A5055907928- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- Autism Spectrum Disorder Research
- Advanced Neuroimaging Techniques and Applications
- Advanced Vision and Imaging
- Optical measurement and interference techniques
- Virology and Viral Diseases
- Optical Coherence Tomography Applications
- Medical Image Segmentation Techniques
- Neural dynamics and brain function
- Advanced Image Processing Techniques
- Blind Source Separation Techniques
- Sparse and Compressive Sensing Techniques
- Genomic variations and chromosomal abnormalities
- Neuroscience and Neural Engineering
Universidade Estadual de Campinas (UNICAMP)
2023-2024
Universidade Federal de Itajubá
2020-2022
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate disorder. These approaches evaluate brain oxygen levels indirectly measure activity compare typical developmental subjects with ASD ones. However, none these works tried classify into...
Genetic factors have been pointed out as the primary root associated with risk of autism. Recent works indicate that approximately 80% autistic people inherited condition from their parents. However, there are no estimates likelihood an parent having child. Using Hidden Markov Models, together data autism heritability, we developed a model to investigate parents generating children. Models double-layered stochastic process, and it consists nonvisible process (not observable) can be predicted...
Identifying and characterizing brain fiber bundles can help to understand many diseases conditions. An important step in this process is the estimation of orientations using Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI). However, obtaining robust orientation estimates demands high-resolution data, leading lengthy acquisitions that are not always clinically available. In work, we explore use automated angular super resolution from faster overcome challenge. Using publicly available...
Background: The last decade was marked by increased neuroscience research involving machine Learning (ML) and medical images such as functional magnetic resonance electroencephalogram (EEG). There are many challenges in this field, including the need for more data a standard presenting results. Since ML models tend to be sensitive input data, different strategies acquisition, preprocessing, feature selection, validation significantly impact results achieved. Therefore, significant variation...
The Corpus Callosum (CC) is a major white matter structure that plays crucial role in the communication between cerebral hemispheres and associated with many subjects' characteristics brain diseases. subdivision of CC into smaller regions, called parcellation, important for detailed analysis structure. However, lack visible landmarks makes parcellation challenging task. In this study, we introduce novel automatic data-driven approach based on Diffusion Tensor Imaging. It uses Tensorial...