Diffusion and structural MRI as potential biomarkers in people with Parkinson's disease and cognitive impairment.

OBJECTIVE: To explore the neuroimage change in Parkinson's disease (PD) patients with cognitive impairments, this study investigated the correlation between plasma biomarkers and morphological brain changes in patients with normal cognition and mild cognitive impairment. The objective was to identify the potential target deposition regions of the plasma biomarkers and to search for the relevant early neuroimaging biomarkers on the basis of different cognitive domains. METHODS: Structural brain MRI and diffusion weighted images were analyzed from 49 eligible PD participants (male/female: 27/22; mean age: 73.4 ± 8.5 years) from a retrospective analysis. Plasma levels of α-synuclein, amyloid beta peptide, and total tau were collected. A comprehensive neuropsychological assessment of the general and specific cognitive domains was performed. Difference between PD patients with normal cognition and impairment was examined. Regression analysis was performed to evaluate the correlation between image-derived index and plasma biomarkers or neuropsychological assessments. RESULTS: Significant correlation was found between plasma Aß-42 level and fractional anisotropy of the middle occipital, angular, and middle temporal gyri of the left brain, as well as plasma T-tau level and the surface area of the isthmus or the average thickness of the posterior part of right cingulate gyrus. Visuospatial and executive function is positively correlated with axial diffusivity in bilateral cingulate gyri. CONCLUSION: In nondemented PD patients, the target regions for plasma deposition might be located in the cingulate, middle occipital, angular, and middle temporal gyri. Changes from multiple brain regions can be correlated to the performance of different cognitive domains. CLINICAL RELEVANCE STATEMENT: Cognitive impairment in Parkinson's disease is primarily linked to biomarkers associated with Alzheimer's disease rather than those related to Parkinson's disease and resembles the frontal variant of Alzheimer's disease, which may guide management strategies for cognitive impairment in Parkinson's disease. KEY POINTS: • Fractional anisotropy, surface area, and thickness in the cingulate, middle occipital, angular, and middle temporal gyri can be significantly correlated with plasma Aß-42 and T-tau level. • Axial diffusivity in the cingulate gyri was correlated with visuospatial and executive function. • The pattern of cognitive impairment in Parkinson's disease can be similar to the frontal variant than typical Alzheimer's disease.

Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network.

Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical Relevance- This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.

A fixel-based analysis of white matter reductions early detects Parkinson disease with mild cognitive impairment.

BACKGROUND: White matter (WM) tract alterations are early signs of cognitive impairment in Parkinson disease (PD) patients. Fixel-based analysis (FBA) has advantages over traditional diffusion tensor imaging in managing complex and crossing fibers. We used FBA to measure fiber-specific changes in patients with PD mild cognitive impairment (PD-MCI) and PD normal cognition (PD-NC). METHODS: Seventy-one patients with PD without dementia were included: 39 PD-MCI and 32 PD-NC. All underwent diffusion-weighted imaging, clinical examinations, and tests to evaluate their cognitive function globally and in five cognitive domains. FBA was used to investigate fiber-tract alterations and compare PD-MCI with PD-NC subjects. Correlations with each cognitive test were analyzed. RESULTS: Patients with PD-MCI were significantly older (P = 0.044), had a higher male-to-female ratio (P = 0.006) and total Unified Parkinson's Disease Rating Scale score (P = 0.001). All fixel-based metrics were significantly reduced within the body of the corpus callosum and superior corona radiata in PD-MCI patients (family-wise error-corrected P value < 0.05) compared with PD-NC patients. The cingulum, superior longitudinal fasciculi, and thalamocortical circuit exhibited predominantly fiber-bundle cross-section (FC) changes. In regression analysis, reduced FC values in cerebellar circuits were associated with poor motor function in PD-MCI patients and poor picture-naming ability in PD-NC patients. CONCLUSIONS: PD-MCI patients have significant WM alterations compared with PD-NC patients. FBA revealed these changes in various bundle tracts, helping us to better understand specific WM changes that are functionally implicated in PD cognitive decline. FBA is potentially useful in detecting early cognitive decline in PD.

Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network.

Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification. Clinical Relevance: This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.

Curriculum Based Multi-Task Learning for Parkinson's Disease Detection.

There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance.

The neural basis of pain during labor.

Characterizing a labor pain-related neural signature is a key prerequisite for devising optimized pharmacologic and nonpharmacologic labor pain relief methods. The aim of this study was to describe the neural basis of labor pain and to provide a brief summary of how epidural anesthesia may affect pain-related neuronal activity during labor. Possible future directions are also highlighted. By taking advantage of functional magnetic resonance imaging, brain activation maps and functional neural networks of women during labor that have been recently characterized were compared between pregnant women who received epidural anesthesia and those who did not. In the subgroup of women who did not receive epidural anesthesia, labor-related pain elicited activations in a distributed brain network that included regions within the primary somatosensory cortex (postcentral gyrus and left parietal operculum cortex) and within the traditional pain network (lentiform nucleus, insula, and anterior cingulate gyrus). The activation maps of women who had been administered epidural anesthesia were found to be different-especially with respect to the postcentral gyrus, the insula, and the anterior cingulate gyrus. Parturients who received epidural anesthesia were also compared with those who did not in terms of functional connectivity from selected sensory and affective regions. When analyzing women who did not receive epidural anesthesia, marked bilateral connections from the postcentral gyrus to the superior parietal lobule, supplementary motor area, precentral gyrus, and the right anterior supramarginal gyrus were observed. In contrast, women who received epidural anesthesia showed fewer connections from the postcentral gyrus-being limited to the superior parietal lobule and supplementary motor area. Importantly, one of the most noticeable effects of epidural anesthesia was observed in the anterior cingulate cortex-a primary region that modulates pain perception. The increased outgoing connectivity from the anterior cingulate cortex in women who received epidural anesthesia indicates that the cognitive control exerted by this area might play a major role in the relief from labor pain. These findings not only affirmed the existence of a brain signature for pain experienced during labor, but they also showed that this signature can be altered by the administration of epidural anesthesia. This finding raises a question about the extent to which the cingulo-frontal cortex may exert top-down influences to gate women's experiences of labor-related pain. Because the anterior cingulate cortex is also involved in the processing and modulation of emotional content, such as fear and anxiety, a related question is about the extent to which the use of epidural anesthesia can affect different components of pain perception. Finally, inhibition of anterior cingulate cortex neurons may represent a potential new therapeutic target for alleviating labor-associated pain.

Diffusion tensor imaging for the differential diagnosis of Parkinsonism by machine learning.

BACKGROUND: There are currently no specific tests for either idiopathic Parkinson's disease or Parkinson-plus syndromes. The study aimed to investigate the diagnostic performance of features extracted from the whole brain using diffusion tensor imaging concerning parkinsonian disorders. METHODS: The retrospective data yielded 625 participants (average age: 61.4 ± 8.2, men/women: 313/312; healthy controls/idiopathic Parkinson's disease/multiple system atrophy/progressive supranuclear palsy: 219/286/51/69) between 2008 and 2017. Diffusion-weighted images were obtained using a 3T MR scanner. The 90th, 50th, and 10th percentiles of fractional anisotropy and mean/axial/radial diffusivity from each parcellated brain area were recorded. Statistical analysis was evaluated based on the features extracted from the whole brain, as determined using discriminant function analysis and support vector machine. 20% of the participants were used as an independent blind dataset with 5 times cross-verification. Diagnostic performance was evaluated by the sensitivity and the F1 score. RESULTS: Diagnoses were accurate for distinguishing idiopathic Parkinson's disease from healthy control and Parkinson-plus syndromes (87.4 ± 2.1% and 82.5 ± 3.9%, respectively). Diagnostic F1 scores varied for Parkinson-plus syndromes with 67.2 ± 3.8% for multiple system atrophy and 71.6 ± 3.5% for progressive supranuclear palsy. For early and late detection of idiopathic Parkinson's disease, the diagnostic performance was 79.2 ± 7.4% and 84.4 ± 6.9%, respectively. The diagnostic performance was 68.8 ± 11.0% and 52.5 ± 8.9% in early and late detection to distinguish different Parkinson-plus syndromes. CONCLUSIONS: Features extracted from diffusion tensor imaging of the whole brain can provide objective evidence for the diagnosis of healthy control, idiopathic Parkinson's disease, and Parkinson-plus syndromes with fair to very good diagnostic performance.

Deep learning based diagnosis of Parkinson's Disease using diffusion magnetic resonance imaging.

The diagnostic performance of a combined architecture on Parkinson's disease using diffusion tensor imaging was evaluated. A convolutional neural network was trained from multiple parcellated brain regions. A greedy algorithm was proposed to combine the models from individual regions into a complex one. Total 305 Parkinson's disease patients (aged 59.9±9.7 years old) and 227 healthy control subjects (aged 61.0±7.4 years old) were enrolled from 3 retrospective studies. The participants were divided into training with ten-fold cross-validation (N = 432) and an independent blind dataset (N = 100). Diffusion-weighted images were acquired from a 3T scanner. Fractional anisotropy and mean diffusivity were calculated and was subsequently parcellated into 90 cerebral regions of interest based on the Automatic Anatomic Labeling template. A convolutional neural network was implemented which contained three convolutional blocks and a fully connected layer. Each convolutional block consisted of a convolutional layer, activation layer, and pooling layer. This model was trained for each individual region. A greedy algorithm was implemented to combine multiple regions as the final prediction. The greedy algorithm predicted the area under curve of 94.1±3.2% from the combination of fractional anisotropy from 22 regions. The model performance analysis showed that the combination of 9 regions is equivalent. The best area under curve was 74.7±5.4% from the right postcentral gyrus. The current study proposed an architecture of convolutional neural network and a greedy algorithm to combine from multiple regions. With diffusion tensor imaging, the algorithm showed the potential to distinguish patients with Parkinson's disease from normal control with satisfactory performance.

Fixel-Based Analysis Effectively Identifies White Matter Tract Degeneration in Huntington's Disease.

Microstructure damage in white matter might be linked to regional and global atrophy in Huntington's Disease (HD). We hypothesize that degeneration of subcortical regions, including the basal ganglia, is associated with damage of white matter tracts linking these affected regions. We aim to use fixel-based analysis to identify microstructural changes in the white matter tracts. To further assess the associated gray matter damage, diffusion tensor-derived indices were measured from regions of interest located in the basal ganglia. Diffusion weighted images were acquired from 12 patients with HD and 12 healthy unrelated controls using a 3 Tesla scanner. Reductions in fixel-derived metrics occurs in major white matter tracts, noticeably in corpus callosum, internal capsule, and the corticospinal tract, which were closely co-localized with the regions of increased diffusivity in basal ganglia. These changes in diffusion can be attributed to potential axonal degeneration. Fixel-based analysis is effective in studying white matter tractography and fiber changes in HD.

Fixel-Based Analysis of White Matter Degeneration in Patients With Progressive Supranuclear Palsy or Multiple System Atrophy, as Compared to Parkinson's Disease.

Introduction: White matter degeneration may contribute to clinical symptoms of parkinsonism. Objective: We used fixel-based analysis (FBA) to compare the extent and patterns of white matter degeneration in different parkinsonian syndromes-including idiopathic Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). Methods: This is a retrospective interpretation of prospectively acquired data of patients recruited in previous studies during 2008 and 2019. Diffusion-weighted images were acquired on a 3-Tesla scanner (diffusion weighting b = 1000 s/mm2-applied along either 64 or 30 non-collinear directions) from 53 patients with PD (men/women: 29/24; mean age: 65.06 ± 5.51 years), 47 with MSA (men/women: 20/27; mean age: 63.00 ± 7.19 years), and 50 with PSP men/women: 20/30; mean age: 65.96 ± 3.14 years). Non-parametric permutation tests were used to detect intergroup differences in fixel-related indices-including fiber density, fiber cross-section, and their combination. Results: Patterns of white matter degeneration were significantly different between PD and atypical parkinsonisms (MSA and PSP). Compared with patients with PD, those with MSA and PSP showed a more extensive white matter involvement-noticeably descending tracts from primary motor cortex to corona radiata and cerebral peduncle. Lesions of corpus callosum were specific to PSP and absent in both MSA and PD. Discussion: FBA identified specific patterns of white matter changes in MSA and PSP patients compared to PD. Our results proved the utility of FBA in evaluation of implied biological processes of white matter changes in parkinsonism. Our study set the stage for future applications of this technique in patients with parkinsonian syndromes.

T1 and T2∗ relaxation time in the parcellated myocardium of healthy Taiwanese participants: A single center study.

BACKGROUND: Quantitative maps from cardiac MRI provide objective information for myocardial tissue. The study aimed to report the T1 and T2∗ relaxation time and its relationship with clinical parameters in healthy Taiwanese participants. METHODS: Ninety-three participants were enrolled between 2014 and 2016 (Males/Females: 43/50; age: 49.7 ± 11.3/49.9 ± 10.3). T1 and T2∗ weighted images were obtained by MOLLI recovery and 3D fully flow compensated gradient echo sequences with a 3T MR scanner, respectively. The T1 map of the myocardium was parcellated into 16 partitions from the American Heart Association. The septal part of basal, mid-cavity, and apical view was selected for the T2∗ map. The difference of quantitative map by sex and age groups were evaluated by Student's TTEST and ANOVA, respectively. The relationship between T1, T2∗ map, and clinical parameters, such as ejection fraction, pulse rate, and blood pressures, were evaluated with partial correlation by controlling BMI and age. RESULTS: Male participants decreased T1 relaxation time in partitions which located in the mid-cavity and apical before 55 years old compared with females (Male/Female: 1143.1.4 ± 72.0-1191.1 ± 37.0/1180.1 ± 54.5-1326.1 ± 113.3 msec, p < 0.01). For female participants, T1 relaxation time was correlated negatively with systolic pressure (p < 0.01) and pulse rate (p < 0.01) before 45 years old. Besides, T1 and T2∗ relaxation time were positively and negatively correlated with ejection fraction and pulse rate after 45 years old in male participants, respectively. Decreased T2∗ relaxation time could be noticed in participants after 45 years old compared with youngers (26.0 ± 6.5/21.9 ± 8.0 msec; 25.2 ± 5.0/21.6 ± 7.2 msec, p < 0.05). CONCLUSION: Reference T1 and T2∗ relaxation time from cardiac MRI in healthy Taiwanese participants were provided with sex and age-dependent manners. The relationship between clinical parameters and T1 or T2∗ relaxation time was also established and could be further investigated for its potential application in healthy/sub-healthy participants.

A Method for the Prediction of Clinical Outcome Using Diffusion Magnetic Resonance Imaging: Application on Parkinson's Disease.

Robust early prediction of clinical outcomes in Parkinson's disease (PD) is paramount for implementing appropriate management interventions. We propose a method that uses the baseline MRI, measuring diffusion parameters from multiple parcellated brain regions, to predict the 2-year clinical outcome in Parkinson's disease. Diffusion tensor imaging was obtained from 82 patients (males/females = 45/37, mean age: 60.9 ± 7.3 years, baseline and after 23.7 ± 0.7 months) using a 3T MR scanner, which was normalized and parcellated according to the Automated Anatomical Labelling template. All patients were diagnosed with probable Parkinson's disease by the National Institute of Neurological Disorders and Stroke criteria. Clinical outcome was graded using disease severity (Unified Parkinson's Disease Rating Scale and Modified Hoehn and Yahr staging), drug administration (levodopa equivalent daily dose), and quality of life (39-item PD Questionnaire). Selection and regularization of diffusion parameters, the mean diffusivity and fractional anisotropy, were performed using least absolute shrinkage and selection operator (LASSO) between baseline diffusion index and clinical outcome over 2 years. Identified features were entered into a stepwise multivariate regression model, followed by a leave-one-out/5-fold cross validation and additional blind validation using an independent dataset. The predicted Unified Parkinson's Disease Rating Scale for each individual was consistent with the observed values at blind validation (adjusted R2 0.76) by using 13 features, such as mean diffusivity in lingual, nodule lobule of cerebellum vermis and fractional anisotropy in rolandic operculum, and quadrangular lobule of cerebellum. We conclude that baseline diffusion MRI is potentially capable of predicting 2-year clinical outcomes in patients with Parkinson's disease on an individual basis.

A longitudinal fixel-based analysis of white matter alterations in patients with Parkinson's disease.

INTRODUCTION: Disruption to white matter pathways is an important contributor to the pathogenesis of Parkinson's disease. Fixel-based analysis has recently emerged as a useful fiber-specific tool for examining white matter structure. In this longitudinal study, we used Fixel-based analysis to investigate white matter changes occurring over time in patients with Parkinson's disease. METHODS: Fifty patients with idiopathic Parkinson's disease (27 men and 23 women; mean age: 61.8 ± 6.1 years), were enrolled. Diffusion-weighted imaging and clinical examinations were performed at three different time points (baseline, first follow-up [after a mean of 24±2 months], and second follow-up [after a mean of 40 ± 3 months]). Additional 76 healthy control subjects (38 men and 38 women; mean age: 62.3 ± 5.5 years) were examined at baseline. The following fixel-based metrics were obtained: fiber density (FD), fiber bundle cross-section (FC), and a combined measure of both (FDC). Paired comparisons of metrics between three different time points were performed in patients. Linear regression was implemented between longitudinal changes of fixel-based metrics and the corresponding modifications in clinical parameters. A family-wise error corrected p < 0.05 was considered statistically significant. RESULTS AND DISCUSSIONS: Early degeneration in the splenium of corpus callosum was identified as a typical alteration of Parkinson's disease over time. At follow-up, we observed significant FDC reductions compared with baseline in white matter, noticeably in corpus callosum; tapetum; cingulum, posterior thalamic radiation, corona radiata, and sagittal stratum. We also identified significant FC decreases that reflected damage to white matter structures involved in Parkinson's disease -related pathways. Fixel-based metrics were found to relate with a deterioration of 39-item Parkinson's Disease Questionnaire, Unified Parkinson's Disease Rating Scale and activity of daily living. A Parkinson's disease -facilitated aging effect was observed in terms of white matter disruption. CONCLUSION: This study provides a thorough fixel-based profile of longitudinal white matter alterations occurring in patients with Parkinson's disease and new evidence of FC as an important role in white matter degeneration in this setting.

Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging.

Progressive supranuclear palsy (PSP) is characterized by a rapid and progressive clinical course. A timely and objective image-based evaluation of disease severity before standard clinical assessments might increase the diagnostic confidence of the neurologist. We sought to investigate whether features from diffusion tensor imaging of the entire brain with a machine learning algorithm, rather than a few pathogenically involved regions, may predict the clinical severity of PSP. Fifty-three patients who met the diagnostic criteria for probable PSP were subjected to diffusion tensor imaging. Of them, 15 underwent follow-up imaging. Clinical severity was assessed by the neurological examinations. Mean diffusivity and fractional anisotropy maps were spatially co-registered, normalized, and parcellated into 246 brain regions from the human Brainnetome atlas. The predictors of clinical severity from a stepwise linear regression model were determined after feature reduction by the least absolute shrinkage and selection operator. Performance estimates were obtained using bootstrapping, cross-validation, and through application of the model in the patients who underwent repeated imaging. The algorithm confidently predicts the clinical severity of PSP at the individual level (adjusted R2: 0.739 and 0.892, p < 0.001). The machine learning algorithm for selection of diffusion tensor imaging-based features is accurate in predicting motor subscale of unified Parkinson's disease rating scale and postural instability and gait disturbance of PSP.

Acupuncture Effect and Mechanism for Treating Pain in Patients With Parkinson's Disease.

Non-motor symptoms of Parkinson's disease (PD) have been receiving increasing attention. Approximately half of patients with PD have experience PD-related pain. We investigated the effect and mechanism of acupuncture in patients with PD who have pain. PD patients with pain were divided into acupuncture group and control group. Nine patients completed acupuncture treatment; seven patients who received only an analgesic agent underwent resting-state functional magnetic resonance imaging (rs-fMRI) twice. fMRI was performed to evaluate the functional connectivity of the brain regions. After treatment, a decrease in total scores on the King's Parkinson's Disease Pain Scale (KPPS) and Unified Parkinson's Disease Rating Scale was observed in the acupuncture group (-46.2 and -21.6%, respectively). In the acupuncture group, increased connectivity was observed in four connections, one in the left hemisphere between the middle temporal gyrus (MTG) and precentral gyrus, and three in the right hemisphere between the postcentral gyrus and precentral gyrus, supramarginal gyrus and precentral gyrus, and MTG and insular cortex. A significant correlation was noted between the changes in functional connectivity and KPPS. The involved connection was between the left middle frontal gyrus and the right precentral gyrus (R = -0.698, P = 0.037). Acupuncture could relieve pain in PD patients by modulating brain regions related to both sensory-discriminative and emotional aspects. The present study might increase the confidence of users that acupuncture is an effective and safe analgesic tool that can relieve PD-related pain.

The effect of spatial resolution on the reproducibility of diffusion imaging when controlled signal to noise ratio.

BACKGROUND: The purpose of the study is to evaluate the reproducibility and repeatability of the compartmental diffusion measurement. METHODS: Two identical whipping cream phantoms and two healthy Sprague-Dawley rats were scanned on a 7T MR scanner, each repeated for three times. Diffusion weighted images were acquired along 30 non-collinear gradient directions, each with four b-values of 750, 1500, 2250 and 3000 s/mm2. Slice thickness and field of view were used to create different combinations of voxel sizes, varied between 1.210 and 2.366 mm3 in phantom and 0.200-0.303 mm3 in rat brains. Multiple averages were used to achieve a controlled signal to noise ratio. RESULTS: Diffusion imaging showed good stability throughout the range of voxel sizes acquired from either the cream phantom or the rat, when the signal to noise ratio is controlled. The reproducibility analysis showed the within-subject coefficient of variation varied between 0.88% and 6.99% for phantom and 0.69%-6.19% for rat. Diffusion imaging is stable among different voxel sizes in 3 aspects: A. from both compartments in phantom and in the rat; B. in measurement of diffusivity and kurtosis and C. along axial, radial and averaged in all directions. CONCLUSION: Diffusion imaging in a heterogeneous but isotropic phantom and in vivo is consistent within the range of spatial resolution in preclinical use and when the signal to noise ratio is fixed. The result is reproducible for repeated measurements.

Potential predictors for health-related quality of life in stroke patients undergoing inpatient rehabilitation.

BACKGROUND: Improving HRQOL is the desired outcome for patients with stroke undergoing inpatient rehabilitation services. This study aimed to comprehensively identify the potential health-related quality of life (HRQOL) predictors in patients with stroke undergoing inpatient rehabilitation within the first year after stroke; thus far, such an investigation has not been conducted. METHODS: We enrolled 119 patients (88 males, 31 females) with stroke, and examined 12 potential predictors: age, sex, stroke type, stroke side, duration after onset, cognition (Mini-Mental State Examination; MMSE), depression (Beck Depression Inventory-II), stroke severity (National Institutes of Health Stroke Scale; NIHSS), upper- and lower-extremity motor function scores of the Fugl-Meyer Assessment (FMA) scale, balance (Berg Balance Scale; BBS), and functional status (Functional Independence Measure). HRQOL was measured using Stroke Impact Scale (SIS) 3.0. RESULTS: NIHSS score predicted the strength domain and total SIS score (41.5% and 41.7% of the variances, respectively). BBS score was a major predictor of mobility and participation/role domains (48.6% and 10% of the variances, respectively). MMSE score predicted the memory and communication domains (22.5% and 36.3% of the variances, respectively). Upper extremity score of the FMA scale predicted the daily living/instrumental activities of daily life and hand function domains (40.3% and 20.6% of the variances, respectively). Stroke side predicted the emotion domain (11.6% of the variance). CONCLUSIONS: NIHSS, MMSE, BBS, FMA, and stroke side predicted most HRQOL domains. These findings suggest that different factors predicted various HRQOL domains in patients with stroke.

Isolation of mesenchymal stem cells from shoulder rotator cuff: a potential source for muscle and tendon repair.

The self-healing potential of each tissue belongs to endogenous stem cells residing in the tissue; however, there are currently no reports mentioned for the isolation of human rotator cuff-derived mesenchymal stem cells (RC-MSCs) since. To isolate RC-MSCs, minced rotator cuff samples were first digested with enzymes and the single cell suspensions were seeded in plastic culture dishes. Twenty-four hours later, nonadherent cells were removed and the adherent cells were further cultured. The RC-MSCs had fibroblast-like morphology and were positive for the putative surface markers of MSCs, such as CD44, CD73, CD90, CD105, and CD166, and negative for the putative markers of hematopoietic cells, such as CD34, CD45, and CD133. Similar to BM-MSCs, RC-MSCs were demonstrated to have the potential to undergo osteogenic, adipogenic, and chondrogenic differentiation. Upon induction in the defined media, RC-MSCs also expressed lineage-specific genes, such as Runx 2 and osteocalcin in osteogenic induction, PPAR-γ and LPL in adipogenic differentiation, and aggrecan and Col2a1 in chondrogenic differentiation. The multipotent feature of RC-MSCs in the myogenic injury model was further strengthened by the increase in myogenic potential both in vitro and in vivo when compared with BM-MSCs. These results demonstrate the successful isolation of MSCs from human rotator cuffs and encourage the application of RC-MSCs in myogenic regeneration.

Efficient expansion of mesenchymal stem cells from mouse bone marrow under hypoxic conditions.

To realize the therapeutic potential of mesenchymal stem cells (MSCs), a large number of high-quality MSCs isolated from different species, such as mouse, were acquired for preclinical animal studies. Surprisingly, isolation and purification of mouse MSCs (mMSCs) is arduous because of the low frequency of MSCs and contamination of haematopoietic cells in culture. We have developed a method based on low density and hypoxic culture to isolate and expand mMSCs from different strains, including BALB/c, C57BL/6J, FVB/N and DBA/2. The cells from all of the strains expanded more rapidly when plated at low density in hypoxic culture compared with normoxic culture. These cells expressed CD44, CD105, CD29 and Sca-1 markers but not CD11b, CD34, CD45 and CD31 markers. Moreover, they were able to differentiate along osteoblastic, adipocytic and chondrocytic lineages. In conclusion, we have developed a robust method for isolation and expansion of mMSCs by combining low-density culture with hypoxic culture.

Benefits of hypoxic culture on bone marrow multipotent stromal cells.

Cultivation of cells is usually performed under atmospheric oxygen tension; however, such a condition does not replicate the hypoxic conditions of normal physiological or pathological status in the body. Recently, the effects of hypoxia on bone marrow multipotent stromal cells (MSCs) have been investigated. In a long-term culture, hypoxia can inhibit senescence, increase the proliferation rate and enhance differentiation potential along the different mesenchymal lineages. Hypoxia also modulates the paracrine effects of MSCs, causing upregulation of various secretable factors, including the vascular endothelial growth factor and interleukin-6, and thereby promoting wound healing and diabetic fracture healing. Finally, hypoxia plays an important role in mobilization and homing of MSCs, primarily by its ability to induce stromal cell-derived factor-1 expression along with its receptor, CXCR4. After transplantation, an ischemic environment, that is the combination of hypoxia and lack of nutrition, can lead to apoptosis or cell death, which can be overcome by the hypoxic preconditioning of MSCs and overexpression of prosurvival genes like Akt, HO-1 and Hsp70. This review emphasizes that hypoxia is an important factor in all major aspects of stem cell biology, and the mechanism involved in the hypoxic inducible factor-1signaling pathway behind these responses is also discussed.