White matter and task-switching in young adults: A Diffusion Tensor Imaging study

The capacity to flexibly switch between different task rules has been previously associated with distributed fronto-parietal networks, predominantly in the left hemisphere for phasic switching sub-processes, and in the right hemisphere for more tonic aspects of task-switching, such as rule maintenance and management. It is thus likely that the white matter (WM) connectivity between these regions is critical in sustaining the flexibility required by task-switching. This study examined the relationship between WM microstructure in young adults and task-switching performance in different paradigms: classical shape-color, spatial and grammatical tasks. The main results showed an association between WM integrity in anterior portions of the corpus callosum (genu and body) and a sustained measure of task-switching performance. In particular, a higher fractional anisotropy and a lower radial diffusivity in these WM regions were associated with smaller mixing costs both in the spatial task-switching paradigm and in the shape-color one, as confirmed by a conjunction analysis. No association was found with behavioral measures obtained in the grammatical task-switching paradigm. The switch costs, a measure of phasic switching processes, were not correlated with WM microstructure in any task. This study shows that a more efficient inter-hemispheric connectivity within the frontal lobes favors sustained task-switching processes, especially with task contexts embedding non-verbal components

Abordagem CNN 2D estendida para o diagnóstico da doença de Alzheimer através de imagens de ressonância magnética estrutural

Orientadores: Leticia Rittner, Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A doença de Alzheimer (AD - Alzheimer's disease) é um tipo de demência que afeta milhões de pessoas em todo o mundo. Até o momento, não há cura para a doença e seu diagnóstico precoce tem sido uma tarefa desafiadora. As técnicas atuais para o seu diagnóstico têm explorado as informações estruturais da Imagem por Ressonância Magnética (MRI - Magnetic Resonance Imaging) em imagens ponderadas em T1. Entre essas técnicas, a rede neural convolucional (CNN - Convolutional Neural Network) é a mais promissora e tem sido usada com sucesso em imagens médicas para uma variedade de aplicações devido à sua capacidade de extração de características. Antes do grande sucesso do aprendizado profundo e das CNNs, os trabalhos que objetivavam classificar os diferentes estágios de AD exploraram abordagens clássicas de aprendizado de máquina e uma meticulosa extração de características, principalmente para classificar testes binários. Recentemente, alguns autores combinaram técnicas de aprendizagem profunda e pequenos subconjuntos do conjunto de dados públicos da Iniciativa de Neuroimagem da Doença de Alzheimer (ADNI - Alzheimer's Disease Neuroimaging Initiative) para prever um estágio inicial da doença explorando abordagens 3D CNN geralmente combinadas com arquiteturas de auto-codificador convolucional 3D. Outros também exploraram uma abordagem de CNN 3D combinando-a ou não com uma etapa de pré-processamento para a extração de características. No entanto, a maioria desses trabalhos focam apenas na classificação binária, sem resultados para AD, comprometimento cognitivo leve (MCI - Mild Cognitive Impairment) e classificação de sujeitos normais (NC - Normal Control). Nosso principal objetivo foi explorar abordagens de CNN 2D para a tarefa de classificação das 3 classes usando imagens de MRI ponderadas em T1. Como objetivo secundário, preenchemos algumas lacunas encontradas na literatura ao investigar o uso de arquiteturas CNN 2D para o nosso problema, uma vez que a maioria dos trabalhos explorou o aprendizado de máquina clássico ou abordagens CNN 3D. Nossa abordagem CNN 2D estendida explora as informações volumétricas dos dados de ressonância magnética, mantendo baixo custo computacional associado a uma abordagem 2D, quando comparados às abordagens 3D. Além disso, nosso resultado supera as outras estratégias para a classificação das 3 classes e comparando o desempenho de nosso modelo com os métodos tradicionais de aprendizado de máquina e 3D CNN. Também investigamos o papel de diferentes técnicas amplamente utilizadas em aplicações CNN, por exemplo, pré-processamento de dados, aumento de dados, transferência de aprendizado e adaptação de domínio para um conjunto de dados brasileiroAbstract: Alzheimer's disease (AD) is a type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer's and its early-diagnosis has been a challenging task. The current techniques for Alzheimer's disease diagnosis have explored the structural information of Magnetic Resonance Imaging (MRI) in T1-weighted images. Among these techniques, deep convolutional neural network (CNN) is the most promising one and has been successfully used in medical images for a variety of applications due to its ability to perform features extraction. Before the great success of deep learning and CNNs, the works that aimed to classify the different stages of AD explored classic machine learning approaches and a meticulous feature engineering extraction, mostly to classify binary tasks. Recently, some authors have combined deep learning techniques and small subsets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public dataset, to predict an early-stage of AD exploring 3D CNN approaches usually combined with 3D convolutional autoencoder architectures. Others have also investigated a 3D CNN approach combining it or not with a pre-processing step for the extraction of features. However, the majority of these papers focus on binary classification only, with no results for Alzheimer's disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) classification. Our primary goal was to explore 2D CNN approaches to tackle the 3-class classification using T1-weighted MRI. As a secondary goal, we filled some gaps we found in the literature by investigating the use of 2D CNN architectures to our problem, since most of the works either explored traditional machine learning or 3D CNN approaches. Our extended-2D CNN explores the MRI volumetric data information while maintaining the low computational costs associated with a 2D approach when compared to 3D-CNNs. Besides, our result overcomes the other strategies for the 3-class classification while analyzing the performance of our model with traditional machine-learning and 3D-CNN methods. We also investigated the role of different widely used techniques in CNN applications, for instance, data pre-processing, data augmentation, transfer-learning, and domain-adaptation to a Brazilian datasetMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica168468/2017-4  CNP

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis

Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of data and minimally fine-tuning them for adaptation to specific tasks. Even so, for 3D medical imaging tasks, we do not know if it is best to pre-train models on natural images, medical images, or even synthetically generated MRI scans or video data. To evaluate these alternatives, here we benchmarked vision transformers (ViTs) and convolutional neural networks (CNNs), initialized with varied upstream pre-training approaches. These methods were then adapted to three unique downstream neuroimaging tasks with a range of difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD) classification, "brain age" prediction. Experimental tests led to the following key observations: 1. Pre-training improved performance across all tasks including a boost of 7.4% for AD classification and 4.6% for PD classification for the ViT and 19.1% for PD classification and reduction in brain age prediction error by 1.26 years for CNNs, 2. Pre-training on large-scale video or synthetic MRI data boosted performance of ViTs, 3. CNNs were robust in limited-data settings, and in-domain pretraining enhanced their performances, 4. Pre-training improved generalization to out-of-distribution datasets and sites. Overall, we benchmarked different vision architectures, revealing the value of pre-training them with emerging datasets for model initialization. The resulting pre-trained models can be adapted to a range of downstream neuroimaging tasks, even when training data for the target task is limited