High Performance And Machine Learning Algorithms For Brain Fmri Data

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High Performance and Machine Learning Algorithms for Brain FMRI Data

Author : Taban Eslami
Publisher :
ISBN 13 :
Total Pages : 118 pages
Book Rating : 4.:/5 (124 download)

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Book Synopsis High Performance and Machine Learning Algorithms for Brain FMRI Data by : Taban Eslami

Download or read book High Performance and Machine Learning Algorithms for Brain FMRI Data written by Taban Eslami and published by . This book was released on 2020 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain disorders are very difficult to diagnose for reasons such as overlapping nature of symptoms, individual differences in brain structure, lack of medical tests and unknown causes of some disorders. The current psychiatric diagnostic process is based on behavioral observation and may be prone to misdiagnosis. Noninvasive brain imaging technologies such as Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) make the process of understanding the structure and function of the brain easier. Quantitative analysis of brain imaging data using machine learning and data mining techniques can be advantageous not only to increase the accuracy of brain disorder diagnosis but also to unravel unknown facts about the complex function of the brain. Research studies have shown functional connectivities of brain contain discriminative patterns that are widely used in a variety of studies such as fMRI classification. In this dissertation, we designed machine learning and deep learning models for diagnosing brain disorders such as ADHD and ASD using fMRI data. In order to reduce the risk of overfitting in deep learning methods, we proposed a data augmentation approach for generating artificial samples from available data. Our models are able to improve the accuracy of classifying healthy samples from patients up to 28%. comparing to state-of-the-art solutions. Analysis of fMRI data considering a huge number of voxels (smallest addressable element of fMRI data) is very time-consuming. One example is computing pairwise functional connections between voxels using measures like Pearson’s correlation. To tackle this issue, we designed two GPU based frameworks based on matrix multiplication for computing pairwise correlations that deliver around 3 times speedup against state-of-the-art GPU based methods. We expanded these frameworks to compute dynamic functional connectivity which involves computing multiple sets of pairwise correlations each associated with specific time windows in original time series followed by designing two methodologies for reducing the space requirements of pairwise correlations.

Machine Learning in Resting-state and Naturalistic FMRI Analysis

Author : Meenakshi Khosla
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Machine Learning in Resting-state and Naturalistic FMRI Analysis by : Meenakshi Khosla

Download or read book Machine Learning in Resting-state and Naturalistic FMRI Analysis written by Meenakshi Khosla and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two brain activity recording paradigms in humans have emerged as increasingly more popular tools for studying brain function in health and in disease, namely resting-state and naturalistic stimulation. These two techniques attempt to capture brain activity 'in the wild' when it is unconstrained by any specific task and thus reflect more naturalistic modes of operation of the brain. The complexity, very high-dimensional nature, a suite of potential applications and lack of standard, straightforward analysis tools make machine learning very attractive for this kind of data. In this thesis, we draw upon recent advances in machine learning, fueled by the success of deep learning, to develop models that can capture the full richness of this data. Resting-state fMRI (rs-fMRI) has enormous potential to advance our understanding of the brain's functional organization and how it is altered by damage or disease. Over the last decade, substantial effort has been devoted to using rs-fMRI for classification of a wide range of neuropsychiatric conditions, such as Alzheimer's disease, schizophrenia, autism spectrum disorder etc. While a growing number of studies have demonstrated the promise of machine learning algorithms for rs-fMRI based clinical or behavioral prediction, most prior models have been limited in their capacity to exploit the richness of the data. The first part of this thesis describes our work on developing novel machine learning approaches for deriving subject level predictions from rs-fMRI scans. We propose a novel volumetric Convolutional Neural Network framework that takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. We showcase our approach on a challenging large-scale dataset and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls. The second part of this thesis is aimed at developing predictive models that can capture information processing within the brain under naturalistic stimulation more stringently than existing approaches. Brain activity recordings of healthy subjects during "free viewing" of movies present a powerful opportunity to build ecologically sound and generalizable models of sensory systems, also known as encoding models. Deep neural networks trained on image or sound recognition tasks have emerged as powerful models of computations underlying sensory processing in the brain, surpassing traditional models of image or sound representation based on Gabor filters and spectro-temporal filters, respectively. While this success is promising, existing encoding models based on deep neural networks have been limited in their focus on limited portions of the sensory space under naturalistic stimulation, ignoring the complex and dynamic interactions of modalities (audio and vision) in this inherently context-rich paradigm. In the second part of this thesis, we will introduce our research with predictive models of cortical responses that aims to capture several critical inductive biases about information processing in the brain : namely, hierarchical processing, assimilation over longer timescales, attentional modulation and multi-sensory auditory-visual interactions. We will describe our efforts in capturing these phenomena in models of the brain and will share our latest findings from this novel computational approach. Finally, we describe our ongoing efforts to characterize neural response properties in the visual cortex under 'ecological' conditions systematically in an entirely data-driven fashion using computational models. Together, our findings illustrate how computational models overcome the tradition of excessive reductionism in cognitive neuroimaging by providing a general-purpose framework that abstracts away from the particulars of the experimental approach and can be used to describe multiple experiments at the same time.

The Livermore Brain

Author :
Publisher :
ISBN 13 :
Total Pages : 17 pages
Book Rating : 4.:/5 (967 download)

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Book Synopsis The Livermore Brain by :

Download or read book The Livermore Brain written by and published by . This book was released on 2016 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or "features" are transformations of the raw data into a form where patterns are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world's largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.

Brain-inspired Machine Learning and Computation for Brain-Behavior Analysis

Author : Rong Chen
Publisher : Frontiers Media SA
ISBN 13 : 2889666832
Total Pages : 290 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Brain-inspired Machine Learning and Computation for Brain-Behavior Analysis by : Rong Chen

Download or read book Brain-inspired Machine Learning and Computation for Brain-Behavior Analysis written by Rong Chen and published by Frontiers Media SA. This book was released on 2021-04-16 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Machine Learning and Interpretation in Neuroimaging

Author : Georg Langs
Publisher : Springer
ISBN 13 : 3642347134
Total Pages : 277 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Machine Learning and Interpretation in Neuroimaging by : Georg Langs

Download or read book Machine Learning and Interpretation in Neuroimaging written by Georg Langs and published by Springer. This book was released on 2012-11-11 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.

Signal Processing and Machine Learning for Biomedical Big Data

Author : Ervin Sejdic
Publisher : CRC Press
ISBN 13 : 1351061216
Total Pages : 1235 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Signal Processing and Machine Learning for Biomedical Big Data by : Ervin Sejdic

Download or read book Signal Processing and Machine Learning for Biomedical Big Data written by Ervin Sejdic and published by CRC Press. This book was released on 2018-07-04 with total page 1235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Machine Learning and Medical Imaging

Author : Guorong Wu
Publisher : Academic Press
ISBN 13 : 0128041145
Total Pages : 514 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Machine Learning and Medical Imaging by : Guorong Wu

Download or read book Machine Learning and Medical Imaging written by Guorong Wu and published by Academic Press. This book was released on 2016-08-11 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Author : Jyotismita Chaki
Publisher : Academic Press
ISBN 13 : 0323983952
Total Pages : 260 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Brain Tumor MRI Image Segmentation Using Deep Learning Techniques by : Jyotismita Chaki

Download or read book Brain Tumor MRI Image Segmentation Using Deep Learning Techniques written by Jyotismita Chaki and published by Academic Press. This book was released on 2021-11-27 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi path, fully convolutional network, cascade convolutional neural networks, Long Short-Term Memory - Recurrent Neural Network and Gated Recurrent Units, and more. The book also highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in brain tumor segmentation. Provides readers with an understanding of deep learning-based approaches in the field of brain tumor segmentation, including preprocessing techniques Integrates recent advancements in the field, including the transformation of low-resolution brain tumor images into super-resolution images using deep learning-based methods, single path Convolutional Neural Network based brain tumor segmentation, and much more Includes coverage of Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN), Gated Recurrent Units (GRU) based Recurrent Neural Network (RNN), Generative Adversarial Networks (GAN), Auto Encoder based brain tumor segmentation, and Ensemble deep learning Model based brain tumor segmentation Covers research Issues and the future of deep learning-based brain tumor segmentation

Advanced Machine Learning Approaches for Brain Mapping

Author : Dajiang Zhu
Publisher : Frontiers Media SA
ISBN 13 : 2832547575
Total Pages : 230 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Advanced Machine Learning Approaches for Brain Mapping by : Dajiang Zhu

Download or read book Advanced Machine Learning Approaches for Brain Mapping written by Dajiang Zhu and published by Frontiers Media SA. This book was released on 2024-04-10 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain mapping is dedicated to using brain imaging techniques such as MRI, CT, PET, EEG, and fNIRS to understand the brain anatomy, structure, and function, and how it contributes to cognition, behavior, and deficits of brain diseases. Recently, machine learning is in a stage of rapid development, and various new technologies are continuously introduced into the field, from traditional approaches

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Author : Nello Cristianini
Publisher : Cambridge University Press
ISBN 13 : 9780521780193
Total Pages : 216 pages
Book Rating : 4.7/5 (81 download)

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Book Synopsis An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by : Nello Cristianini

Download or read book An Introduction to Support Vector Machines and Other Kernel-based Learning Methods written by Nello Cristianini and published by Cambridge University Press. This book was released on 2000-03-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

Dictionary Learning Algorithms for Functional Magnetic Resonance Imaging

Author : Muhammad Usman Khalid
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)

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Book Synopsis Dictionary Learning Algorithms for Functional Magnetic Resonance Imaging by : Muhammad Usman Khalid

Download or read book Dictionary Learning Algorithms for Functional Magnetic Resonance Imaging written by Muhammad Usman Khalid and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The detection of regional activity and estimation of communication networks are two critical features to enable functional understanding of a human brain. In this regard, functional magnetic resonance imaging (fMRI) has emerged as a powerful noninvasive neuroimaging modality to investigate not only the neural activity within the different isolated brains regions but also how different brain regions communicate with each other during cognition and resting state. Similar to other imaging modalities, the functional connectivity networks for fMRI data are usually determined by following a data-driven approach, which typically consists of statistical similarity measures such as estimation of correlation matrix, and machine learning methods such as matrix decomposition and exploratory techniques. Unlike hypothesis-driven approach for activation detection that requires prior knowledge about the shape of hemodynamic response function (HRF), the main advantage of data-driven approach is its independence from predetermined paradigm knowledge, hence, its applicability to both activation detection and functional connectivity estimation. However, the trained bases from the standard decomposition techniques and data transformation bases from exploratory techniques are not very meaningful and can give rise to issues such as computational inefficiency and inability to retrieve spatiotemporal ground truth with sufficient conviction, respectively. To remedy these problems, matrix decomposition is adapted to fMRI data by considering sparse assumption based on the spatial characteristics and autocorrelation assumption based on temporal characteristics of the fMRI data to develop novel sparse dictionary learning algorithms for this work. In proposed algorithms, better assumptions about underlying structure of the data are taken into account, which yield bases that are relatively closer to the artificially generated ground truth. For every proposed algorithm, an empirical study is introduced for quantitative and comparative analysis with existing data-driven techniques. To validate the proposed algorithms, they are applied to six different fMRI data-sets and it has been shown that they outperform existing state state-of-the-art-methods in terms of statistical performance of map and network retrieval for activation detection and functional connectivity, respectively.

Algorithms for High-performance Brain-computer Interfaces

Author : Guy Wilson (Researcher in neurosciences)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

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Book Synopsis Algorithms for High-performance Brain-computer Interfaces by : Guy Wilson (Researcher in neurosciences)

Download or read book Algorithms for High-performance Brain-computer Interfaces written by Guy Wilson (Researcher in neurosciences) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain-computer interfaces (BCIs) are a set of technologies that enable people to interact with devices by directly translating neural activity into command signals. Noninvasive methods such as EEG and fMRI allow minimal-risk access to brain signals but are limited by low spatial and temporal resolution, respectively. By contrast, intracortical BCIs (iBCIs) benefit from unparalleled spatial and temporal resolution recordings of local neuronal ensembles, resulting in some of the highest-performance communication systems to date. While promising, several key challenges remain for wider adoption of these technologies. First, iBCIs in particular suffer from signal instability that cause performance degradation across time. A key challenge for clinical translation of these iBCI cursor systems is ensuring robust, long-term control for end users without manual retraining. Second, while these technologies have enabled point-and-click based typing interfaces approaching 8-10 words per minute, an ideal interface would match the bandwidth of conversational speech (roughly 150 words per minute). Third, intracortical systems require brain surgery, which has inherent risks due to the invasive nature of the procedure. Developing noninvasive approaches for silent speech decoding may therefore be more suitable for a subset of patients. In this work, we discuss three advances that tackle these key areas by leveraging modern machine learning methods alongside high signal-to-noise (SNR) recording systems. First, we present an unsupervised recalibration procedure for improving cursor BCI robustness. Using data from our clinical trial participant, we highlight the extent and timescales of neural feature drift. We demonstrate how existing state-of-the-art procedures are seemingly ill-equipped to deal with long-term drift using both offline data as well as simulation models. We then introduce a novel procedure that leverages task structure to recalibrate a system automatically, and demonstrate superior performance in-silico and in closed-loop in our participant. Second, we lay the groundwork for intracortical speech BCI efforts by prototyping a system in an offline manner using microelectrode arrays in dorsal motor cortex. We demonstrate that, despite being located in a nontraditional brain area for speech decoding, our arrays provide high SNR compared to existing approaches. Third, we leverage skin-like flexible electronics and deep learning for silent speech decoding from electromyography (EMG) signals.

High Performance Computing

Author : Michèle Weiland
Publisher : Springer Nature
ISBN 13 : 3030343561
Total Pages : 682 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis High Performance Computing by : Michèle Weiland

Download or read book High Performance Computing written by Michèle Weiland and published by Springer Nature. This book was released on 2019-12-02 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of 13 workshops held at the 34th International ISC High Performance 2019 Conference, in Frankfurt, Germany, in June 2019: HPC I/O in the Data Center (HPC-IODC), Workshop on Performance & Scalability of Storage Systems (WOPSSS), Workshop on Performance & Scalability of Storage Systems (WOPSSS), 13th Workshop on Virtualization in High-Performance Cloud Computing (VHPC '18), 3rd International Workshop on In Situ Visualization: Introduction and Applications, ExaComm: Fourth International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, International Workshop on OpenPOWER for HPC (IWOPH18), IXPUG Workshop: Many-core Computing on Intel, Processors: Applications, Performance and Best-Practice Solutions, Workshop on Sustainable Ultrascale Computing Systems, Approximate and Transprecision Computing on Emerging Technologies (ATCET), First Workshop on the Convergence of Large Scale Simulation and Artificial Intelligence, 3rd Workshop for Open Source Supercomputing (OpenSuCo), First Workshop on Interactive High-Performance Computing, Workshop on Performance Portable Programming Models for Accelerators (P^3MA). The 48 full papers included in this volume were carefully reviewed and selected. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include HPC computer architecture and hardware; programming models, system software, and applications; solutions for heterogeneity, reliability, power efficiency of systems; virtualization and containerized environments; big data and cloud computing; and artificial intelligence.

Machine Learning Algorithms for Pattern Discovery in Spatio-temporal Data With Application to Brain Imaging Analysis

Author : Nima Asadi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Machine Learning Algorithms for Pattern Discovery in Spatio-temporal Data With Application to Brain Imaging Analysis by : Nima Asadi

Download or read book Machine Learning Algorithms for Pattern Discovery in Spatio-temporal Data With Application to Brain Imaging Analysis written by Nima Asadi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Temporal networks have become increasingly pervasive in many real-world applications. Due to the existence of diverse and evolving entities in such networks, understanding the structure and characterizing patterns in them is a complex task. A prime real-world example of such networks is the functional connectivity of the brain. These networks are commonly generated by measuring the statistical relationship between the oxygenation level-dependent signal of spatially separate regions of the brain over the time of an experiment involving a task being performed or at rest in an MRI scanner. Due to certain characteristics of fMRI data, such as high dimensionality and high noise level, extracting spatio-temporal patterns in such networks is a complicated task. Therefore, it is necessary for state-of-the-art data-driven analytical methods to be developed and employed for this domain. In this thesis, we suggest methodological tools within the area of spatio-temporal pattern discovery to explore and address several questions in the domain of computational neuroscience. One of the important objectives in neuroimaging research is the detection of informative brain regions for characterizing the distinction between the activation patterns of the brains among groups with different cognitive conditions. Popular approaches for achieving this goal include the multivariate pattern analysis(MVPA), regularization-based methods, and other machine learning based approaches. However, these approaches suffer from a number of limitations, such as requirement of manual tuning of parameter as well as incorrect identification of truly informative regions in certain cases. We therefore propose a maximum relevance minimum redundancy search algorithm to alleviate these limitations while increasing the precision of detection of infor- mative activation clusters. The second question that this thesis work addresses is how to detect the temporal ties in a dynamic connectivity network that are not formed at random or due to local properties of the nodes. To explore the solution to this problem, a null model is proposed that estimates the latent characteristics of the distributions of the temporal links through optimization, followed by a statistical test to filter the links whose formation can be reduced to the local properties of their interacting nodes. We demonstrate the benefits of this approach by applying it to a real resting state fMRI dataset, and provide further discussion on various aspects and advantages of it. Lastly, this dissertation delves into the task of learning a spatio-temporal representation to discover contextual patterns in evolutionary structured data. For this purpose, a representation learning approach is proposed based on the transformer model to extract the spatio-temporal contextual information from the fMRI data. Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative set of features can specially benefit the analysis of fMRI data due to the complexities and dynamic dependencies present in such datasets. The proposed framework takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features which can in turn be used in var- ious downstream tasks such as classification, feature extraction, and statistical analysis. This architecture uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. The benefits of this framework are demonstrated by applying it to two resting state fMRI datasets, and further discussion is provided on various aspects and advantages of it over a number of commonly adopted architectures.

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology

Author : Seyed Mostafa Kia
Publisher : Springer Nature
ISBN 13 : 3030668436
Total Pages : 319 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology by : Seyed Mostafa Kia

Download or read book Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology written by Seyed Mostafa Kia and published by Springer Nature. This book was released on 2020-12-30 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Handbook of Functional MRI Data Analysis

Author : Russell A. Poldrack
Publisher : Cambridge University Press
ISBN 13 : 1139498363
Total Pages : 239 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Handbook of Functional MRI Data Analysis by : Russell A. Poldrack

Download or read book Handbook of Functional MRI Data Analysis written by Russell A. Poldrack and published by Cambridge University Press. This book was released on 2011-08-22 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.