In 1991, the Biophysics Research Institute at the Medical College of Wisconsin was among the first groups to develop functional Magnetic Resonance Imaging (fMRI). Our story is unique on a few levels: We didn’t have knowledge of the ability to image human brain activation with MRI using blood oxygenation dependent (BOLD) contrast until early August of 1991 when we attended the Society for Magnetic Resonance in Medicine (SMRM) meeting in San Francisco, yet we produced our first BOLD-based maps of motor cortex activation about a month later. The effort started with two graduate students, Eric Wong and myself. Only a few days prior to that extremely important SMRM meeting, we had developed human echo planar imaging (EPI) capability in-house. Wong designed, built, and interfaced a head gradient coil made out of sewer pipe, wire, and epoxy to a standard GE 1.5 T MRI scanner. Also, a few months prior to building this human head gradient coil he developed the EPI pulse sequences and image reconstruction. All of these efforts were towards a different goal – for demonstration of Wong’s novel approach to perfusion imaging in the human brain. Following SMRM, where a plenary lecture by Tom Brady from MGH opened our eyes to human brain activation imaging using BOLD contrast, and where we learned that EPI was extremely helpful if not critical to its success, we worked quickly to achieve our first results on September 14, 1991. The story is also unique in that Jim Hyde had set up the Biophysics Research Institute to be optimal for just this type of rapidly advancing basic technology research. It was well equipped for hardware development, had open and dynamic collaborative relationships with other departments, hospitals on campus, and GE, and had a relatively flat hierarchy and relaxed, flexible, collegial atmosphere internally. Since these first brain activation results, MCW Biophysics has continued to be at the forefront of functional MRI innovation, having helped to pioneer real time fMRI, high-resolution fMRI, and functional connectivity mapping.
To investigate patterns and correlates of cortical thickness in adolescent males with autism spectrum disorders (ASD) versus matched typically developing controls, we applied kernel canonical correlation analysis to whole brain cortical thickness with the explaining variables of diagnosis, age, full-scale IQ, and their interactions. The analysis found that canonical variates (patterns of cortical thickness) correlated with each of these variables. The diagnosis- and age-by-diagnosis-related canonical variates showed thinner cortex for participants with ASD, which is consistent with previous studies using a univariate analysis. In addition, the multivariate statistics found larger affected regions with higher sensitivity than those found using univariate analysis. An IQ-related effect was also found with the multivariate analysis. The effects of IQ and age-by-IQ interaction on cortical thickness differed between the diagnostics groups. For typically developing adolescents, IQ was positively correlated with cortical thickness in orbitofrontal, postcentral and superior temporal regions, and greater thinning with age was seen in dorsal frontal areas in the superior IQ (> 120) group. These associations between IQ and cortical thickness were not seen in the ASD group. Differing relationships between IQ and cortical thickness implies independent associations between measures of intelligence and brain structure in ASD versus typically developing controls. We discuss these findings vis-à-vis prior results obtained utilizing univariate methods.
autism spectrum disorders; kernel canonical correlation analysis; varimax rotation; cortical thickness; developmental change
A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R2* and initial signal intensity (S0) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R2* and S0 change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like “functional network” components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.
fMRI; Resting state; Multi-echo; BOLD; TE dependence; ICA; Denoising; Subcortical; Connectivity
Primate inferior temporal (IT) cortex is thought to contain a high-level representation of objects at the interface between vision and semantics. This suggests that the perceived similarity of real-world objects might be predicted from the IT representation. Here we show that objects that elicit similar activity patterns in human IT (hIT) tend to be judged as similar by humans. The IT representation explained the human judgments better than early visual cortex, other ventral-stream regions, and a range of computational models. Human similarity judgments exhibited category clusters that reflected several categorical divisions that are prevalent in the IT representation of both human and monkey, including the animate/inanimate and the face/body division. Human judgments also reflected the within-category representation of IT. However, the judgments transcended the IT representation in that they introduced additional categorical divisions. In particular, human judgments emphasized human-related additional divisions between human and non-human animals and between man-made and natural objects. hIT was more similar to monkey IT than to human judgments. One interpretation is that IT has evolved visual-feature detectors that distinguish between animates and inanimates and between faces and bodies because these divisions are fundamental to survival and reproduction for all primate species, and that other brain systems serve to more flexibly introduce species-dependent and evolutionarily more recent divisions.
object perception; vision; neuronal representation; fMRI; representational similarity analysis; human; primate
About 150 researchers around the world convened at the Chateau Lake Louise on Feb 20-23, 2011 to present and discuss the latest research in human and animal imaging and spectroscopy at field strengths of 7 Tesla or above (termed Ultra High Field or UHF) at the third ISMRM-sponsored high field workshop. The clear overall message from the workshop presentations and discussion is that UHF imaging is gaining momentum with regard to new clinically relevant findings, anatomic and fMRI results, susceptibility contrast advancements, solutions to high field related image quality challenges, and to generally pushing the limits of resolution and speed of high field imaging. This meeting report is organized in a manner reflecting the meeting organization itself, covering the seven sessions that were approximately titled: 1. High field overview from head to body to spectroscopy. 2. Susceptibility imaging. 3. Proffered session on susceptibility, ultra fast imaging, unique contrast at 7T and angiography. 4. Neuroscience applications. 5. Proffered session on coils, shimming, parallel imaging, diffusion tensor imaging, and MRI-PET fusion, 6. High field animal imaging and spectroscopy, as well as a vendor overview, and 7. Cutting edge technology at 7T.
Human inferior temporal cortex contains category-selective visual regions, including the fusiform face area (FFA) and the parahippocampal place area (PPA). These regions are defined by their greater category-average activation to the preferred category (faces and places, respectively) relative to nonpreferred categories. The approach of investigating category-average activation has left unclear to what extent category selectivity holds for individual object images. Here we investigate single-image activation profiles to address (1) whether each image from the preferred category elicits greater activation than any image outside the preferred category (categorical ranking), (2) whether there are activation differences within and outside the preferred category (gradedness), and (3) whether the activation profile falls off continuously across the category boundary or exhibits a discontinuity at the boundary (category step). We used functional magnetic resonance imaging to measure the activation elicited in the FFA and PPA by each of 96 object images from a wide range of categories, including faces and places, but also humans and animals, and natural and manmade objects. Results suggest that responses in FFA and PPA exhibit almost perfect categorical ranking, are graded within and outside the preferred category, and exhibit a category step. The gradedness within the preferred category was more pronounced in FFA; the category step was more pronounced in PPA. These findings support the idea that these regions have category-specific functions, but are also consistent with a distributed object representation emphasizing categories while still distinguishing individual images.
We make a few additional points regarding our discussion with Sirotin and Das’ 2009 Nature paper and their 2010 Neuroimage response to our commentary. While we find their data interesting in itself, we remain concerned with how the data are interpreted by the authors. We discuss two categories of methodological issues that limit the conclusions one can draw from their results. (1) The measures of fit quality between the optical and electrical data: kernel shape variation, variance of predicted/measured signals, and R2, interact with each other and are confounded by the fact that one condition has a lower signal magnitude and therefore, lower signal-to-noise-ratio (SNR). (2) Hemodynamic responses to distinct events will be incorrectly or inefficiently estimated if the hemodynamic responses overlap across periodic trials that are not jittered and have an inter-trial interval less than 15 seconds. Most importantly, the overlapping responses across trials might cause transient effects that look similar to the anticipatory effects presented by Sirotin and Das. While their study demonstrates a potentially useful way to probe neurovascular coupling, we believe the current results have little practical relevance for interpreting hemodynamic measures of neural activity such as those used in fMRI. We conclude by making several suggestions for future analyses, which might help elucidate the mechanisms behind these observations and lead to a better understanding of how these observations relate to hemodynamic based measures of neural activation.
In their 2009 Nature article: “Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity,” Yevginiy Sirotin and Aniruddha Das suggest that hemodynamic signals, the basis of functional MRI (fMRI), can arise without any measurable neuronal activity. They report that hemodynamic signals in visual cortex were associated with and time-locked to the anticipation of a visual stimulus, and importantly, without any associated neuronal activity as measured with direct electrophysiological recordings. In this commentary, we demonstrate, using an assessment of their own data, that their claims are not strongly supported. In fact, we found that specific LFP frequency ranges predicted with a high degree of accuracy, the “dark” or “anticipatory” hemodynamic response. For other frequency ranges, we found differences in phase but not magnitude of the measured and predicted hemodynamic response. Importantly, when comparing simply the magnitude as well as the time series standard deviation of the electrophysiological recordings with those of the measured hemodynamic responses, we found a direct correspondence of the dark/stimulated magnitude and standard deviation between the electrophysiological recordings and the hemodynamic responses. All of these analyses strongly imply that anticipatory hemodynamic responses are, in fact, accurately predicted in phase and magnitude by several LFP frequency bands, and are predicted in standard deviation and magnitude by the standard deviation and magnitude of even a wider range of LFP frequencies. We argue that rather than casting doubt on fMRI signal changes, these studies open up an interesting window into exploring more subtle neurovascular relationships.
Aerobic activity is a powerful stimulus for improving mental health and for generating structural changes in the brain. We review the literature documenting these structural changes and explore exactly where in the brain these changes occur as well as the underlying substrates of the changes including neural, glial, and vasculature components. Aerobic activity has been shown to produce different types of changes in the brain. The presence of novel experiences or learning is an especially important component in how these changes are manifest. We also discuss the distinct time courses of structural brain changes with both aerobic activity and learning as well as how these effects might differ in diseased and elderly groups.
exercise; plasticity; hippocampus; neurogenesis; angiogenesis; learning; environmental enrichment; aging
A multimodal neuroimaging study of virtual spatial navigation extends the role of the hippocampal theta rhythm to human memory and self-directed learning.
The hippocampus is crucial for episodic or declarative memory and the theta rhythm has been implicated in mnemonic processing, but the functional contribution of theta to memory remains the subject of intense speculation. Recent evidence suggests that the hippocampus might function as a network hub for volitional learning. In contrast to human experiments, electrophysiological recordings in the hippocampus of behaving rodents are dominated by theta oscillations reflecting volitional movement, which has been linked to spatial exploration and encoding. This literature makes the surprising cross-species prediction that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. We examined the links between theta, spatial exploration, and memory encoding by designing an interactive human spatial navigation paradigm combined with multimodal neuroimaging. We used both non-invasive whole-head Magnetoencephalography (MEG) to look at theta oscillations and Functional Magnetic Resonance Imaging (fMRI) to look at brain regions associated with volitional movement and learning. We found that theta power increases during the self-initiation of virtual movement, additionally correlating with subsequent memory performance and environmental familiarity. Performance-related hippocampal theta increases were observed during a static pre-navigation retrieval phase, where planning for subsequent navigation occurred. Furthermore, periods of the task showing movement-related theta increases showed decreased fMRI activity in the parahippocampus and increased activity in the hippocampus and other brain regions that strikingly overlap with the previously observed volitional learning network (the reverse pattern was seen for stationary periods). These fMRI changes also correlated with participant's performance. Our findings suggest that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. These findings directly extend the role of the hippocampus in spatial exploration in rodents to human memory and self-directed learning.
Neural activity both within and across brain regions can oscillate in different frequency ranges (such as alpha, gamma, and theta frequencies), and these different ranges are associated with distinct functions. In behaving rodents, for example, theta rhythms (4–12 Hz) in the hippocampus are prominent during the initiation of movement and have been linked to spatial exploration. Recent evidence in humans, however, suggests that the human hippocampus is involved in guiding self-directed learning. This suggests that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. In this study, we tested whether there is a human analogue for the movement-initiation-related theta rhythm found in the rodent hippocampus by using a virtual navigation paradigm, combined with non-invasive recordings and functional imaging techniques. Our recordings showed that, indeed, theta power increases are linked to movement initiation. We also examined the relationship to memory encoding, and we found that hippocampal theta oscillations related to pre-retrieval planning predicted memory performance. Imaging results revealed that periods of the task showing movement-related theta also showed increased activity in the hippocampus, as well as other brain regions associated with self-directed learning. These findings directly extend the role of the hippocampal theta rhythm in rodent spatial exploration to human memory and self-directed learning.
A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher’s linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function-kernel) support vector machines. We compared these classifiers’ accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher’s linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60-minutes per subject with 3T fMRI.
Multi-voxel pattern analysis; decoding; classification analysis; fMRI; normalization
Face recognition is a complex cognitive process that requires distinguishable neuronal representations of individual faces. Previous functional magnetic resonance imaging (fMRI) studies using the “fMRI-adaptation” technique have suggested the existence of face-identity representations in face-selective regions, including the fusiform face area (FFA). Here, we present face-identity adaptation findings that are not well explained in terms of face-identity representations. We performed blood-oxygen level–dependent (BOLD) fMRI measurements, while participants viewed familiar faces that were shown repeatedly throughout the experiment. We found decreased activation for repeated faces in face-selective regions, as expected based on previous studies. However, we found similar effects in regions that are not face-selective, including the parahippocampal place area (PPA) and early visual cortex (EVC). These effects were present for exact-image (same view and lighting) as well as different-image (different view and/or lighting) repetition, but more widespread for exact-image repetition. Given the known functional properties of PPA and EVC, it appears unlikely that they contain domain-specific face-identity representations. Alternative interpretations include general attentional effects and carryover of activation from connected regions. These results remind us that fMRI stimulus-change effects can have a range of causes and do not provide conclusive evidence for a neuronal representation of the changed stimulus property.
attention; face-identity; fMRI-adaptation; fusiform face area; neuronal representation
Inferior temporal (IT) object representations have been intensively studied in monkeys and humans, but representations of the same particular objects have never been compared between the species. Moreover, IT’s role in categorization is not well understood. Here, we presented monkeys and humans with the same images of real-world objects and measured the IT response pattern elicited by each image. In order to relate the representations between the species and to computational models, we compare response-pattern dissimilarity matrices. IT response patterns form category clusters, which match between man and monkey. The clusters correspond to animate and inanimate objects; within the animate objects, faces and bodies form subclusters. Within each category, IT distinguishes individual exemplars, and the within-category exemplar similarities also match between the species. Our findings suggest that primate IT across species may host a common code, which combines a categorical and a continuous representation of objects.
Functional MRI (fMRI) is a non-invasive brain imaging methodology that started in 1991 and allows human brain activation to be imaged at high resolution within only a few minutes. Because it has extremely high sensitivity, is relatively easy to implement, and can be performed on most standard clinical MRI scanners. It continues to grow at an explosive rate throughout the world. Over the years, at any given time, fMRI has been defined by only a handful of major topics that have been the focus of researchers using and developing the methodology. In this review, I attempt to take a snapshot of the field of fMRI as it is in mid-2009 by discussing the seven topics that I feel are most on the minds of fMRI researchers. The topics are, in no particular order or grouping: (1) Clinical impact, (2) Utilization of individual functional maps, (3) fMRI signal interpretation, (4) Pattern effect mapping and decoding, (5) Endogenous oscillations, (6) MRI technology, and (7) Alternative functional contrast mechanisms. Most of these topics are highly interdependent, each advancing as the others advance. While most fMRI involves applications towards clinical or neuroscience questions, all applications are fundamentally dependent on advances in basic methodology as well as advances in our understanding of the relationship between neuronal activity and fMRI signal changes. This review neglects almost completely an in-depth discussion of applications. Rather the discussions are on the methods and interpretation.
fMRI; functional MRI; BOLD contrast; brain imaging; hemoglobin; cerebral blood flow; cognition; brain mapping; Magnetic Resonance Imaging; human brain; clinical applications
Recent studies suggested that fMRI voxel patterns can convey information represented in columnar-scale neuronal population codes, even when spatial resolution is insufficient to directly image the patterns of columnar selectivity (Kamitani & Tong 2005; Haynes & Rees 2005). Sensitivity to subvoxel-scale pattern information, or “fMRI hyperacuity”, would greatly enhance the power of fMRI when combined with pattern-information analysis techniques (Kriegeskorte & Bandettini 2007). An individual voxel might weakly reflect columnar-level information if the columns within its boundaries constituted a slightly unbalanced sample of columnar selectivities (Kamitani & Tong 2005), providing a possible mechanism for fMRI hyperacuity. However, Op de Beeck (in press) suggests that a coarse-scale neuronal organization rather than fMRI hyperacuity may explain the presence of the information in the fMRI patterns. Here we argue (a) that the present evidence does not rule out fMRI hyperacuity, (b) that the mechanism originally suggested for fMRI hyperacuity (biased sampling by averaging within each voxel's boundaries; Kamitani & Tong 2005) will only produce very weak sensitivity to fine-grained pattern information, and (c) that an alternative mechanism (voxel as complex spatiotemporal filter) is physiologically more accurate and promises stronger sensitivity to fine-grained pattern information: We know that each voxel samples the neuronal activity pattern through a unique fine-grained structure of venous vessels that supply its blood-oxygen-level-dependent signal. At the simplest level, the drainage domain of a venous vessel may sample the neuronal pattern with a selectivity bias (Gardner in press; Shmuel et al. in press). Beyond biased drainage domains, we illustrate with a simple simulation how temporal properties of the hemodynamics (e.g. the speed of the blood in the capillary bed) can shape spatial properties of a voxel's filter (e.g. how finely structured it is). This suggests that a voxel, together with its signal-supplying vasculature, may best be thought of as a complex spatiotemporal filter. Such a filter may well have greater sensitivity to high spatial frequencies than the Gaussian or averaging-box kernels typically invoked to characterize voxel sampling (compact kernels, both of which would act like anti-aliasing filters that minimize such sensitivity). Importantly, the complex-spatiotemporal-filter hypothesis of fMRI hyperacuity can account for the observed robustness to slight shifts of the voxel grid caused by head motion: Because the fine-grained components of the filter are vascular, they will remain in a constant relationship to the neuronal patterns sampled as the voxel grid is slightly shifted.
During Pavlovian conditioning the expression of a conditioned response is typically taken as evidence that an association between a conditioned stimulus (CS) and an unconditioned stimulus (UCS) has been formed. However, learning-related changes in the unconditioned response (UCR) produced by a predictable UCS can also develop. Learning-related reductions in UCR magnitude are often referred to as UCR diminution. In the present study, we examined UCR diminution in the functional magnetic resonance imaging (fMRI) signal by pairing supra and sub-threshold CS presentations with a UCS. UCR diminution was observed within several brain regions associated with fear learning and memory including the insula, inferior parietal lobe, ventromedial prefrontal cortex (PFC), dorsomedial PFC, and dorsolateral PFC. CS perception appeared to mediate UCR diminution within the ventromedial PFC and posterior cingulate cortex. UCRs within these regions were larger when the UCS followed an unperceived compared to a perceived CS. UCS expectancies appeared to modulate UCRs within the dorsomedial PFC, dorsolateral PFC, insula, and inferior parietal lobe. Activity within these regions showed an inverse relationship with participants’ UCS expectancies, such that as UCS expectancy increased UCR magnitude decreased. In addition, activity within the dorsomedial PFC, dorsolateral PFC, and insula showed a linear relationship with unconditioned skin conductance response expression. These findings demonstrate UCR diminution within the fMRI signal, and suggest that UCS expectancies modulate prefrontal cortex responses to aversive stimuli. In turn, prefrontal cortex activity appears to modulate the expression of unconditioned SCRs.
Verbal fluency tasks have been widely used to evaluate language and executive control processes in the human brain. FMRI studies of verbal fluency, however, have used either silent word generation (which provides no behavioral measure) or cued generation of single words in order to contend with speech-related motion artifacts. In this study, we use a recently developed paradigm design to investigate the neural correlates of verbal fluency during overt, free recall, word generation so that performance and brain activity could be evaluated under conditions that more closely mirror standard behavioral test demands. We investigated verbal fluency to both letter and category cues in order to evaluate differential involvement of specific frontal and temporal lobe sites as a function of retrieval cue type, as suggested by previous neuropsychological and neuroimaging investigations. In addition, we incorporated both a task switching manipulation and an automatic speech condition in order to modulate the demand placed on executive functions. We found greater activation in the left hemisphere during category and letter fluency tasks, and greater right hemisphere activation during automatic speech. We also found that letter and category fluency tasks were associated with differential involvement of specific regions of the frontal and temporal lobes. These findings provide converging evidence that letter and category fluency performance is dependent on partially distinct neural circuitry. They also provide strong evidence that verbal fluency can be successfully evaluated in the MR environment using overt, self-paced, responses.
An increasing number of fMRI studies are using the correlation of low-frequency fluctuations between brain regions, believed to reflect synchronized variations in neuronal activity, to infer “functional connectivity”. In studies of autism spectrum disorder (ASD), decreases in this measure of connectivity have been found by focusing on the response to task modulation, by using only the rest periods, or by analyzing purely resting-state data. This difference in connectivity, however, could result from a number of different mechanisms – differences in noise, task-related fluctuations, task performance, or spontaneous neuronal activity. In this study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing control subjects by examining the residual fluctuations occurring on top of the fMRI response to an overt verbal fluency task. We find decreased correlations of these residuals (a decreased “connectivity”) in ASD subjects. Furthermore, we find that this decrease was not due to task-related effects, block-to-block variations in task performance, or increased noise, and the difference was greatest when primarily rest periods are considered. These findings suggest that the estimate of disrupted functional connectivity in ASD is likely driven by differences in task-unrelated neuronal fluctuations.
Variations in the subject's heart rate and breathing pattern have been shown to result in significant fMRI signal changes, mediated in part by non-neuronal physiological mechanisms such as global changes in levels of arterial CO2. When these physiological changes are correlated with a task, as may happen in response to emotional stimuli or tasks that change levels of arousal, a concern arises that non-neuronal physiologically-induced signal changes may be misinterpreted as reflecting task-related neuronal activation. The purpose of this study is to provide information that can help in determining whether task activation maps are influenced by task-correlated physiological noise, particularly task-correlated breathing changes. We also compare different strategies to reduce the influence of physiological noise. Two paradigms are investigated — 1) a lexical decision task where some subjects showed task-related breathing changes, and 2) a task where subjects were instructed to hold their breath during the presentation of contrast-reversing checkerboard, an extreme case of task-correlated physiological noise. Consistent with previous literature, we find that MRI signal changes correlated with variations in breathing depth and rate have a characteristic spatial and temporal profile that is different from the typical activation-induced BOLD response. The delineation of activation in the presence of task correlated breathing changes was improved either by independent component analysis, or by including specific nuisance regressors in a regression analysis. The difference in the spatial and temporal characteristics of physiological-induced and neuronal-induced fluctuations exploited by these strategies suggests that activation can be studied even in the presence of task-correlated physiological changes.
Voxel-Based Morphometry (VBM) has been used for several years to study differences in brain structure between populations. Recently, a longitudinal version of VBM has been used to show changes in gray matter associated with relatively short periods of training. In the present study we use fMRI and three different standard implementations of longitudinal VBM: SPM2, FSL, and SPM5 to assess functional and structural changes associated with a simple learning task. Behavioral and fMRI data clearly showed a significant learning effect. However, initially positive VBM results were found to be inconsistent across minor perturbations of the analysis technique and ultimately proved to be artifactual. When alignment biases were controlled for and recommended statistical procedures were used, no significant changes in grey matter density were found. This work, initially intended to show structural and functional changes with learning, rather demonstrates some of the potential pitfalls of existing longitudinal VBM methods and prescribes that these tools be applied and interpreted with extreme caution.
Physiological fluctuations resulting from the heart beat and respiration are a dominant source of noise in fMRI, particularly at high field strengths. Commonly used physiological noise correction techniques, such as RETROspective Image CORection (RETROICOR), rely critically on the timing of the image acquisition relative to the heart beat, but do not account for the effects of subject motion. Such motion affects the fluctuation amplitude, yet volume registration can distort the timing information. In this study, we aimed to systematically determine the optimal order of volume registration, slice-time correction and RETROICOR in their traditional forms. In addition, we evaluate the sensitivity of RETROICOR to timing errors introduced by the slice acqusition, and we develop a new method of accounting for timing errors introduced by volume registration into physiological correction (motion-modified RETROICOR). Both simulation and resting data indicate that the temporal standard deviation is reduced most by performing volume registration before RETROICOR and slice-time correction after RETROCIOR. While simulations indicate that physiological noise correction with regressors constructed on a slice-by-slice basis more accurately modeled physiological noise compared to using the same regressors for the entire volume, the difference between these regression techniques in subject data was minimal. The motion-modified RETROICOR showed marked improvement in simulations with varying amounts of subject motion, reducing the temporal standard deviation by up to 36% over the traditional RETROICOR. Though to a lesser degree than in simulation, the motion-modified RETROICOR performed better in nearly every voxel in the brain in both high- and low-resolution subject data.
Distinct aspects of our fearful experiences appear to be mediated by separate explicit and implicit memory processes. To identify brain regions that support these separate memory processes, we measured contingency awareness, conditional fear expression, and functional magnetic resonance imaging signal during a Pavlovian fear conditioning procedure in which tones that predicted an aversive event were presented at supra and sub-threshold volumes. Contingency awareness developed in conjunction with learning-related hippocampal and parahippocampal activity on perceived conditioning trials only. In contrast, conditional fear and differential amygdala activity developed on both perceived and unperceived trials, regardless of whether contingency awareness was expressed. These findings demonstrate the distinct roles of these brain regions in explicit and implicit fear memory processes.
Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.
Beginning in the 1990's, substantial advances have been made in the ability to image the living human brain. Functional MRI, PET, and other modalities have been developed to provide a rich means for assessing brain function and structure across spatial and temporal dimensions. Such methods are now the preferred means to examine the brain in vivo, with several thousand articles now appearing in the literature each year. The next era of human brain imaging is upon us now as technological developments reach a level where data can be processed quickly and combined with other biological information to provide fundamentally new applications and insights. This new era will involve and require the collaborative participation of leading research groups from around the world to share information and expertise for understanding observed effects and synthesizing these into new knowledge. One particular community that is gaining in its prominence in the field is that of the Pacific Rim, whose collective research efforts present an important corpus of research effort into brain structure and function. The Pacific Rim represents an important collection of researchers interested in the greater sharing of ideas. In this special issue of Brain Imaging and Behavior, we focus on emerging areas of research that utilize brain imaging methodology, and discuss how current developments are driving the expansion of functional imaging research. Moreover, we focus on the robust interaction of researchers from around the Pacific Rim whose collaborations are significantly shaping the future of brain imaging.
Perceptual decision making is a multi-stage process where incoming sensory information is used to select one option from several alternatives. Researchers typically have adopted one of two conceptual frameworks to define the criteria for determining whether a brain region is involved in decision computations. One framework, building on single-unit recordings in monkeys, posits that activity in a region involved in decision making reflects the accumulation of evidence toward a decision threshold, thus showing the lowest level of BOLD signal during the hardest decisions. The other framework instead posits that activity in a decision-making region reflects the difficulty of a decision, thus showing the highest level of BOLD signal during the hardest decisions. We had subjects perform a face detection task on degraded face images while we simultaneously recorded BOLD activity. We searched for brain regions where changes in BOLD activity during this task supported either of these frameworks by calculating the correlation of BOLD activity with reaction time – a measure of task difficulty. We found that the right supplementary eye field, right frontal eye field, and right inferior frontal gyrus had increased activity relative to baseline that positively correlated with reaction time, while the left superior frontal sulcus and left middle temporal gyrus had decreased activity relative to baseline that negatively correlated with reaction time. We propose that a simple mechanism that scales a region's activity based on task demands can explain our results.
evidence accumulation; face perception; fMRI