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Analysis of fMRI Single Subject Data in the Fourier Domain Acquired Using a Multiple Input Stimulus Experimental Design

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Journal of Signal and Information Processing 2012年第4期3卷 469-480页

作者： Daniel Rio Robert Rawlings Lawrence Woltz jodi gilman Daniel Hommer Laboratory of Neuroimaging and Genetics Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown USA NIAAA Columbia USA 3Synergy Research Inc. Monrovia USA Section of Brain Electrophysiology and Imaging LCTS NIAAA National Institutes of Health Bethesda USA Synergy Research Inc. Monrovia USA

Analysis of functional MRI (fMRI) blood oxygenation level dependent (BOLD) data is typically carried out in the time domain where the data has a high temporal correlation. These analyses usually employ parametric models of the hemodynamic response function (HRF) where either pre-whitening of the data is attempted or autoregressive (AR) models are employed to model the noise. Statistical analysis then proceeds via regression of the convolution of the HRF with the input stimuli. This approach has limitations when considering that the time series collected are embedded in a brain image in which the AR model order may vary and pre-whitening techniques may be insufficient for handling faster sampling times. However fMRI data can be analyzed in the Fourier domain where the assumptions made as to the structure of the noise can be less restrictive and hypothesis tests are straightforward for single subject analysis, especially useful in a clinical setting. This allows for experiments that can have both fast temporal sampling and event-related designs where stimuli can be closely spaced in time. Equally important, statistical analysis in the Fourier domain focuses on hypothesis tests based on nonparametric estimates of the hemodynamic transfer function (HRF in the frequency domain). This is especially important for experimental designs involving multiple states (drug or stimulus induced) that may alter the form of the response function. In this context a univariate general linear model in the Fourier domain has been applied to analyze BOLD data sampled at a rate of 400 ms from an experiment that used a two-way ANOVA design for the deterministic stimulus inputs with inter-stimulus time intervals chosen from Poisson distributions of equal intensity.

关键词： fMRI (BOLD) Time Series Analysis Single Subject Analysis Fourier Domain Statistical Analysis Complex General Linear Model Alcoholism Research

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Brain Signaling in Psychiatric Disorders: What Can They Tell Us in the Absence of Behavioral Differences?

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Journal of Behavioral and Brain Science 2015年第8期5卷 333-337页

作者： jodi M. gilman James M. Bjork Timothy E. Wilens Center for Addiction Medicine Massachusetts General Hospital Boston MA USA Department of Psychiatry Harvard Medical School Boston MA USA Department of Psychiatry Virginia Commonwealth University Richmond VA USA Clinical and Research Programs in Pediatric Psychopharmacology Adult ADHD Massachusetts General Hospital Boston MA USA

This is a commentary on the often-observed phenomenon of observing task-based brain signaling differences between clinical populations and healthy control participants in the absence of any behavioral decrements in the clinical group. We offer several explanations for why brain-based differences amid normative performance may be of interest to researchers and clinicians. First, neural processing in the clinical group may not be as efficient as that in the control group. Second, differences in activation could reveal important differences in the cognition behind the (normative) behavior. Third, differences in activation may be prognostic biomarkers of injury or decline. In addition, we contend that similar behavior between groups is important in properly interpreting brain data. Finally, we offer caveats and future directions to further clarify brain mechanisms underlying behavior in clinical populations.

关键词： fMRI Neuroimaging Normative Behavior Normative Performance Neural Efficiency Prognostic Biomarker

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