White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer’s Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLDFMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between subject populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.
Multisite contributions are essential to improve the reliability and statistical power of imaging studies but introduce a complexity because of different acquisition protocols and scanners. The hemodynamic response function (HRF) is the transform that relates neural activity to the measured blood oxygenation level-dependent (BOLD) signal in MRI and contains information about the latency, amplitude, and duration of neuronal activations. Acquisition variabilities, without adding harmonization techniques, can severely limit our ability to characterize spatial effects. To address this problem, we propose to study and remove variabilities of the sampling rate and scanners on estimates of the HRF. We computed the HRF using a blind deconvolution method in 547 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) across 62 sites and 18 scanners. The approach consists of studying the changes of the response according to repetition times (TR) and scanner models. We applied ComBAT, a statistical multi-site harmonization technique, to evaluate and reduce the scanner and repetition time effects and used the Wilcoxon rank sum test to assess the performance of the harmonization. Results show high scanner and repetition time variabilities (|d| ≥ 0.38, p = 4.5 × 10!") across features, indicating that using harmonization is crucial in multi-site studies. ComBAT successfully removes the sampling effects and reduces the variance between scanners for 7 out of 10 of the HRF features (|d| ≤ 0.05, p = 0.0052). Scanners effects have been characterized on multi-site datasets, but the repetition time impact has been less studied. We showed that the use of different values of repetition time leads to changes in HRF behavior. Regression modeling changes in the HRF on the harmonized data are not significant (p = 0.0401) which does not allow to conclude how HRF changes with aging.
KEYWORDS: Functional magnetic resonance imaging, Matrices, Databases, White matter, Signal processing, Signal detection, Reliability, Quality control, Neuroimaging, Correlation coefficients
Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.
KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Optogenetics, Blue lasers, Thermometry, Neurons, Electrophysiology, Brain mapping, Signal detection
Combining functional Magnetic Resonance Imaging (fMRI) with cell-type selective optogenetic stimulation offers a unique chance to dissect brain functional networks and probe causal connections. Our team employed opto-fMRI and opto-electrophysiology to map the brain circuits of the secondary somatosensory cortex (S2) in nonhuman primate brains after unilateral transfection with AAV5 and AAV9 constructs of blue light opsin ChR2 with CaMKIIa promoter, largely specific to excitatory neurons. Our results revealed that blue light stimulation of varying intensities (1, 2, 4, 8, 16, and 24 mW) in the transfected S2 hand region elicited robust Local Field Potentials (LFPs) and spiking activity. Blue laser evoked LFP power increases peaked at 16 mW. Delivery of blue laser to transfected S2 evoked robust BOLD signal changes locally and in distant cortical and subcortical brain regions, including bilateral MCC, posterior insula, thalamus, bilateral area 3b/1, and contralateral S2 cortices. As expected, green light stimulation did not produce detectable spiking and LFP activity, but it did lead to robust BOLD signal changes in both local and distant brain regions. To monitor possible heating effects from laser stimulation, we developed an MRI method that measures temperature by computing the phase information of fMRI images. We measured small temperature increases at high laser power (e.g., 24 mW delivered through a 200 μm diameter optical fiber) but not at low laser powers (1,2, 4, and 8 mW). The low power green light-associated BOLD signal changes require further elucidation but suggest some opto-fMRI findings should be interpreted with caution.
Background: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder, in which pathological alterations are seen in both gray matter (GM) and white matter (WM). To date functional MRI (fMRI) studies of AD have been exclusively focused on GM, since blood oxygenation level dependent (BOLD) signals in WM are relatively weak and thus ignored in practice. Our recent work provides compelling evidence that BOLD fluctuations in brain WM are reliably detectable and reflect neural activities, offering the potential of investigating the functional connectivity in WM. Purpose: In this study, we aim to apply our fMRI analysis method to the investigation of functional alterations in WM during the progression of AD. Method: Raw resting state fMRI data of normal subjects and patients (total n=290, 5 diagnostic groups) were obtained from the Alzheimer’s Disease Neuroimaging Initiative database. Each fMRI image was parcellated into 82 GM regions and 48 WM bundles. Temporal correlation between each pair of GM and WM was calculated and the correlations of all pairs constituted a functional correlation matrix (FCM) for each subject. The FCMs were averaged within each diagnostic group, and differences in the averaged FCMs between the normal group and each disease group were sought. Result: Differences in functional correlations progressively enlarge as the disease evolves, and fornix and ventral entorhinal cortices exhibited most pronounced differences between the normal and disease groups. Conclusion: Functional connectivity in WM may serve as a novel neuroimaging biomarker for the progression of AD.
Quantitative fat-water MRI (FWMRI) methods provide valuable information about the distribution, volume, and composition of adipose tissue (AT). Ultra high field FWMRI of animal models may have the potential to provide insights into the progression of obesity and its comorbidities. Here, we present quantitative FWMRI with all known confounder corrections on a 15.2T preclinical scanner for noninvasive in vivo monitoring of an established diet-induced obesity mouse model. Male C57BL/6J mice were placed on a low-fat (LFD) or a high-fat diet (HFD). Three-dimensional (3-D) multiple gradient echo MRI at 15.2T was performed at baseline, 4, 8, 12, and 16 weeks after diet onset. A 3-D fat-water separation algorithm and additional processing were used to generate proton-density fat fraction (PDFF), local magnetic field offset, and R2* maps. We examined these parameters in perirenal AT ROIs from LFD and HFD mice. The data suggest that PDFF, local field offset, and R2* have different time course behaviors between LFD and HFD mice over 16 weeks. This work suggests FWMRI at 15.2T may be a useful tool for longitudinal studies of adiposity due to the advantages of ultra high field although further investigation is needed to understand the observed time course behavior.
Modern magnetic resonance imaging (MRI) brain atlases are high quality 3-D volumes with specific structures labeled in the volume. Atlases are essential in providing a common space for interpretation of results across studies, for anatomical education, and providing quantitative image-based navigation. Extensive work has been devoted to atlas construction for humans, macaque, and several non-primate species (e.g., rat). One notable gap in the literature is the common squirrel monkey – for which the primary published atlases date from the 1960’s. The common squirrel monkey has been used extensively as surrogate for humans in biomedical studies, given its anatomical neuro-system similarities and practical considerations. This work describes the continued development of a multi-modal MRI atlas for the common squirrel monkey, for which a structural imaging space and gray matter parcels have been previously constructed. This study adds white matter tracts to the atlas. The new atlas includes 49 white matter (WM) tracts, defined using diffusion tensor imaging (DTI) in three animals and combines these data to define the anatomical locations of these tracks in a standardized coordinate system compatible with previous development. An anatomist reviewed the resulting tracts and the inter-animal reproducibility (i.e., the Dice index of each WM parcel across animals in common space) was assessed. The Dice indices range from 0.05 to 0.80 due to differences of local registration quality and the variation of WM tract position across individuals. However, the combined WM labels from the 3 animals represent the general locations of WM parcels, adding basic connectivity information to the atlas.
In obesity, fat-water MRI (FWMRI) methods provide valuable information about adipose tissue (AT) distribution. AT is known to undergo complex metabolic and endocrine changes in association with chronic inflammation including iron overloading. Here, we investigate the potential for FWMRI parameters (fat signal fraction (FSF), local magnetic field offset, and T2*) to be sensitive to AT inflammatory changes in an established diet-induced obesity mouse model. Male C57BL/6J mice were placed on a low fat (LFD) or a high fat diet (HFD). 3D multi- gradient-echo MRI at 15.2T was performed at baseline, 4, 8, 12, and 16 weeks after diet onset. A 3D fat-water separation algorithm and additional processing was used to generate FSF, local field offset, and T2* maps. We examined these parameters in perirenal AT ROIs from HFD and LFD mice. Results: The data suggest that FSF, local field offset, and T2* can differentiate time course behavior between inflamed and control AT (increasing FSF, decreasing local field offset, increasing followed by decreasing T2*). The biophysical mechanisms of these observed changes are not well understood and require further study. To the best of our knowledge, we report the first evidence that FWMRI can provide biomarkers sensitive to AT inflammation, and that FWMRI has the potential for longitudinal non-invasive assessment of AT inflammation in obesity.
We investigate the use of a recent technique for shape analysis of brain substructures in identifying learning disabilities
in third-grade children. This Riemannian technique provides a quantification of differences in shapes of parameterized
surfaces, using a distance that is invariant to rigid motions and re-parameterizations. Additionally, it provides an optimal
registration across surfaces for improved matching and comparisons. We utilize an efficient gradient based method to
obtain the optimal re-parameterizations of surfaces. In this study we consider 20 different substructures in the human
brain and correlate the differences in their shapes with abnormalities manifested in deficiency of mathematical skills in
106 subjects. The selection of these structures is motivated in part by the past links between their shapes and cognitive
skills, albeit in broader contexts. We have studied the use of both individual substructures and multiple structures jointly
for disease classification. Using a leave-one-out nearest neighbor classifier, we obtained a 62.3% classification rate based
on the shape of the left hippocampus. The use of multiple structures resulted in an improved classification rate of 71.4%.
Deformation Based Morphometry (DBM) is a relatively new method used for characterizing anatomical differences
among populations. DBM is based on the analysis of the deformation fields generated by non-rigid registration
algorithms, which warp the individual volumes to one standard coordinate system. Although several studies have
compared non-rigid registration algorithms for segmentation tasks, few studies have compared the effect of the
registration algorithm on population differences that may be uncovered through DBM. In this study, we compared DBM
results obtained with five well established non-rigid registration algorithms on the corpus callosum (CC) in thirteen
subjects with Williams Syndrome (WS) and thirteen Normal Control (NC) subjects. The five non-rigid registration
algorithms include: (1) The Adaptive Basis Algorithm (ABA); (2) Image Registration Toolkit (IRTK); (3) FSL
Nonlinear Image Registration Tool (FSL); (4) Automatic Registration Tools (ART); and (5) the normalization algorithm
available in SPM8. For each algorithm, the 3D deformation fields from all subjects to the atlas were obtained and used to
calculate the Jacobian determinant (JAC) at each voxel in the mid-sagittal slice of the CC. The mean JAC maps for each
group were compared quantitatively across different nonrigid registration algorithms. An ANOVA test performed on the
means of the JAC over the Genu and the Splenium ROIs shows the JAC differences between nonrigid registration
algorithms are statistically significant over the Genu for both groups and over the Splenium for the NC group. These
results suggest that it is important to consider the effect of registration when using DBM to compute morphological
differences in populations.
Magnetic resonance diffusion tensor imaging (DTI) is being widely used to reconstruct brain white matter (WM) fiber
tracts. For further characterization of the tracts, the fibers with similar courses often need to be grouped into a fiber
bundle that corresponds to certain underlying WM anatomic structure. In addition, the alignments of fibers from different
studies are often desirable for bundle comparisons and group analysis.
In this work, a novel registration algorithm based on fiber-to-bundle matching was proposed to address the above two
needs. Using an Expectation Maximization (EM) algorithm, the proposed method is capable of estimating a Thin-Plate-
Spline transformation that optimally aligns whole-brain target fiber sets with a reference bundle model. Based on the
resulting transformations, the fibers from different target datasets can all be warped into the reference coordinate system
for comparisons and group analysis. The fibers can be further automatically labeled according to their similarity to the
reference model.
The algorithm was evaluated with eight human brain DTI data volumes acquired in vivo at 3T. After registration, the
warped target bundles exhibit good similarity to the reference bundles. Quantitative experiments further demonstrated
that the detected target bundles agree with ground truth obtained by manual segmentation at a sub-voxel accuracy.
KEYWORDS: Tumors, Image registration, Breast, Magnetic resonance imaging, Detection and tracking algorithms, Tissues, Image resolution, Computer simulations, Mammography, Breast cancer
Although useful for the detection of breast cancers, conventional imaging methods, including mammography and
ultrasonography, do not provide adequate information regarding response to therapy. Dynamic contrast enhanced MRI
(DCE-MRI) has emerged as a promising technique to provide relevant information on tumor status. Consequently,
accurate longitudinal registration of breast MR images is critical for the comparison of changes induced by treatment at
the voxel level. In this study, a nonrigid registration algorithm is proposed to allow for longitudinal registration of breast
MR images obtained throughout the course of treatment. We accomplish this by modifying the adaptive bases algorithm
(ABA) through adding a tumor volume preserving constraint in the cost function. The registration results demonstrate
the proposed algorithm can successfully register the longitudinal breast MR images and permit analysis of the parameter
maps. We also propose a novel validation method to evaluate the proposed registration algorithm quantitatively. These
validations also demonstrate that the proposed algorithm constrains tumor deformation well and performs better than the
unconstrained ABA algorithm.
Mathematical difficulty affects approximately 5-9% of the population. Studies on individuals with dyscalculia, a
neurologically based math disorder, provide important insight into the neural correlates of mathematical ability. For
example, cognitive theories, neuropsychological studies, and functional neuroimaging studies in individuals with
dyscalculia suggest that the bilateral parietal lobes and intraparietal sulcus are central to mathematical performance. The
purpose of the present study was to investigate morphological differences in a group of third grade children with poor
math skills. We compare population averages of children with low math skill (MD) to gender and age matched controls
with average math ability. Anatomical data were gathered with high resolution MRI and four different population
averaging methods were used to study the effect of the normalization technique on the results. Statistical results based on
the deformation fields between the two groups show anatomical differences in the bilateral parietal lobes, right frontal
lobe, and left occipital/parietal lobe.
3D intra- and inter-subject registration of image volumes is important for tasks that include measurements and
quantification of temporal/longitudinal changes, atlas-based segmentation, deriving population averages, or voxel and
tensor-based morphometry. A number of methods have been proposed to tackle this problem but few of them have
focused on the problem of registering whole body image volumes acquired either from humans or small animals. These
image volumes typically contain a large number of articulated structures, which makes registration more difficult than
the registration of head images, to which the vast majority of registration algorithms have been applied. To solve this
problem, we have previously proposed an approach, which initializes an intensity-based non-rigid registration algorithm
with a point based registration technique [1, 2]. In this paper, we introduce new constraints into our non-rigid registration
algorithm to prevent the bones from being deformed inaccurately. Results we have obtained show that the new
constrained algorithm leads to better registration results than the previous one.
The importance of small animal imaging in fundamental and clinical research is growing rapidly. These studies typically involve micro PET, micro MR, and micro CT images as well as optical or fluorescence images. Histological images are also often used to complement and/or validate the in vivo data. As is the case for human studies, automatic registration of these imaging modalities is a critical component of the overall analysis process, but, the small size of the animals and thus the limited spatial resolution of the in vivo images present specific challenges. In this paper, we propose a series of methods and techniques that permit the inter-subject registration of micro MR and histological images. We then compare results obtained by registering directly MR volumes to each other using a non-rigid registration algorithm we have developed at our institution with results obtained by registering first the MR volumes to their corresponding histological volume, which we reconstruct from 2D cross-sections, and then registering histological volumes to each other. We show that the second approach is preferable.
KEYWORDS: Signal to noise ratio, Signal detection, Tomography, Signal attenuation, Sensors, 3D scanning, Polymers, Scanners, Optical scanning, Interference (communication)
This optical tomography scanner for imaging (3D) applied density distribution as been evaluated for uniformity, resolution, and linearity. Signal to noise ratio of about 1000:1 in both line projection and in back projection reconstructed image has been obtained. Radiation-sensitive BANGR polymer gels were studied using this system. Dose as low as 10 cGy up to 10 Gy for 6 MV X-ray and 6 MeV electrons from linear accelerator have been measured. Since the gels can be made UVlight sensitive, dose distribution could be used to measure exposure using this technique.
James Duncan, Peng-Cheng Shi, Amir Amimi, R. Todd Constable, Lawrence Staib, Donald Dione, QingXin Shi, Elliot Heller, Michael Singer, Amit Chakraborty, Glynn Robinson, John Gore, Albert Sinusas
This paper describes efforts aimed at more accurately and objectively determining and quantifying the local, regional, and global function of the left ventricle (LV) of the heart under both normal and ischemic conditions. These measurements and evaluations are made using non-invasive, 3-D, cardiac diagnostic imaging sequences (i.e., 4-D data) and rely on an approach that follows the shape properties of the endocardial and epicardial surfaces of the LV over the entire cardiac cycle. Our efforts involve the development of an acute infarct animal model that permits us to establish the validity of our noninvasive image analysis algorithms, as well as permits us to study the efficacy of using in vivo, image-derived measures of function for predicting regional myocardial viability (immediately post mortem). We first describe the experimental setup for the animal model, including the use of implanted imaging-opaque markers that assist in setting up a gold standard against which image-derived measurements can be evaluated. Next, the imaging techniques are described, and finally the image analysis methods and their comparison to the validation technique are discussed.
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