with neuromyelitis optica spectrum disorders (NMOSDs). Objective: To investigate resting state (RS) functional connectivity (FC) abnormalities within the main cognitive networks in NMOSD patients and their correlation with cognitive performance. Methods: We acquired RS fMRI from 25 NMOSD patients and 30 healthy controls (HC). Patients underwent an extensive neuropsychological evaluation. Between-group RS FC comparisons and correlations with cognitive performance were assessed on the main cognitive RS networks identified by independent component analysis. Results: NMOSD patients showed higher RS FC versus HC in the precuneus of the default mode network (DMN) and right working memory network (WMN), as well as in several frontoparietal regions of the salience network (SN) and bilateral WMNs. Reduced frontal RS FC in NMOSD versus HC was detected in the left WMN. Increased RS FC in the DMN and right WMN was correlated with better cognitive performance, while decreased RS FC in the left WMN was associated with worse cognitive performance. Conclusion: Cognitive-network reorganization occurs in NMOSD. Clinico-imaging correlations suggest an adaptive role of increased RS FC. Conversely, reduced RS FC seems to be a maladaptive mechanism associated with a worse cognitive performance. © The Author(s), 2019.

Background: Functional magnetic resonance imaging (fMRI) correlates of cognitive deficits have not been thoroughly studied in patients with neuromyelitis optica spectrum disorders (NMOSDs). Objective: To investigate resting state (RS) functional connectivity (FC) abnormalities within the main cognitive networks in NMOSD patients and their correlation with cognitive performance. Methods: We acquired RS fMRI from 25 NMOSD patients and 30 healthy controls (HC). Patients underwent an extensive neuropsychological evaluation. Between-group RS FC comparisons and correlations with cognitive performance were assessed on the main cognitive RS networks identified by independent component analysis. Results: NMOSD patients showed higher RS FC versus HC in the precuneus of the default mode network (DMN) and right working memory network (WMN), as well as in several frontoparietal regions of the salience network (SN) and bilateral WMNs. Reduced frontal RS FC in NMOSD versus HC was detected in the left WMN. Increased RS FC in the DMN and right WMN was correlated with better cognitive performance, while decreased RS FC in the left WMN was associated with worse cognitive performance. Conclusion: Cognitive-network reorganization occurs in NMOSD. Clinico-imaging correlations suggest an adaptive role of increased RS FC. Conversely, reduced RS FC seems to be a maladaptive mechanism associated with a worse cognitive performance. © The Author(s), 2019.

Objective Since astrocytes at the blood-brain barrier are targeted by neuromyelitis optica spectrum disorder (NMOSD), this study aims to assess whether patients with NMOSD have a subclinical accumulation of brain water and if it differs according to disease activity. Methods Seventy-seven aquaporin-4-positive patients with NMOSD and 105 healthy controls were enrolled at two European centres. Brain dual-echo turbo spin-echo MR images were evaluated and maps of T2 relaxation time (T2rt) in the normal-appearing white matter (NAWM), grey matter and basal ganglia were obtained. Patients with a clinical relapse within 1 month before or after MRI acquisition were defined 'active'. Differences between patients and controls were assessed using z-scores of T2rt obtained with age-adjusted and sex-adjusted linear models from each site. A stepwise binary logistic regression was run on clinical and MRI variables to identify independent predictors of disease activity. Results Patients had increased T2rt in both white and grey matter structures (p range: 0.014 to <0.0001). Twenty patients with NMOSD were defined active. Despite similar clinical and MRI features, active patients had a significantly increased T2rt in the NAWM and grey matter compared with those clinically stable (p range: 0.010-0.002). The stepwise binary logistic regression selected the NAWM as independently associated with disease activity (beta=2.06, SE=0.58, Nagelkerke R 2 =0.46, p<0.001). Conclusions In line with the research hypothesis, patients with NMOSD have increased brain T2rt. The magnitude of this alteration might be useful for identifying those patients with active disease. © 2022 BMJ Publishing Group. All rights reserved.

Background: The understanding of disease pathophysiology is pivotal for tailored treatments. The spatial distribution of brain damage relies on the regional antigen expression and the local balance of susceptibility and protective elements. Objective: As proof-of-concept, we investigated the spatial association between brain damage and gene expression in aquaporin-4-IgG-positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD). Methods: In this multicenter cross-sectional study, 90 AQP4 + NMOSD patients and 94 age-matched healthy controls underwent a brain magnetic resonance imaging (MRI). We used T2-hyperintense lesion probability maps and white/gray matter atrophy as proxies of inflammation and neurodegeneration. The association with the expression of 266 candidate genes was obtained with the Multimodal Environment for Neuroimaging and Genomic Analysis platform. A functional-enrichment analysis investigated overrepresented biological processes. Results: In AQP4 + NMOSD, T2-hyperintense lesions were mainly periventricular; atrophy mostly involved the visual pathway. The expression of AQP4 and complement (C4a and C5) was associated with both inflammation and neurodegeneration. Complement activation and regulation/uptake of the insulin-like growth factor were the most relevant enriched pathways. Nonspecific pathways related to DNA synthesis and repair were associated with brain atrophy. Conclusions: Quantitative MRI and gene expression atlas identified the key elements of AQP4 + NMOSD pathophysiology. This analysis could help in understanding the pathophysiology of antibody-mediated autoimmune disorders. © The Author(s), 2024.

Background: The understanding of disease pathophysiology is pivotal for tailored treatments. The spatial distribution of brain damage relies on the regional antigen expression and the local balance of susceptibility and protective elements. Objective: As proof-of-concept, we investigated the spatial association between brain damage and gene expression in aquaporin-4-IgG-positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD). Methods: In this multicenter cross-sectional study, 90 AQP4 + NMOSD patients and 94 age-matched healthy controls underwent a brain magnetic resonance imaging (MRI). We used T2-hyperintense lesion probability maps and white/gray matter atrophy as proxies of inflammation and neurodegeneration. The association with the expression of 266 candidate genes was obtained with the Multimodal Environment for Neuroimaging and Genomic Analysis platform. A functional-enrichment analysis investigated overrepresented biological processes. Results: In AQP4 + NMOSD, T2-hyperintense lesions were mainly periventricular; atrophy mostly involved the visual pathway. The expression of AQP4 and complement (C4a and C5) was associated with both inflammation and neurodegeneration. Complement activation and regulation/uptake of the insulin-like growth factor were the most relevant enriched pathways. Nonspecific pathways related to DNA synthesis and repair were associated with brain atrophy. Conclusions: Quantitative MRI and gene expression atlas identified the key elements of AQP4 + NMOSD pathophysiology. This analysis could help in understanding the pathophysiology of antibody-mediated autoimmune disorders. © The Author(s), 2024.