216 research outputs found
Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge
Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew’s correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds
Connectomics has emerged as a powerful tool in neuroimaging and has spurred
recent advancements in statistical and machine learning methods for
connectivity data. Despite connectomes inhabiting a matrix manifold, most
analytical frameworks ignore the underlying data geometry. This is largely
because simple operations, such as mean estimation, do not have easily
computable closed-form solutions. We propose a geometrically aware neural
framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic
mean of a collections of symmetric positive definite (SPD) matrices. The
mSPD-NN is comprised of bilinear fully connected layers with tied weights and
utilizes a novel loss function to optimize the matrix-normal equation arising
from Fr\'echet mean estimation. Via experiments on synthetic data, we
demonstrate the efficacy of our mSPD-NN against common alternatives for SPD
mean estimation, providing competitive performance in terms of scalability and
robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in
multiple experiments on rs-fMRI data and demonstrate that it uncovers stable
biomarkers associated with subtle network differences among patients with
ADHD-ASD comorbidities and healthy controls.Comment: Accepted into IPMI 202
Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis
Purpose: Autism Spectrum Disorder (ASD) is diagnosed through observation or interview assessments, which is time-consuming, subjective, and with questionable validity and reliability. Thus, we aimed to evaluate the role of machine learning (ML) with neuroimaging data to provide a reliable classification of ASD. Methods: A systematic search of PubMed, Scopus, and Embase was conducted to identify relevant publications. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the studies’ quality. A bivariate random-effects model meta-analysis was employed to evaluate the pooled sensitivity, the pooled specificity, and the diagnostic performance through the hierarchical summary receiver operating characteristic (HSROC) curve of ML with neuroimaging data in classifying ASD. Meta-regression was also performed. Results: Forty-four studies (5697 ASD and 6013 typically developing individuals [TD] in total) were included in the quantitative analysis. The pooled sensitivity for differentiating ASD from TD individuals was 86.25 95% confidence interval [CI] (81.24, 90.08), while the pooled specificity was 83.31 95% CI (78.12, 87.48) with a combined area under the HSROC (AUC) of 0.889. Higgins I2 (> 90%) and Cochran’s Q (p < 0.0001) suggest a high degree of heterogeneity. In the bivariate model meta-regression, a higher pooled specificity was observed in studies not using a brain atlas (90.91 95% CI [80.67, 96.00], p = 0.032). In addition, a greater pooled sensitivity was seen in studies recruiting both males and females (89.04 95% CI [83.84, 92.72], p = 0.021), and combining imaging modalities (94.12 95% [85.43, 97.76], p = 0.036). Conclusion: ML with neuroimaging data is an exciting prospect in detecting individuals with ASD but further studies are required to improve its reliability for usage in clinical practice
The effect of using multiple connectivity metrics in brain Functional Connectivity studies
Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa, Faculdade de Ciências, 2022Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to assist as a
diagnostic or prognostic tool for a diverse set of neurological and neuropsychiatric disorders, which are
often difficult to differentiate. fMRI focuses on the study of the brain functional Connectome, which is
characterized by the functional connections and neuronal activity among different brain regions, also
interpreted as communications between pairs of regions. This Functional Connectivity (FC) is quantified
through the statistical dependences between brain regions’ blood-oxygen-level-dependent (BOLD)
signals time-series, being traditionally evaluated by correlation coefficient metrics and represented as
FC matrices. However, several studies underlined limitations regarding the use of correlation metrics to
fully capture information from these signals, leading investigators towards different statistical metrics
that would fill those shortcomings. Recently, investigators have turned their attention to Deep Learning
(DL) models, outperforming traditional Machine Learning (ML) techniques due to their ability to
automatically extract relevant information from high-dimensional data, like FC data, using these models
with rs-fMRI data to improve diagnostic predictions, as well as to understand pathological patterns in
functional Connectome, that can lead to the discovery of new biomarkers. In spite of very encouraging
performances, the black-box nature of DL algorithms makes difficult to know which input information
led the model to a certain prediction, restricting its use in clinical settings.
The objective of this dissertation is to exploit the power of DL models, understanding how FC
matrices created from different statistical metrics can provide information about the brain FC, beyond
the conventionally used correlation family. Two publicly available datasets where studied, the ABIDE I dataset, composed by healthy and autism spectrum disease (ASD) individuals, and the ADHD-200
dataset, with typically developed controls and individuals with attention-deficit/hyperactive disorder
(ADHD). The computation of the FC matrices of both datasets, using different statistical metrics, was
performed in MATLAB using MULAN’s toolbox functions, encompassing the correlation coefficient,
non-linear correlation coefficient, mutual information, coherence and transfer entropy. The
classification of FC data was performed using two DL models, the improved ConnectomeCNN model
and the innovative ConnectomeCNN-Autoencoder model. Moreover, another goal is to study the effect
of a multi-metric approach in classification performances, combining multiple FC matrices computed
from the different statistical metrics used, as well as to study the use of Explainable Artificial
Intelligence (XAI) techniques, namely Layer-wise Relevance Propagation method (LRP), to surpass the
black-box problem of DL models used, in order to reveal the most important brain regions in ADHD.
The results show that the use of other statistical metrics to compute FC matrices can be a useful
complement to the traditional correlation metric methods for the classification between healthy subjects
and subjects diagnosed with ADHD and ASD. Namely, non-linear metrics like h2 and mutual
information, achieved similar and, in some cases, even slightly better performances than correlation
methods. The use of FC multi-metric, despite not showing improvements in classification performance
compared to the best individual method, presented promising results, namely the ability of this approach
to select the best features from all the FC matrices combined, achieving a similar performance in relation
to the best individual metric in each of the evaluation measures of the model, leading to a more complete
classification. The LRP analysis applied to ADHD-200 dataset proved to be promising, identifying brain
regions related to the pathophysiology of ADHD, which are in broad accordance with FC and structural
study’s findings.A ressonância magnética funcional em estado de repouso (rs-fMRI) tem o potencial de ser uma
ferramenta auxiliar de diagnóstico ou prognóstico para um conjunto diversificado de distúrbios
neurológicos e neuropsiquiátricos, que muitas vezes são difíceis de diferenciar. A análise de dados de
rs-fMRI recorre muitas vezes ao conceito de conectoma funcional do cérebro, que se caracteriza pelas
conexões funcionais entre as diferentes regiões do cérebro, sendo estas conexões interpretadas como
comunicações entre diferentes pares de regiões cerebrais. Esta conectividade funcional é quantificada
através de dependências estatísticas entre os sinais fMRI das regiões cerebrais, sendo estas
tradicionalmente calculadas através da métrica coeficiente de correlação, e representadas através de
matrizes de conectividade funcional. No entanto, vários estudos demonstraram limitações em relação ao
uso de métricas de correlação, em que estas não conseguem capturar por completo todas as informações
presentes nesses sinais, levando os investigadores à procura de diferentes métricas estatísticas que
pudessem preencher essas lacunas na obtenção de informações mais completas desses sinais.
O estudo destes distúrbios neurológicos e neuropsiquiátricos começou por se basear em técnicas
como mapeamento paramétrico estatístico, no contexto de estudos de fMRI baseados em tarefas. Porém,
essas técnicas apresentam certas limitações, nomeadamente a suposição de que cada região cerebral atua
de forma independente, o que não corresponde ao conhecimento atual sobre o funcionamento do cérebro.
O surgimento da rs-fMRI permitiu obter uma perspetiva mais global e deu origem a uma vasta literatura
sobre o efeito de patologias nos padrões de conetividade em repouso, incluindo tentativas de diagnóstico
automatizado com base em biomarcadores extraídos dos conectomas. Nos últimos anos, os
investigadores voltaram a sua atenção para técnicas de diferentes ramos de Inteligência Artificial, mais
propriamente para os algoritmos de Deep Learning (DL), uma vez que são capazes de superar os
algoritmos tradicionais de Machine Learning (ML), que foram aplicados a estes estudos numa fase
inicial, devido à sua capacidade de extrair automaticamente informações relevantes de dados de alta
dimensão, como é o caso dos dados de conectividade funcional. Esses modelos utilizam os dados obtidos
da rs-fMRI para melhorar as previsões de diagnóstico em relação às técnicas usadas atualmente em
termos de precisão e rapidez, bem como para compreender melhor os padrões patológicos nas conexões
funcionais destes distúrbios, podendo levar à descoberta de novos biomarcadores. Apesar do notável
desempenho destes modelos, a arquitetura natural em caixa-preta dos algoritmos de DL, torna difícil
saber quais as informações dos dados de entrada que levaram o modelo a executar uma determinada
previsão, podendo este utilizar informações erradas dos dados para alcançar uma dada inferência,
restringindo o seu uso em ambientes clínicos.
O objetivo desta dissertação, desenvolvida no Instituto de Biofísica e Engenharia Biomédica, é
explorar o poder dos modelos DL, de forma a avaliar até que ponto matrizes de conectividade funcional
criadas a partir de diferentes métricas estatísticas podem fornecer mais informações sobre a
conectividade funcional do cérebro, para além das métricas de correlação convencionalmente usadas
neste tipo de estudos. Foram estudados dois conjuntos de dados bastante utilizados em estudos de
Neurociência e que estão disponíveis publicamente: o conjunto de dados ABIDE-I, composto por
indivíduos saudáveis e indivíduos com doenças do espectro do autismo (ASD), e o conjunto de dados
ADHD-200, com controlos tipicamente desenvolvidos e indivíduos com transtorno do défice de atenção
e hiperatividade (ADHD).
Numa primeira fase foi realizada a computação das matrizes de conetividade funcional de ambos os
conjuntos de dados, usando as diferentes métricas estatísticas. Para isso, foi desenvolvido código de
MATLAB, onde se utilizam as séries temporais dos sinais BOLD obtidas dos dois conjuntos de dados
para criar essas mesmas matrizes de conectividade funcional, incorporando funções de diferentes
métricas estatísticas da caixa de ferramentas MULAN, compreendendo o coeficiente de correlação, o
coeficiente de correlação não linear, a informação mútua, a coerência e a entropia de transferência. De
seguida, a classificação dos dados de conectividade funcional, de forma a avaliar o efeito do uso de
diferentes métricas estatísticas para a criação de matrizes de conectividade funcional na discriminação
de sujeitos saudáveis e patológicos, foi realizada usando dois modelos de DL. O modelo
ConnectomeCNN melhorado e o modelo inovador ConnectomeCNN-Autoencoder foram desenvolvidos
com recurso à biblioteca de Redes Neuronais Keras, juntamente com o seu backend Tensorflow, ambos
em Python. Estes modelos, desenvolvidos previamente no Instituto de Biofísica e Engenharia
Biomédica, tiveram de ser otimizados de forma a obter a melhor performance, onde vários parâmetros
dos modelos e do respetivo treino dos mesmos foram testados para os dados a estudar. Pretendeu-se
também estudar o efeito de uma abordagem multi-métrica nas tarefas de classificação dos sujeitos de
ambos os conjuntos de dados, sendo que, para estudar essa abordagem as diferentes matrizes calculadas
a partir das diferentes métricas estatísticas utilizadas, foram combinadas, sendo usados os mesmos
modelos que foram aplicados às matrizes de conectividade funcional de cada métrica estatística
individualmente. É importante realçar que na abordagem multi-métrica também foi realizada a
otimização dos parâmetros dos modelos utilizados e do respetivo treino, de modo a conseguir a melhor
performance dos mesmos para estes dados. Para além destes dois objetivos, estudou-se o uso de técnicas
de Inteligência Artificial Explicável (XAI), mais especificamente o método Layer-wise Relevance
Propagation (LRP), com vista a superar o problema da caixa-preta dos modelos de DL, com a finalidade
de explicar como é que os modelos estão a utilizar os dados de entrada para realizar uma dada previsão.
O método LRP foi aplicado aos dois modelos utilizados anteriormente, usando como dados de entrada
o conjunto de dados ADHD-200, permitindo assim revelar quais as regiões cerebrais mais importantes
no que toca a um diagnóstico relacionado com o ADHD.
Os resultados obtidos mostram que o uso de outras métricas estatísticas para criar as matrizes de
Conectividade Funcional podem ser um complemento bastante útil às métricas estatísticas
tradicionalmente utilizadas para a classificação entre indivíduos saudáveis e indivíduos como ASD e
ADHD. Nomeadamente métricas estatísticas não lineares como o h2 e a informação mútua, obtiveram
desempenhos semelhantes e, em alguns casos, desempenhos ligeiramente melhores em relação aos
desempenhos obtidos por métodos de correlação, convencionalmente usados nestes estudos de
conectividade funcional. A utilização da multi-métrica de conectividade funcional, apesar de não
apresentar melhorias no desempenho geral da classificação em relação ao melhor método das matrizes
de conectividade funcional individuais do conjunto de métricas estatísticas abordadas, apresenta
resultados que justificam a exploração mais aprofundada deste tipo de abordagem, de forma a
compreender melhor a complementaridade das métricas e a melhor maneira de as utilizar. O uso do
método LRP aplicado ao conjunto de dados do ADHD-200 mostrou a sua aplicabilidade a este tipo de
estudos e a modelos de DL, identificando as regiões cerebrais mais relacionadas à fisiopatologia do
diagnóstico do ADHD que são compatíveis com o que é reportado por diversos estudos de conectividade
funcional e estudos de alterações estruturais associados a esta doença. O facto destas técnicas de XAI
demonstrarem como é que os modelos de DL estão a usar os dados de entrada para efetuar as previsões,
pode significar uma mais rápida e aceite adoção destes algoritmos em ambientes clínicos. Estas técnicas
podem auxiliar o diagnóstico e prognóstico destes distúrbios neurológicos e neuropsiquiátricos, que são
na maioria das vezes difíceis de diferenciar, permitindo aos médicos adquirirem um conhecimento em
relação à previsão realizada e poder explicar a mesma aos seus pacientes
Blending generative models with deep learning for multidimensional phenotypic prediction from brain connectivity data
Network science as a discipline has provided us with foundational machinery to study complex relational entities such as social networks, genomics, econometrics etc. The human brain is a complex network that has recently garnered immense interest within the data science community. Connectomics or the study of the underlying connectivity patterns in the brain has become an important field of study for the characterization of various neurological disorders such as Autism, Schizophrenia etc. Such connectomic studies have provided several fundamental insights into its intrinsic organisation and implications on our behavior and health.
This thesis proposes a collection of mathematical models that are capable of fusing information from functional and structural connectivity with phenotypic information. Here, functional connectivity is measured by resting state functional MRI (rs-fMRI), while anatomical connectivity is captured using Diffusion Tensor Imaging (DTI). The phenotypic information of interest could refer to continuous measures of behavior or cognition, or may capture levels of impairment in the case of neuropsychiatric disorders.
We first develop a joint network optimization framework to predict clinical severity from rs-fMRI connectivity matrices. This model couples two key terms into a unified optimization framework: a generative matrix factorization and a discriminative linear regression model. We demonstrate that the proposed joint inference strategy is successful in generalizing to prediction of impairments in Autism Spectrum Disorder (ASD) when compared with several machine learning, graph theoretic and statistical baselines. At the same time, the model is capable of extracting functional brain biomarkers that are informative of individual measures of clinical severity. We then present two modeling extensions to non-parametric and neural network regression models that are coupled with the same generative framework.
Building on these general principles, we extend our framework to incorporate multimodal information from Diffusion Tensor Imaging (DTI) and dynamic functional connectivity. At a high level, our generative matrix factorization now estimates a time-varying functional decomposition. At the same time, it is guided by anatomical connectivity priors in a graph-based regularization setup. This connectivity model is coupled with a deep network that predicts multidimensional clinical characterizations and models the temporal dynamics of the functional scan. This framework allows us to simultaneously explain multiple impairments, isolate stable multi-modal connectivity signatures, and study the evolution of various brain states at rest.
Lastly, we shift our focus to end-to-end geometric frameworks. These are designed to characterize the complementarity between functional and structural connectivity data spaces, while using clinical information as a secondary guide. As an alternative to the previous generative framework for functional connectivity, our representation learning scheme of choice is a matrix autoencoder that is crafted to reflect the underlying data geometry. This is coupled with a manifold alignment model that maps from function to structure and a deep network that maps to phenotypic information. We demonstrate that the model reliably recovers structural connectivity patterns across individuals, while robustly extracting predictive yet interpretable brain biomarkers. Finally, we also present a preliminary analytical and experimental exposition on the theoretical aspects of the matrix autoencoder representation
Scalable Machine Learning Methods for Massive Biomedical Data Analysis.
Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional connectomes, which are high dimensional correlation maps generated from resting state functional MRI.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111354/1/takanori_1.pd
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ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors
Classification and early detection of dementia and cognitive decline with magnetic resonance imaging
Dementie is een
verwoestende ziekte waar wereldwijd miljoenen mensen aan leiden. De meest
voorkomende oorzaak van dementie is de ziekte van Alzheimer. Voor het
ontwikkelen van effectieve behandelingen is het belangrijk om dementie in een
vroeg stadium te detecteren.
Traditioneel alzheimeronderzoek is voornamelijk gericht op groepsverschillen
tussen patiënten en controles. Recent onderzoek is deels verschoven naar
individuele classificatie met machine learning. In dit proefschrift onderzoeken
we het gebruik van magnetic resonance imaging (MRI) voor automatische detectie
van de ziekte van Alzheimer, en vroege detectie van cognitieve achteruitgang.
In dit proefschrift laten we zien dat het combineren van MRI modaliteiten de
classificatie kan verbeteren. Ook laten we zien dat diffusie MRI een goede maat
is om alzheimer te diagnosticeren.
Bij toepassing van dezelfde methoden op een groep presymptomatische gendragers
die amyloïdangiopathie zullen ontwikkelen vonden we geen verschillen tussen de
gendragers en controles. Tevens waren we niet in staat om cognitieve
achteruitgang na 4 jaar te voorspellen in een groep ouderen met verhoogd risico
op achteruitgang.
Met MRI kunnen betrouwbare individuele uitspraken gedaan kan worden over patiënten,
maar het is met de huidige methoden niet gevoelig voor vroege detectie van
cognitieve achteruitgang.Alzheimer NederlandLUMC / Geneeskund
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