Richard C. Reynolds

Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We first replicate the previously reported head-motion bias on correlation coefficients using data generously contributed by Power et al. (2012). We then show that while motion can be the source of artifact in correlations, the distance-dependent bias-taken to be a manifestation of the motion effect on correlation-is exacerbated by the use of GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that WMeLOCAL, as subset of the ANATICOR discussed by Jo et al. (2010), denoising approach results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI

Surface-based brain imaging analysis is increasingly being used for detailed analysis of the topology of brain activation patterns and changes in cerebral gray matter. Here we present SUMA, a new interface for visualizing and performing surface-based brain imaging analysis that is tightly coupled to AFNI - a volume-based brain imaging analysis suite. The interactive part of SUMA is used for rapid and interactive surface and data visualization, access and manipulations with direct link to the volumetric data rendered in AFNI. The batch-mode part of SUMA allows for surface based operations such as geometry and data smoothing, surface to volume domain mapping in both directions and node-based statistical and computational tools. We also present methods for mapping low resolution functional data onto the cortical surface while preserving the topological information present in the volumetric data and detail an efficient procedure for performing cross-subject, surface-based analysis with minimal interpolation of the functional data.

Evidence of Left Inferior Frontal–Premotor Structural and Functional Connectivity Deficits in Adults Who Stutter

The neurophysiological basis for stuttering may involve deficits that affect dynamic interactions among neural structures supporting fluid speech processing. Here, we examined functional and structural connectivity within corticocortical and thalamocortical loops in adults who stutter. For functional connectivity, we placed seeds in the left and right inferior frontal Brodmann area 44 (BA44) and in the ventral lateral nucleus (VLN) of the thalamus. Subject-specific seeds were based on peak activation voxels captured during speech and nonspeech tasks using functional magnetic resonance imaging. Psychophysiological interaction (PPI) was used to find brain regions with heightened functional connectivity with these cortical and subcortical seeds during speech and nonspeech tasks. Probabilistic tractography was used to track white matter tracts in each hemisphere using the same seeds. Both PPI and tractrography supported connectivity deficits between the left BA44 and the left premotor regions, while connectivity among homologous right hemisphere structures was significantly increased in the stuttering group. No functional connectivity differences between BA44 and auditory regions were found between groups. The functional connectivity results derived from the VLN seeds were less definitive and were not supported by the tractography results. Our data provide strongest support for deficient left hemisphere inferior frontal to premotor connectivity as a neural correlate of stuttering.

Sensory stimulation activates both motor and sensory components of the swallowing system

Volitional swallowing in humans involves the coordination of both brainstem and cerebral swallowing control regions. Peripheral sensory inputs are necessary for safe and efficient swallowing, and their importance to the patterned components of swallowing has been demonstrated. However, the role of sensory inputs to the cerebral system during volitional swallowing is less clear. We used four conditions applied during functional magnetic resonance imaging to differentiate between sensory, motor planning, and motor execution components for cerebral control of swallowing. Oral air pulse stimulation was used to examine the effect of sensory input, covert swallowing was used to engage motor planning for swallowing, and overt swallowing was used to activate the volitional swallowing system. Breath-holding was also included to determine whether its effects could account for the activation seen during overt swallowing. Oral air pulse stimulation, covert swallowing and overt swallowing all produced activation in the primary motor cortex, cingulate cortex, putamen and insula. Additional regions of the swallowing cerebral system that were activated by the oral air pulse stimulation condition included the primary and secondary somatosensory cortex and thalamus. Although air pulse stimulation was on the right side only, bilateral cerebral activation occurred. On the other hand, covert swallowing minimally activated sensory regions, but did activate the supplementary motor area and other motor regions. Breath-holding did not account for the activation during overt swallowing. The effectiveness of oral-sensory stimulation for engaging both sensory and motor components of the cerebral swallowing system demonstrates the importance of sensory input in cerebral swallowing control.

Direct imaging of macrovascular and microvascular contributions to BOLD fMRI in layers IV-V of the rat whisker-barrel cortex

The spatiotemporal characteristics of the hemodynamic response to increased neural activity were investigated at the level of individual intracortical vessels using BOLD-fMRI in a well-established rodent model of somatosensory stimulation at 11.7 T. Functional maps of the rat barrel cortex were obtained at 150 × 150 × 500 μm spatial resolution every 200 ms. The high spatial resolution allowed separation of active voxels into those containing intracortical macro vessels, mainly vein/venules (referred to as macrovasculature), and those enriched with arteries/capillaries and small venules (referred to as microvasculature) since the macro vessel can be readily mapped due to the fast T2 decay of blood at 11.7 T. The earliest BOLD response was observed within layers IV-V by 0.8s following stimulation and encompassed mainly the voxels containing the microvasculature and some confined macrovasculature voxels. By 1.2s, the BOLD signal propagated to the macrovasculature voxels where the peak BOLD signal was 2-3 times higher than that of the microvasculature voxels. The BOLD response propagated in individual venules/veins far from neuronal sources at later times. This was also observed in layers IV-V of the barrel cortex after specific stimulation of separated whisker rows. These results directly visualized that the earliest hemodynamic changes to increased neural activity occur mainly in the microvasculature and spread toward the macrovasculature. However, at peak response, the BOLD signal is dominated by penetrating venules even at layers IV-V of the cortex.

Neural Mechanisms of Frustration in Chronically Irritable Children

OBJECTIVE Irritability is common in children and adolescents and is the cardinal symptom of disruptive mood dysregulation disorder, a new DSM-5 disorder, yet its neural correlates remain largely unexplored. The authors conducted a functional MRI study to examine neural responses to frustration in children with severe mood dysregulation. METHOD The authors compared emotional responses, behavior, and neural activity between 19 severely irritable children (operationalized using criteria for severe mood dysregulation) and 23 healthy comparison children during a cued-attention task completed under nonfrustrating and frustrating conditions. RESULTS Children in both the severe mood dysregulation and the healthy comparison groups reported increased frustration and exhibited decreased ability to shift spatial attention during the frustration condition relative to the nonfrustration condition. However, these effects of frustration were more marked in the severe mood dysregulation group than in the comparison group. During the frustration condition, participants in the severe mood dysregulation group exhibited deactivation of the left amygdala, the left and right striatum, the parietal cortex, and the posterior cingulate on negative feedback trials, relative to the comparison group (i.e., between-group effect) and to the severe mood dysregulation groups responses on positive feedback trials (i.e., within-group effect). In contrast, neural response to positive feedback during the frustration condition did not differ between groups. CONCLUSIONS In response to negative feedback received in the context of frustration, children with severe, chronic irritability showed abnormally reduced activation in regions implicated in emotion, attention, and reward processing. Frustration appears to reduce attention flexibility, particularly in severely irritable children, which may contribute to emotion regulation deficits in this population. Further research is needed to relate these findings to irritability specifically, rather than to other clinical features of severe mood dysregulation.

Functional imaging analysis contest (FIAC) analysis according to AFNI and SUMA

The Functional Imaging Analysis Contest (FIAC) datasets were analyzed with the AFNI software package. Two types of linear regression analyses were carried out: “fixed shape” hemodynamic response, where a preselected incomplete gamma function is used to model each brief activation episode, and “variable shape” analysis, where the temporal shape of the response model in each stimulus block class is allowed to vary separately in each voxel. These time series regressions were carried out both in the volume and on the original data projected to individual standardized cortical surface models. Intersubject analyses were carried out voxel‐wise on the regression amplitudes obtained from these time series results, using a multi‐way within‐subject analysis of variance (ANOVA). Group analysis of the block design demonstrated a significant repetition suppression of the BOLD signal within blocks in the superior and middle temporal gyrus. This effect may represent differences in the response to the first stimulus following a period of silence compared to the remaining sentences in the block. Analyzing the event‐related data, Brodmann area 31 showed significant sentence effect and consecutive‐sentence repetition effect. However, no significant speaker effect was found; these results may be consistent with the instructions to the subjects that they would be tested on the sentence content. Sentence by speaker interaction effects were found in bilateral middle temporal gyrus, left inferior frontal, and left inferior temporal gyrus. Hum Brain Mapp, 2006.

Methods for cleaning the BOLD fMRI signal

&NA; Blood oxygen‐level‐dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non‐neuronal fluctuations of instrumental, physiological or subject‐specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion‐related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data‐driven approaches and using external recordings. Data‐driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task‐based and resting state fMRI studies. HighlightsNumerous techniques are available for denoising the BOLD fMRI signal.Motion‐related artifacts and physiological noise fluctuations are the main targets.Phase‐based and multi‐echo fMRI can help to improve the performance of denoising.There exist multiple equally‐efficient alternatives to global signal regression.There is no “best” method for preprocessing, but there are incorrect methods.

Parametric Modulation of Neural Activity by Emotion in Youth With Bipolar Disorder, Youth With Severe Mood Dysregulation, and Healthy Volunteers

CONTEXT Youth with bipolar disorder (BD) and those with severe, nonepisodic irritability (severe mood dysregulation [SMD]) exhibit amygdala dysfunction during facial emotion processing. However, studies have not compared such patients with each other and with comparison individuals in neural responsiveness to subtle changes in facial emotion; the ability to process such changes is important for social cognition. To evaluate this, we used a novel, parametrically designed faces paradigm. OBJECTIVE To compare activation in the amygdala and across the brain in BD patients, SMD patients, and healthy volunteers (HVs). DESIGN Case-control study. SETTING Government research institute. PARTICIPANTS Fifty-seven youths (19 BD, 15 SMD, and 23 HVs). MAIN OUTCOME MEASURE Blood oxygenation level-dependent data. Neutral faces were morphed with angry and happy faces in 25% intervals; static facial stimuli appeared for 3000 milliseconds. Participants performed hostility or nonemotional facial feature (ie, nose width) ratings. The slope of blood oxygenation level-dependent activity was calculated across neutral-to-angry and neutral-to-happy facial stimuli. RESULTS In HVs, but not BD or SMD participants, there was a positive association between left amygdala activity and anger on the face. In the neutral-to-happy whole-brain analysis, BD and SMD participants modulated parietal, temporal, and medial-frontal areas differently from each other and from that in HVs; with increasing facial happiness, SMD patients demonstrated increased, and BD patients decreased, activity in the parietal, temporal, and frontal regions. CONCLUSIONS Youth with BD or SMD differ from HVs in modulation of amygdala activity in response to small changes in facial anger displays. In contrast, individuals with BD or SMD show distinct perturbations in regions mediating attention and face processing in association with changes in the emotional intensity of facial happiness displays. These findings demonstrate similarities and differences in the neural correlates of facial emotion processing in BD and SMD, suggesting that these distinct clinical presentations may reflect differing dysfunctions along a mood disorders spectrum.

AFNI and Clustering: False Positive Rates Redux

In response to reports of inflated false positive rate (FPR) in FMRI group analysis tools, a series of replications, investigations, and software modifications were made to address this issue. While these investigations continue, significant progress has been made to adapt AFNI to fix such problems. Two separate lines of changes have been made. First, a long-tailed model for the spatial correlation of the FMRI noise characterized by autocorrelation function (ACF) was developed and implemented into the 3dClustSim tool for determining the cluster-size threshold to use for a given voxel-wise threshold. Second, the 3dttest++ program was modified to do randomization of the voxel-wise t-tests and then to feed those randomized t-statistic maps into 3dClustSim directly for cluster-size threshold determination-without any spatial model for the ACF. These approaches were tested with the Beijing subset of the FCON-1000 data collection. The first approach shows markedly improved (reduced) FPR, but in many cases is still above the nominal 5%. The second approach shows FPRs clustered tightly about 5% across all per-voxel p-value thresholds ≤ 0.01. If t-tests from a univariate GLM are adequate for the group analysis in question, the second approach is what the AFNI group currently recommends for thresholding. If more complex per-voxel statistical analyses are required (where permutation/randomization is impracticable), then our current recommendation is to use the new ACF modeling approach coupled with a per-voxel p-threshold of 0.001 or below. Simulations were also repeated with the now infamously “buggy” version of 3dClustSim: the effect of the bug on FPRs was minimal (of order a few percent).