Neuroinformatics
For more than 20 years, MRI has played a fundamental in cognitive neuroscience. The introduction of ultra-high field MRI scanners (≥ 7 Tesla) has significantly enhanced data quality. However, traditional data analysis methods designed for 3T MRI often fall short in the ultra-high field domain, prompting the need for more sophisticated approaches. The goal of our team is to develop advanced machine learning techniques customized for magnetic resonance imaging of the human brain. By merging neuroscience with machine learning, we aim to reveal fresh perspectives beyond traditional analysis approaches. Specifically, we have developed a range of novel analysis techniques ranging from high-precision segmentation of 9.4T MRI data, super-resolution to predictive modelling.
Autism Spectrum Disorder (ASD) is a common psychiatric condition characterized by atypical cognitive, emotional and social patterns. With millions of people affected worldwide, early diagnosis is essential for positive outcomes. However, current diagnostic methods, which rely on behavioral observations, are time-consuming, subjective, and require trained clinicians. In addition, common assessments of ASD lack objectivity and transparency.
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Mental health issues, particularly conditions such as Autism Spectrum Disorder (ASD), are a major global concern. The lack of reliable biomarkers poses a challenge for timely and accurate diagnosis, which relies solely on behavioral observations. This study explores the intersection of deep learning, explicable AI, and ASD diagnosis, with the goal of extracting meaningful biomarkers from brain imaging data. The present study complements an earlier study in which we developed a predictive modelling approach for ASD.
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Ultra-high field (UHF) MRI often suffers from motion artifacts, which can degrade image quality and affect subsequent analysis. In our study, we present an innovative solution to this problem by using deep features from pre-trained convolutional neural networks (CNNs) to correct motion artifacts in MRI.
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Automatic image segmentation in brain magnetic resonance imaging is essential for various applications, as it is a prerequisite for upstream tasks in computational neuroimaging analysis. Most notably, highly precise segmentation of the cortical gray matter is required for depth-specific fMRI studies, where even minor errors can significantly impact subsequent analyses.
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Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)).
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MRI super-resolution (SR) and de-noising tasks are fundamental challenges in the field of deep learning, which have traditionally been treated as distinct tasks with separately paired training data. We propose an innovative method that addresses both tasks simultaneously using a single deep learning model, eliminating the need for explicitly paired noisy and clean images during training.
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A number of recent studies have investigated machine learning techniques for predicting individual behaviour from fMRI. Even though encouraging results have been obtained, excessive scan times - especially in resting state fMRI - are a limiting factor.
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Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI).
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One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. In this study, we propose a non-parametric and threshold-free framework called LISA to address this demand.
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