Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Post-hoc physiological waveform extraction from motion estimation in simultaneous multislice (SMS) functional MRI using separate stack processing

PURPOSE

Motion estimation is an essential step in functional MRI (fMRI) preprocessing. Usually, fMRI processing software packages (eg, FSL and AFNI) automatically estimate motion parameters in order to counteract the effects of motion. However, the time courses of the motion estimation for fMRI data also contain information about physiological processes. Here, we show that respiration and cardiac signals can be extracted from motion estimation at significantly higher bandwidth than is possible with current methods.

METHOD

To detect motion at high effective temporal resolution (HighRes), the motion parameters of stacks of simultaneously acquired slices were estimated separately, then combined. This method was validated by extracting physiological motion signals from resting state fMRI (rsfMRI) data (Enhanced Nathan Kline Institute-Rockland Sample) and comparing them to respiration belt and pulse oximeter signals.

RESULTS

HighRes motion time-courses with an effective sampling rate of 15.5 and 11.4 Hz were extracted from repetition time (TR) = 0.645 and 1.4 s data, respectively. Respiration waveforms were extracted with significantly higher accuracy than the original motion parameters. Even cardiac waveforms could be extracted, despite the fact that the sampling time or TR values were too long to sample cardiac frequencies.

CONCLUSION

HighRes motion traces provide insight into the subjects' motion at higher frequencies than can be estimated using standard techniques. In its simplest form, this technique can recover accurate respiration signals and may reveal additional complexity in brain motion.

Effects of using different software packages for BOLD analysis in planning a neurosurgical treatment in patients with brain tumours

BACKGROUND

The authors of the present thesis carried out a comparative analysis of three different computer software packages - FSL, SPM and FuncTool - for the processing of fMRI scans.

PURPOSE

The aim of the thesis was the analysis of the volume of regions functionally active during the stimulation of the centres evaluated as well as the location of those regions in relation to the tumour boundaries, and then the comparison of the results.

MATERIAL AND METHODS

Thirty eight patients with a diagnosed tumour of the left hemisphere, qualified for a neurosurgical operation, underwent fMRI. The functions of speech, motion and sensation were evaluated. Imaging data were processed for all the acquired scans with the use of each of the three software packages assessed.

RESULTS

For the FuncTool software package the calculated differences in the distances were several times greater than those calculated using FSL and SPM. The differences in the volume of the functionally active regions derived from the calculations with the use of the FSL and SPM software packages were statistically different for four out of the six functions evaluated.

CONCLUSIONS

The conclusions of the analysis in question showed that the FSL and SPM packages could be used interchangeably in the functional mapping of the brain with the purpose of the planning of neurosurgical operations. The FuncTool software package is less precise than FSL and SPM.

Optimizing data processing to improve the reproducibility of single-subject functional magnetic resonance imaging

INTRODUCTION

High reproducibility is critical for ensuring the confidence needed to use functional magnetic resonance imaging (fMRI) activation maps for presurgical planning.

METHODS

In this study, the comparison of different motion correction methods, spatial smoothing methods, regression methods, and thresholding methods was performed to see whether specific data processing methods can be employed to improve the reproducibility of single-subject fMRI activation. Three test-retest metrics were used: the percent difference in activation volume (PDAV), the difference in the center of mass (DCM), and the Dice Similarity Coefficient (DSC).

RESULTS

The PDAV was minimized when using little or no spatial smoothing and AMPLE thresholding. The DCM was minimized when using affine motion correction and little or no spatial smoothing. The DSC was improved when using affine motion correction and generous spatial smoothing. However, it is believed that the overlap metric may be unsuitable for testing fMRI reproducibility.

CONCLUSION

Processing methods to improve fMRI reproducibility were determined. Importantly, the processing methods needed to improve reproducibility were dependent on the fMRI activation metric of interest.

Improved motion correction of submillimetre 7T fMRI time series with Boundary-Based Registration (BBR)

Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. For instance, the method results in a 15% increase in linear discriminant contrast, a cross-validated estimate of multivariate discriminability. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

The Contribution of Functional Magnetic Resonance Imaging to the Understanding of the Effects of Acute Physical Exercise on Cognition

The fact that a single bout of acute physical exercise has a positive impact on cognition is well-established in the literature, but the neural correlates that underlie these cognitive improvements are not well understood. Here, the use of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers great potential, which is just starting to be recognized. This review aims at providing an overview of those studies that used fMRI to investigate the effects of acute physical exercises on cerebral hemodynamics and cognition. To this end, a systematic literature survey was conducted by two independent reviewers across five electronic databases. The search returned 668 studies, of which 14 studies met the inclusion criteria and were analyzed in this systematic review. Although the findings of the reviewed studies suggest that acute physical exercise (e.g., cycling) leads to profound changes in functional brain activation, the small number of available studies and the great variability in the study protocols limits the conclusions that can be drawn with certainty. In order to overcome these limitations, new, more well-designed trials are needed that (i) use a more rigorous study design, (ii) apply more sophisticated filter methods in fMRI data analysis, (iii) describe the applied processing steps of fMRI data analysis in more detail, and (iv) provide a more precise exercise prescription.

Animal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization

Animal whole-brain functional magnetic resonance imaging (fMRI) provides a non-invasive window into brain activity. A collection of associated methods aims to replicate observations made in humans and to identify the mechanisms underlying the distributed neuronal activity in the healthy and disordered brain. Animal fMRI studies have developed rapidly over the past years, fueled by the development of resting-state fMRI connectivity and genetically encoded neuromodulatory tools. Yet, comparisons between sites remain hampered by lack of standardization. Recently, we highlighted that mouse resting-state functional connectivity converges across centers, although large discrepancies in sensitivity and specificity remained. Here, we explore past and present trends within the animal fMRI community and highlight critical aspects in study design, data acquisition, and post-processing operations, that may affect the results and influence the comparability between studies. We also suggest practices aimed to promote the adoption of standards within the community and improve between-lab reproducibility. The implementation of standardized animal neuroimaging protocols will facilitate animal population imaging efforts as well as meta-analysis and replication studies, the gold standards in evidence-based science.

Resting State fMRI: Going Through the Motions

Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.

Neuroimaging of pain in animal models: a review of recent literature

Neuroimaging of pain in animals allows us to better understand mechanisms of pain processing and modulation. In this review, we discuss recently published brain imaging studies in rats, mice, and monkeys, including functional magnetic resonance imaging (MRI), manganese-enhanced MRI, positron emission tomography, and electroencephalography. We provide an overview of innovations and limitations in neuroimaging techniques, as well as results of functional brain imaging studies of pain from January 1, 2016, to October 10, 2018. We then discuss how future investigations can address some bias and gaps in the field. Despite the limitations of neuroimaging techniques, the 28 studies reinforced that transition from acute to chronic pain entails considerable changes in brain function. Brain activations in acute pain were in areas more related to the sensory aspect of noxious stimulation, including primary somatosensory cortex, insula, cingulate cortex, thalamus, retrosplenial cortex, and periaqueductal gray. Pharmacological and nonpharmacological treatments modulated these brain regions in several pain models. On the other hand, in chronic pain models, brain activity was observed in regions commonly associated with emotion and motivation, including prefrontal cortex, anterior cingulate cortex, hippocampus, amygdala, basal ganglia, and nucleus accumbens. Neuroimaging of pain in animals holds great promise for advancing our knowledge of brain function and allowing us to expand human subject research. Additional research is needed to address effects of anesthesia, analysis approaches, sex bias and omission, and potential effects of development and aging.

Circuit activity underlying a distinct modulator of prepulse inhibition

Prepulse inhibition (PPI), the diminished eye blink response to a startling pulse induced by a prepulse, is regulated by brainstem, and modulated by cerebral, processes. Attentional modulation by the prepulse (AMP), a potential biomarker of psychotic disorders, differs from other modulatory processes because it only occurs if the interval between the prepulse and pulse exceeds 100 ms (>PP100). Videotaped eye blinks were measured during fMRI scanning in 15 healthy subjects hearing 64 pulse alone, 64 PP60 and 64 PP120 trials in a rapid event-related design. Because attentional influences on PPI vary spontaneously, we posited AMP could be isolated by comparing eye blink and Blood Oxygen Level Dependent covariation during the two PP trial types. Behavioral regressor coefficients reflecting significant covariation covered the insula and auditory cortices during PP120 but not PP60 trials. Clusters within the right anterior insula and auditory cortex were specific to AMP. Functional connections (FCs) between cerebral ROIs implicated in PPI were stronger during PP120 trials. The four FCs that were individually stronger during PP120 trials involved the right insula or auditory cortex and three were not present during PP60 trials. Converging evidence indicates the right insula is the hub of a network underlying AMP.

Comparison of prospective head motion correction with NMR field probes and an optical tracking system

PURPOSE

The aim of this study was to compare prospective head motion correction and motion tracking abilities of two tracking systems: Active NMR field probes and a Moiré phase tracking camera system using an optical marker.

METHODS

Both tracking systems were used simultaneously on human subjects. The prospective head motion correction was compared in an MP2RAGE and a gradient echo sequence. In addition, the motion tracking trajectories for three subjects were compared against each other and their correlation and deviations were analyzed.

RESULTS

With both tracking systems motion artifacts were visibly reduced. The precision of the field probe system was on the order of 50 µm for translations and 0.03° for rotations while the camera's was approximately 5 µm and 0.007°. The comparison of the measured trajectories showed close correlation and an average absolute deviation below 500 µm and 0.5°.

CONCLUSION

This study presents the first in vivo comparison between NMR field probes and Moiré phase tracking. For the gradient echo images, the field probes had a similar motion correction performance as the optical tracking system. For the MP2RAGE measurement, however, the camera yielded better results. Still, both tracking systems substantially decreased image artifacts in the presence of subject motion. Thus, the motion tracking modality should be chosen according to the specific requirements of the experiment while considering the desired image resolution, refresh rate, and head coil constraints.

Challenges and techniques for presurgical brain mapping with functional MRI

Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.

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fMRI is increasingly used for presurgical brain mapping for surgical planning.

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Understanding of the limitations of fMRI is critical for its clinical use.

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Clinical fMRI's challenges and potential solutions are discussed.

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Intraoperative use and validation of fMRI are discussed.

Evolutionary and Modern Image Content Differentially Influence the Processing of Emotional Pictures

From an evolutionary perspective, environmental threats relevant for survival constantly challenged human beings. Current research suggests the evolution of a fear processing module in the brain to cope with these threats. Recently, humans increasingly encountered modern threats (e.g., guns or car accidents) in addition to evolutionary threats (e.g., snakes or predators) which presumably required an adaptation of perception and behavior. However, the neural processes underlying the perception of these different threats remain to be elucidated. We investigated the effect of image content (i.e., evolutionary vs. modern threats) on the activation of neural networks of emotion processing. During functional magnetic resonance imaging (fMRI) 41 participants watched affective pictures displaying evolutionary-threatening, modern-threatening, evolutionary-neutral and modern-neutral content. Evolutionary-threatening stimuli evoked stronger activations than modern-threatening stimuli in left inferior frontal gyrus and thalamus, right middle frontal gyrus and parietal regions as well as bilaterally in parietal regions, fusiform gyrus and bilateral amygdala. We observed the opposite effect, i.e., higher activity for modern-threatening than for evolutionary-threatening stimuli, bilaterally in the posterior cingulate and the parahippocampal gyrus. We found no differences in subjective arousal ratings between the two threatening conditions. On the valence scale though, subjects rated modern-threatening pictures significantly more negative than evolutionary-threatening pictures, indicating a higher level of perceived threat. The majority of previous studies show a positive relationship between arousal rating and amygdala activity. However, comparing fMRI results with behavioral findings we provide evidence that neural activity in fear processing areas is not only driven by arousal or valence, but presumably also by the evolutionary content of the stimulus. This has also fundamental methodological implications, in the sense to suggest a more elaborate classification of stimulus content to improve the validity of experimental designs.

Methods for cleaning the BOLD fMRI signal

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.

Progress toward openness, transparency, and reproducibility in cognitive neuroscience

Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.

Isolated assessment of translation or rotation severely underestimates the effects of subject motion in fMRI data

Subject motion has long since been known to be a major confound in functional MRI studies of the human brain. For resting-state functional MRI in particular, data corruption due to motion artefacts has been shown to be most relevant. However, despite 6 parameters (3 for translations and 3 for rotations) being required to fully describe the head's motion trajectory between timepoints, not all are routinely used to assess subject motion. Using structural (n = 964) as well as functional MRI (n = 200) data from public repositories, a series of experiments was performed to assess the impact of using a reduced parameter set (translation_only and rotation_only) versus using the complete parameter set. It could be shown that the usage of 65 mm as an indicator of the average cortical distance is a valid approximation in adults, although care must be taken when comparing children and adults using the same measure. The effect of using slightly smaller or larger values is minimal. Further, both translation_only and rotation_only severely underestimate the full extent of subject motion; consequently, both translation_only and rotation_only discard substantially fewer datapoints when used for quality control purposes (“motion scrubbing”). Finally, both translation_only and rotation_only severely underperform in predicting the full extent of the signal changes and the overall variance explained by motion in functional MRI data. These results suggest that a comprehensive measure, taking into account all available parameters, should be used to characterize subject motion in fMRI.

Special considerations/technical limitations of blood-oxygen-level-dependent functional magnetic resonance imaging

In this review, limitations affecting the results of presurgical mapping with blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) are discussed. There is a great need to standardize fMRI acquisition and analysis methods and establish guidelines to address quality control issues. Several national and international organizations are formulating guidelines and standards for both clinical and research applications of BOLD fMRI. Consensus regarding management of these issues will likely both improve the clinical standard of care and enhance future research applications of fMRI.

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction

Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.

Functional connectivity of the hippocampus in temporal lobe epilepsy: feasibility of a task-regressed seed-based approach

OBJECTIVE

Resting-state functional connectivity (FC) has revealed marked network dysfunction in patients with temporal lobe epilepsy (TLE) compared to healthy controls. However, the nature and the location of these changes have not been fully elucidated nor confirmed by other methodologies. We assessed the presence of hippocampal FC changes in TLE based on the low frequency residuals of task-related functional magnetic resonance imaging data after the removal of task-related activation [i.e., task-regressed functional connectivity MRI (fcMRI)].

METHOD

We employed a novel, task-regressed approach to quantify hippocampal FC, and compare hippocampal FC in 17 patients with unilateral TLE (9 left) with 17 healthy controls.

RESULTS

Our results suggest widespread FC reductions in the mesial cortex associated with the default mode network (DMN), and some local FC increases in the lateral portions of the right hemisphere. We found more pronounced FC decreases in the left hemisphere than in the right, and these FC decreases were greatest in patients with left TLE. Moreover, the FC reductions observed between the hippocampus and posterior cingulate, inferior parietal, paracentral regions are in agreement with previous resting state studies.

CONCLUSIONS

Consistent with the existing literature, FC reductions in TLE appear widespread with prominent reductions in the medial portion of the DMN. Our data expand the literature by demonstrating that reductions in FC may be greatest in the left hemisphere and in patients with left TLE. Overall, our findings suggest that task-regressed FC is a viable alternative to resting state and that future studies may extract similar information on network connectivity from already existing datasets.

Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points

Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.

Functional magnetic resonance imaging: comparison between activation maps and computation pipelines in a clinical context

In this study new evaluation strategies for comparing different Statistical Parametric Maps computed from fMRI time-series analysis software tools are proposed. The aim of our work is to assess and quantitatively evaluate the statistical agreement of activation maps. Some pre-processing steps are necessary to compare SPMs (Statistical Parametric Maps), including segmentation and co-registration. The study of the statistical agreement is carried out following two ways. The first way considers SPMs as the result of two classification processes and extracts confusion matrix and Cohen's kappa index to assess agreement. Some considerations will be made on the statistical dependence of classes and a new formulation of kappa index will be used for overcoming this problem. The second way considers SPMs as two 3D images, and computes the similarity of SPMs images with a fuzzy formulation of the Jaccard Index. Several experiments were conducted both to assess the performance of the proposed evaluation tools and to compare activation maps computation pipelines from two widely used software tools in a clinical context.

Optimizing preprocessing and analysis pipelines for single-subject fMRI: 2. Interactions with ICA, PCA, task contrast and inter-subject heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or "pipeline") may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets.

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3-10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747-771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89-95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or "pipeline") in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods.

How reliable are the results from functional magnetic resonance imaging?

Functional magnetic resonance imaging (fMRI) is one of the most important methods for in vivo investigation of cognitive processes in the human brain. Within the last two decades, an explosion of research has emerged using fMRI, revealing the underpinnings of everything from motor and sensory processes to the foundations of social cognition. While these results have revealed the potential of neuroimaging, important questions regarding the reliability of these results remain unanswered. In this paper, we take a close look at what is currently known about the reliability of fMRI findings. First, we examine the many factors that influence the quality of acquired fMRI data. We also conduct a review of the existing literature to determine if some measure of agreement has emerged regarding the reliability of fMRI. Finally, we provide commentary on ways to improve fMRI reliability and what questions remain unanswered. Reliability is the foundation on which scientific investigation is based. How reliable are the results from fMRI?

An fMRI study examining effects of acute D-cycloserine during symptom provocation in spider phobia

BACKGROUND

Exposure-based therapy for anxiety disorders is believed to operate on the basis of fear extinction. Studies have shown acute administration of D-cycloserine (DCS) enhances fear extinction in animals and facilitates exposure therapy in humans, but the neural mechanisms are not completely understood. To date, no study has examined neural effects of acute DCS in anxiety-disordered populations.

METHODS

Two hours prior to functional magnetic resonance imaging scanning, 23 spider-phobic and 23 non-phobic participants were randomized to receive DCS 100 mg or placebo. During scanning, participants viewed spider, butterfly, and Gaussian-blurred baseline images in a block-design paradigm. Diagnostic and treatment groups were compared regarding differential activations to spider versus butterfly stimuli.

RESULTS

In the phobic group, DCS enhanced prefrontal (PFC), dorsal anterior cingulate (ACC), and insula activations. For controls, DCS enhanced ventral ACC and caudate activations. There was a positive correlation between lateral PFC and amygdala activation for the placebo-phobic group. Reported distress during symptom provocation was correlated with amygdala activation in the placebo-phobic group and orbitofrontal cortex activation in the DCS-phobic group.

CONCLUSIONS

Results suggest that during initial phobic symptom provocation DCS enhances activation in regions involved in cognitive control and interoceptive integration, including the PFC, ACC, and insular cortices for phobic participants.

Bootstrap generation and evaluation of an fMRI simulation database

Computer simulations have played a critical role in functional magnetic resonance imaging (fMRI) research, notably in the validation of new data analysis methods. Many approaches have been used to generate fMRI simulations, but there is currently no generic framework to assess how realistic each one of these approaches may be. In this article, a statistical technique called parametric bootstrap was used to generate a simulation database that mimicked the parameters found in a real database, which comprised 40 subjects and five tasks. The simulations were evaluated by comparing the distributions of a battery of statistical measures between the real and simulated databases. Two popular simulation models were evaluated for the first time by applying the bootstrap framework. The first model was an additive mixture of multiple components and the second one implemented a non-linear motion process. In both models, the simulated components included the following brain dynamics: a baseline, physiological noise, neural activation and random noise. These models were found to successfully reproduce the relative variance of the components and the temporal autocorrelation of the fMRI time series. By contrast, the level of spatial autocorrelation was found to be drastically low using the additive model. Interestingly, the motion process in the second model intrisically generated some slow time drifts and increased the level of spatial autocorrelations. These experiments demonstrated that the bootstrap framework is a powerful new tool that can pinpoint the respective strengths and limitations of simulation models.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly used fMRI preprocessing steps and optimized the associated multivariate CVA-based, single-subject processing pipelines with the NPAIRS (nonparametric prediction, activation, influence and reproducibility resampling) performance metrics [prediction accuracy and statistical parametric image (SPI) reproducibility] on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.FEAT, NPAIRS.GLM and NPAIRS.CVA software packages (or modules) using a novel second-level CVA. We found that for the block-design data, (a) slice timing correction and global intensity normalization have little consistent impact on the fMRI processing pipeline, but spatial smoothing, temporal detrending or high-pass filtering, and motion correction significantly improved pipeline performance across all subjects; (b) the combined optimization of spatial smoothing, temporal detrending and CVA model parameters on average improved between-subject reproducibility; and (c) the most important pipeline choices include univariate or multivariate statistical models and spatial smoothing. This study suggests that considering options other than simply using GLM with a fixed spatial filter may be of critical importance in determining activation patterns in BOLD fMRI studies.

False positives in imaging genetics

Imaging genetics provides an enormous amount of functional-structural data on gene effects in living brain, but the sheer quantity of potential phenotypes raises concerns about false discovery. Here, we provide the first empirical results on false positive rates in imaging genetics. We analyzed 720 frequent coding SNPs without significant association with schizophrenia and a subset of 492 of these without association with cognitive function. Effects on brain structure (using voxel-based morphometry, VBM) and brain function, using two archival imaging tasks, the n-back working memory task and an emotional face matching task, were studied in whole brain and regions of interest and corrected for multiple comparisons using standard neuroimaging procedures. Since these variants are unlikely to impact relevant brain function, positives obtained provide an upper empirical estimate of the false positive association rate. In a separate analysis, we randomly permuted genotype labels across subjects, removing any true genotype-phenotype association in the data, to derive a lower empirical estimate. At a set correction level of 0.05, in each region of interest and data set used, the rate of positive findings was well below 5% (0.2-4.1%). There was no relationship between the region of interest and the false positive rate. Permutation results were in the same range as empirically derived rates. The observed low rates of positives provide empirical evidence that the type I error rate is well controlled by current commonly used correction procedures in imaging genetics, at least in the context of the imaging paradigms we have used. In fact, our observations indicate that these statistical thresholds are conservative.