Information-based functional brain mapping

How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration

Recent advances in MRI receiver and coil technologies have significantly improved image signal-to-noise ratios (SNR) and thus temporal SNR (TSNR). These gains in SNR and TSNR have allowed the detection of fMRI signal changes at higher spatial resolution and therefore have increased the potential to localize small brain structures such as cortical layers and columns. The majority of current fMRI processing strategies employ multi-subject averaging and therefore require spatial smoothing and normalization, effectively negating these gains in spatial resolution higher than about 10 mm3. Reliable detection of activation in single subjects at high resolution is becoming a more common desire among fMRI researchers who are interested in comparing individuals rather than populations. Since TSNR decreases with voxel volume, detection of activation at higher resolutions requires longer scan durations. The relationship between TSNR, voxel volume and detectability is highly non-linear. In this study, the relationship between TSNR and the necessary fMRI scan duration required to obtain significant results at varying P values is determined both experimentally and theoretically. The results demonstrate that, with a TSNR of 50, detection of activation of above 2% requires at most 350 scan volumes (when steps are taken to remove the influence of physiological noise from the data). Importantly, these results also demonstrate that, for activation magnitude on the order of 1%, the scan duration required is more sensitive to the TSNR level than at 2%. This study showed that with voxel volumes of approximately 10 mm3 at 3 T, and a corresponding TSNR of approximately 50, the required number of time points that guarantees detection of signal changes of 1% is about 860, but if TSNR increases by only 20%, the time for detection decreases by more than 30%. More than just being an exercise in numbers, these results imply that imaging of columnar resolution (effect size=1% and assuming a TR of 1 s) at 3 T will require either 10 min for a TSNR of 60 or 40 min for a TSNR of 30. The implication is that at these resolutions, TSNR is likely to be critical for determining success or failure of an experiment.

Mapping the MRI voxel volume in which thermal noise matches physiological noise--implications for fMRI

This work addresses the choice of the imaging voxel volume in blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). Noise of physiological origin that is present in the voxel time course is a prohibitive factor in the detection of small activation-induced BOLD signal changes. If the physiological noise contribution dominates over the temporal fluctuation contribution in the imaging voxel, further increases in the voxel signal-to-noise ratio (SNR) will have diminished corresponding increases in temporal signal-to-noise (TSNR), resulting in reduced corresponding increases in the ability to detect activation induced signal changes. On the other hand, if the thermal and system noise dominate (suggesting a relatively low SNR) further decreases in SNR can prohibit detection of activation-induced signal changes. Here we have proposed and called the "suggested" voxel volume for fMRI the volume where thermal plus system-related and physiological noise variances are equal. Based on this condition we have created maps of fMRI suggested voxel volume from our experimental data at 3T, since this value will spatially vary depending on the contribution of physiologic noise in each voxel. Based on our fast EPI segmentation technique we have found that for gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) brain compartments the mean suggested cubical voxel volume is: (1.8 mm)3, (2.1 mm)3 and (1.4 mm)3, respectively. Serendipitously, (1.8 mm)3 cubical voxel volume for GM approximately matches the cortical thickness, thus optimizing BOLD contrast by minimizing partial volume averaging. The introduced suggested fMRI voxel volume can be a useful parameter for choice of imaging volume for functional studies.

Functional magnetic resonance imaging investigation of overlapping lateral occipitotemporal activations using multi-voxel pattern analysis

Several functional areas are proposed to reside in human lateral occipitotemporal cortex, including the motion-selective human homolog of macaque area MT (hMT), object-form-selective lateral occipital complex (LO), and body-selective extrastriate body area (EBA). Indeed, several functional magnetic resonance imaging (fMRI) studies have reported significant activation overlap among these regions. The standard interpretation of this overlap would be that the common areas of activation reflect engagement of common neural systems. Alternatively, motion, object form, and body form may be processed independently within this general region. To distinguish these possibilities, we first analyzed the lateral occipitotemporal responses to motion, objects, bodies, and body parts with whole-brain group-average analyses and within-subjects functional region of interest (ROI) analyses. The activations elicited by these stimuli, each relative to a matched control, overlapped substantially in the group analysis. When hMT, LO, and EBA were defined functionally within subjects, each ROI in each hemisphere (except right-hemisphere hMT) showed significant selectivity for motion, intact objects, bodies, and body parts, although only the peak voxel of each region was tested. In contrast, multi-voxel analyses of variations in selectivity patterns revealed that visual motion, object form, and the form of the human body elicited three relatively independent patterns of fMRI activity in lateral occipitotemporal cortex. Multi-voxel approaches, in contrast to other methods, can reveal the functional significance of overlapping fMRI activity in extrastriate cortex and, by extension, elsewhere in the brain.

The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data

In the present study, we compared the effects of temporal compression (averaging across multiple scans) and space selection (i.e. selection of "regions of interest" from the whole brain) on single-subject and multi-subject classification of fMRI data using the support vector machine (SVM). Our aim was to investigate various data transformations that could be applied before training the SVM to retain task discriminatory variance while suppressing irrelevant components of variance. The data were acquired during a blocked experiment design: viewing unpleasant (Class 1), neutral (Class 2) and pleasant pictures (Class 3). In the multi-subject level analysis, we used a "leave-one-subject-out" approach, i.e. in each iteration, we trained the SVM using data from all but one subject and tested its performance in predicting the class label of the this last subject's data. In the single-subject level analysis, we used a "leave-one-block-out" approach, i.e. for each subject, we selected randomly one block per condition to be the test block and trained the SVM using data from the remaining blocks. Our results showed that in a single-subject level both temporal compression and space selection improved the SVM accuracy. However, in a multi-subject level, the temporal compression improved the performance of the SVM, but the space selection had no effect on the classification accuracy.

Decoding mental states from brain activity in humans

Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.

Beyond mind-reading: multi-voxel pattern analysis of fMRI data

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

Functional MRI today

Most brain imaging researchers would agree with the assertion that functional MRI (fMRI) is progressing. Since fMRI began in 1991, the number of people, papers, and abstracts related to fMRI has been increasing; the technology and methodology has shown advances in robustness and sophistication; the physiology of the signal is better understood; and, even though it hasn't yet made significant headway into the clinical setting, applications are widening. Questions that stem from this optimistic and perhaps overly general set of observations include those that ask what the ultimate theoretical and practical limits of fMRI are and how close are we to approaching these limits. In this commentary, I attempt to provide a snapshot of fMRI as it exists at the end of 2005, and to give a clear impression that not only are we progressing by "dotting the i's and crossing the t's" but that fundamental changes in fMRI methodology and processing are being put forth as the field matures.