Author: Valladares Velez, Jonathan A ; Llerena Payango, Andrea F ; Portillo Rivera, Ligia I ; Yorio, Diana ; Correa Diaz, Edgar P ; Ortiz Salas, Paola A ; Van Sijtveld, Ivonne ; Pereira, Daniel ; Treviño Frenk, Irene ; Weiser, Roberto ; Larrategui, Mario ; Rocha, Valeria ; Rojas, Juan I ; Lana Peixoto, Marco ; de Queiroz, André Luiz Guimarães ; Miranda, Jhair ; Santos Pujols, Biany ; Armien, Blas ; Corea, Karla ; Galleguillos, Lorna ; Legnani, Cecilia ; de Escobar, Romy ; Rodriguez Salinas, Luis C ; Diaz de la Fe, Amado ; Zuluaga Rodas, María I ; Candanedo, Awilda ; Patrucco, Liliana ; Soto Del Castillo, Ibis ; Araujo, Pahola ; Delgado, Jaime ; Gortari, José I ; Soares Neto, Herval Ribeiro ; Cristiano, Edgardo ; Thompson, Arnold ; Oliveira de Cuba, Irma ; Sorondo, Noelia ; Becker, Jefferson ; Baños-Hernández, Lilia Anahi ; Diaz, Alejandro ; Garcia, Karla ; Guio Sanchez, Claudia ; Carrillo, Gabriela ; Higgie, Juan Ramón ; Gracia, Fernando ; Valderrama, Carlos ; Ramirez, Deyanira ; Acosta Camargo, Laura C ; Benzadon, Aron ; Garcia Valle, Luis A ; Ramirez Sanchez, Nicia E ; Carnero Contentti, Edgar ; Lopez, Ericka

The objective of the study was to develop a global data share initiative to collect data from many sources (registries and observational databases) of NMOSD (neuromyelitis optica spectrum disorder) patients in LATAM (Latin America) to describe the frequency of patients untreated in our region to focus access and educational projects to improve patients care and long-term outcomes. METHODS: This was a non-interventional, multicenter cross-sectional study that included NMOSD patients from LATAM. Data were acquired through independent registries and observational single and multicenter cohorts from 16 countries (Argentina n = 379, Brazil n = 250, Chile n = 50, Colombia n = 250, Ecuador n = 47, Mexico n = 101, Uruguay n = 10, Venezuela n = 31, Aruba n = 4, Cuba n = 13, El Salvador n = 7, Guatemala n = 23, Honduras n = 7, Nicaragua n = 6, Panama n = 42 and Dominican Republic n = 48). Data homogenization was conducted using the data dictionary provided by each custodian in a single, centralized database with variables loaded in a homogeneous manner. RESULTS: A total of 1264 patients were included from three nationwide registries and seven observational cohorts from LATAM. 90.8 % (n = 1148) patients were receiving long-term treatment for NMOSD, while 9.2 % (n = 116) were untreated. Most frequent treatment received in the analyzed population was rituximab (56.7 %), followed by azathioprine (28 %). Despite the availability of novel therapeutic options such as satralizumab, eculizumab, and inebilizumab, these were used in <5 % of AQP4-IgG-positive patients CONCLUSION: The study helps to understand how patients are being treated in the region and to develop educational and access strategies to improve patients care.

01 May 2025·Neuropsychologia

Investigating social cognition in Multiple Sclerosis: Does Implicit Biological Motion processing affect visuo-spatial attention?

Article

Author: Brunetti, Marcella ; Bertoli, Massimo ; Graziano, Francesca ; Bonventre, Sofia ; De Cesaris, Martina ; Tomassini, Valentina

The perception of Biological Motion (BM) is critical for understanding social cues. A limited number of moving light dots resembling a moving individual, can suggest social intention information, providing an attentional orienting. This ability to predict other's intentions from BM cues refers to social cognition, an ability impaired in several neurological diseases. As in patients with Multiple Sclerosis (MS) an impairment in visuo-spatial attention and social cognition has been observed, we aim to investigate the possible differences in the visuo-spatial attention between MS patients and healthy individuals by using BM stimuli as cues. We tested 37 MS patients and 40 healthy controls (HC), who performed a modified central cue Posner task, using BM stimuli as cues that are not always predictive of the target location. They were represented by Point Light Walker (PLW) configuration, a series of dots arranged at the human joints and walking through the left or right of the screen, shown in the global (whole body) or local (two dots, indicating the feet of the PLW) configuration. MS patients exhibited overall slower responses compared to HC. In MS patients, a weaker advantage of valid trial over invalid ones was evident when cue had a local than global BM configuration. Also, MS patients showed a slower performance than HC in valid trials with local BM cues. The difference between groups was attenuated when the cue had a global BM configuration. These findings suggest possible impairment of local BM cue processing in MS patients, possibly due to delays or deficits in interpreting feet motion as biological information, reducing the cue's predictive power.

01 May 2025·Computer Methods and Programs in Biomedicine

Explainable time-to-progression predictions in multiple sclerosis

Article

Author: Lugaresi, Alessandra ; de Gans, Koen ; van der Walt, Anneke ; D'hondt, Robbe ; Vens, Celine ; Prevost, Julie ; Csepany, Tunde ; Weinstock-Guttman, Bianca ; Carroll, William M ; Mrabet, Saloua ; Ramanathan, Sudarshini ; Spitaleri, Daniele ; Lalive, Patrice ; Reddel, Stephen ; Al-Harbi, Talal ; Van Wijmeersch, Bart ; Laureys, Guy ; Solaro, Claudio ; Aerts, Sofie ; Rozsa, Csilla ; van Pesch, Vincent ; Roos, Izanne ; Havrdova, Eva Kubala ; Peeters, Liesbet ; Dedja, Klest ; Singhal, Bhim ; Sánchez-Menoyo, José Luis ; Hardy, Todd A ; Prat, Alexandre ; Butzkueven, Helmut ; Tomassini, Valentina ; McCombe, Pamela ; Kalincik, Tomas ; Deri, Norma ; Kermode, Allan G ; Gerlach, Oliver ; Alroughani, Raed ; Al-Asmi, Abdullah ; Patti, Francesco ; Ozakbas, Serkan ; Khoury, Samia J ; Sá, Maria José

BACKGROUNDPrognostic machine learning research in multiple sclerosis has been mainly focusing on black-box models predicting whether a patients' disability will progress in a fixed number of years. However, as this is a binary yes/no question, it cannot take individual disease severity into account. Therefore, in this work we propose to model the time to disease progression instead. Additionally, we use explainable machine learning techniques to make the model outputs more interpretable.METHODSA preprocessed subset of 29,201 patients of the international data registry MSBase was used. Disability was assessed in terms of the Expanded Disability Status Scale (EDSS). We predict the time to significant and confirmed disability progression using random survival forests, a machine learning model for survival analysis. Performance is evaluated on a time-dependent area under the receiver operating characteristic and the precision-recall curves. Importantly, predictions are then explained using SHAP and Bellatrex, two explainability toolboxes, and lead to both global (population-wide) as well as local (patient visit-specific) insights.RESULTSOn the task of predicting progression in 2 years, the random survival forest achieves state-of-the-art performance, comparable to previous work employing a random forest. However, here the random survival forest has the added advantage of being able to predict progression over a longer time horizon, with AUROC >60% for the first 10 years after baseline. Explainability techniques further validated the model by extracting clinically valid insights from the predictions made by the model. For example, a clear decline in the per-visit probability of progression is observed in more recent years since 2012, likely reflecting globally increasing use of more effective MS therapies.CONCLUSIONThe binary classification models found in the literature can be extended to a time-to-event setting without loss of performance, thus allowing a more comprehensive prediction of patient prognosis. Furthermore, explainability techniques proved to be key to reach a better understanding of the model and increase validation of its behaviour.

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