The Statistical Analysis of Functional MRI Data (Statistics for Biology and Health)

The Statistical Analysis of Functional MRI Data (Statistics for Biology and Health): Medicine & Health Science Books @ leondumoulin.nl
Table of contents

• Evaluation of Statistical Inference on Empirical Resting State fMRI
• New Research In
• INTRODUCTION
• Statistical Approaches to Functional Neuroimaging Data

The preliminary data-driven cluster analysis defines neural processing clusters with similar patterns of activity, but generally does not quantify or test the degree of association between the intracluster voxels. In addition, one may conduct classical hypothesis testing using a Wald or likelihood ratio test for each neural processing cluster to evaluate the null hypothesiss of no functional connectivity between voxels within the cluster versus the one-sided alternative of positive functional connectivity. Correlations between voxels may arise due to neurophysiological influences as well as to background or non-neurophysiological sources, such those induced by the scanner and by image preprocessing.

When evaluating the functional connectivity between two brain regions, it is important to account for the background spatial correlation inherent in neuroimaging data. Breakspear and colleagues [ 64 ] developed a wavelet-based nonparametric technique for testing the null hypothesis that the correlations between two selected brain regions are typical of the data set and not unique to the regions of interest. This was achieved through spatio-temporal resampling of the data in the wavelet domain.

Patel and colleagues [ 65 ] extended this work to a 4-D wavelet-based nonparametric approach for determining whether the functional connectivity observed in an experiment is significantly greater than the background correlation. Specifically, they presented a spatio-temporal wavelet packet resampling method that generates surrogate data that preserves only the average background correlation within an axial slice, across axial slices, and through each voxel time series, while excluding the specific correlations due to true functional relationships.

For functional neuroimaging data, effective connectivity addresses the influence that one neuronal system exerts upon others [ 66 ] and how this varies with the experimental context. Structural equation modeling SEM is a well-established statistical technique that has applications to effective connectivity analyses. SEM focuses on the covariance structure that reflects associations between the variables. Parameter estimation in SEM minimizes differences between the observed covariances and those implied by a path or structural model.

The parameters of the SEM represent the connection strengths between the brain activity measurements in different regions and correspond to measures of effective connectivity. SEM also has been used to describe changes in effective connectivity associated with memory tasks [ 69 , 70 ]. Dynamic causal modeling DCM is a general method to estimate effective connectivity from neuroimaging data in a Bayesian framework [ 71 , 72 ].

DCM regards the brain as a deterministic nonlinear dynamic system that receives inputs and that produces outputs [ 72 ]. DCM parameterizes effective connectivity in terms of coupling, representing the influence of one brain region on another. DCM attempts to estimate coupling parameters by measuring the responses to perturbations in the specified system [ 72 ]. PLS regression looks for orthogonal factors called latent variables that perform a decomposition of both neural responses Y and experimental variables predictors X simultaneously, such that these factors explain as much as possible of the covariance between X and Y.

PLS measures cross-block correlations and creates a new set of variables optimized for maximum covariance using the fewest possible dimensions [ 69 ]. It is ideally suited for data that have highly correlated dependent measures, such as neuroimaging data [ 77 - 79 ]. FMRI and PET play important roles in defining the neural basis of illness and of risk factors for diseases such as major psychiatric disorders [ 80 , 81 , 82 , 83 ].

To increase the translational significance of functional neuroimaging techniques to clinical practice, an important area of research involves methods for predicting disease progression and treatment response. Numerous studies have linked observed neural processing characteristics from fMRI and PET with disease development or with clinical responses to treatment [ 84 , 85 , 86 , 87 , 88 ].

For example, several authors have established associations between treatment response and pre- to post-treatment changes in measured brain activity [ 89 , 90 , 91 , 92 , 93 , 94 , 95 ]. The potential clinical insights gained from evaluating both baseline and post-treatment scans are offset in practice due to the unavailability of post-treatment scans at the time that a clinician makes treatment decisions for a particular patient.

The predicted post-treatment brain activity maps, along with the observed baseline brain scans, provided a physician with objective and clinically relevant information that he or she may incorporate into the treatment selection process. The predictive model of Guo represents an important first step in attempting to aid treatment decisions by using functional neuroimaging data and provides a foundation upon which future research, including that on predicting clinical symptom responses, can build.

The first level of the hierarchy models within-subject temporal activation effects, while the second level modeled the subject-specific effects in terms of population parameters. The predicted post-treatment maps follow from the conditional probability distribution of the post-treatment maps given the pre-treatment maps. They illustrated their method using PET data from a study of working memory in patients with schizophrenia.

The predicted post-treatment maps from the Bayesian hierarchical model were quite accurate when compared to the observed post-treatment maps Figure 5. To implement the predictive framework in practice, a clinician obtains data from a patient prior to initiating a new treatment and applies the predictive algorithm to the baseline data to predict patterns of post-treatment brain activity under various treatment alternatives.

These predicted maps can then contribute to clinical decision-making regarding treatment options. One benefit of the Bayesian framework is that it allows updating of the predictive model after obtaining both pre- and post-treatment scans on more individuals. Predicted a and observed b post-treatment brain activity regional cerebral blood flow measurements for four schizophrenia patients, corresponding to a working memory task. The axial slice shown is 6mm below the anterior commissure. Notable differences exist between the predicted post-treatment distributed patterns of brain activity for these patients, and there is a high degree of concordance between the observed and predicted maps for each patient.

The massive amounts of data collected in functional neuroimaging studies pose challenges for implementing statistical analyses. Fortunately, substantial advances have been made in the development of software for processing and for statistical analyses of neuroimaging data. Table 1 shows software tools that are helpful for implementing several of the statistical techniques described in this article.

Some of the software tools provide essentially comprehensive environments for processing and analyzing functional neuroimaging data, while others are more specialized, implementing very specific analyses. Numerous other tools exist for implementing statistical analyses of functional neuroimaging data, but we do not attempt to provide an exhaustive list here. Neuroimaging statistics is an emerging area that has helped to foster the widespread use of functional neuroimaging technology for research and clinical applications.

Statistics plays a pivotal role in functional neuroimaging research in its interplay with other fields, such as neuroscience and imaging physics. Using probabilistic arguments and modeling techniques, statistics makes valuable contributions concerning methods for the design and conduct of neuroimaging experiements and tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. The massive data structures and complex patterns of correlations pose challenges for many conventional statistical methods.

These and other complications give rise to the development of new statistical methods and the adaptation of existing approaches for applications to functional neuroimaging data. The advancements in neuroimaging statistical methodologies provide effective means for processing, exploring, analyzing, and visualizing fMRI and PET data. Applications of functional neuroimaging statistical methods have provided us with a better understanding of the neural basis of cognitions, emotions, behaviors, and the pathophysiology of psychiatric and neurologic disorders.

Evaluation of Statistical Inference on Empirical Resting State fMRI

We have developed methods that enable the conduct and analyses of activation studies that attempt to identify brain regions revealing task-related changes in measured activity. In addition, we have developed methods to analyze data from functional connectivity studies that seek to determine which brain regions show similar patterns of activation when performing an experimental task. Statistical prediction for functional neuroimaging data is a nascent area that stands to have important clinical applications.

The recent development of predictive models for fMRI and PET data demonstrate the feasibility of providing accurate predictions of brain activity maps following treatment. Methodology for statistical prediction based on functional neuroimaging data represents an important area for future research, and preliminary work in this area provides a promising outlook for the potential utility of functional neuroimaging data in a clinical setting.

The authors thank Dr. Clinton Kilts and Mr. We also thank Ms. Angela Thomas for her assistance with manuscript preparation. National Institutes of Health; Grant number: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form.

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National Center for Biotechnology Information , U. Neuroimaging Clin N Am. Author manuscript; available in PMC Jul F DuBois Bowman , Ph. Gordana Derado 3 Ph. Address correspondence, including proofs and reprints, to: Atlanta, GA , Phone: Atlanta, GA , tel: The publisher's final edited version of this article is available at Neuroimaging Clin N Am.

See other articles in PMC that cite the published article. Synopsis The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiements and by providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Linear model, spatial model, functional connectivity, independent component analysis, clustering, prediction, Bayesian hierarchical model. Open in a separate window. Stage 1 Model The first stage of the model characterizes distributed brain activity associated with various experimental conditions, separately for each individual.

Spatial Models Complex neural networks act as pathways that enable interactive neural processing. Inference Procedures Statistical inferences for activation studies typically target the voxel-level e. Region of Interest Inferences In some cases, researchers seek to determine whether there is evidence of task-related changes in brain activity within a particular region, rather than to determine globally where task-related activations occur, leading to ROI analyses.

Nonparametric methods Complexities in fMRI and PET data may support the use of nonparametric methods for finding appropriate thresholds for statistical inferences. Clustering Cluster analysis is a data-driven method that can assist in identifying voxels exhibiting similar patterns of task-related brain activity. Independent Component Analysis Independent component analysis ICA is becoming increasingly popular for analyzing functional neuroimaging data [ 55 , 56 , 57 ]. Single subject ICA The basic goal of ICA is to express the observed 3-D brain images as linear combinations of statistically independent latent component signals.

Group ICA Scientific objectives in functional neuroimaging studies often target conclusions that generalize to groups of individuals, so we now discuss methods for performing ICA on multi-subject functional neuroimaging data. Bayesian Hierarchical Modeling One may also implement parametric model-based analyses to quantify functional connectivity and provide statistical evidence regarding the likelihood of functional connectivity between brain regions.

Spatio-temporal modeling Our spatial model [ 22 ] establishes a unified framework to address objectives for activation studies and provide information on functional connectivity in the brain. Nonparametric Wavelet-Based Methods Correlations between voxels may arise due to neurophysiological influences as well as to background or non-neurophysiological sources, such those induced by the scanner and by image preprocessing.

Extensions Causal Associations Structural Equation Modeling For functional neuroimaging data, effective connectivity addresses the influence that one neuronal system exerts upon others [ 66 ] and how this varies with the experimental context.

Dynamic Causal Modeling Dynamic causal modeling DCM is a general method to estimate effective connectivity from neuroimaging data in a Bayesian framework [ 71 , 72 ]. Table 1 List of available software tools. Note that this table is not intended to provide a comprehensive list of available software, but instead to provide readers with some direction on how to implement some of the statistical techniques described in this article. Acknowledgments The authors thank Dr. The neural basis of addiction: A pathology of motivation and choice. American Journal of Psychiatry.

Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine. To smooth or not to smooth? Cambridge University Press; Introduction to functional magnetic resonance imaging. A general statistical analysis for fMRI data.

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Lange N, Zeger SL. Non-linear fourier time series analysis for human brain mapping by functional magnetic resonance imaging with discussion Applied Statistics, Journal of the Royal Statistical Society, Series C. A method for removal of global effects from fMRI time series. General multilevel linear modeling for group analysis in FMRI. Analysis of fMRI time-series -Revisited again. Bowman FD, Kilts C. Modeling intra-subject correlation among repeated scans in positron emission tomography PET neuroimaging data. Penny W, Holmes A. Elsevier Academic Press; New York: Local maxima and the expected euler characteristic of excursion sets of, F, and t fields.

Advances in Applied Probability. A three-dimensional statistical analysis for rCBF activation studies in the human brain. Journal of Cerebral Blood Flow and Metabolism. Non-parametric analysis of statistic images from functional mapping experiments. Raz J, Turetsky B. A Spatio-temporal regression model for the analysis of functional MRI data. Bayesian spatiotemporal inference in functional magnetic resonance imaging. Fully Bayesian spatio-temporal modeling of fMRI data.

Bayesian fMRI time series analysis with spatial priors.

New Research In

Spatiotemporal modeling of localized brain activity. Spatio-temporal models for region of interest analyses of functional neuroimaging data. Journal of the American Statistical Association. Analysis of functional MRI time series. Statistical parametric maps in functional imaging: A general linear approach.

Statistical methods of estimation and inference for functional MR image analysis. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Nichols T, Hayasaka S.

Controlling the family wise error rate in functional neuroimaging: Statistical Methods in Medical Research.

INTRODUCTION

An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Implementation and application of a brain template for multiple volumes of interest. Region of interest based analysis of functional imaging data. Characterizing dynamic brain responses with fMRI: Nonparametric permutation tests for functional neuroimaging: Wavelets and statistical analysis of functional magnetic resonance images of the human brain. The principal component analysis of large data sets.

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Alphabetically prepared and cross-referenced, The guide of study and Quantitative tools In Psychology offers: To ensure continued progress, fMRI experimentalists want to be assured that the instruments, experimental protocols, and data analysis paradigms have been vetted by experts and work correctly. The ease of analysis afforded by some of the software programs belies the complexity of the methods. This ease of use does not release experimentalists from their responsibility to validate findings using established statistical principles 12 , Judicious use of nonparametric methods can, as Eklund et al.

However, application of nonparametric methods cannot be the universal solution, nor did Eklund et al. The current discussion shows that the validity of fMRI data analysis paradigms has not been uniformly established and needs continued in-depth investigation. As a consequence, all of the biophysics, neurophysiology, and neuroanatomy that underlie fMRI should be used to design experiments, formulate statistical models, and analyze the data to increase the signal-to-noise ratio and information extraction.

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• Statistical Approaches to Functional Neuroimaging Data.
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• Evaluation of Statistical Inference on Empirical Resting State fMRI.

Achieving more accurate fMRI data analyses is a challenging interdisciplinary task that requires concerted collaborations among physicists, statisticians, and neuroscientists who, together, can question the current approaches more deeply and construct more accurate analysis methods. In an ideal fMRI statistical analysis, the relationships among the voxels would take account of the spatial and temporal properties of the experiment and the scanner thermal noise The ideal fMRI acquisition scheme would be accompanied by a characterization of these spatial and temporal processes so that the subsequent data analysis can correctly take them into account Sharing data and methods would greatly expedite validation 9.

BRAIN , the report of the NIH Brain Initiative, recommends fostering interdisciplinary collaborations among neuroscientists, physicists, engineers, statisticians, and mathematicians to properly collect, analyze, and interpret the data that result from the development of new neuroscience tools https: The current exchange identifies fMRI as an existing tool that is perfect for pursuing such a collaboration.

A possible goal could be to increase fMRI signal-to-noise ratios so that the technique can be used reliably to make inferences about an individual subject in a given paradigm. Developing statistical methods based on detailed modeling of the fMRI process opens the door to using more direct, informative inference paradigms based on estimated effect sizes, confidence intervals, and Bayesian posterior assessments rather than more indirect approaches based on significance tests and P values.

Linking statistical methodology development and fundamental fMRI research is crucial for developing more accurate analysis methods, attributing accurate scientific interpretations to results, and ensuring the reliability and reproducibility of fMRI studies. These points have been made before. However, their significance has perhaps not been considered to the extent required.

Statistical Approaches to Functional Neuroimaging Data

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