Childhood Emotional Abuse Moderates Associations Among Corticomotor White Matter Structure and Stress Neuromodulators in Women With and Without Depression.

Adverse caregiving during development can produce long-lasting changes to neural, endocrine, and behavioral responses to stress, and is strongly related to elevated risk of adult psychopathology. While prior experience of adversity is associated with altered sympathetic nervous system (SNS) and hypothalamic-pituitary-adrenal (HPA) axis activity, the underlying neural pathways are not completely understood. In a double-blind crossover study, we used diffusion tensor imaging (DTI) to examine whether variation in white matter structure predicts differences in HPA-SNS interactions as a function of early adversity. Participants included 74 women who exhibited a wide range of depression severity and/or childhood emotional abuse (EA). Participants attended two experimental sessions during which they were administered 20 mg cortisol (CORT) or placebo and after 90 min, viewed emotionally laden pictures while undergoing MRI scanning. Immediately after emotional picture-viewing, we collected salivary alpha-amylase (sAA) to index SNS activation. We tested whether EA moderated the relation between fractional anisotropy (FA), a measure of white matter fiber structure, and sAA. In the placebo condition, for participants with minimal history of EA, higher FA in corticomotor projections was negatively correlated with sAA, whereas in participants with severe EA, the correlation was trending in the opposite direction. Following CORT administration, FA and sAA were not related, suggesting that SNS tone during acute cortisol elevation may depend on neural pathways other than corticomotor projections. The results suggest that at baseline-though not during cortisol elevation-increased FA in these tracts is associated with lower levels of SNS activity in women with minimal EA, but not in women with severe EA. These findings provide evidence that corticomotor projections may be a key component of altered neural circuitry in adults with history of maltreatment, and may be related to alterations in stress neuromodulators in psychopathology.

Intrathecal antibody distribution in the rat brain: surface diffusion, perivascular transport and osmotic enhancement of delivery.

KEY POINTS: It is unclear precisely how macromolecules (e.g. endogenous proteins and exogenous immunotherapeutics) access brain tissue from the cerebrospinal fluid (CSF). We show that transport at the brain-CSF interface involves a balance between Fickian diffusion in the extracellular spaces at the brain surface and convective transport in perivascular spaces of cerebral blood vessels. Intrathecally-infused antibodies exhibited size-dependent access to the perivascular spaces and tunica media basement membranes of leptomeningeal arteries. Perivascular access and distribution of full-length IgG could be enhanced by intrathecal co-infusion of hyperosmolar mannitol. Pores or stomata present on CSF-facing leptomeningeal cells ensheathing blood vessels in the subarachnoid space may provide unique entry sites into the perivascular spaces from the CSF. These results illuminate new mechanisms likely to govern antibody trafficking at the brain-CSF interface with relevance for immune surveillance in the healthy brain and insights into the distribution of therapeutic antibodies. ABSTRACT: The precise mechanisms governing the central distribution of macromolecules from the cerebrospinal fluid (CSF) to the brain and spinal cord remain poorly understood, despite their importance for physiological processes such as antibody trafficking for central immune surveillance, as well as several ongoing intrathecal clinical trials. In the present study, we clarify how IgG and smaller single-domain antibodies (sdAb) distribute throughout the whole brain in a size-dependent manner after intrathecal infusion in rats using ex vivo fluorescence and in vivo three-dimensional magnetic resonance imaging. Antibody distribution was characterized by diffusion at the brain surface and widespread distribution to deep brain regions along the perivascular spaces of all vessel types, with sdAb accessing a four- to seven-fold greater brain area than IgG. Perivascular transport involved blood vessels of all caliber and putative smooth muscle and astroglial basement membrane compartments. Perivascular access to smooth muscle basement membrane compartments also exhibited size-dependence. Electron microscopy was used to show stomata on leptomeningeal coverings of blood vessels in the subarachnoid space as potential access points allowing substances in the CSF to enter the perivascular space. Osmolyte co-infusion significantly enhanced perivascular access of the larger antibody from the CSF, with intrathecal 0.75 m mannitol increasing the number of perivascular profiles per slice area accessed by IgG by ∼50%. The results of the present study reveal potential distribution mechanisms for endogenous IgG, which is one of the most abundant proteins in the CSF, as well as provide new insights with respect to understanding and improving the drug delivery of macromolecules to the central nervous system via the intrathecal route.

The Impact of Intracranial Tumor Proximity to White Matter Tracts on Morbidity and Mortality: A Retrospective Diffusion Tensor Imaging Study.

Background: Using diffusion tensor imaging (DTI) in neurosurgical planning allows identification of white matter tracts and has been associated with a reduction in postoperative functional deficits. Objective: This study explores the relationship between the lesion-to-tract distance (LTD) and postoperative morbidity and mortality in patients with brain tumors in order to evaluate the role of DTI in predicting postoperative outcomes. Methods: Adult patients with brain tumors (n = 60) underwent preoperative DTI. Three major white matter pathways (superior longitudinal fasciculi [SLF], cingulum, and corticospinal tract) were identified using DTI images, and the shortest LTD was measured for each tract. Postoperative morbidity and mortality information was collected from electronic medical records. Results: The ipsilesional corticospinal tract LTD and left SLF LTD were significantly associated with the occurrence rate of total postoperative motor (P = .018) and language (P < .001) deficits, respectively. The left SLF LTD was also significantly associated with the occurrence rate of new postoperative language deficits (P = .003), and the LTD threshold that best predicted this occurrence was 1 cm (P < .001). Kaplan­Meier log-rank survival analyses in patients having high-grade tumors demonstrated a significantly higher mortality for patients with a left SLF LTD <1 cm (P = .01). Conclusion: Measuring tumor proximity to major white matter tracts using DTI can inform clinicians of the likelihood of postoperative functional deficits. A distance of 1 cm or less from eloquent white matter structures most significantly predicts the occurrence of new deficits with current surgical and imaging techniques.

Challenges and Opportunities: Building a Relationship Between a Department of Biomedical Engineering and a Medical School.

A department of biomedical engineering can significantly enhance the impact of their research and training programs if a productive relationship with a medical school can be established. In order to develop such a relationship, significant hurdles must be overcome. This editorial summarizes some of the major challenges and opportunities for a department of biomedical engineering as they seek to build or enhance a relationship with a medical school. The ideas were formulated by engaging the collective wisdom from the Council of Chairs of the biomedical engineering departments.

Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data.

Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor, and executive control RSNs from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method's utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians' expert interpretation.

Uptake and retention of manganese contrast agents for PET and MRI in the rodent brain.

Manganese-enhanced magnetic resonance imaging (MRI) is an established neuroimaging method for signal enhancement, tract tracing, and functional studies in rodents. Along with the increasing availability of combined positron emission tomography (PET) and MRI scanners, the recent development of the positron-emitting isotope 52 Mn has prompted interest in the use of Mn2+ as a dual-modality contrast agent. In this work, we characterized and compared the uptake of systemically delivered Mn2+ and radioactive 52 Mn2+ in the rat brain for MRI and PET, respectively. Additionally, we examined the biodistribution of two formulations of 52 Mn2+ in the rat. In MRI, maximum uptake was observed one day following delivery of the highest MnCl2 dose tested (60 mg/kg), with some brain regions showing delayed maximum enhancement 2-4 days following delivery. In PET, we observed low brain uptake after systemic delivery, with a maximum of approximately 0.2% ID/g. We also studied the effect of final formulation vehicle (saline compared with MnCl2 ) on 52 Mn2+ organ biodistribution and brain uptake. We observed that the addition of bulk Mn2+ carrier to 52 Mn2+ in solution resulted in significantly reduced 52 Mn2+ uptake in the majority of organs, including the brain. These results lay the groundwork for further development of 52 Mn PET or dual Mn-enhanced PET-MR neuroimaging in rodents, and indicate several interesting potential applications of 52 Mn PET in other organs and systems. Copyright © 2016 John Wiley & Sons, Ltd.

Data-driven optimized flip angle selection for T1 estimation from spoiled gradient echo acquisitions.

PURPOSE: Define criteria for selection of optimal flip angle sets for T1 estimation and evaluate effects on T1 mapping. THEORY AND METHODS: Flip angle sets for spoiled gradient echo-based T1 mapping were selected by minimizing T1 estimate variance weighted by the joint density of M0 and T1 in an initial acquisition. The effect of optimized flip angle selection on T1 estimate error was measured using simulations and experimental data in the human and rat brain. RESULTS: For two-point acquisitions, optimized angle sets were similar to those proposed by other groups and, therefore, performed similarly. For multipoint acquisitions, optimal angle sets for T1 mapping in the brain consisted of a repetition of two angles. Implementation of optimal angles reduced T1 estimate variance by 30-40% compared with a multipoint acquisition using a range of angles. Performance of the optimal angle set was equivalent to that of a repetition of the two-angle set selected using criteria proposed by other researchers. CONCLUSION: Repetition of two carefully selected flip angles notably improves the precision of resulting T1 estimates compared with acquisitions using a range of flip angles. This work provides a flexible and widely applicable optimization method of particular use for those who repeatedly perform T1 estimation. Magn Reson Med 76:792-802, 2016. © 2015 Wiley Periodicals, Inc.

In Vivo Tracking of Human Neural Progenitor Cells in the Rat Brain Using Magnetic Resonance Imaging Is Not Enhanced by Ferritin Expression.

Rapid growth in the field of stem cell research has generated a lot of interest in their therapeutic use, especially in the treatment of neurodegenerative diseases. Specifically, human neural progenitor cells (hNPCs), unique in their capability to differentiate into cells of the neural lineage, have been widely investigated due to their ability to survive, thrive, and migrate toward injured tissues. Still, one of the major roadblocks for clinical applicability arises from the inability to monitor these cells following transplantation. Molecular imaging techniques, such as magnetic resonance imaging (MRI), have been explored to assess hNPC transplant location, migration, and survival. Here we investigated whether inducing hNPCs to overexpress ferritin (hNPCs(Fer)), an iron storage protein, is sufficient to track these cells long term in the rat striatum using MRI. We found that increased hypointensity on MRI images could establish hNPC(Fer) location. Unexpectedly, however, wild-type hNPC transplants were detected in a similar manner, which is likely due to increased iron accumulation following transplantation-induced damage. Hence, we labeled hNPCs with superparamagnetic iron oxide (SPIO) nanoparticles to further increase iron content in an attempt to enhance cell contrast in MRI. SPIO-labeling of hNPCs (hNPCs-SPIO) achieved increased hypointensity, with significantly greater area of decreased T2* compared to hNPC(Fer) (p < 0.0001) and all other controls used. However, none of the techniques could be used to determine graft rejection in vivo, which is imperative for understanding cell behavior following transplantation. We conclude that in order for cell survival to be monitored in preclinical and clinical settings, another molecular imaging technique must be employed, including perhaps multimodal imaging, which would utilize MRI along with another imaging modality.

Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: task and statistical threshold effects on language lateralization.

BACKGROUND AND PURPOSE: Functional magnetic resonance imaging (fMRI) is a non-invasive pre-surgical tool used to assess localization and lateralization of language function in brain tumor and vascular lesion patients in order to guide neurosurgeons as they devise a surgical approach to treat these lesions. We investigated the effect of varying the statistical thresholds as well as the type of language tasks on functional activation patterns and language lateralization. We hypothesized that language lateralization indices (LIs) would be threshold- and task-dependent. MATERIALS AND METHODS: Imaging data were collected from brain tumor patients (n = 67, average age 48 years) and vascular lesion patients (n = 25, average age 43 years) who received pre-operative fMRI scanning. Both patient groups performed expressive (antonym and/or letter-word generation) and receptive (tumor patients performed text-reading; vascular lesion patients performed text-listening) language tasks. A control group (n = 25, average age 45 years) performed the letter-word generation task. RESULTS: Brain tumor patients showed left-lateralization during the antonym-word generation and text-reading tasks at high threshold values and bilateral activation during the letter-word generation task, irrespective of the threshold values. Vascular lesion patients showed left-lateralization during the antonym and letter-word generation, and text-listening tasks at high threshold values. CONCLUSION: Our results suggest that the type of task and the applied statistical threshold influence LI and that the threshold effects on LI may be task-specific. Thus identifying critical functional regions and computing LIs should be conducted on an individual subject basis, using a continuum of threshold values with different tasks to provide the most accurate information for surgical planning to minimize post-operative language deficits.

The influence of physiological noise correction on test-retest reliability of resting-state functional connectivity.

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

Intrinsically germanium-69-labeled iron oxide nanoparticles: synthesis and in-vivo dual-modality PET/MR imaging.

Intrinsically germanium-69-labeled super-paramagnetic iron oxide nanoparticles are synthesized via a newly developed, fast and highly specific chelator-free approach. The biodistribution pattern and the feasibility of (69) Ge-SPION@PEG for in vivo dual-modality positron emission tomography/magnetic resonance (PET/MR) imaging and lymph-node mapping are investigated, which represents the first example of the successful utilization of a (69) Ge-based agent for PET/MR imaging.

In vivo tracking of human neural progenitor cells in the rat brain using bioluminescence imaging.

BACKGROUND: Stem cell therapies appear promising for treating certain neurodegenerative disorders and molecular imaging methods that track these cells in vivo could answer some key questions regarding their survival and migration. Bioluminescence imaging (BLI), which relies on luciferase expression in these cells, has been used for this purpose due to its high sensitivity. NEW METHOD: In this study, we employ BLI to track luciferase-expressing human neural progenitor cells (hNPC(Luc2)) in the rat striatum long-term. RESULTS: We show that hNPC(Luc2) are detectable in the rat striatum. Furthermore, we demonstrate that using this tracking method, surviving grafts can be detected in vivo for up to 12 weeks, while those that were rejected do not produce bioluminescence signal. We also demonstrate the ability to discern hNPC(Luc2) contralateral migration. COMPARISON WITH EXISTING METHODS: Some of the advantages of BLI compared to other imaging methods used to track progenitor/stem cells include its sensitivity and specificity, low background signal and ability to distinguish surviving grafts from rejected ones over the long term while the blood-brain barrier remains intact. CONCLUSIONS: These new findings may be useful in future preclinical applications developing cell-based treatments for neurodegenerative disorders.

The effect of scan length on the reliability of resting-state fMRI connectivity estimates.

There has been an increasing use of functional magnetic resonance imaging (fMRI) by the neuroscience community to examine differences in functional connectivity between normal control groups and populations of interest. Understanding the reliability of these functional connections is essential to the study of neurological development and degenerate neuropathological conditions. To date, most research assessing the reliability with which resting-state functional connectivity characterizes the brain's functional networks has been on scans between 3 and 11 min in length. In our present study, we examine the test-retest reliability and similarity of resting-state functional connectivity for scans ranging in length from 3 to 27 min as well as for time series acquired during the same length of time but excluding half the time points via sampling every second image. Our results show that reliability and similarity can be greatly improved by increasing the scan lengths from 5 min up to 13 min, and that both the increase in the number of volumes as well as the increase in the length of time over which these volumes was acquired drove this increase in reliability. This improvement in reliability due to scan length is much greater for scans acquired during the same session. Gains in intersession reliability began to diminish after 9-12 min, while improvements in intrasession reliability plateaued around 12-16 min. Consequently, new techniques that improve reliability across sessions will be important for the interpretation of longitudinal fMRI studies.

Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data.

The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10(-7)). A linear SVR age predictor performed reasonably well in continuous age prediction (R (2) = 0.419, p-value < 1 × 10(-8)). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.

Association of functional magnetic resonance imaging indices with postoperative language outcomes in patients with primary brain tumors.

OBJECT: Functional MRI (fMRI) has the potential to be a useful presurgical planning tool to treat patients with primary brain tumor. In this study the authors retrospectively explored relationships between language-related postoperative outcomes in such patients and multiple factors, including measures estimated from task fMRI maps (proximity of lesion to functional activation area, or lesion-to-activation distance [LAD], and activation-based language lateralization, or lateralization index [LI]) used in the clinical setting for presurgical planning, as well as other factors such as patient age, patient sex, tumor grade, and tumor volume. METHODS: Patient information was drawn from a database of patients with brain tumors who had undergone preoperative fMRI-based language mapping of the Broca and Wernicke areas. Patients had performed a battery of tasks, including word-generation tasks and a text-versus-symbols reading task, as part of a clinical fMRI protocol. Individually thresholded task fMRI activation maps had been provided for use in the clinical setting. These clinical imaging maps were used to retrospectively estimate LAD and LI for the Broca and Wernicke areas. RESULTS: There was a relationship between postoperative language deficits and the proximity between tumor and Broca area activation (the LAD estimate), where shorter LADs were related to the presence of postoperative aphasia. Stratification by tumor location further showed that for posterior tumors within the temporal and parietal lobes, more bilaterally oriented Broca area activation (LI estimate close to 0) and a shorter Wernicke area LAD were associated with increased postoperative aphasia. Furthermore, decreasing LAD was related to decreasing LI for both Broca and Wernicke areas. Preoperative deficits were related to increasing patient age and a shorter Wernicke area LAD. CONCLUSIONS: Overall, LAD and LI, as determined using fMRI in the context of these paradigms, may be useful indicators of postsurgical outcomes. Whereas tumor location may influence postoperative deficits, the results indicated that tumor proximity to an activation area might also interact with how the language network is affected as a whole by the lesion. Although the derivation of LI must be further validated in individual patients by using spatially specific statistical methods, the current results indicated that fMRI is a useful tool for predicting postoperative outcomes in patients with a single brain tumor.

Sparse and optimal acquisition design for diffusion MRI and beyond.

PURPOSE: Diffusion magnetic resonance imaging (MRI) in combination with functional MRI promises a whole new vista for scientists to investigate noninvasively the structural and functional connectivity of the human brain-the human connectome, which had heretofore been out of reach. As with other imaging modalities, diffusion MRI data are inherently noisy and its acquisition time-consuming. Further, a faithful representation of the human connectome that can serve as a predictive model requires a robust and accurate data-analytic pipeline. The focus of this paper is on one of the key segments of this pipeline-in particular, the development of a sparse and optimal acquisition (SOA) design for diffusion MRI multiple-shell acquisition and beyond. METHODS: The authors propose a novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and a novel and effective semistochastic and moderately greedy combinatorial search strategy with simulated annealing to locate the optimum design or configuration. The goal of the optimality criteria is threefold: first, to maximize uniformity of the diffusion measurements in each shell, which is equivalent to maximal incoherence in angular measurements; second, to maximize coverage of the diffusion measurements around each radial line to achieve maximal incoherence in radial measurements for multiple-shell acquisition; and finally, to ensure maximum uniformity of diffusion measurement directions in the limiting case when all the shells are coincidental as in the case of a single-shell acquisition. The approach taken in evaluating the stability of various acquisition designs is based on the condition number and the A-optimal measure of the design matrix. RESULTS: Even though the number of distinct configurations for a given set of diffusion gradient directions is very large in general-e.g., in the order of 10(232) for a set of 144 diffusion gradient directions, the proposed search strategy was found to be effective in finding the optimum configuration. It was found that the square design is the most robust (i.e., with stable condition numbers and A-optimal measures under varying experimental conditions) among many other possible designs of the same sample size. Under the same performance evaluation, the square design was found to be more robust than the widely used sampling schemes similar to that of 3D radial MRI and of diffusion spectrum imaging (DSI). CONCLUSIONS: A novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and an effective search strategy for finding the best configuration have been developed. The results are very promising, interesting, and practical for diffusion MRI acquisitions.

Extremely efficient and deterministic approach to generating optimal ordering of diffusion MRI measurements.

PURPOSE: Diffusion MRI measurements are typically acquired sequentially with unit gradient directions that are distributed uniformly on the unit sphere. The ordering of the gradient directions has significant effect on the quality of dMRI-derived quantities. Even though several methods have been proposed to generate optimal orderings of gradient directions, these methods are not widely used in clinical studies because of the two major problems. The first problem is that the existing methods for generating highly uniform and antipodally symmetric gradient directions are inefficient. The second problem is that the existing methods for generating optimal orderings of gradient directions are also highly inefficient. In this work, the authors propose two extremely efficient and deterministic methods to solve these two problems. METHODS: The method for generating nearly uniform point set on the unit sphere (with antipodal symmetry) is based upon the notion that the spacing between two consecutive points on the same latitude should be equal to the spacing between two consecutive latitudes. The method for generating optimal ordering of diffusion gradient directions is based on the idea that each subset of incremental sample size, which is derived from the prescribed and full set of gradient directions, must be as uniform as possible in terms of the modified electrostatic energy designed for antipodally symmetric point set. RESULTS: The proposed method outperformed the state-of-the-art method in terms of computational efficiency by about six orders of magnitude. CONCLUSIONS: Two extremely efficient and deterministic methods have been developed for solving the problem of optimal ordering of diffusion gradient directions. The proposed strategy is also applicable to optimal view-ordering in three-dimensional radial MRI.

The Effects of Computational Method, Data Modeling, and TR on Effective Connectivity Results.

As the use of effective connectivity as become more popular, it is important to understand how the results from different analyses compare with each other, as the results from studies employing differing methods for determining connectivity may not reach the same conclusion. Simulated fMRI time series data were used to compare the results from four of the more commonly used computational methods, structural equation modeling, autoregressive analysis, Granger causality, and dynamic causal modeling to determine which may be better suited to the task. The results show that all three methods are able to detect changes in system dynamics. Structural equation modeling appeared to be the least sensitive to changes in TR or source of variance, and Granger causality the most sensitive. The results also suggest that improved reporting on data analyses is necessary, and employing an effect statistic to depict results may remove some of the ambiguity in comparing results across studies using differing methods to determine connectivity.

Functional neuroimaging correlates of finger-tapping task variations: an ALE meta-analysis.

Finger-tapping tasks are one of the most common paradigms used to study the human motor system in functional neuroimaging studies. These tasks can vary both in the presence or absence of a pacing stimulus as well as in the complexity of the tapping task. A voxel-wise, coordinate-based meta-analysis was performed on 685 sets of activation foci in Talairach space gathered from 38 published studies employing finger-tapping tasks. Clusters of concordance were identified within the primary sensorimotor cortices, supplementary motor area, premotor cortex, inferior parietal cortices, basal ganglia, and anterior cerebellum. Subsequent analyses performed on subsets of the primary set of foci demonstrated that the use of a pacing stimulus resulted in a larger, more diverse network of concordance clusters, in comparison to varying the complexity of the tapping task. The majority of the additional concordance clusters occurred in regions involved in the temporal aspects of the tapping task, rather than its execution. Tapping tasks employing a visual pacing stimulus recruited a set of nodes distinct from the results observed in those tasks employing either an auditory or no pacing stimulus, suggesting differing cognitive networks when integrating visual or auditory pacing stimuli into simple motor tasks. The relatively uniform network of concordance clusters observed across the more complex finger-tapping tasks suggests that further complexity, beyond the use of multi-finger sequences or bimanual tasks, may be required to fully reveal those brain regions necessary to execute truly complex movements.