Comprehensive mathematical simulation of functional magnetic resonance imaging time series including motion-related image distortion and spin saturation effect

There has been vast interest in determining the feasibility of functional magnetic resonance imaging (fMRI) as an accurate method of imaging brain function for patient evaluations. The assessment of fMRI as an accurate tool for activation localization largely depends on the software used to process the time series data. The performance evaluation of different analysis tools is not reliable unless truths in motion and activation are known. Lack of valid truths has been the limiting factor for comparisons of different algorithms. Until now, currently available phantom data do not include comprehensive accounts of head motion. While most fMRI studies assume no interslice motion during the time series acquisition in fMRI data acquired using a multislice and single-shot echo-planar imaging sequence, each slice is subject to a different set of motion parameters. In this study, in addition to known three-dimensional motion parameters applied to each slice, included in the time series computation are geometric distortion from field inhomogeneity and spin saturation effect as a result of out-of-plane head motion. We investigated the effect of these head motion-related artifacts and present a validation of the mapping slice-to-volume (MSV) algorithm for motion correction and activation detection against the known truths. MSV was evaluated, and showed better performance in comparison with other widely used fMRI data processing software, which corrects for head motion with a volume-to-volume realignment method. Furthermore, improvement in signal detection was observed with the implementation of the geometric distortion correction and spin saturation effect compensation features in MSV.     

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.     

Complementary aspects of diffusion imaging and fMRI; I: structure and function

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.     

To smooth or not to smooth? ROC analysis of perfusion fMRI data

Blood oxygenation level dependent (BOLD) contrast has been widely used for visualizing regional neural activation. Temporal filtering and parameter estimation algorithms are generally used to account for the intrinsic temporal autocorrelation present in BOLD data. Arterial spin labeling perfusion imaging is an emerging methodology for visualizing regional brain function both at rest and during activation. Perfusion contrast manifests different noise properties compared with BOLD contrast, represented by the even distribution of noise power and spatial coherence across the frequency spectrum. Consequently, different strategies are expected to be employed in the statistical analysis of functional magnetic resonance imaging (fMRI) data based on perfusion contrast. In this study, the effect of different analysis methods upon signal detection efficacy, as assessed by receiver operator characteristic (ROC) measures, was examined for perfusion fMRI data. Simulated foci of neural activity of varying amplitude and spatial extent were added to resting perfusion data, and the accuracy of each analysis was evaluated by comparing the results with the known distribution of pseudo-activation. In contrast to the BOLD fMRI, temporal smoothing or filtering reduces the power of perfusion fMRI data analyses whereas spatial smoothing is beneficial to the efficacy of analyses.     

Statistical methods for detecting activated regions in functional MRI of the brain

Two statistical tests for detecting activated pixels in functional MRI (fMRI) data are presented. The first test (t-test) is the optimal solution to the problem of detecting a known activation signal in Gaussian white noise. The results of this test are shown to be equivalent to the cross-correlation method that is widely used for activation detection in fMRI. The second test (F test) is the optimal solution when the measured data are modeled to consist of an unknown activation signal that lies in a known lower dimensional subspace of the measurement space with added Gaussian white noise. A model for the signal subspace based on a truncated trigonometric Fourier series is proposed for periodic activation–baseline imaging paradigms. The advantage of the second method is that it does not assume any information about the shape or delay of the activation signal, except that it is periodic with the same period as the activation–baseline pattern. The two models are applied to experimental echo-planar fMRI data sets and the results are compared.     

Nonlinear responses of cerebral blood volume, blood flow and blood oxygenation signals during visual stimulation

Hemodynamic-based functional magnetic resonance imaging (fMRI) techniques provide a great utility for noninvasive functional brain mapping. However, because the hemodynamic signals reflect underlying neural activity indirectly, characterization of these signals following brain activation is essential for experimental design and data interpretation. In this report, the linear (or nonlinear) responses to neuronal activation of three hemodynamic parameters based primarily on changes of cerebral blood volume, blood flow and blood oxygenation were investigated by testing these hemodynamic responses' additivity property. Using a recently developed fMRI technique that acquires vascular space occupancy (VASO), arterial spin labeling (ASL) perfusion and blood oxygenation level-dependent (BOLD) signals simultaneously, the additivity property of the three hemodynamic responses in human visual cortex was assessed using various visual stimulus durations. Experiments on healthy volunteers showed that all three hemodynamic-weighted signals responded nonlinearly to stimulus durations less than 4 s, with the degree of nonlinearity becoming more severe as the stimulus duration decreased. Vascular space occupancy and ASL perfusion signals showed similar nonlinearity properties, whereas the BOLD signal was the most nonlinear. These data suggest that caution should be taken in the interpretation of hemodynamic-based signals in fMRI.     

Informatics methods to enable sharing of quantitative imaging research data

Introduction

The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community.

Methods

We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN.

Results

There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network.

Conclusions

As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers.     

A preliminary study of functional abnormalities in aMCI subjects during different episodic memory tasks

Functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of mild cognitive impairment (MCI) and early Alzheimer's disease (AD), because the memory dysfunction may occur before structural degeneration is obvious. In this research, we investigated the functional abnormalities of subjects with amnestic MCI (aMCI) using three episodic memory paradigms that are relevant to different memory domains in both encoding and recognition phases. Both whole-brain analysis and region-of-interest (ROI) analysis of the medial temporal lobes (MTL), which are central to the memory formation and retrieval, were used to compare the efficiency of the different memory paradigms and the functional difference between aMCI subjects and normal control subjects. We also investigated the impact of using different functional activation measurements in ROI analysis. This pilot study could facilitate the use of fMRI activations in the MTL as a marker for early detection and monitoring progression of AD.     

Surface-based functional magnetic resonance imaging analysis of partial brain echo planar imaging data at 1.5 T

Surface-based functional magnetic resonance imaging (fMRI) analysis is more sensitive and accurate than volume-based analysis for detecting neural activation. However, these advantages are less important in practical fMRI experiments with commonly used 1.5-T magnetic resonance devices because of the resolution gap between the echo planar imaging data and the cortical surface models. We expected high-resolution segmented partial brain echo planar imaging (EPI) data to overcome this problem, and the activation patterns of the high-resolution data could be different from the low-resolution data. For the practical applications of surface-based fMRI analysis using segmented EPI techniques, the effects of some important factors (e.g., activation patterns, registration and local distortions) should be intensively evaluated because the results of surface-based fMRI analyses could be influenced by them. In this study, we demonstrated the difference between activations detected from low-resolution EPI data, which were covering whole brain, and high-resolution segmented EPI data covering partial brain by volume- and surface-based analysis methods. First, we compared the activation maps of low- and high-resolution EPI datasets detected by volume- and surface-based analyses, with the spatial patterns of activation clusters, and analyzed the distributions of activations in occipital lobes. We also analyzed the high-resolution EPI data covering motor areas and fusiform gyri of human brain, and presented the differences of activations detected by volume- and surface-based methods.     

High-resolution, multiple gradient-echo functional MRI at 1.5 T

A multiple gradient echo, high resolution imaging method is proposed to better visualize different sources of activation in functional magnetic resonance imaging (fMRI) experiments. Eight echoes are collected from 30 ms to 205 ms with an echo spacing of 25 ms. All echoes show significant activation, but each echo reveals its own pattern of activation. From this variability, it appears that large vessel contributions can be separated from small vessel contributions using a fuzzy cluster analysis across echo times. The results demonstrate the importance of a multiple gradient echo data acquisition approach in localizing various vascular contributions to brain activation in fMRI.     

Discussion on the choice of separated components in fMRI data analysis by spatial independent component analysis

By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. Included simulations show the possibility of the spatial ICA on discriminating asynchronous activations or different response patterns in an fMRI data set. An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.     

Quantification of venous blood signal contribution to BOLD functional activation in the auditory cortex at 3 T

Most modern techniques for functional magnetic resonance imaging (fMRI) rely on blood-oxygen-level-dependent (BOLD) contrast as the basic principle for detecting neuronal activation. However, the measured BOLD effect depends on a transfer function related to neurophysiological changes accompanying electrical neural activation. The spatial accuracy and extension of the region of interest are determined by vascular effect, which introduces incertitude on real neuronal activation maps. Our efforts have been directed towards the development of a new methodology that is capable of combining morphological, vascular and functional information; obtaining new insight regarding foci of activation; and distinguishing the nature of activation on a pixel-by-pixel basis. Six healthy volunteers were studied in a parametric auditory functional experiment at 3 T; activation maps were overlaid on a high-resolution brain venography obtained through a novel technique. The BOLD signal intensities of vascular and nonvascular activated voxels were analyzed and compared: it was shown that nonvascular active voxels have lower values for signal peak (P<10(-7)) and area (P<10(-8)) with respect to vascular voxels. The analysis showed how venous blood influenced the measured BOLD signals, supplying a technique to filter possible venous artifacts that potentially can lead to misinterpretation of fMRI results. This methodology, although validated in the auditory cortex activation, maintains a general applicability to any cortical fMRI study, as the basic concepts on which it relies on are not limited to this cortical region. The results obtained in this study can represent the basis for new methodologies and tools that are capable of adding further characterization to the BOLD signal properties.     

Flexible statistical modelling detects clinical functional magnetic resonance imaging activation in partially compliant subjects

Clinical functional magnetic resonance imaging (fMRI) occasionally fails to detect significant activation, often due to variability in task performance. The present study seeks to test whether a more flexible statistical analysis can better detect activation, by accounting for variance associated with variable compliance to the task over time. Experimental results and simulated data both confirm that even at 80% compliance to the task, such a flexible model outperforms standard statistical analysis when assessed using the extent of activation (experimental data), goodness of fit (experimental data), and area under the operator characteristic curve (simulated data). Furthermore, retrospective examination of 14 clinical fMRI examinations reveals that in patients where the standard statistical approach yields activation, there is a measurable gain in model performance in adopting the flexible statistical model, with little or no penalty in lost sensitivity. This indicates that a flexible model should be considered, particularly for clinical patients who may have difficulty complying fully with the study task.     

Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets

A new approach in studying interregional functional connectivity using functional magnetic resonance imaging (fMRI) is presented. Functional connectivity may be detected by means of cross correlating time course data from functionally related brain regions. These data exhibit high temporal coherence of low frequency fluctuations due to synchronized blood flow changes. In the past, this fMRI technique for studying functional connectivity has been applied to subjects that performed no prescribed task ("resting" state). This paper presents the results of applying the same method to task-related activation datasets. Functional connectivity analysis is first performed in areas not involved with the task. Then a method is devised to remove the effects of activation from the data using independent component analysis (ICA) and functional connectivity analysis is repeated. Functional connectivity, which is demonstrated in the "resting brain," is not affected by tasks which activate unrelated brain regions. In addition, ICA effectively removes activation from the data and may allow us to study functional connectivity even in the activated regions.     

fMRI pain activation in the periaqueductal gray in healthy volunteers during the cold pressor test

The periaqueductal gray (PAG), a brain area belonging to the descending pain modulatory system, plays a crucial role in pain perception. Little information is available on the relationship between PAG activation and perceived pain intensity. In this study, we acquired functional magnetic resonance imaging (fMRI) scans from the PAG during the cold pressor test, a model for tonic pain, in 12 healthy volunteers. fMRI data were acquired with a 12-channel head-coil and a 3-Tesla scanner and analyzed with Statistical Parametric Mapping (SPM8) software.     

Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis

Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.     

Improved temporal clustering analysis method applied to whole-brain data in acupuncture fMRI study

Temporal clustering analysis (TCA) has been proposed as a method for detecting the brain responses of a functional magnetic resonance imaging (fMRI) time series when the time and location of activation are completely unknown. But TCA is not suitable for treating the time series of the whole brain due to the existence of many inactive pixels. In theory, active pixels are located only in gray matter (GM). In this study, SPM2 was used to segment functional images into GM, white matter and cerebrospinal fluid, and only the pixels in GM were considered. Thus, most of inactive pixels are deleted, so that the sensitivity of TCA is greatly improved in the analysis of the whole brain. The same set of acupuncture fMRI data was treated using both conventional TCA and modified TCA (MTCA) for comparing their analytical ability. The results clearly show a significant improvement in the sensitivity achieved by MTCA.     

Derivative temporal clustering analysis: detecting prolonged neuronal activity

Temporal clustering analysis (TCA) and independent component analysis (ICA) are promising data-driven techniques in functional magnetic resonance imaging (fMRI) experiments to obtain brain activation maps in conditions with unknown temporal information regarding the neuronal activity. Although comparable to ICA in detecting transient neuronal activities, TCA fails to detect prolonged plateau brain activations. To eliminate this pitfall, a novel derivative TCA (DTCA) method was introduced and its algorithms with different subtraction intervals were tested on simulated data with a pattern of prolonged plateau brain activation. It was found that the best performance of DTCA method in generating functional maps could be obtained if the subtraction interval is equal to or larger than the length of the rising time of the fMRI response. The DTCA method and its theoretical predication were further investigated and validated using in vivo fMRI data sets. By removing the limitations in the previous TCA, DTCA has shown its powerful capability in detecting prolonged plateau neuronal activities.     

A variant of logistic transfer function in Infomax and a postprocessing procedure for independent component analysis applied to fMRI data

Independent component analysis with Infomax algorithm can separate functional magnetic resonance imaging (fMRI) data into independent spatial components (brain activation maps) and their associated time courses. In the current study, we propose a variant of the logistic transfer function in Infomax, referred to as a-logistic Infomax, and a postprocessing procedure to combine a consistently task-related (CTR) component with transiently task-related (TTR) components for a better definition of brain functional localization. This a-logistic Infomax introduced parameter a into the standard logistic transfer function of conventional Infomax algorithm. For postprocessing method, we suggest the use of a stepwise linear regression of CTR and TTR components to fit reference function and then to sum up with different weights only those with significant contributions to the reference function in order to obtain a task component activation map. The effectiveness of both approaches on separating components and functional localization was evaluated with simulated and real fMRI data.     

In vivo mapping of functional domains and axonal connectivity in cat visual cortex using magnetic resonance imaging

Noninvasive cognitive neuroimaging studies based on functional magnetic resonance imaging (fMRI) are of ever-increasing importance for basic and clinical neurosciences. The explanatory power of fMRI could be greatly expanded, however, if the pattern of the neuronal circuitry underlying functional activation could be made visible in an equally noninvasive manner. In this study, blood oxygenation level-dependent (BOLD)-based fMRI and diffusion tensor imaging (DTI) were performed in the same cat visual cortex, and the foci of fMRI activation utilized as seeding points for 3D DTI fiber reconstruction algorithms, thus providing the map of the axonal circuitry underlying visual information processing. The methods developed in this study will lay the foundation for in vivo neuroanatomy and the ability for noninvasive longitudinal studies of brain development.