Box-Jenkins intervention analysis of functional magnetic resonance imaging data

The brain landscape of the two-hit model of posttraumatic stress disorder

The neurophysiological mechanisms underlying the development of posttraumatic stress disorder (PTSD) are poorly understood. Here we test a proposal that PTSD symptoms reflect fixed, highly correlated neural networks resulting from massive engagement of sensory inputs and the sequential involvement of those projections to limbic areas. Three-tesla functional magnetic resonance imaging (fMRI) data were acquired at rest in 15 veterans diagnosed with PTSD and 21 healthy control veterans from which zero-lag cross correlations between 50 brain areas (N = 1,225 pairs) were computed and analyzed. The brain areas were assigned to tiers based on the neurocircuitry of successively converging sensory pathways proposed by Jones and Powell (Jones EG, Powell TP. Brain 93: 793-820, 1970). The primary analyses assessed normalized proportional differences in cross correlation strength within and across tiers in veterans with PTSD and control veterans. Compared with control veterans, cross correlation strength was higher in veterans with PTSD, within and across tiers of areas involved in processing sensory inputs, and systematically increased from sensory processing areas to limbic areas. The functional relevance of this hypercorrelation was further documented by the finding that the severity of self-reported PTSD symptomatology was positively associated with higher neural correlations.NEW & NOTEWORTHY The neurophysiological mechanisms underlying the development of PTSD are poorly understood. Here we document that massive engagement of sensory modalities during trauma exposure leads to fixed, hypercorrelated frontal, parietal, temporal, and limbic networks, reflecting the successive integration of salient sensory inputs along the framework of Jones and Powell.

Long-Term Predictive and Feedback Encoding of Motor Signals in the Simple Spike Discharge of Purkinje Cells

Most hypotheses of cerebellar function emphasize a role in real-time control of movements. However, the cerebellum’s use of current information to adjust future movements and its involvement in sequencing, working memory, and attention argues for predicting and maintaining information over extended time windows. The present study examines the time course of Purkinje cell discharge modulation in the monkey (Macaca mulatta) during manual, pseudo-random tracking. Analysis of the simple spike firing from 183 Purkinje cells during tracking reveals modulation up to 2 s before and after kinematics and position error. Modulation significance was assessed against trial shuffled firing, which decoupled simple spike activity from behavior and abolished long-range encoding while preserving data statistics. Position, velocity, and position errors have the most frequent and strongest long-range feedforward and feedback modulations, with less common, weaker long-term correlations for speed and radial error. Position, velocity, and position errors can be decoded from the population simple spike firing with considerable accuracy for even the longest predictive (-2000 to -1500 ms) and feedback (1500 to 2000 ms) epochs. Separate analysis of the simple spike firing in the initial hold period preceding tracking shows similar long-range feedforward encoding of the upcoming movement and in the final hold period feedback encoding of the just completed movement, respectively. Complex spike analysis reveals little long-term modulation with behavior. We conclude that Purkinje cell simple spike discharge includes short- and long-range representations of both upcoming and preceding behavior that could underlie cerebellar involvement in error correction, working memory, and sequencing.

A compact and realistic cerebral cortical layout derived from prewhitened resting-state fMRI time series: Cherniak's adjacency rule, size law, and metamodule grouping upheld

We used hierarchical tree clustering to derive a functional organizational chart of 52 human cortical areas (26 per hemisphere) from zero-lag correlations calculated between single-voxel, prewhitened, resting-state BOLD fMRI time series in 18 subjects. No special “resting-state networks” were identified. There were four major features in the resulting tree (dendrogram). First, there was a strong clustering of homotopic, left-right hemispheric areas. Second, cortical areas were concatenated in multiple, partially overlapping clusters. Third, the arrangement of the areas revealed a layout that closely resembled the actual layout of the cerebral cortex, namely an orderly progression from anterior to posterior. And fourth, the layout of the cortical areas in the tree conformed to principles of efficient, compact layout of components proposed by Cherniak. Since the tree was derived on the basis of the strength of neural correlations, these results document an orderly relation between functional interactions and layout, i.e., between structure and function.

True associations between resting fMRI time series based on innovations

We calculated voxel-by-voxel pairwise crosscorrelations between prewhitened resting-state BOLD fMRI time series recorded from 60 cortical areas (30 per hemisphere) in 18 human subjects (nine women and nine men). Altogether, more than a billion-and-a-quarter pairs of BOLD time series were analyzed. For each pair, a crosscorrelogram was computed by calculating 21 crosscorrelations, namely at zero lag ± 10 lags of 2 s duration each. For each crosscorrelogram, in turn, the crosscorrelation with the highest absolute value was found and its sign, value, and lag were retained for further analysis. In addition, the crosscorrelations at zero lag (irrespective of the location of the peak) were also analyzed as a special case. Based on known varying density of anatomical connectivity, we distinguished four general brain groups for which we derived summary statistics of crosscorrelations between voxels within an area (group I), between voxels of paired homotopic areas across the two hemispheres (group II), between voxels of an area and all other voxels in the same (ipsilateral) hemisphere (group III), and voxels of an area and all voxels in the opposite (contralateral) hemisphere (except those in the homotopic area) (group IV). We found the following. (a) Most of the crosscorrelogram peaks occurred at zero lag, followed by ± 1 lag; (b) over all groups, positive crosscorrelations were much more frequent than negative ones; (c) average crosscorrelation was highest for group I, and decreased progressively for groups II-IV; (d) the ratio of positive over negative crosscorrelations was highest for group I and progressively smaller for groups II-IV; (e) the highest proportion of positive crosscorrelations (with respect to all positive ones) was observed at zero lag; and (f) the highest proportion of negative crosscorrelations (with respect to all negative ones) was observed at lag = 2. These findings reveal a systematic pattern of crosscorrelations with respect to their sign, magnitude, lag and brain group, as defined above. Given that these groups were defined along a qualitative gradient of known overall anatomical connectivity, our results suggest that functional interactions between two voxels may simply reflect the density of such anatomical connectivity between the areas to which the voxels belong.

New criteria for assessing fit quality in dynamic contrast-enhancedT1-weighted MRI for perfusion and permeability imaging

Contrast-enhanced (CE) MRI provides in vivo physiological information that cannot be obtained by conventional imaging methods. This information is generally extracted by using models to represent the circulation of contrast agent in the body. However, the results depend on the quality of the fit obtained with the chosen model. Therefore, one must check the fit quality to avoid working on physiologically irrelevant parameters. In this study two dimensionless criteria-the fraction of modeling information (FMI) and the fraction of residual information (FRI)-are proposed to identify errors caused by poor fit. These are compared with more conventional criteria, namely the quadratic error and the correlation coefficient, both theoretically and with the use of simulated and real CE-MRI data. The results indicate the superiority of the new criteria. It is also shown that these new criteria can be used to detect oversimplified models.

Model assessment and model building in fMRI

Model quality is rarely assessed in fMRI data analyses and less often reported. This may have contributed to several shortcomings in the current fMRI data analyses, including: (1) Model mis-specification, leading to incorrect inference about the activation-maps, SPM[t] and SPM[F]; (2) Improper model selection based on the number of activated voxels, rather than on model quality; (3) Under-utilization of systematic model building, resulting in the common but suboptimal practice of using only a single, pre-specified, usually over-simplified model; (4) Spatially homogenous modeling, neglecting the spatial heterogeneity of fMRI signal fluctuations; and (5) Lack of standards for formal model comparison, contributing to the high variability of fMRI results across studies and centers. To overcome these shortcomings, it is essential to assess and report the quality of the models used in the analysis. In this study, we applied images of the Durbin-Watson statistic (DW-map) and the coefficient of multiple determination (R(2)-map) as complementary tools to assess the validity as well as goodness of fit, i.e., quality, of models in fMRI data analysis. Higher quality models were built upon reduced models using classic model building. While inclusion of an appropriate variable in the model improved the quality of the model, inclusion of an inappropriate variable, i.e., model mis-specification, adversely affected it. Higher quality models, however, occasionally decreased the number of activated voxels, whereas lower quality or inappropriate models occasionally increased the number of activated voxels, indicating that the conventional approach to fMRI data analysis may yield sub-optimal or incorrect results. We propose that model quality maps become part of a broader package of maps for quality assessment in fMRI, facilitating validation, optimization, and standardization of fMRI result across studies and centers. Hum. Brain Mapping 20:227-238, 2003.

Wavelet transform-based Wiener filtering of event-related fMRI data

The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a method based on Wiener filtering in the wavelet domain is developed and demonstrated for denoising event-related fMRI data. Application of the technique to simulated and experimental data demonstrates that the technique is effective in reducing noise while preserving neuronal activity-induced response.

Plurality and resemblance in fMRI data analysis

We apply nine analytic methods employed currently in imaging neuroscience to simulated and actual BOLD fMRI signals and compare their performances under each signal type. Starting with baseline time series generated by a resting subject during a null hypothesis study, we compare method performance with embedded focal activity in these series of three different types whose magnitudes and time courses are simple, convolved with spatially varying hemodynamic responses, and highly spatially interactive. We then apply these same nine methods to BOLD fMRI time series from contralateral primary motor cortex and ipsilateral cerebellum collected during a sequential finger opposition study. Paired comparisons of results across methods include a voxel-specific concordance correlation coefficient for reproducibility and a resemblance measure that accommodates spatial autocorrelation of differences in activity surfaces. Receiver-operating characteristic curves show considerable model differences in ranges less than 10% significance level (false positives) and greater than 80% power (true positives). Concordance and resemblance measures reveal significant differences between activity surfaces in both data sets. These measures can assist researchers by identifying groups of models producing similar and dissimilar results, and thereby help to validate, consolidate, and simplify reports of statistical findings. A pluralistic strategy for fMRI data analysis can uncover invariant and highly interactive relationships between local activity foci and serve as a basis for further discovery of organizational principles of the brain. Results also suggest that a pluralistic empirical strategy coupled formally with substantive prior knowledge can help to uncover new brain-behavior relationships that may remain hidden if only a single method is employed.