Peter A. Bandettini

The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

Dynamic functional connectivity: promise, issues, and interpretations.

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

Functional magnetic resonance imaging of complex human movements

Functional magnetic resonance imaging (FMRI) is a new, noninvasive imaging tool thought to measure changes related to regional cerebral blood flow (rCBF). Previous FMRI studies have demonstrated functional changes within the primary cerebral cortex in response to simple activation tasks, but it is unknown whether FMRI can also detect changes within the nonprimary cortex in response to complex mental activities. We therefore scanned six right-handed healthy subjects while they performed self-paced simple and complex finger movements with the right and left hands. Some subjects also performed the tasks at a fixed rate (2 Hz) or imagined performing the complex task. Functional changes occurred (1) in the contralateral primary motor cortex during simple, self-paced movements; (2) in the contralateral (and occasionally ipsilateral) primary motor cortex, the supplementary motor area (SMA), the premotor cortex of both hemispheres, and the contralateral somatosensory cortex during complex, self-paced movements; (3) with less intensity during paced movements, presumably due to the slower movement rates associated with the paced (relative to self-paced) condition; and (4) in the SMA and, to a lesser degree, the premotor cortex during imagined complex movements. These preliminary results are consistent with hierarchical models of voluntary motor control.

Representational similarity analysis - connecting the branches of systems neuroscience.

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience.

Matching categorical object representations in inferior temporal cortex of man and monkey.

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, ITs 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.

A general mechanism for perceptual decision-making in the human brain

Findings from single-cell recording studies suggest that a comparison of the outputs of different pools of selectively tuned lower-level sensory neurons may be a general mechanism by which higher-level brain regions compute perceptual decisions. For example, when monkeys must decide whether a noisy field of dots is moving upward or downward, a decision can be formed by computing the difference in responses between lower-level neurons sensitive to upward motion and those sensitive to downward motion. Here we use functional magnetic resonance imaging and a categorization task in which subjects decide whether an image presented is a face or a house to test whether a similar mechanism is also at work for more complex decisions in the human brain and, if so, where in the brain this computation might be performed. Activity within the left dorsolateral prefrontal cortex is greater during easy decisions than during difficult decisions, covaries with the difference signal between face- and house-selective regions in the ventral temporal cortex, and predicts behavioural performance in the categorization task. These findings show that even for complex object categories, the comparison of the outputs of different pools of selectively tuned neurons could be a general mechanism by which the human brain computes perceptual decisions.

QUIPSS II with thin-slice TI1 periodic saturation: A method for improving accuracy of quantitative perfusion imaging using pulsed arterial spin labeling

Quantitative imaging of perfusion using a single subtraction, second version (QUIPSS II) is a pulsed arterial spin labeling (ASL) technique for improving the quantitation of perfusion imaging by minimizing two major systematic errors: the variable transit delay from the distal edge of the tagged region to the imaging slices, and the contamination by intravascular signal from tagged blood that flows through the imaging slices. However, residual errors remain due to incomplete saturation of spins over the slab‐shaped tagged region by the QUIPSS II saturation pulse, and spatial mismatch of the distal edge of the saturation and inversion slice profiles. By replacing the original QUIPSS II saturation pulse with a train of thin‐slice periodic saturation pulses applied at the distal end of the tagged region, the accuracy of perfusion quantitation is improved. Results of single and multislice studies are reported. Magn Reson Med 41:1246–1254, 1999.

Neural Correlates of Visual Working Memory: fMRI Amplitude Predicts Task Performance

We used fMRI to investigate how moment-to-moment neural activity contributes to success or failure on individual trials of a visual working memory (WM) task. We found that different nodes of a distributed cortical network were activated to a greater extent for correct compared to incorrect trials during stimulus encoding, memory maintenance during delays, and at test. A logistic regression analysis revealed that the fMRI signal amplitude during the delay interval in a network of frontoparietal regions predicted successful performance on a trial-by-trial basis. Differential delay activity occurred even for only those trials in which BOLD activity during encoding was strong, demonstrating that it was not a simple consequence of effective versus ineffective encoding. Our results indicate that accurate memory depends on strong sustained signals that span the delay interval of WM tasks.

Functional magnetic resonance imaging (FMRI) of the human brain

Functional magnetic resonance imaging (FMRI) can provide detailed images of human brain that reflect localized changes in cerebral blood flow and oxygenation induced by sensory, motor, or cognitive tasks. This review presents methods for gradient-recalled echo-planar functional magnetic resonance imaging (FMRI). Also included is a discussion of the hypothesized basis of FMRI, imaging hardware, a unique visual stimulation apparatus, image post-processing and statistical analysis. Retinotopic mapping of striate and extrastriate visual cortex is discussed as an example application. The described echo-planar technique permitted acquisition of an image in 40 ms with a repetition rate of up to 2 per second. However, FMRI responses are slow compared to changes in neural activity. Onset of a visual checkerboard test pattern evoked a response that was delayed by 1-2 s and reached 90% of peak in 5 s. Return to baseline following stimulation was slightly slower. Alternating control (blank) and test (checkerboard) patterns every 20 s induced a cyclic response that was detected in the presence of noise using a cross-correlation technique that was verified by parametric statistics. FMRI revealed retinotopically organized patterns of visually evoked activity in response to annular stimuli that increased in visual field eccentricity. Retinotopy was also observed with test patterns rotated around the fixation point (center of gaze). Results from repeated tests 1 week apart were highly similar. Compared to passive viewing, an active visual discrimination task enhanced responses from extrastriate association cortex.

Relationship Between Finger Movement Rate and Functional Magnetic Resonance Signal Change in Human Primary Motor Cortex

Functional magnetic resonance imaging (FMRI) is a noninvasive technique for mapping regional brain changes in response to sensory, motor, or cognitive activation tasks. Interpretation of these activation experiments may be confounded by more elementary task parameters, such as stimulus presentation or movement rates. We examined the effect of movement rate on the FMRI response recorded from the contralateral primary motor cortex. Four right-handed healthy subjects performed flexion-extension movements of digits 2–5 of the right hand at rates of 1, 2, 3, 4, or 5 Hz. Results of this study indicated a positive linear relationship between movement rate and FMRI signal change. Additionally, the number of voxels demonstrating functional activity increased significantly with faster movement rates. The magnitude of the signal change at each movement rate remained constant over the course of three 8-min scanning series. These findings are similar to those of previous rate studies of the visual and auditory system performed with positron emission tomography (PET) and FMRI.