FERNANDO MITSUO SUMIYA
Projetos de Pesquisa
Unidades Organizacionais
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
2 resultados
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- CRISIS AFAR: an international collaborative study of the impact of the COVID-19 pandemic on mental health and service access in youth with autism and neurodevelopmental conditions(2023) VIBERT, Bethany; SEGURA, Patricia; GALLAGHER, Louise; GEORGIADES, Stelios; PERVANIDOU, Panagiota; THURM, Audrey; ALEXANDER, Lindsay; ANAGNOSTOU, Evdokia; AOKI, Yuta; BIRKEN, Catherine S.; BISHOP, Somer L.; BOI, Jessica; BRAVACCIO, Carmela; BRENTANI, Helena; CANEVINI, Paola; CARTA, Alessandra; CHARACH, Alice; COSTANTINO, Antonella; COST, Katherine T.; CRAVO, Elaine A.; CROSBIE, Jennifer; DAVICO, Chiara; DONNO, Federica; FUJINO, Junya; GABELLONE, Alessandra; GEYER, Cristiane T.; HIROTA, Tomoya; KANNE, Stephen; KAWASHIMA, Makiko; KELLEY, Elizabeth; KIM, Hosanna; KIM, Young Shin; KIM, So Hyun; KORCZAK, Daphne J.; LAI, Meng-Chuan; MARGARI, Lucia; MARZULLI, Lucia; MASI, Gabriele; MAZZONE, Luigi; MCGRATH, Jane; MONGA, Suneeta; MOROSINI, Paola; NAKAJIMA, Shinichiro; NARZISI, Antonio; NICOLSON, Rob; NIKOLAIDIS, Aki; NODA, Yoshihiro; NOWELL, Kerri; POLIZZI, Miriam; PORTOLESE, Joana; RICCIO, Maria Pia; SAITO, Manabu; SCHWARTZ, Ida; SIMHAL, Anish K.; SIRACUSANO, Martina; SOTGIU, Stefano; STROUD, Jacob; SUMIYA, Fernando; TACHIBANA, Yoshiyuki; TAKAHASHI, Nicole; TAKAHASHI, Riina; TAMON, Hiroki; TANCREDI, Raffaella; VITIELLO, Benedetto; ZUDDAS, Alessandro; LEVENTHAL, Bennett; MERIKANGAS, Kathleen; MILHAM, Michael P.; MARTINO, Adriana DiBackgroundHeterogeneous mental health outcomes during the COVID-19 pandemic are documented in the general population. Such heterogeneity has not been systematically assessed in youth with autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDD). To identify distinct patterns of the pandemic impact and their predictors in ASD/NDD youth, we focused on pandemic-related changes in symptoms and access to services.MethodsUsing a naturalistic observational design, we assessed parent responses on the Coronavirus Health and Impact Survey Initiative (CRISIS) Adapted For Autism and Related neurodevelopmental conditions (AFAR). Cross-sectional AFAR data were aggregated across 14 European and North American sites yielding a clinically well-characterized sample of N = 1275 individuals with ASD/NDD (age = 11.0 +/- 3.6 years; n females = 277). To identify subgroups with differential outcomes, we applied hierarchical clustering across eleven variables measuring changes in symptoms and access to services. Then, random forest classification assessed the importance of socio-demographics, pre-pandemic service rates, clinical severity of ASD-associated symptoms, and COVID-19 pandemic experiences/environments in predicting the outcome subgroups.ResultsClustering revealed four subgroups. One subgroup-broad symptom worsening only (20%)-included youth with worsening across a range of symptoms but with service disruptions similar to the average of the aggregate sample. The other three subgroups were, relatively, clinically stable but differed in service access: primarily modified services (23%), primarily lost services (6%), and average services/symptom changes (53%). Distinct combinations of a set of pre-pandemic services, pandemic environment (e.g., COVID-19 new cases, restrictions), experiences (e.g., COVID-19 Worries), and age predicted each outcome subgroup.LimitationsNotable limitations of the study are its cross-sectional nature and focus on the first six months of the pandemic.ConclusionsConcomitantly assessing variation in changes of symptoms and service access during the first phase of the pandemic revealed differential outcome profiles in ASD/NDD youth. Subgroups were characterized by distinct prediction patterns across a set of pre- and pandemic-related experiences/contexts. Results may inform recovery efforts and preparedness in future crises; they also underscore the critical value of international data-sharing and collaborations to address the needs of those most vulnerable in times of crisis.
- Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal(2023) FRANCO, Felipe O.; OLIVEIRA, Jessica S.; PORTOLESE, Joana; SUMIYA, Fernando M.; SILVA, Andreia F.; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.; BRENTANI, HelenaBackgroundAutism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets.MethodsThe previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm.ResultsThe test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset.ConclusionIn summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.