Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion Magnetic Resonance Imaging With Histology Verification.

PURPOSE: The goal of this study is to validate the muscle architecture derived from both ex vivo and in vivo diffusion-weighted magnetic resonance imaging (dMRI) of the human tongue with histology of an ex vivo tongue. METHOD: dMRI was acquired with a 200-direction high angular resolution diffusion imaging (HARDI) diffusion scheme for both a postmortem head (imaged within 48 hr after death) and a healthy volunteer. After MRI, the postmortem head was fixed and the tongue excised for hematoxylin and eosin (H&E) staining and histology imaging. Structure tensor images were generated from the stained images to better demonstrate muscle fiber orientations. The tongue muscle fiber orientations, estimated from dMRI, were visualized using the tractogram, a novel representation of crossing fiber orientations, and compared against the histology images of the ex vivo tongue. RESULTS: Muscle fibers identified in the tractograms showed good correspondence with those appearing in the histology images. We further demonstrated tongue muscle architecture in in vivo tractograms for the entire tongue. CONCLUSION: The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue and may potentially benefit planning and evaluation of oral surgery and research on speech and swallowing.

Three-dimensional elastic image registration based on strain energy minimization: application to prostate magnetic resonance imaging.

The use of magnetic resonance (MR) imaging in conjunction with an endorectal coil is currently the clinical standard for the diagnosis of prostate cancer because of the increased sensitivity and specificity of this approach. However, imaging in this manner provides images and spectra of the prostate in the deformed state because of the insertion of the endorectal coil. Such deformation may lead to uncertainties in the localization of prostate cancer during therapy. We propose a novel 3-D elastic registration procedure that is based on the minimization of a physically motivated strain energy function that requires the identification of similar features (points, curves, or surfaces) in the source and target images. The Gauss-Seidel method was used in the numerical implementation of the registration algorithm. The registration procedure was validated on synthetic digital images, MR images from prostate phantom, and MR images obtained on patients. The registration error, assessed by averaging the displacement of a fiducial landmark in the target to its corresponding point in the registered image, was 0.2 ± 0.1 pixels on synthetic images. On the prostate phantom and patient data, the registration errors were 1.0 ± 0.6 pixels (0.6 ± 0.4 mm) and 1.8 ± 0.7 pixels (1.1 ± 0.4 mm), respectively. Registration also improved image similarity (normalized cross-correlation) from 0.72 ± 0.10 to 0.96 ± 0.03 on patient data. Registration results on digital images, phantom, and prostate data in vivo demonstrate that the registration procedure can be used to significantly improve both the accuracy of localized therapies such as brachytherapy or external beam therapy and can be valuable in the longitudinal follow-up of patients after therapy.

Postnatal Guinea Pig Brain Development, as Revealed by Magnetic Resonance and Diffusion Kurtosis Imaging.

This study used in vivo magnetic resonance imaging (MRI) to identify age dependent brain structural characteristics in Dunkin Hartley guinea pigs. Anatomical T2-weighted images, diffusion kurtosis (DKI) imaging, and T2 relaxometry measures were acquired from a cohort of male guinea pigs from postnatal day (PND) 18-25 (juvenile) to PND 46-51 (adolescent) and PND 118-123 (young adult). Whole-brain diffusion measures revealed the distinct effects of maturation on the microstructural complexity of the male guinea pig brain. Specifically, fractional anisotropy (FA), as well as mean, axial, and radial kurtosis in the corpus callosum, amygdala, dorsal-ventral striatum, and thalamus significantly increased from PND 18-25 to PND 118-123. Age-related alterations in DKI measures within these brain regions paralleled the overall alterations observed in the whole brain. Age-related changes in FA and kurtosis in the gray matter-dominant parietal cerebral cortex and dorsal hippocampus were less pronounced than in the other brain regions. The regional data analysis revealed that between-age changes of diffusion kurtosis metrics were more pronounced than those observed in diffusion tensor metrics. The age-related anatomical differences reported here may be important determinants of the age-dependent neurobehavior of guinea pigs in different tasks.

A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging.

Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI, Ktrans using dynamic contrast-enhanced MRI, and 3-dimensional-MR spectroscopy. A pathologist reviewed all 223 specimens and marked cancerous regions on each and graded them with Gleason scores, which served as the ground truth to validate our prediction model. In cancer aggressiveness prediction, the average area under the receiver operating characteristic curve (AUC) value was 0.73 with 95% confidence interval (0.72-0.74) and the average sensitivity and specificity were 0.72 (0.71-0.73) and 0.73 (0.71-0.75), respectively. For the cancer detection model, the average AUC value was 0.68 (0.66-0.70) and the average sensitivity and specificity were 0.73 (0.70-0.77) and 0.62 (0.60-0.68), respectively. Our method included capability to handle class imbalance using adaptive boosting with random undersampling. In addition, our method was noninvasive and allowed for nonsubjective disease characterization, which provided physician information to make personalized treatment decision.

Associations between interhemispheric functional connectivity and the Automated Neuropsychological Assessment Metrics (ANAM) in civilian mild TBI.

This study investigates cognitive deficits and alterations in resting state functional connectivity in civilian mild traumatic brain injury (mTBI) participants with high and low symptoms. Forty-one mTBI participants completed a resting state fMRI scan and the Automated Neuropsychological Assessment Metrics (ANAM) during initial testing (<10 days of injury) and a 1 month follow up. Data were compared to 30 healthy control subjects. Results from the ANAM demonstrate that mTBI participants performed significantly worse than controls on the code substitution delayed subtest (p = 0.032). [corrected]. Among the mTBI patients, high symptom mTBI participants performed worse than those with low symptoms on the code substitution delayed (p = 0.017), code substitution (p = 0.012), repeated simple reaction time (p = 0.031), and weighted throughput score (p = 0.019). [corrected]. Imaging results reveal that during the initial visit, low symptom mTBI participants had reduced interhemispheric functional connectivity (IH-FC) within the lateral parietal lobe (p = 0.020); however, during follow up, high symptom mTBI participants showed reduced IH-FC compared to the control group within the dorsolateral prefrontal cortex (DLPFC) (p = 0.013). Reduced IH-FC within the DLPFC during the follow-up was associated with reduced cognitive performance. Together, these findings suggest that reduced rs-FC may contribute to the subtle cognitive deficits noted in high symptom mTBI participants compared to control subjects and low symptom mTBI participants.

Modulation of resting state functional connectivity of the motor network by transcranial pulsed current stimulation.

The effects of transcranial pulsed current stimulation (tPCS) on resting state functional connectivity (rs-FC) within the motor network were investigated. Eleven healthy participants received one magnetic resonance imaging (MRI) session with three resting state functional MRI (rs-fMRI) scans, one before stimulation (PRE-STIM) to collect baseline measures, one during stimulation (STIM), and one after 13 min of stimulation (POST-STIM). Rs-FC measures during the STIM and POST-STIM conditions were compared to the PRE-STIM baseline. Regions of interest for the rs-FC analysis were extracted from the significantly activated clusters obtained during a finger tapping motor paradigm and included the right primary motor cortex (R M1), left primary motor cortex (L M1), supplemental motor area (SMA), and cerebellum (Cer). The main findings were reduced rs-FC between the left M1 and surrounding motor cortex, and increased rs-FC between the left M1 and left thalamus during stimulation, but increased rs-FC between the Cer and right insula after stimulations. Bivariate measures of connectivity demonstrate reduced strength of connectivity for the whole network average (p=0.044) and reduced diversity of connectivity for the network average during stimulation (p=0.024). During the POST-STIM condition, the trend of reduced diversity for the network average was statistically weaker (p=0.071). In conclusion, while many of the findings are comparable to previous reports using simultaneous transcranial direct current stimulation (tDCS) and fMRI acquisition, we also demonstrate additional changes in connectivity patterns that are induced by tPCS.

Longitudinal and prognostic evaluation of mild traumatic brain injury: A 1H-magnetic resonance spectroscopy study.

In the majority of patients with mild traumatic brain injury (mTBI), brain tissue impairment is undetectable by computed tomography and/or structural magnetic resonance imaging. Even in confirmed cases of head injury, conventional neuroimaging methods lack sensitivity in predicting neuropsychological outcomes of patients. The objectives of this study were to (1) cross-sectionally determine deviations in the neurometabolic profile of patients with mTBI from healthy controls at different stages of mTBI using tightly controlled examination windows, and (2) determine associations between acute neurometabolic markers of mTBI and chronic neurocognitive performance. Patients were examined at the early subacute (n=43; 5.44 ± 3.15 days post-injury (DPI)), late subacute (n=33; 37.00 ± 12.26 DPI) and chronic (n=27; 195.30 ± 19.60 DPI) stages of mTBI. Twenty-one neurologically intact subjects were used as controls. Proton magnetic resonance spectroscopy imaging ((1)H-MRSI) was used to obtain metabolic measurements from different brain regions. The Automated Neuropsychological Assessment Metrics (ANAM) was used for cognitive evaluation of patients at the chronic stage of mTBI. Measurements in the thalamus and centrum semiovale (CSV) emerged as the most indicative of injury and were used to predict neurocognitive outcome. The major findings of this study are (1) decreases in Cho/Cre (choline-to-creatine ratio) measured in the thalamus (p=0.042) and CSV (p=0.017) at the late subacute stage of mTBI; (2) positive associations of early subacute Cre measurements in the CSV with chronic ANAM scores measuring performance in delayed (r=0.497, p=0.019) and immediate (r=0.391, p=0.072) code substitution. These findings show that metabolic measurements in the thalamus and CSV can potentially serve as diagnostic and prognostic markers of mTBI.

Reliability estimation of grouped functional imaging data using penalized maximum likelihood.

We analyzed grouped fMRI data and developed a reliability analysis for such data using the method of penalized maximum likelihood (ML). Specifically, this technique was applied to a somatosensory paradigm that used a mechanical probe to provide noxious stimuli to the foot, and a paradigm consisting of four levels of graded peripheral neuromuscular electrical stimulation (NMES). In each case, reliability maps of activation were generated. Receiver operating characteristic (ROC) curves were constructed in the case of the graded NMES paradigm for each level of stimulation, which revealed an increase in the specificity of activation with increasing stimulation levels. In addition, penalized ML was used to determine whether the grouped reliability maps obtained from one stimulus level were significantly different from those obtained at other levels. The results show a significant difference (P < 0.01) in the reliability of activation from one stimulation level to the next. These results are in agreement with those obtained using generalized linear modeling (GLM). While the reliability maps generated are not directly comparable, they are qualitatively similar to those obtained by controlling the expected false discovery rate (FDR). The proposed methodology can be used to objectively compare activation maps between groups, as well as to perform reliability assessments. Furthermore, this method potentially can be used to assess the longitudinal effect of treatment therapies within a group.