Intrinsic Inter-subject Variability in Functional Neuroimaging

Intrinsic Inter-subject Variability in Functional Neuroimaging PDF

Author: Shruti Gopal Vij

Publisher:

Published: 2016

Total Pages: 256

ISBN-13:

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"The holy grail of brain imaging is the identification of a biomarker, which can identify an abnormality that can be used to diagnose disease and track the effectiveness of treatment and disease progression. Typically approaches that search for biomarkers start by identifying mean differences between groups of patients and healthy controls. However, combining data from different subjects and groups to be able to make meaningful inferences is not trivial. The structure of the brain in each individual is unique in size and shape as well as in the relative location of anatomical landmarks (e.g. sulci and gyri). When looking for mean differences in functional images, this issue is exacerbated by the presence of variability in functional localization, i.e. variability in the location of functional regions in the brain. This is notably an important reason to focus on looking for inter-individual differences or variability. Inter-subject variability in neuroimaging experiments is often viewed as noise. The analyses are setup in a manner to ignore this variability assuming that a global spatial normalization brings the data into the same space. Nonetheless, functional activation patterns can be impacted by variability in multiple ways for e.g., there could be spatial variability of the maps or variability in the spectral composition of the timecourses or variability in the connectivity between the activation patterns identified. The overarching problem this thesis seeks to contribute to, is seeking improved measures to quantify biologically significant spatial, spectral and connectivity based variability and to identify associated cognitive or behavioral differences in the distribution of brain networks. We have successfully shown that different (spatial and spectral) measures of variability in blind source separated functional activation patterns underline previously unexplained characteristics that help in discerning schizophrenia patients from healthy controls. Additionally, we show that variance measures in dynamic connectivity between networks in healthy controls can justify relationship between connectivity patterns and executive functioning abilities."--Abstract.

Machine Learning and Interpretation in Neuroimaging

Machine Learning and Interpretation in Neuroimaging PDF

Author: Georg Langs

Publisher: Springer

Published: 2012-11-11

Total Pages: 277

ISBN-13: 3642347134

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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.

fMRI Neurofeedback

fMRI Neurofeedback PDF

Author: Michelle Hampson

Publisher: Academic Press

Published: 2021-10-09

Total Pages: 366

ISBN-13: 0128224363

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fMRI Neurofeedback provides a perspective on how the field of functional magnetic resonance imaging (fMRI) neurofeedback has evolved, an introduction to state-of-the-art methods used for fMRI neurofeedback, a review of published neuroscientific and clinical applications, and a discussion of relevant ethical considerations. It gives a view of the ongoing research challenges throughout and provides guidance for researchers new to the field on the practical implementation and design of fMRI neurofeedback protocols. This book is designed to be accessible to all scientists and clinicians interested in conducting fMRI neurofeedback research, addressing the variety of different knowledge gaps that readers may have given their varied backgrounds and avoiding field-specific jargon. The book, therefore, will be suitable for engineers, computer scientists, neuroscientists, psychologists, and physicians working in fMRI neurofeedback. Provides a reference on fMRI neurofeedback covering history, methods, mechanisms, clinical applications, and basic research, as well as ethical considerations Offers contributions from international experts—leading research groups are represented, including from Europe, Japan, Israel, and the United States Includes coverage of data analytic methods, study design, neuroscience mechanisms, and clinical considerations Presents a perspective on future translational development

Handbook of Biomedical Imaging

Handbook of Biomedical Imaging PDF

Author: Nikos Paragios

Publisher: Springer

Published: 2015-03-24

Total Pages: 501

ISBN-13: 038709749X

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This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses. It is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging PDF

Author: Fei Wang

Publisher: Springer

Published: 2012-11-13

Total Pages: 287

ISBN-13: 3642354289

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This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

Mental Processes in the Human Brain

Mental Processes in the Human Brain PDF

Author: Jon Driver

Publisher: Oxford University Press (UK)

Published: 2008-02-21

Total Pages: 308

ISBN-13: 0199230617

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Advanced methods for imaging brain structure and activity are leading to sophisticated accounts of how mental processes are implemented in the brain. This title provides an overview of the advances and future challenges in understanding the neurobiological basis of mental processes that are characteristically human.

Pattern Analysis of the Human Connectome

Pattern Analysis of the Human Connectome PDF

Author: Dewen Hu

Publisher: Springer

Published: 2020-11-20

Total Pages: 0

ISBN-13: 9789813295254

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This book presents recent advances in pattern analysis of the human connectome. The human connectome, measured by magnetic resonance imaging at the macroscale, provides a comprehensive description of how brain regions are connected. Based on machine learning methods, multiviarate pattern analysis can directly decode psychological or cognitive states from brain connectivity patterns. Although there are a number of works with chapters on conventional human connectome encoding (brain-mapping), there are few resources on human connectome decoding (brain-reading). Focusing mainly on advances made over the past decade in the field of manifold learning, sparse coding, multi-task learning, and deep learning of the human connectome and applications, this book helps students and researchers gain an overall picture of pattern analysis of the human connectome. It also offers valuable insights for clinicians involved in the clinical diagnosis and treatment evaluation of neuropsychiatric disorders.

Single-Trial Analyses of Behavioural and Neuroimaging Data in Perception and Decision-Making

Single-Trial Analyses of Behavioural and Neuroimaging Data in Perception and Decision-Making PDF

Author: Paul Sajda

Publisher: Frontiers Media SA

Published: 2012

Total Pages: 130

ISBN-13: 2889190234

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The cognitive psychology of perception and decision-making is at a cross-road. Most studies still employ categorical designs, a priori classified stimuli and perform statistical evaluations across subjects. However, a shift has been observed in recent years towards parametric designs in which the information content of stimuli is systematically manipulated to study the single-trial dynamics of behaviour (reaction times, eye movements) and brain activity (EEG, MEG, fMRI). By using the information contained in the variance of individual trials, the single-trial approach goes beyond the activity of the average brain: it reveals the specificity of information processing in individual subjects, across tasks and stimulus space, revealing both inter-individual commonalties and differences. This Research Topic provides theoretical and empirical support for the study of single-trial data. Topics of particular interest include: 1. description of the richness of information in single-trials and how it can be successfully extracted; 2. statistical issues related to measures of central tendency, control for multiple comparisons, multivariate approaches, hierarchical modelling and characterization of individual differences; 3. how manipulation of the stimulus space can allow for a direct mapping of stimulus properties onto brain activity to infer dynamics of information processing and information content of brain states; 4. how results from different brain imaging techniques can be integrated at the single-trial level.