1,721,103 research outputs found
Multi-view Brain Network Prediction from a Source View Using Sample Selection via CCA-Based Multi-kernel Connectomic Manifold Learning
No full textSeveral challenges emerged from the dataclysm of neuroimaging datasets spanning both healthy and disordered brain spectrum. In particular, samples with missing data views (e.g., functional imaging modality) constitute a hurdle to conventional big data learning techniques which ideally would be trained using a maximum number of samples across all views. Existing works on predicting target data views from a source data view mainly used brain images such as predicting PET image from MRI image. However, to the best of our knowledge, predicting a set of target brain networks from a source network remains unexplored. To ll this gap, a multi-kernel manifold learning (MKML) framework is proposed to learn how to predict multi-view brain networks from a source network to impute missing views in a connectomic dataset. Prior to performing multiple kernel learning of multi-view data, it is typically assumed that the source and target data come from the same distribution. However, multi-view connectomic data can be drawn from different distributions. In order to build robust predictors for predicting target multi-view networks from a source network view, it is necessary to take into account the shift between the source and target domains. Hence, we first estimate a mapping function that transforms the source and the target domains into a shared space where their correlation is maximized using canonical correlation analysis (CCA). Next, we nest the projected training and testing source samples into a connectomic manifold using multiple kernel learning, where we identify the most similar training samples to the testing source network. Given a testing subject, we introduce a cross-domain trust score to assess the reliability of each selected training sample for the target prediction task. Our model outperformed both conventional MKML technique and the proposed CCA-based MKMLtechnique without enhancement by trust scores
Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes
No full textIn this study, we aim to predict emotional intelligence scores from functional connectivity data acquired at different timepoints. To enhance the generalizability of the proposed predictive model to new data and accurate identification of most relevant neural correlates with different facets of the human intelligence, we propose a joint support vector machine and support vector regression (SVM + SVR) model. Specifically, we first identify most discriminative connections between subjects with high vs low emotional intelligence scores in the SVM step and then perform a multi-variate linear regression using these connections to predict the target emotional intelligence score in the SVR step. Our methodoutperformed existing methods including the Connectome-based Predictive Model (CPM) using functional connectivity data simultaneously acquired with the intelligence scores. The most predictive connections of intelligence included brain regions involved in processing of emotions and social behaviour
Data-specific feature selection method identification for most reproducible connectomic feature discovery fingerprinting brain states
Get PDFMachine learning methods present unprecedented opportunities to advance our understanding of the connectomics of brain disorders. With the proliferation of extremely high-dimensional connectomic data drawn from multiple neuroimaging sources (e.g., functional and structural MRIs), effective feature selection (FS) methods have become indispensable components for (i) disentangling brain states (e.g., early vs late mild cognitive impairment) and (ii) identifying connectional features that might serve as biomarkers for treatment. Strangely, despite the extensive work on identifying stable discriminative features using a particular FS method, the challenge of choosing the best one from a large pool of existing FS techniques for optimally achieving (i) and (ii) using a dataset of interest remains unexplored. In essence, the question that we aim to address in this work is: “Given a set of feature selection methods {FS1,&FSk}, and a dataset of interest, which FS method might produce the most reproducible and ‘trustworthy’ connectomic features that accurately differentiate between two brain states?” This paper is an attempt to address this question by evaluating the performance of a particular feature selection for a specific data type in fulfilling criteria (i) and (ii). To this aim, we propose to model the relationships between a set of FS methods using a multi-graph architecture, where each graph quantifies the feature reproducibility power between graph nodes at a fixed number of top ranked features. Next, we integrate the reproducibility graphs with a discrepancy graph which captures the difference in classification performance between FS methods. This allows to identify, for a dataset of interest, the ‘central’ node with the highest degree, which reveals the most reliable and reproducible FS method for the target brain state classification task along with the most discriminative features fingerprinting these brain states. We evaluated our method on multi-view brain connectomic data for late mild cognitive impairment vs Alzheimer’s disease classification. Our experiments give insights into reproducible connectional features fingerprinting late dementia brain states.</p
Intact connectional morphometricity learning using multi-view morphological brain networks with application to autism spectrum disorder
Get PDFThe morphology of anatomical brain regions can be affected by neurological disorders, including dementia and schizophrenia, to various degrees. Hence, identifying the morphological signature of a specific brain disorder can improve diagnosis and better explain how neuroanatomical changes associate with function and cognition. To capture this signature, a landmark study introduced, brain morphometricity, a global metric defined as the proportion of phenotypic variation that can be explained by brain morphology derived from structural brain MRI scans. However, this metric is limited to investigating morphological changes using low-order measurements (e.g., regional volumes) and overlooks how these changes can be related to each other (i.e., how morphological changes in region A are influenced by changes in region B). Furthermore, it is derived from a pre-defined anatomical similarity matrix using a Gaussian function, which might not be robust to outliers and constrains the locality of data to a fixed bandwidth. To address these limitations, we propose the intact connectional brain morphometricity (ICBM), a metric that captures the variation of connectional changes in brain morphology. In particular, we use multi-view morphological brain networks estimated from multiple cortical attributes (e.g., cortical thickness) to learn an intact space that first integrates the morphological network views into a unified space. Next, we learn a multi-view morphological similarity matrix in the intact space by adaptively assigning neighbors for each data sample based on local connectivity. The learned similarity capturing the shared traits across morphological brain network views is then used to derive our ICBM via a linear mixed effect model. Our framework shows the potential of the proposed ICBM in capturing the connectional neuroanatomical signature of brain disorders such as Autism Spectrum Disorder.</p
XmoNet:a Fully Convolutional Network for Cross-Modality MR Image Inference
No full textMagnetic resonance imaging (MRI) can generate multimodal scans with complementary contrast information, capturing various anatomical or functional properties of organs of interest. But whilst the acquisition of multiple modalities is favourable in clinical and research settings, it is hindered by a range of practical factors that include cost and imaging artefacts.We propose XmoNet, a deep-learning architecture based on fully convolutional networks (FCNs) that enables cross-modality MR image inference. This multiple branch architecture operates on various levels of image spatial resolutions, encoding rich feature hierarchies suited for this image generation task. We illustrate the utility of XmoNet in learning the mapping between heterogeneous T1- and T2-weighted MRI scans for accurate and realistic image synthesis in a preliminary analysis. Our ndings support scaling the work to include larger samples and additional modalities
Multi-view Brain Network Prediction from a Source View Using Sample Selection via CCA-Based Multi-kernel Connectomic Manifold Learning
Get PDFSeveral challenges emerged from the dataclysm of neuroimaging datasets spanning both healthy and disordered brain spectrum. In particular, samples with missing data views (e.g., functional imaging modality) constitute a hurdle to conventional big data learning techniques which ideally would be trained using a maximum number of samples across all views. Existing works on predicting target data views from a source data view mainly used brain images such as predicting PET image from MRI image. However, to the best of our knowledge, predicting a set of target brain networks from a source network remains unexplored. To ll this gap, a multi-kernel manifold learning (MKML) framework is proposed to learn how to predict multi-view brain networks from a source network to impute missing views in a connectomic dataset. Prior to performing multiple kernel learning of multi-view data, it is typically assumed that the source and target data come from the same distribution. However, multi-view connectomic data can be drawn from different distributions. In order to build robust predictors for predicting target multi-view networks from a source network view, it is necessary to take into account the shift between the source and target domains. Hence, we first estimate a mapping function that transforms the source and the target domains into a shared space where their correlation is maximized using canonical correlation analysis (CCA). Next, we nest the projected training and testing source samples into a connectomic manifold using multiple kernel learning, where we identify the most similar training samples to the testing source network. Given a testing subject, we introduce a cross-domain trust score to assess the reliability of each selected training sample for the target prediction task. Our model outperformed both conventional MKML technique and the proposed CCA-based MKMLtechnique without enhancement by trust scores
Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes
Get PDFIn this study, we aim to predict emotional intelligence scores from functional connectivity data acquired at different timepoints. To enhance the generalizability of the proposed predictive model to new data and accurate identification of most relevant neural correlates with different facets of the human intelligence, we propose a joint support vector machine and support vector regression (SVM + SVR) model. Specifically, we first identify most discriminative connections between subjects with high vs low emotional intelligence scores in the SVM step and then perform a multi-variate linear regression using these connections to predict the target emotional intelligence score in the SVR step. Our methodoutperformed existing methods including the Connectome-based Predictive Model (CPM) using functional connectivity data simultaneously acquired with the intelligence scores. The most predictive connections of intelligence included brain regions involved in processing of emotions and social behaviour
AI in film and media: a dual-edged sword of creative empowerment and labour displacement
No full texthis paper explores AI’s paradoxical role in the film and media industries as both an empowering force for creative democratization and a disruptive agent contributing to labour displacement. Through historical and legal precedents, conceptual frameworks, and empirical observations, it outlines the transformative, ethical, and policy implications of AI in media
XmoNet:a Fully Convolutional Network for Cross-Modality MR Image Inference
Get PDFMagnetic resonance imaging (MRI) can generate multimodal scans with complementary contrast information, capturing various anatomical or functional properties of organs of interest. But whilst the acquisition of multiple modalities is favourable in clinical and research settings, it is hindered by a range of practical factors that include cost and imaging artefacts.We propose XmoNet, a deep-learning architecture based on fully convolutional networks (FCNs) that enables cross-modality MR image inference. This multiple branch architecture operates on various levels of image spatial resolutions, encoding rich feature hierarchies suited for this image generation task. We illustrate the utility of XmoNet in learning the mapping between heterogeneous T1- and T2-weighted MRI scans for accurate and realistic image synthesis in a preliminary analysis. Our ndings support scaling the work to include larger samples and additional modalities
Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study
Get PDFMotion management strategies are crucial for radiotherapy of mobile tumours in order to ensure proper target coverage, save organs at risk and prevent interplay effects. We present a feasibility study for an inter-fractional, patient-specific motion model targeted at active beam scanning proton therapy. The model is designed to predict dense lung motion information from 2D abdominal ultrasound images. In a pretreatment phase, simultaneous ultrasound and magnetic resonance imaging are used to build a regression model. During dose delivery, abdominal ultrasound imaging serves as a surrogate for lung motion prediction. We investigated the performance of the motion model on five volunteer datasets. In two cases, the ultrasound probe was replaced after the volunteer has stood up between two imaging sessions. The overall mean prediction error is 2.9 mm and 3.4 mm after repositioning and therefore within a clinically acceptable range. These results suggest that the ultrasound-based regression model is a promising approach for inter-fractional motion management in radiotherapy
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