The effects of motion on parametric fMRI analysis techniques

Methods of Analysis: Functional MRI for Presurgical Planning

There are many technical and nontechnical steps involved in a successful clinical functional MRI (fMRI) scan. The output from scanning and analysis can only be as good as the input, so task instruction and rehearsal are the most important steps during an clinical fMRI procedure. Properly pre-processed data significantly affects statistical analysis, which has a great impact on image interpretation. Even though there is general agreement on how to process clinical fMRI data, such as algorithms for head motion detection and correction, the theory and practicalities associated with data processing remain complex and constantly evolving.

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.

Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index

Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

Test-retest variability underlying fMRI measurements

INTRODUCTION

A high test-retest reliability is of pivotal importance for many disciplines in fMRI research. To assess the current limits of fMRI reliability, we estimated the variability in true underlying Blood Oxygen Level Dependent (BOLD) activation, with which we mean the variability that would be found in the theoretical case when we could obtain an unlimited number of scans in each measurement.

METHODS

In this test-retest study, subjects were scanned twice with one week apart, while performing a visual and a motor inhibition task. We addressed the nature of the variability in the underlying BOLD signal, by separating for each brain area and each subject the between-session differences in the spatial pattern of BOLD activation, and the global (whole brain) changes in the amplitude of the spatial pattern of BOLD activation.

RESULTS

We found evidence for changes in the true underlying spatial pattern of BOLD activation for both tasks across the two sessions. The sizes of these changes in pattern activation were approximately 16% of the total activation within the pattern, irrespective of brain area and task. After spatial smoothing, this variability was greatly reduced, which suggests it takes place at a small spatial scale. The mean between-session differences in the amplitude of activation across the whole brain were 13.8% for the visual task and 23.4% for the motor inhibition task.

CONCLUSIONS

Between-session changes in the true underlying spatial pattern of BOLD activation are always present, but occur at a scale that is consistent with partial voluming effects or spatial distortions. We found no evidence that the reliability of the spatial pattern of activation differs systematically between brain areas. Consequently, between-session changes in the amplitude of activation are probably due to global effects. The observed variability in amplitude across sessions warrants caution when interpreting fMRI estimates of height of brain activation. A Matlab implementation of the used algorithm is available for download at www.ni-utrecht.nl/downloads/ura.

Group specific optimisation of fMRI processing steps for child and adult data

Motion is a major issue in functional magnetic resonance imaging (fMRI) dataseries and causes artifacts or increased overall noise obscuring signals of interest. It is particularly important to be able to control for and correct these artifacts when dealing with child data. We analysed the data from 35 children (4-8 years old) and 13 adults (18-30 years old) during an emotional face paradigm. The children were split into low and high motion groups on the basis of having less or more than an estimated maximal movement of one voxel (3.75 mm) and one degree of rotation in any motion direction between any pair of scans in the run. Several different preprocessing steps were evaluated for their ability to correct for the excess motion using agnostic canonical variates analysis (aCVA) in the NPAIRS (Nonparametric, Prediction, Activation, Influence, Reproducibility, re-Sampling) framework. The adult dataset was reasonably stable whereas the motion-prone child datasets benefited greatly from motion parameter regression (MPR). Motion parameter regression had a strong beneficial impact on all datasets, a result that was largely unaffected by other preprocessing choices; however, motion correction on its own did not have as much impact. The low motion child group subjected to MPR had reproducibility values at par with those of the adult group, but needed almost twice as many subjects to achieve this result, indicating weaker responses in young children. The aCVA showed greater sensitivity to the task response pattern than the mixed effects general linear model (mGLM) in the expected face processing regions, although the mGLM showed more responses in some other areas. This work illustrates that preprocessing choices must be made in a group-specific fashion to optimise fMRI results.

Magnetic field shift due to mechanical vibration in functional magnetic resonance imaging

Mechanical vibrations of the gradient coil system during readout in echo-planar imaging (EPI) can increase the temperature of the gradient system and alter the magnetic field distribution during functional magnetic resonance imaging (fMRI). This effect is enhanced by resonant modes of vibrations and results in apparent motion along the phase encoding direction in fMRI studies. The magnetic field drift was quantified during EPI by monitoring the resonance frequency interleaved with the EPI acquisition, and a novel method is proposed to correct the apparent motion. The knowledge on the frequency drift over time was used to correct the phase of the k-space EPI dataset. Since the resonance frequency changes very slowly over time, two measurements of the resonance frequency, immediately before and after the EPI acquisition, are sufficient to remove the field drift effects from fMRI time series. The frequency drift correction method was tested "in vivo" and compared to the standard image realignment method. The proposed method efficiently corrects spurious motion due to magnetic field drifts during fMRI.

Comparison of fMRI motion correction software tools

Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion. Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The "accurate" parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the "fast" parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either. The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement.

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs). Neural activation detection is formulated at the voxel level in terms of time coupling between the sequence of hemodynamic response onsets (HROs) observed in the fMRI signal, and an HSMESM of the hidden sequence of task-induced neural activations. The sequence of HRO events is derived from a continuous wavelet transform (CWT) of the fMRI signal. The brain activation HSMESM is built from the timing information of the input stimulation protocol. The rich mathematical framework of HSMESMs makes these models an effective and versatile approach for fMRI data analysis. Solving for the HSMESM Evaluation and Learning problems enables the model to automatically detect neural activation embedded in a given set of fMRI signals, without requiring any template basis function or prior shape assumption for the fMRI response. Solving for the HSMESM Decoding problem allows to enrich brain mapping with activation lag mapping, activation mode visualizing, and hemodynamic response function analysis. Activation detection results obtained on synthetic and real epoch-related fMRI data demonstrate the superiority of the HSMESM mapping method with respect to a real application case of the statistical parametric mapping (SPM) approach. In addition, the HSMESM mapping method appears clearly insensitive to timing variations of the hemodynamic response, and exhibits low sensitivity to fluctuations of its shape.

Ethical issues in the clinical application of fMRI: factors affecting the validity and interpretation of activations

The ability of functional magnetic resonance imaging (fMRI) to localize activations in a single patient, along with the safety and widespread availability of this methodology, has lead to an increasing use of fMRI for clinical purposes such as pre-surgical planning. As methodology continues to improve and more experience with fMRI in the clinical setting is acquired, clinical functional neuroimaging will likely have an increasing influence over patient care. Therefore, ethical use of fMRI, as with other medical techniques, requires understanding the factors impacting the interpretation of the methodology. Issues affecting the validity and interpretation of clinical functional neuroimaging, including effects of altered hemodynamic response function, head motion, and structural changes in the brain, are reviewed. The distinction between correlated and necessary activation in a clinical context is discussed. Different types of statistical errors in fMRI analysis are described, along with their consequences to the patient. Finally, for the future of clinical fMRI development, the need for normative patient data, as well as standardized tasks, scan protocols, and data analyses, is discussed.

Deterministic and stochastic features of fMRI data: implications for analysis of event-related experiments

As the limits of stimuli presentation rates are explored in event-related fMRI design, there is a greater need to assess the implications of averaging raw fMRI data. Selective averaging assumes that the fMRI signal consists of task-dependent signal, random noise, and non-task dependent brain signal that can be modeled as random noise so that it tends to zero when averaged over a practical number of trials. We recorded a total of four fMRI data series from two normal subjects (subject 1, axially acquired; subject 2, coronally acquired) performing a simple visual event-related task and a water phantom with the same fMRI scanner imaging parameters. To determine which fraction of the fMRI data was deterministic as opposed to random, we created different data subsets by taking the odd or even time points of the full data sets. All data sets were first dimension-reduced with principal component analysis (PCA) and separated into 100 spatially independent components with independent component analysis (ICA). The mutual information between best-matching pairs of components selected from full data set-subset comparisons was plotted for each data set. Visual inspection suggested that 45-85 components were reproducible, and hence deterministic, accounting for 79-97% of the variance, respectively, in the raw data. The reproducible components exhibited much less trial-to-trial variability than the raw data from even the most activated voxel. Many (22-47) of reproducible components were significantly affected by stimulus presentation (P < 0.001). The most significantly-stimulus-correlated component was strongly time-locked to stimulus presentation and was directly stimulus correlated, corresponding to occipital brain regions. However, other spatially distinct task-related components demonstrated variable temporal relationships with the most significantly-stimulus-correlated component. Our results suggest that the majority of the variance in fMRI data is in fact deterministic, and support the notion that the data consist of differing components with differing temporal relationships to visual stimulation. They further suggest roles for restricting interpretations of the spatial extent of activation from event-related designs to a specific region of interest (ROI) and/or first separating the data into spatially independent components. Averaging the time courses of spatially independent components time-locked to stimulus presentation may prevent possible biases in the estimates of the spatial and temporal extent of stimulus-correlated activation and of trial-to-trial variability.

Driving plasticity in human adult motor cortex is associated with improved motor function after brain injury

Changes in somatosensory input can remodel human cortical motor organization, yet the input characteristics that promote reorganization and their functional significance have not been explored. Here we show with transcranial magnetic stimulation that sensory-driven reorganization of human motor cortex is highly dependent upon the frequency, intensity, and duration of stimulus applied. Those patterns of input associated with enhanced excitability (5 Hz, 75% maximal tolerated intensity for 10 min) induce stronger cortical activation to fMRI. When applied to acutely dysphagic stroke patients, swallowing corticobulbar excitability is increased mainly in the undamaged hemisphere, being strongly correlated with an improvement in swallowing function. Thus, input to the human adult brain can be programmed to promote beneficial changes in neuroplasticity and function after cerebral injury.

A simple and fast technique for on-line fMRI data analysis

In the present work a simple technique for fMRI data analysis is presented. Artifacts due to random and stimulus-correlated motions are corrected without image registration procedures. The first step of our procedure is the calculation of the raw activation map by correlation analysis. The task related motion artifacts arise at the tissue interfaces, including vessels: when image intensity gradient is calculated the high values correspond to interface regions. To eliminate stimulus-correlated motion artifacts the intensity gradient image, obtained from the fMRI data set, is compared to the raw activation map. Since small random motions decrease the value of the correlation coefficient (R) of the external pixels of the activation areas, in the last step of our analysis procedures the clusters are extended to connected pixels having R values smaller than the defined threshold. Each cluster is expanded until the R value of the cluster average intensity is kept constant. The procedure has been tested with both GRE and EPI studies. The presented approach is a fast and robust technique useful for preliminary or on-line analysis of fMRI data.

A study of the cortical processing of ano-rectal sensation using functional MRI

Investigation of human ano-rectal physiology has concentrated largely on understanding the motor control of defecation and continence mechanisms. However, little is known of the physiology of ano-rectal sensation. There are important differences in the afferent innervation and sensory perception between the rectum and anal canal. This suggests that there could also be differences in the brain's processing of sensation from these two areas; however, this possibility remains unexplored. The aim of our study was to identify the cerebral areas processing anal (somatic) and rectal (visceral) sensation in healthy adults, using functional MRI. Eight male subjects with an age range of 21-39 years were studied on two separate occasions, one for rectal and the other for anal stimulation studies. Single shot gradient echo planar imaging was performed using a 1.5 tesla Phillips MRI scanner. For each subject, a series of 40 image sets containing 24 slices of the brain was obtained during periods of rapid phasic non-painful distension of the rectum or anal canal, alternating with rest periods, without stimulation. After motion correction, the images were analysed using cross correlation to identify the cerebral areas activated by the stimulus. Rectal stimulation resulted in bilateral activation of the inferior primary somatosensory, secondary somatosensory, sensory association, insular, peri-orbital, anterior cingulate and prefrontal cortices. Anal canal stimulation resulted in activation of areas similar to rectal stimulation, but the primary somatosensory cortex was activated at a more superior level, and there was no anterior cingulate activation. In conclusion, anal and rectal sensation resulted in a similar pattern of cortical activation, including areas involved with spatial discrimination, attention and affect. The differences in sensory perception from these two regions can be explained by their different representation in the primary somatosensory cortex. The anterior cingulate cortex was only activated by rectal stimulation, suggesting that the viscera have a greater representation on the limbic cortex than somatic structures, and this explains the greater autonomic responses evoked by visceral sensation in comparison with somatic sensation.

Data mining in brain imaging

Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of task-activation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.

A study of analysis parameters that influence the sensitivity of event-related fMRI analyses

To assess the effect of various analysis parameters on the sensitivity of event-related fMRI analyses, 36 analyses were performed on a single fMRI data-set, varying parameters along four axes: (1) resampled voxel size; (2) spatial smoothing; (3) temporal smoothing; and (4) the set of basis functions used to model event-related responses. Sensitivity (i.e., the probability of detecting an activation given it exists) was assessed in terms of Z scores and by a metric for corrected P values, the negative log of the expected Euler characteristic. Sixteen brain regions distributed across cortical and subcortical areas were included in the meta-analysis. Main effects on sensitivity were found for resampled voxel size, spatial smoothing, temporal smoothing, and the set of basis functions chosen. The analysis parameters that generally produced the most sensitive analyses were a 2-mm(3) resampled voxel size, 10-mm spatial smoothing, 4-s temporal smoothing, and a basis set comprising a hemodynamic response function and its temporal derivative.