JOANA TEIXEIRA PORTOLESE PASCALICCHIO
Projetos de Pesquisa
Unidades Organizacionais
Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
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bookPart Intervenção multidisciplinar em crianças e adolescentes com transtorno do espectro autista e deficiência intelectual(2016) BOARATI, Miguel Angelo; MORAIS, Rosa Magaly Campêlo Borba de; PORTOLESE, Joana; DREYER, Margareth Ramos Mari; SATO, Flávia M.; PANTANO, Telma- A randomised clinical pilot trial to test the effectiveness of parent training with video modelling to improve functioning and symptoms in children with autism spectrum disorders and intellectual disability(2020) BORDINI, D.; PAULA, C. S.; CUNHA, G. R.; CAETANO, S. C.; BAGAIOLO, L. F.; RIBEIRO, T. C.; MARTONE, M. C. C.; PORTOLESE, J.; MOYA, A. C.; BRUNONI, D.; BOSA, C.; BRENTANI, H.; COGO-MOREIRA, H.; MARI, J. de JesusBackground Poor eye contact and joint attention are early signs of autism spectrum disorder (ASD) and important prerequisites for developing other socio-communicative skills. Teaching parents evidence-based techniques to improve these skills can impact the overall functioning of children with ASD. We aimed to analyse the impact of conducting a group parent-training intervention with video modelling to improve the intelligent quotient (IQ), social and communication functioning and to minimise symptoms in children with ASD and intellectual disability (ID). Methods Study design: A multicentre, single-blinded, randomised clinical pilot trial of parent training using video modelling was conducted.Sample: Sixty-seven parents of children with ASD, aged between 3 and 6 years and with IQs between 50 and 70, were randomised: 34 to the intervention group and 33 to the control group.Intervention program: The intervention group received parent training over 22 sessions, and the control group received the standard community treatment.Instruments: Pre-evaluation and post-evaluation (week 28), the following were used: Autism Diagnostic Interview, Vineland Adaptive Behaviour Scale I, Snijders-Oomen Nonverbal Intelligence Test, Autism Behaviour Checklist and Hamilton Depression Rating Scale.Data Analysis: Intention to treat and complier-average causal effect (CACE) were used to estimate the effects of the intervention. Results There was a statistically significant improvement in the Vineland standardized communication scores in CACE (Cohen'sd = 0.260). There was a non-statistically significant decrease in autism symptomatology (Autism Behaviour Checklist total scores) and a significant increase in the non-verbal IQ in the intervention group. After the false discovery rate correction was applied, IQ remained statistically significant under both paradigms. The effect size for this adjusted outcome under the intention-to-treat paradigm was close to 0.4, and when considering adherence (CACE), the effect sizes were more robust (IQ's Cohen'sd = 0.433). Conclusions Parent training delivered by video modelling can be a useful technique for improving the care given to children with ASD and ID, particularly in countries that lack specialists.
- Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data(2021) V, Thiago Cardoso; MICHELASSI, Gabriel C.; FRANCO, Felipe O.; SUMIYA, Fernando M.; PORTOLESE, Joana; BRENTANI, Helena; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.The use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD.
- Environmental Influences Measured by Epigenetic Clock and Vulnerability Components at Birth Impact Clinical ASD Heterogeneity(2021) REIS, Viviane Neri de Souza; TAHIRA, Ana Carolina; GASTALDI, Vinicius Daguano; MARI, Paula; PORTOLESE, Joana; SANTOS, Ana Cecilia Feio dos; LISBOA, Bianca; MARI, Jair; CAETANO, Sheila C.; BRUNONI, Decio; BORDINI, Daniela; PAULA, Cristinane Silvestre de; VENCIO, Ricardo Z. N.; QUACKENBUSH, John; BRENTANI, HelenaAlthough Autism Spectrum Disorders (ASD) is recognized as being heavily influenced by genetic factors, the role of epigenetic and environmental factors is still being established. This study aimed to identify ASD vulnerability components based on familial history and intrauterine environmental stress exposure, explore possible vulnerability subgroups, access DNA methylation age acceleration (AA) as a proxy of stress exposure during life, and evaluate the association of ASD vulnerability components and AA to phenotypic severity measures. Principal Component Analysis (PCA) was used to search the vulnerability components from 67 mothers of autistic children. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 had a higher correlation with biological factors (psychiatric family history and gestational complications). Comparing the methylome between above and below PC1 average subgroups we found 11,879 statistically significant differentially methylated probes (DMPs, p < 0.05). DMPs CpG sites were enriched in variably methylated regions (VMRs), most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability components score moderated by epigenetic clock AA was associated with Vineland Total score (p = 0.0036, adjR(2) = 0.31), suggesting risk factors with stress burden can influence ASD phenotype.
- 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.
- Classification of Autism Spectrum Disorder Severity Using Eye Tracking Data Based on Visual Attention Model(2021) REVERS, Mirian C.; OLIVEIRA, Jessica S.; FRANCO, Felipe O.; PORTOLESE, Joana; V, Thiago Cardoso; SILVA, Andreia F.; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.; BRENTANI, HelenaComputer-aided diagnosis using eye tracking data is classically based on regions of interest in the image. However, in recent years, the modeling of visual attention by saliency maps has shown better results. Wang et al., considering 3-layered saliency model that incorporated pixel-level, objectlevel, and semantic-level attributes, showed differences in the performance of eye tracking in autism spectrum disorder (ASD) and better characterized these differences by looking at which attributes were used, providing meaningful clinical results about the disorder. Our hypothesis is that the context interpretation would be worse according to the severity of ASD, consequently, the eye tracking data processed based on visual attention model (VAM) could be used to classify patients with ASD according to gravity. In this context, the present work proposes: 1) based on VAM, using Image Processing and Artificial Intelligence to learn a model for each group (severe and non-severe), from eye tracking data, and 2) a supervised classifier that, based on the models learned, performs the severity diagnosis. The classifier using the saliency maps was able to identify and separate the groups with an average accuracy of 88%. The most important features were the presence of face and skin color, in other words, semantic features.
bookPart Transtorno do espectro autista no adulto(2021) STUMP, Gabriela Viegas; PORTOLESE, Joana; LOWENTHAL, Rosane- UNDERSTANDING THE ROLE OF RARE EXONIC DELETERIOUS VARIANTS IN AUTISM SPECTRUM DISORDERS HETEROGENEITY USING NORMATIVE MODELS(2021) GOMES, Catarina; PORTOLESE, Joana; VENCIO, Ricardo Z. N.; BRENTANI, Helena
- Computer-aided autism diagnosis based on visual attention models using eye tracking(2021) OLIVEIRA, Jessica S.; FRANCO, Felipe O.; REVERS, Mirian C.; SILVA, Andreia F.; PORTOLESE, Joana; BRENTANI, Helena; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.
bookPart Tratamento de transtorno do espectro autista(2021) PORTOLESE, Joana; PACíFICO, Claudia Romano; BAGAIOLO, Leila; ROLIM, Deborah; SATO, Fábio; POLANCZYK, Guilherme Vanoni; BRENTANI, Helena