Statistical Analysis fMRI Graduate Course November 2, 2005

When do we not need statistical analysis? Inter-ocular Trauma Test (Lockhead, personal communication)

Why use statistical analyses? Replaces simple subtractive methods –Signal highly corrupted by noise Typical SNRs: 0.2 – 0.5 –Sources of noise Thermal variation (unstructured) Physiological, task variability (structured) Assesses quality of data –How reliable is an effect? –Allows distinction of weak, true effects from strong, noisy effects

Statistical Parametric Maps 1. Brain maps of statistical quality of measurement –Examples: correlation, regression approaches –Displays likelihood that the effect observed is due to chance factors –Typically expressed in probability (e.g., p < 0.001) 2. Effect size –Determined by comparing task-related variability and non-task-related variability –Signal change divided by noise (SNR) –Typically expressed as t or z statistics

What are our statistics for?

Which is more important to avoid: Type I or Type II errors?

Simple Hypothesis-Driven Analyses Common –t-test across conditions –Fourier –t-test at time points –Correlation General Linear Model Other tests –Kolmogorov-Smirnov –Iterative Connectivity Mapping

t – Tests across Conditions Compares difference between means to population variability –Uses t distribution –Defined as the likely distribution due to chance between samples drawn from a single population Commonly used across conditions in blocked designs Subset of general linear model 5%

Drift Artifact and t-Test

Fourier Analysis Fourier transform: converts information in time domain to frequency domain –Used to change a raw time course to a power spectrum –Hypothesis: any repetitive/blocked task should have power at the task frequency BIAC function: FFTMR –Calculates frequency and phase plots for time series data. Equivalent to correlation in frequency domain Subset of general linear model –Same as if used sine and cosine as regressors

12s on, 12s off Frequency (Hz) Power

t / z – Tests across Time Points Determines whether a single data point in an epoch is significantly different from baseline BIAC Tool: tstatprofile –Creates: Avg_V*.img StdDev_V*.img ZScore_V*.img

Correlation Special case of General Linear Model –Blocked t-test is equivalent to correlation with square wave function –Allows use of any reference waveform Correlation coefficient describes match between observation and expectation –Ranges from -1 to 1 –Amplitude of response does not affect correlation directly BIAC tool: tstatprofile

Problems with Correlation Approaches Limited by choice of HDR –Poorly chosen HDR can significantly impair power Examples from previous weeks –May require different correlations across subjects Assume that correlation template is Gaussian Assume random variation around HDR –Do not model variability contributing to noise (e.g., scanner drift) Such variability is usually removed in preprocessing steps –Do not model interactions between successive events

Kolmogorov – Smirnov (KS) Test Statistical evaluation of differences in cumulative density function –Cf. t-test evaluates differences in mean Useful if distributions have same mean but different shape ABC

Iterative Connectivity Mapping Acquire two data sets –1: Defines regions of interest and hypothetical connections –2: Evaluates connectivity based on low frequency correlations Use of Continuous Data Sets –Null Data –Task Data –Can see connections between functional areas (e.g., between Broca’s and Wernicke’s Areas) Hampson et al., Hum. Brain. Map., 2002

Use of Continuous Tasks to Evaluate Functional Connectivity Hampson et al., Hum. Brain. Map., 2002

The General Linear Model

Basic Concepts of the GLM GLM treats the data as a linear combination of model functions plus noise –Model functions have known shapes –Amplitude of functions are unknown –Assumes linearity of HDR; nonlinearities can be modeled explicitly GLM analysis determines set of amplitude values that best account for data –Usual cost function: least-squares deviance of residual after modeling (noise)

Signal, noise, and the General Linear Model Measured Data Amplitude (solve for) Design Model Noise Cf. Boynton et al., 1996

Form of the GLM Data = N Time Points Model N Time Points Model Functions * Amplitudes Model Functions + Noise

Implementation of GLM in SPM Model Parameters Images

The Problem of Multiple Comparisons

P < (32 voxels)P < 0.01 (364 voxels)P < 0.05 (1682 voxels)

A BC t = 2.10, p < 0.05 (uncorrected)t = 3.60, p < (uncorrected)t = 7.15, p < 0.05, Bonferroni Corrected

Options for Multiple Comparisons Statistical Correction (e.g., Bonferroni) –Gaussian Field Theory –False discovery rate Cluster Analyses ROI Approaches

Statistical Corrections If more than one test is made, then the collective alpha value is greater than the single-test alpha –That is, overall Type I error increases One option is to adjust the alpha value of the individual tests to maintain an overall alpha value at an acceptable level –This procedure controls for overall Type I error –Known as Bonferroni Correction

Bonferroni Correction Very severe correction –Results in very strict significance values for even medium data sets –Typical brain may have about 15,000-20,000 functional voxels PType1 ~ 1.0 ; Corrected alpha ~ Greatly increases Type II error rate Is not appropriate for correlated data –If data set contains correlated data points, then the effective number of statistical tests may be greatly reduced –Most fMRI data has significant correlation

Gaussian Field Theory Approach developed by Worsley and colleagues to account for multiple comparisons –Forms basis for much of SPM Provides false positive rate for fMRI data based upon the smoothness of the data –If data are very smooth, then the chance of noise points passing threshold is reduced

Cluster Analyses Assumptions –Assumption I: Areas of true fMRI activity will typically extend over multiple voxels –Assumption II: The probability of observing an activation of a given voxel extent can be calculated Cluster size thresholds can be used to reject false positive activity –Forman et al., Mag. Res. Med. (1995) –Xiong et al., Hum. Brain Map. (1995)

How many foci of activation? Data from motor/visual event-related task (used in laboratory)

How large should clusters be? At typical alpha values, even small cluster sizes provide good correction –Spatially Uncorrelated Voxels At alpha = 0.001, cluster size 3 reduces Type 1 rate to << per voxel –Highly correlated Voxels Smoothing (FW = 0.5 voxels) increases needed cluster size to 7 or more voxels Efficacy of cluster analysis depends upon shape and size of fMRI activity –Not as effective for non-convex regions –Power drops off rapidly if cluster size > activation size Data from Forman et al., 1995

False Discovery Rate Controls the expected proportion of false positive values among suprathreshold values –Genovese, Lazar, and Nichols (2002, NeuroImage) –Does not control for chance of any face positives FDR threshold determined based upon observed distribution of activity –So, sensitivity increases because metric becomes more lenient as voxels become significant –Weak familywise Type I error rate

ROI Comparisons Changes basis of statistical tests –Voxels: ~16,000 –ROIs : ~ 1 – 100 Each ROI can be thought of as a very large volume element (e.g., voxel) –Anatomically-based ROIs do not introduce bias Potential problems with using functional ROIs –Functional ROIs result from statistical tests –Therefore, they cannot be used (in themselves) to reduce the number of comparisons

Are there differences between voxel-wise and ROI analyses?

Summary of Multiple Comparison Correction Basic statistical corrections are often too severe for fMRI data What are the relative consequences of different error types? –Correction decreases Type I rate: false positives –Correction increases Type II rate: misses Alternate approaches may be more appropriate for fMRI –Cluster analyses –Region of interest approaches –Smoothing and Gaussian Field Theory –False Discovery Rate

Fixed and Random Effects Comparisons

How do we compare across subjects? Fixed-effects Model –Assumes that effect is constant (“fixed”) in the population –Uses data from all subjects to construct statistical test –Examples Averaging across subjects before a t-test Taking all subjects’ data and then doing an ANOVA –Allows inference to subject sample Random-effects Model –Assumes that effect varies across the population –Accounts for inter-subject variance in analyses –Allows inferences to population from which subjects are drawn –Especially important for group comparisons –Required by many reviewers/journals

How are random-effects models run? Assumes that activation parameters may vary across subjects –Since subjects are randomly chosen, activation parameters may vary within group –Fixed-effects models assume that parameters are constant across individuals Calculates descriptive statistic for each subject –i.e., t-test for each subject based on correlation Uses all subjects’ statistics in a one-sample t-test –i.e., another t-test based only on significance maps

Summary of Hypothesis Tests Simple experimental designs –Blocked: t-test, Fourier analysis –Event-related: correlation, t-test at time points Complex experimental designs –Regression approaches (GLM) Critical problem: Minimization of Type I Error –Strict Bonferroni correction is too severe –Cluster analyses improve –Accounting for smoothness of data also helps Use random-effects analyses to allow generalization to the population

Data Driven Analyses

Independent Components Analysis

IPS FMC aINS pMFG Partial Least Squares

Why conduct data-driven analyses? Powerful tools for exploring data –PCA, ICA: Intrinsic, spatially stationary patterns of activity in dataset –Clustering: Collections of voxels with similar time courses of activity –PLS: How those patterns of activity maximally differentiate experimental conditions Allows segmentation of nuisance factors Provides check on hypothesis-driven analyses