Literature DB >> 25309940

Brain Imaging Analysis.

F Dubois Bowman1.   

Abstract

The increasing availability of brain imaging technologies has led to intense neuroscientific inquiry into the human brain. Studies often investigate brain function related to emotion, cognition, language, memory, and numerous other externally induced stimuli as well as resting-state brain function. Studies also use brain imaging in an attempt to determine the functional or structural basis for psychiatric or neurological disorders and, with respect to brain function, to further examine the responses of these disorders to treatment. Neuroimaging is a highly interdisciplinary field, and statistics plays a critical role in establishing rigorous methods to extract information and to quantify evidence for formal inferences. Neuroimaging data present numerous challenges for statistical analysis, including the vast amounts of data collected from each individual and the complex temporal and spatial dependence present. We briefly provide background on various types of neuroimaging data and analysis objectives that are commonly targeted in the field. We present a survey of existing methods targeting these objectives and identify particular areas offering opportunities for future statistical contribution.

Entities:  

Keywords:  DTI; Neuroimaging; activation; connectivity; fMRI; prediction

Year:  2014        PMID: 25309940      PMCID: PMC4189192          DOI: 10.1146/annurev-statistics-022513-115611

Source DB:  PubMed          Journal:  Annu Rev Stat Appl        ISSN: 2326-8298            Impact factor:   5.810


  82 in total

1.  On clustering fMRI time series.

Authors:  C Goutte; P Toft; E Rostrup; F Nielsen; L K Hansen
Journal:  Neuroimage       Date:  1999-03       Impact factor: 6.556

2.  Bayesian estimation of dynamical systems: an application to fMRI.

Authors:  K J Friston
Journal:  Neuroimage       Date:  2002-06       Impact factor: 6.556

3.  Cluster analysis of fMRI data using dendrogram sharpening.

Authors:  Larissa Stanberry; Rajesh Nandy; Dietmar Cordes
Journal:  Hum Brain Mapp       Date:  2003-12       Impact factor: 5.038

4.  A Bayesian hierarchical framework for spatial modeling of fMRI data.

Authors:  F DuBois Bowman; Brian Caffo; Susan Spear Bassett; Clinton Kilts
Journal:  Neuroimage       Date:  2007-08-24       Impact factor: 6.556

5.  Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes.

Authors:  Andre Marquand; Matthew Howard; Michael Brammer; Carlton Chu; Steven Coen; Janaina Mourão-Miranda
Journal:  Neuroimage       Date:  2009-10-29       Impact factor: 6.556

6.  Analysis of fMRI data by blind separation into independent spatial components.

Authors:  M J McKeown; S Makeig; G G Brown; T P Jung; S S Kindermann; A J Bell; T J Sejnowski
Journal:  Hum Brain Mapp       Date:  1998       Impact factor: 5.038

7.  Spatio-Spectral Mixed Effects Model for Functional Magnetic Resonance Imaging Data.

Authors:  Hakmook Kang; Hernando Ombao; Crystal Linkletter; Nicole Long; David Badre
Journal:  J Am Stat Assoc       Date:  2012       Impact factor: 5.033

8.  BSMac: a MATLAB toolbox implementing a Bayesian spatial model for brain activation and connectivity.

Authors:  Lijun Zhang; Sanjay Agravat; Gordana Derado; Shuo Chen; Belinda J McIntosh; F DuBois Bowman
Journal:  J Neurosci Methods       Date:  2011-11-10       Impact factor: 2.390

9.  Modeling inter-subject variability in FMRI activation location: a Bayesian hierarchical spatial model.

Authors:  Lei Xu; Timothy D Johnson; Thomas E Nichols; Derek E Nee
Journal:  Biometrics       Date:  2009-12       Impact factor: 2.571

10.  Classification of structural MRI images in Alzheimer's disease from the perspective of ill-posed problems.

Authors:  Ramon Casanova; Fang-Chi Hsu; Mark A Espeland
Journal:  PLoS One       Date:  2012-10-10       Impact factor: 3.240

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  12 in total

Review 1.  Heterogeneity and individuality: microRNAs in mental disorders.

Authors:  Leif G Hommers; Katharina Domschke; Jürgen Deckert
Journal:  J Neural Transm (Vienna)       Date:  2014-11-14       Impact factor: 3.575

2.  A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.

Authors:  Ryan Warnick; Michele Guindani; Erik Erhardt; Elena Allen; Vince Calhoun; Marina Vannucci
Journal:  J Am Stat Assoc       Date:  2018-05-16       Impact factor: 5.033

3.  Degree-based statistic and center persistency for brain connectivity analysis.

Authors:  Kwangsun Yoo; Peter Lee; Moo K Chung; William S Sohn; Sun Ju Chung; Duk L Na; Daheen Ju; Yong Jeong
Journal:  Hum Brain Mapp       Date:  2016-09-04       Impact factor: 5.038

4.  Age sensitive associations of adolescent substance use with amygdalar, ventral striatum, and frontal volumes in young adulthood.

Authors:  Michael Windle; Joshua C Gray; Karlo Mankit Lei; Allen W Barton; Gene Brody; Steven R H Beach; Adrianna Galván; James MacKillop; Uraina S Clark; Lawrence H Sweet
Journal:  Drug Alcohol Depend       Date:  2018-03-14       Impact factor: 4.492

5.  Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.

Authors:  Shuo Chen; F DuBois Bowman; Yishi Xing
Journal:  Comput Stat Data Anal       Date:  2019-07-09       Impact factor: 1.681

6.  Bayesian Models for fMRI Data Analysis.

Authors:  Linlin Zhang; Michele Guindani; Marina Vannucci
Journal:  Wiley Interdiscip Rev Comput Stat       Date:  2015 Jan-Feb

7.  A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data.

Authors:  Shuo Chen; F DuBois Bowman; Helen S Mayberg
Journal:  Biometrics       Date:  2015-10-26       Impact factor: 2.571

8.  Thalamocortical and Intracortical Inputs Differentiate Layer-Specific Mouse Auditory Corticocollicular Neurons.

Authors:  Bernard J Slater; Stacy K Sons; Georgiy Yudintsev; Christopher M Lee; Daniel A Llano
Journal:  J Neurosci       Date:  2018-10-25       Impact factor: 6.167

9.  Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: Comparison of linear mixed-effect models.

Authors:  Alicia S Chua; Svetlana Egorova; Mark C Anderson; Mariann Polgar-Turcsanyi; Tanuja Chitnis; Howard L Weiner; Charles R G Guttmann; Rohit Bakshi; Brian C Healy
Journal:  Neuroimage Clin       Date:  2015-07-02       Impact factor: 4.881

10.  A spatial Bayesian latent factor model for image-on-image regression.

Authors:  Cui Guo; Jian Kang; Timothy D Johnson
Journal:  Biometrics       Date:  2021-01-13       Impact factor: 2.571

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