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To explore the application effect of nursing intervention guided by King’s interactive standard theory in patients with lumbar disc herniation undergoing conservative treatment. Total of 136 patients with lumbar disc herniation admitted to our hospital from June 2019 to May 2020 were randomly divided into interactive nursing group and routine nursing group. The interactive nursing group was given nursing intervention guided by King’s interactive standard theory and the routine nursing group was given routine nursing guidance. The disease cognition rate, incidence of adverse behavior, lumbar function and pain degree were compared between the two groups. The disease cognition rates of main symptoms, disease type, familiarity with conservative treatment, necessity of hard bed rest, feasible surgical treatment in severe cases and causes of disease in interactive nursing group were higher than those in routine nursing group (p

Background Elective total knee replacement (TKR) is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify those patients who are at particularly high risk who could then be targeted with preventative interventions. We aimed to develop a simple model to help inform treatment choices. Methods We trained and externally validated adverse event prediction models for patients with TKR using electronic health records (EHR) and claims data from the US (OPTUM, CCAE, MDCR, and MDCD) and general practice data in the UK (IQVIA Medical Research Database ([IMRD], incorporating data from The Health Improvement Network [THIN], a Cegedim database). The target population consisted of patients undergoing a primary TKR, aged ≥40 years and registered in any of the contributing data sources for ≥1 year before surgery. LASSO logistic regression models were developed for four adverse outcomes: post-operative (90-day) mortality, venous thromboembolism (VTE), readmission, and long-term (5-year) revision surgery. A second model was developed with a reduced feature set to increase interpretability and usability. Findings A total of 508,082 patients were included, with sample size per data source ranging from 1,853 to 158,549 patients. Overall, 90-day mortality, VTE, and readmission prevalence occurred in a range of 0.20%-0.32%, 1.7%-3.0% and 2.2%-4.8%, respectively. Five-year revision surgery was observed in 1.5%-3.1% of patients. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and yielded an AUROC of 0.70 when externally validated on THIN. We then developed a 12 variable model which achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. The discriminative performances of the models predicting 90-day VTE, readmission, and 5-year revision were consistently poor across the datasets (AUROC Interpretation We developed and externally validated a simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR. Our model had a greater discriminative ability than the Charlson Comorbidity Index in predicting 90-day mortality. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and for appropriate precautions to be taken for those at high risk. The other outcomes examined had low performance. Funding This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. The sponsor of the study did not have any involvement in the writing of the manuscript or the decision to submit it for publication. The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). TDS is funded by the Department of Health of the Generalitat de Catalunya under the Strategic Plan for Research and Innovation in Health (PERIS; SLT002/16/00308). The views expressed in this publication are those of the authors and not those of the NHS, the National Institute for Health Research or the Department of Health. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Key Points Question Is it possible to predict adverse events following total knee replacement? Findings Mortality was the only adverse event studied that we were able to predict with adequate performance. We produced a 12 variable prediction model for 90-day post-operative mortality that achieved an AUROC of 0.77 on internal test validation (Optum) and 0.71 when externally validated in THIN. The model also showed adequate calibration. Meaning Patients can now be presented with an accurate risk assessment for short term mortality such that they are well-informed before the decision for surgery is taken. Importance Total Knee Replacement is generally a safe, effective procedure that is performed on thousands of patients each year. However, a small number of those patients will experience adverse events. Due to the surgery’s elective nature, a well calibrated, high performing risk model could pre-emptively inform the patient and clinician decision making process and help to guide preventative treatment.

Background Dysphagia is a common symptom and may be a cause of death in patients with spinocerebellar ataxias (SCAs). However, little is known about at which disease stage dysphagia becomes clinically relevant. Therefore, our study aims to investigate the prevalence of dysphagia in different disease stages of SCA 1, 2, 3 and 6. Methods We studied 237 genetically confirmed patients with SCA 1, 2, 3, 6 from the Clinical Research Consortium for SCAs and investigated the prevalence of self-reported dysphagia and the association between dysphagia and other clinical characteristics. We further stratified ataxia severity and studied the prevalence of dysphagia at each disease stage. Results Dysphagia was present in 59.9% of SCA patients. Patients with dysphagia had a longer disease duration and more severe ataxia than patients without dysphagia (patients with dysphagia vs. without dysphagia, disease duration (years): 14.51 ± 8.91 vs. 11.22 ± 7.82, p = .001, scale for the assessment and rating of ataxia [SARA]: 17.90 ± 7.74 vs. 13.04 ± 7.51, p = .000). Dysphagia was most common in SCA1, followed by SCA3, SCA 6, and SCA 2. Dysphagia in SCA1 and 3 was associated robustly with ataxia severity, whereas this association was less obvious in SCA2 and 6, demonstrating genotype-specific clinical variation. Conclusion Dysphagia is a common clinical symptom in SCAs, especially in the severe disease stage. Understanding dysphagia in SCA patients can improve the care of these patients and advance knowledge on the roles of the cerebellum and brainstem control in swallowing.

To assess the efficacy and safety of drug-eluting beads transarterial chemoembolization (DEB-TACE) in liver cancer patients with different times of previous conventional transarterial chemoembolization (cTACE) treatments. 367 liver cancer patients about to receive DEB-TACE treatment were enrolled in this prospective cohort study. All patients were divided into no previous cTACE group (NPC group), 1–2 times previous cTACE group (PC group) and triple or above previous cTACE group (TPC group) according to the times of previous cTACE treatments. There was no difference in complete response (CR) (P = 0.671) and objective response rate (ORR) (P = 0.062) among three groups. Additionally, no difference in overall survival (OS) among groups (P = 0.899) was found. As to liver function, most liver function indexes were deteriorative at 1 week after DEB-TACE operation, but returned to baseline at 1–3 months after DEB-TACE operation in all three groups, while percentage of abnormal total bile acid (TBA) patients was higher in TPC group than NPC and PC groups at 1–3 month post-DEB-TACE (P = 0.018). As for safety profiles, the incidence of pain during DEB-TACE operation was lower in TPC group compared to NPC and PC groups (P = 0.005), while no difference of other adverse events was found during and 1 month post-DEB-TACE treatment among three groups. DEB-TACE treatment was equally efficient and tolerated in liver cancer patients with different times of previous cTACE treatments.

Background For a variety of sporadic neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis, it is well-established that ethnicity does affect the disease phenotypes. However, how ethnicity contributes to the clinical symptoms and disease progressions in monogenetic disorders, such as spinocerebellar ataxia type 3 (SCA3), remains less studied. Methods We used multivariable linear and logistical regression models in 257 molecularly-confirmed SCA3 patients (66 Caucasians, 43 African Americans, and 148 Asians [composed of 131 Chinese and 17 Asian Americans]) to explore the influence of ethnicity on age at onset (AAO), ataxia severity, and non-ataxia symptoms (i.e. depression, tremor, and dystonia). Results We found that Asians had significantly later AAO, compared to Caucasians (β = 4.75, p = 0.000) and to African Americans (β = 6.64, p = 0.000) after adjusting for the pathological CAG repeat numbers in ATXN3. African Americans exhibited the most severe ataxia as compared to Caucasians (β = 3.81, p = 0.004) and Asians (β = 4.39, p = 0.001) after taking into consideration of the pathological CAG repeat numbers in ATXN3 and disease duration. Caucasians had a higher prevalence of depression than African Americans (β = 1.23, p = 0.040). Ethnicity had no influence on tremor or dystonia. Conclusions Ethnicity plays an important role in clinical presentations of SCA3 patients, which could merit further clinical studies and public health consideration. These results highlight the role of ethnicity in monogenetic, neurodegenerative disorders.

Introduction Pulmonary arterial hypertension (PAH) is characterised by vascular remodelling of pulmonary arterioles. Disrupted iron homeostasis linked to elevated hepcidin levels has been observed in PAH patients, and disruption of the hepcidin/ferroportin axis at the level of the pulmonary vasculature cells has been shown to contribute to proliferation of pulmonary artery smooth muscle cells. A role for Pulmonary artery endothelial cells (PAECs) linked to hepcidin has not been investigated. Objectives In this study we explored the influences of hepcidin-25 on PAEC gene expression targeting BMPRII, known to be dysfunctional in PAH. Methods Cells were challenged with Hepcidin-25 (1 μg/mL) or for comparison IL-6 (10 ng/mL). Transcriptional regulation was analysed by RT-PCR, protein expression by immunocytochemistry. Results Novel findings demonstrate that BMPRII mRNA expression is significantly down regulated in PAECs challenged with hepcidin-25 over a time course from 1 hour to 5 hours; figure 1 illustrates findings at 3 hours. IL-6 challenge was not able to replicate this response over the same time frame. In addition, Western blot analysis of cell lysates (n=2) showed an obvious loss of BMPRII protein expression in Hepcidin-25 challenged cells when compared to control and IL-6 challenged cell lysates. Conclusion This is the first report linking hepcidin-25 activity to potentially dysfunctional BMPRII responses in PAECs. Given the established role of hepcidin as regulator of cellular iron levels, a role for downstream signaling linked to iron accumulation in PAECs may offer a plausible mechanism for these observations and warrants further investigation. These studies may provide novel insights regarding emerging concepts of hepcidin driven proliferative and second messenger responses of relevance to PAH.

Objective The aim of this study is to evaluate the correlation between resting state functional MRI (RS-fMRI) activity and motor and cognitive impairment in spinocerebellar ataxia type 6 (SCA6). Methods Twelve patients with genetically confirmed SCA6 and 14 age matched healthy controls were imaged with RS-fMRI. Whole brain gray matter was automatically parcellated into 1000 regions of interest (ROIs). For each ROI, the first eigenvariate of voxel time courses was extracted. For each patient, Pearson correlation coefficients between each pair of ROI time courses were calculated across the 1000 ROIs. The set of average control correlation coefficients were fed as an undirected weighted adjacency matrix into the Rubinov and Sporns (2010) modularity algorithm. The intranetwork global efficiency of the thresholded adjacency sub-matrix was calculated and correlated with ataxia scores and cognitive performance. Results SCA6 patients showed mild cognitive impairments in executive function and visual-motor processing compared to control subjects. These neuropsychological impairments were correlated with decreased RS functional connectivity (FC) in the attention network. Conclusions Mild cognitive executive functions and visual-motor coordination impairments seen in SCA6 patients correlate with decreased resting-state connectivity in the attention network, suggesting a possible metric for the study of cognitive dysfunction in cerebellar disease. Hum Brain Mapp 38:3001–3010, 2017. © 2017 Wiley Periodicals, Inc.

Automatic and accurate cerebellum parcellation has long been a challenging task due to the relative surface complexity and large anatomical variation of the human cerebellum. An inaccurate segmentation will inevitably bias further studies. In this paper we present an automatic approach for the quality control of cerebellum parcellation based on shape analysis in a hierarchical structure. We assume that the overall shape variation of a segmented structure comes from both population and segmentation variation. In this hierarchical structure, the higher level shape mainly captures the population variation of the human cerebellum, while the lower level shape captures both population and segmentation variation. We use a partial least squares regression to combine the lower level and higher level shape information. By compensating for population variation, we show that the estimated segmentation variation is highly correlated with the accuracy of the cerebellum parcellation results, which not only provides a confidence measurement of the cerebellum parcellation, but also gives some clues about when a segmentation software may fail in real scenarios.

BACKGROUND: Cerebellar degenerative pathology has been identified in tremor patients; however, how the degenerative pathology could contribute to tremor remains unclear. If the cerebellar degenerative pathology can directly drive tremor, one would hypothesize that tremor is likely to occur in the diseases of cerebellar ataxia and follows the disease progression in such disorders. METHODS: To further test this hypothesis, we studied the occurrence of tremor in different disease stages of classical cerebellar degenerative disorders: spinocerebellar ataxias (SCAs). We further separately analyzed postural tremor and rest tremor, two forms of tremor that both involve the cerebellum. We also explored tremor in different subtypes of SCAs. RESULTS: We found that 18.1% of SCA patients have tremor. Interestingly, SCA patients with tremor have worse ataxia than those without tremor. When stratifying patients into mild, moderate, and severe disease stages according to the severity of ataxia, moderate and severe SCA patients more commonly have tremor than those with mild ataxia, the effect most prominently observed in postural tremor of SCA3 and SCA6 patients. Finally, tremor can independently contribute to worse functional status in SCA2 patients, even after adjusting for ataxia severity. CONCLUSIONS: Tremor is more likely to occur in the severe stage of cerebellar degeneration when compared to mild stages. Our results partially support the cerebellar degenerative model of tremor.

To better understand cerebellum-related diseases and functional mapping of the cerebellum, quantitative measurements of cerebellar regions in magnetic resonance (MR) images have been studied in both clinical and neurological studies. Such studies have revealed that different spinocerebellar ataxia (SCA) subtypes have different patterns of cerebellar atrophy and that atrophy of different cerebellar regions is correlated with specific functional losses. Previous methods to automatically parcellate the cerebellum-that is, to identify its sub-regions-have been largely based on multi-atlas segmentation. Recently, deep convolutional neural network (CNN) algorithms have been shown to have high speed and accuracy in cerebral sub-cortical structure segmentation from MR images. In this work, two three-dimensional CNNs were used to parcellate the cerebellum into 28 regions. First, a locating network was used to predict a bounding box around the cerebellum. Second, a parcellating network was used to parcellate the cerebellum using the entire region within the bounding box. A leave-one-out cross validation of fifteen manually delineated images was performed. Compared with a previously reported state-of-the-art algorithm, the proposed algorithm shows superior Dice coefficients. The proposed algorithm was further applied to three MR images of a healthy subject and subjects with SCA6 and SCA8, respectively. A Singularity container of this algorithm is publicly available.

Chronic rejection is the major cause of long-term heart allograft failure, characterized by tissue infiltration by recipient T cells with indirect allospecificity. Phosphoinositol-3-kinase p110δ is a key mediator of T cell receptor signaling, regulating both T cell activation and migration of primed T cells to non-lymphoid antigen-rich tissue. We investigated the effect of genetic or pharmacologic inactivation of PI3K p110δ on the development of chronic allograft rejection in a murine model in which HY-mismatched male hearts were transplanted into female recipients. We show that suppression of p110δ activity significantly attenuates the development of chronic rejection of heart grafts in the absence of any additional immunosuppressive treatment by impairing the localization of antigen-specific T cells to the grafts, while not inducing specific T cell tolerance. p110δ pharmacologic inactivation is effective when initiated after transplantation. Targeting p110δ activity might be a viable strategy for the treatment of heart chronic rejection in humans.

The cerebellar peduncles, comprising the superior cerebellar peduncles (SCPs), the middle cerebellar peduncle (MCP), and the inferior cerebellar peduncles (ICPs), are white matter tracts that connect the cerebellum to other parts of the central nervous system. Methods for automatic segmentation and quantification of the cerebellar peduncles are needed for objectively and efficiently studying their structure and function. Diffusion tensor imaging (DTI) provides key information to support this goal, but it remains challenging because the tensors change dramatically in the decussation of the SCPs (dSCP), the region where the SCPs cross. This paper presents an automatic method for segmenting the cerebellar peduncles, including the dSCP. The method uses volumetric segmentation concepts based on extracted DTI features. The dSCP and noncrossing portions of the peduncles are modeled as separate objects, and are initially classified using a random forest classifier together with the DTI features. To obtain geometrically correct results, a multi-object geometric deformable model is used to refine the random forest classification. The method was evaluated using a leave-one-out cross-validation on five control subjects and four patients with spinocerebellar ataxia type 6 (SCA6). It was then used to evaluate group differences in the peduncles in a population of 32 controls and 11 SCA6 patients. In the SCA6 group, we have observed significant decreases in the volumes of the dSCP and the ICPs and significant increases in the mean diffusivity in the noncrossing SCPs, the MCP, and the ICPs. These results are consistent with a degeneration of the cerebellar peduncles in SCA6 patients.

Ocular motor abnormalities reflect the varied neuropathology of spinocerebellar ataxias (SCAs) and may serve to clinically distinguish the different SCAs. We analyzed the various eye movement abnormalities detected prospectively at the baseline visit during a large multicenter natural history study of SCAs 1, 2, 3, and 6.The data were prospectively collected from 12 centers in the United States in patients with SCAs 1, 2, 3, and 6, as part of the Clinical Research Consortium for Spinocerebellar Ataxias (NIH-CRC-SCA). Patient characteristics, ataxia rating scales, the Unified Huntington Disease Rating Scale functional examination, and clinical staging were used. Eye movement abnormalities including nystagmus, disorders of saccades and pursuit, and ophthalmoparesis were recorded, and factors influencing their occurrence were examined.A total of 301 patients participated in this study, including 52 patients with SCA 1, 64 with SCA 2, 117 with SCA 3, and 68 with SCA 6. Although no specific ocular motor abnormality was pathognomonic to any SCA, significant differences were noted in their occurrence among different disorders. SCA 6 was characterized by frequent occurrence of nystagmus and abnormal pursuit and rarity of slow saccades and ophthalmoparesis and SCA 2 by the frequent occurrence of slow saccades and infrequent nystagmus and dysmetric saccades. SCA 1 and SCA 3 subjects had a more even distribution of eye movement abnormalities.Prospective data from a large cohort of patients with SCAs 1, 2, 3, and 6 provide statistical validation that the SCAs exhibit distinct eye movement abnormalities that are useful in identifying the genotypes. Many of the abnormalities correlate with greater disease severity measures.

Background Dystonia is a common feature in spinocerebellar ataxias (SCAs). Whether the presence of dystonia is associated with different rate of ataxia progression is not known. Objectives To study clinical characteristics and ataxia progression in SCAs with and without dystonia. Methods We studied 334 participants with SCA 1, 2, 3 and 6 from the Clinical Research Consortium for Spinocerebellar Ataxias (CRC-SCA) and compared the clinical characteristics of SCAs with and without dystonia. We repeatedly measured ataxia progression by the Scale for Assessment and Rating of Ataxia every 6 months for 2 years. Regression models were employed to study the association between dystonia and ataxia progression after adjusting for age, sex and pathological CAG repeats. We used logistic regression to analyze the impact of different repeat expansion genes on dystonia in SCAs. Results Dystonia was most commonly observed in SCA3, followed by SCA2, SCA1, and SCA6. Dystonia was associated with longer CAG repeats in SCA3. The CAG repeat number in TBP normal alleles appeared to modify the presence of dystonia in SCA1. The presence of dystonia was associated with higher SARA scores in SCA1, 2, and 3. Although relatively rare in SCA6, the presence of dystonia was associated with slower progression of ataxia. Conclusions The presence of dystonia is associated with greater severity of ataxia in SCA1, 2, and 3, but predictive of a slower progression in SCA6. Complex genetic interactions among repeat expansion genes can lead to diverse clinical symptoms and progression in SCAs.

To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain–computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects’ recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77[Formula: see text]s, when compared with 79.2% accuracy and a decision time of 3.01[Formula: see text]s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects’ cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

BACKGROUND AND PURPOSE Site-specific degeneration patterns of the infratentorial brain in relation to phylogenetic origins may relate to symptoms in patients with spinocerebellar degeneration, but the patterns are still unclear. We investigated macro- and microstructural changes of the infratentorial brain based on phylogenetic origins and their correlation with symptoms in patients with spinocerebellar ataxia type 6. MATERIALS AND METHODS MR images of 9 patients with spinocerebellar ataxia type 6 and 9 age- and sex-matched controls were obtained. We divided the infratentorial brain on the basis of phylogenetic origins and performed an atlas-based analysis. Comparisons of the 2 groups and a correlation analysis assessed with the International Cooperative Ataxia Rating Scale excluding age effects were performed. RESULTS A significant decrease of fractional volume and an increase of mean diffusivity were seen in all subdivisions of the cerebellum and in all the cerebellar peduncles except mean diffusivity in the inferior cerebellar peduncle in patients compared with controls (P < .0001 to

In vitro human cadaveric biomechanical study. To investigate the roles of transverse atlantal ligament (TAL) and longitudinal ligament (LL) of the upper cervical spine (UCS) in maintaining atlantoaxial stability. China. Six intact UCS specimens were harvested and embedded in polymethylmethacrylate. Three-dimensional movements including flexion, extension, right and left lateral bending, and axial rotation, as well as the C1–C2 displacement in flexion (atlantodental interval, ADI), were tested on specimens with the following state sequentially: (1) intact (intact group), (2) TAL transected (TAL group) and (3) TAL and LL disrupted (TAL+LL group) using an electromechanical testing machine. Compared with intact group, the flexion/extension motion range and ADI were significantly higher in TAL group when the loading was 10 N or >100 N. However, no significant differences were detected between the two groups within a range of physiological loading (10–100 N). Similarly, significant differences in right–left lateral bending and axial rotation between TAL and intact groups occurred only when the loading was 150 N. However, when both of the TAL and LL were resected, the atlantoaxial joint showed obvious instability compared with TAL or intact group, which were further demonstrated in the analyses of the three-dimensional movements (significant differences at any loading). Within physiological loading range, the LLs have sufficient capacities to maintain the stability of atlantoaxial joint even if there are TAL injuries in atlas fractures.

ObjectiveTo systematically review evidence regarding ataxia treatment.MethodsA comprehensive systematic review was performed according to American Academy of Neurology methodology.ConclusionsFor patients with episodic ataxia type 2, 4-aminopyridine 15 mg/d probably reduces ataxia attack frequency over 3 months (1 Class I study). For patients with ataxia of mixed etiology, riluzole probably improves ataxia signs at 8 weeks (1 Class I study). For patients with Friedreich ataxia or spinocerebellar ataxia (SCA), riluzole probably improves ataxia signs at 12 months (1 Class I study). For patients with SCA type 3, valproic acid 1,200 mg/d possibly improves ataxia at 12 weeks. For patients with spinocerebellar degeneration, thyrotropin-releasing hormone possibly improves some ataxia signs over 10 to 14 days (1 Class II study). For patients with SCA type 3 who are ambulatory, lithium probably does not improve signs of ataxia over 48 weeks (1 Class I study). For patients with Friedreich ataxia, deferiprone possibly worsens ataxia signs over 6 months (1 Class II study). Data are insufficient to support or refute the use of numerous agents. For nonpharmacologic options, in patients with degenerative ataxias, 4-week inpatient rehabilitation probably improves ataxia and function (1 Class I study); transcranial magnetic stimulation possibly improves cerebellar motor signs at 21 days (1 Class II study). For patients with multiple sclerosis–associated ataxia, the addition of pressure splints possibly has no additional benefit compared with neuromuscular rehabilitation alone (1 Class II study). Data are insufficient to support or refute use of stochastic whole-body vibration therapy (1 Class III study).

Purpose To determine cognitive impairment patterns in patients with spinocerebellar ataxia type 6 (SCA6) compared to patients with idiopathic late-onset cerebellar ataxia (ILOCA). Methods Neurocognitive testing was conducted on 21 SCA6, nine ILOCA, and 27 controls subjects. Intergroup differences were assessed using the Wilcoxon signed-ranked test or Student's t-test. Principal component analysis (PCA) was performed on nine cognitive variables, and Hotelling's T-squared test assessed group-specific differences. Pearson's correlations assessed changes in cognitive performance and disease progression. Intra-group differences among SCA6 were examined in a post-hoc analysis. Results SCA6 and ILOCA patients showed impairment in visuo-spatial executive function, phonemic verbal fluency, and semantic-verb word generation. ILOCA showed impairment in mental flexibility/response inhibition, verbal learning, semantic-noun verbal fluency, and forward numerical working memory. Within the first three principal components, SCA6 and ILOCA differed from controls and from each other. Verbal working and immediate visuo-spatial memory correlated with disease duration for SCA6. For ILOCA, Mini-Mental Status Exam and RCF copy correlated with disease duration. Conclusion Differing patterns of cognitive dysfunction were seen in SCA6 and ILOCA. PCA suggested that distinct SCA6 subgroups may exist, SCA61 with significant ILOCA overlap in several cognitive deficits, and SCA62 showing deficits in visuo-spatial performance only.

Background and Purpose— Strokes can be distinguished from benign peripheral causes of acute vestibular syndrome using bedside oculomotor tests (head impulse test, nystagmus, test-of-skew). Using head impulse test, nystagmus, test-of-skew is more sensitive and less costly than early magnetic resonance imaging for stroke diagnosis in acute vestibular syndrome but requires expertise not routinely available in emergency departments. We sought to begin standardizing the head impulse test, nystagmus, test-of-skew diagnostic approach for eventual emergency department use through the novel application of a portable video-oculography device measuring vestibular physiology in real time. This approach is conceptually similar to ECG to diagnose acute cardiac ischemia. Methods— Proof-of-concept study (August 2011 to June 2012). We recruited adult emergency department patients with acute vestibular syndrome defined as new, persistent vertigo/dizziness, nystagmus, and (1) nausea/vomiting, (2) head motion intolerance, or (3) new gait unsteadiness. We recorded eye movements, including quantitative horizontal head impulse testing of vestibulo-ocular-reflex function. Two masked vestibular experts rated vestibular findings, which were compared with final radiographic gold-standard diagnoses. Masked neuroimaging raters determined stroke or no stroke using magnetic resonance imaging of the brain with diffusion-weighted imaging obtained 48 hours to 7 days after symptom onset. Results— We enrolled 12 consecutive patients who underwent confirmatory magnetic resonance imaging. Mean age was 61 years (range 30–73), and 10 were men. Expert-rated video-oculography–based head impulse test, nystagmus, test-of-skew examination was 100% accurate (6 strokes, 6 peripheral vestibular). Conclusions— Device-based physiological diagnosis of vertebrobasilar stroke in acute vestibular syndrome should soon be possible. If confirmed in a larger sample, this bedside eye ECG approach could eventually help fulfill a critical need for timely, accurate, efficient diagnosis in emergency department patients with vertigo or dizziness who are at high risk for stroke.

Cerebellar dysfunction can lead to a wide range of movement disorders. Studying the cerebellar atrophy pattern associated with different cerebellar disease types can potentially help in diagnosis, prognosis, and treatment planning. In this paper, we present a landmark based shape analysis pipeline to classify healthy control and different ataxia types and to visualize the characteristic cerebellar atrophy patterns associated with different types. A highly informative feature representation of the cerebellar structure is constructed by extracting dense homologous landmarks on the boundary surfaces of cerebellar sub-structures. A diagnosis group classifier based on this representation is built using partial least square dimension reduction and regularized linear discriminant analysis. The characteristic atrophy pattern for an ataxia type is visualized by sampling along the discriminant direction between healthy controls and the ataxia type. Experimental results show that the proposed method can successfully classify healthy controls and different ataxia types. The visualized cerebellar atrophy patterns were consistent with the regional volume decreases observed in previous studies, but the proposed method provides intuitive and detailed understanding about changes of overall size and shape of the cerebellum, as well as that of individual lobules.

The feasibility of using indirect CT lymphography (CT-LG) to guide sentinel lymph node (SLN) biopsy in a rabbit model of tongue VX2 carcinoma with cervical lymph node metastasis was studied. Tongue VX2 carcinoma with cervical lymph node metastasis was induced in 19 rabbits by injecting VX2 carcinoma suspension into the tongue submucosa. SLN biopsy was performed under the guidance of indirect CT-LG. SLN identification was performed by indirect CT-LG combined with blue dye injection in 2 rabbits. Tongue SLNs were identified preoperatively by indirect CT-LG and blue-stained SLNs were visualized intraoperatively. Only one SLN was enhanced in each side of the neck, lateral to the larynx-tracheal region. CT attenuation values of the enhanced SLNs were 782.4+/-46.6, 443.1+/-68.5, 180.3+/-20.6 and 80.5+/-10.7 HU at 1, 5, 15 and 20 min after contrast injection. Overall, ipsilateral SLN identification rate was 97.4% and contralateral SLN identification rate was 100%. Ipsilateral SLN metastasis was verified in all rabbits (100%), bilateral SLN metastasis occurred in 8 rabbits (42%), and micrometastasis was found in 3 rabbits (16%). Indirect CT-LG may be useful for guiding SLN biopsies in tongue cancer. Combining indirect CT-LG with blue dye injection may improve preoperative and intraoperative SLN identification.

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method-supervised classification-was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers-linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)-were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Background Depression is a common comorbidity in spinocerebellar ataxias (SCAs) but its association with ataxia progression is not well understood. Objectives To study the prevalence and influence of depressive symptoms in SCAs. Methods We studied 300 participants with SCA 1, 2, 3 and 6 from the Clinical Research Consortium for Spinocerebellar Ataxias (CRC-SCA) and repeatedly measured depressive symptoms by the 9-item Patient Health Questionnaire (PHQ-9) along with other clinical features including ataxia, functional status, and quality of life every 6 months for 2 years. We employed regression models to study the effects of depressive symptoms on clinical progression indexed by Scale for Assessment and Rating of Ataxia (SARA), Unified Huntington's Disease Rating Scale Part IV (UHDRS-IV) and EQ5D after adjusting for age, sex and pathological CAG repeats. Results Comorbid depression is common in SCAs (26%). Although the baseline prevalence of depression was similar among different SCA types, suicidal ideation was more frequently reported in SCA3 (65%). Depressive symptoms were associated with SARA scores but did not significantly progress over time within 2 years or deteriorate by increased numbers of pathological CAG repeats. The effects of depression on ataxia progression varied across different SCA types. Nevertheless, depression had consistently negative and significant impact on functional status and quality of life in all SCAs, even after accounting for ataxia progression. Conclusions Depressive symptoms are not simply the consequence of motor disability in SCAs. Comorbid depression per se contributes to different health outcomes and deserves more attention when caring patients with SCAs.

We recorded divergence eye movements after short (4 s) and long (36 s) periods of sustained symmetrical convergence (30 degrees) in nine normal human subjects using the search coil technique. Following the long period of convergence, alignment after the initial 1,250 ms of divergence was more converged than after the short period of convergence, showing short-term "phoria adaptation". The first 1,000 ms of divergence, however, could be slower, faster or relatively unchanged, depending upon the subject. A change in the timing and/or amplitude of associated saccades (which accelerate ongoing vergence) between the long and short stimuli accounted for much of the difference in the rate of divergence. The differences in saccade pattern during early divergence following the long and short periods of convergence may reflect changes in attentional focus (to near or to far).

Two hereditary ataxia syndromes show distinct profiles of region-specific atrophy and ocular motor deficits. Selective pontine atrophy is associated with slowed saccades in ataxin-2 mutations, and selective floccular atrophy is associated with impaired pursuit and gaze-holding abnormalities in Ca(V)2.1 mutations. Although the flocculus seems to be spared relative to the pons in ataxin-2 mutations, and pursuit and gaze-holding appear to be relatively normal, these can be difficult to assess at the bedside, as corrective saccades are also slow and hard to discern. Here, we show the presence of significant floccular atrophy compared with controls in both ataxin-2 and Ca(V)2.1 mutations, which raises the possibility that abnormalities of smooth pursuit or gaze-holding are present in both conditions.

Since stereoscopic images provide observers with both realistic and discomfort viewing experience, it is necessary to investigate the determinants of visual discomfort. By considering that foreground object draws most attention when human observing stereoscopic images. This paper proposes a new foreground object based visual comfort assessment (VCA) metric. In the first place, a suitable segmentation method is applied to disparity map and then the foreground object is ascertained as the one having the biggest average disparity. In the second place, three visual features being average disparity, average width and spatial complexity of foreground object are computed from the perspective of visual attention. Nevertheless, object’s width and complexity do not consistently influence the perception of visual comfort in comparison with disparity. In accordance with this psychological phenomenon, we divide the whole images into four categories on the basis of different disparity and width, and exert four different models to more precisely predict its visual comfort in the third place. Experimental results show that the proposed VCA metric outperformance other existing metrics and can achieve a high consistency between objective and subjective visual comfort scores. The Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are over 0.84 and 0.82, respectively.

Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease on brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived features to first segment and then parcellate the thalamus. We incorporate fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features. Combining these multiple information sources allows us to identify discriminating dimensions and thus parcellate the thalamic nuclei. A hierarchical random forest framework with a multidimensional feature per voxel, first distinguishes thalamus from background, and then separates each group of thalamic nuclei. Using a leave one out cross-validation on 12 subjects we have a mean Dice score of 0.805 and 0.799 for the left and right thalami, respectively. We also report overlap for the thalamic nuclear groups.

Cerebellar ataxia is a progressive neuro-degenerative disease that has multiple genetic versions, each with a characteristic pattern of anatomical degeneration that yields distinctive motor and cognitive problems. Studying this pattern of degeneration can help with the diagnosis of disease subtypes, evaluation of disease stage, and treatment planning. In this work, we propose a learning framework using MR image data for discriminating a set of cerebellar ataxia types and predicting a disease related functional score. We address the difficulty in analyzing high-dimensional image data with limited training subjects by: 1) training weak classifiers/regressors on a set of image subdomains separately, and combining the weak classifier/regressor outputs to make the decision; 2) perturbing the image subdomain to increase the training samples; 3) using a deep learning technique called the stacked auto-encoder to develop highly representative feature vectors of the input data. Experiments show that our approach can reliably classify between one of four categories (healthy control and three types of ataxia), and predict the functional staging score for ataxia.

The thalamus sub-cortical gray matter structure consists of contiguous nuclei, each individually responsible for communication between various cerebral cortex and midbrain regions. These nuclei are differentially affected in neurodegenerative diseases such as multiple sclerosis and Alzheimer's. However thalamic parcellation of the nuclei, manual or automatic, is difficult given the limited contrast in any particular magnetic resonance (MR) modality. Several groups have had qualitative success differentiating nuclei based on spatial location and fiber orientation information in diffusion tensor imaging (DTI). In this paper, we extend these principles by combining these discriminating dimensions with structural MR and derived information, and by building random forest learners on the resultant multi-modal features. In training, we form a multi-dimensional feature per voxel, which we associate with a nucleus classification from a manual rater. Learners are trained to differentiate thalamus from background and thalamic nuclei from other nuclei. These learners inform the external forces of a multiple object level set model. Our cross-validated quantitative results on a set of twenty subjects show the efficacy and reproducibility of our results.

The superior cerebellar peduncles (SCPs) are white matter tracts that serve as the major efferent pathways from the cerebellum to the thalamus. With diffusion tensor images (DTI), tractography algorithms or volumetric segmentation methods have been able to reconstruct part of the SCPs. However, when the fibers cross, the primary eigenvector (PEV) no longer represents the primary diffusion direction. Therefore, at the crossing of the left and right SCP, known as the decussation of the SCPs (dSCP), fiber tracts propagate incorrectly. To our knowledge, previous methods have not been able to segment the SCPs correctly. In this work, we explore the diffusion properties and seek to volumetrically segment the complete SCPs. The non-crossing SCPs and dSCP are modeled as different objects. A multi-object geometric deformable model is employed to define the boundaries of each piece of the SCPs, with the forces derived from diffusion properties as well as the PEV. We tested our method on a software phantom and real subjects. Results indicate that our method is able to the resolve the crossing and segment the complete SCPs with repeatability.

The thalamus is a sub-cortical gray matter structure that relays signals between the cerebral cortex and midbrain. It can be parcellated into the thalamic nuclei which project to different cortical regions. The ability to automatically parcellate the thalamic nuclei could lead to enhanced diagnosis or prognosis in patients with some brain disease. Previous works have used diffusion tensor images (DTI) to parcellate the thalamus, using either tensor similarity or cortical connectivity as information driving the parcellation. In this paper, we propose a method that uses the diffusion tensors in a different way than previous works to guide a multiple object geometric deformable model (MGDM) for parcellation. The primary eigenvector (PEV) is used to indicate the homogeneity of fiber orientations. To remove the ambiguity due to the fact that the PEV is an orientation, we map the PEV into a 5D space known as the Knutsson space. An edge map is then generated from the 5D vector to show divisions between regions of aligned PEV’s. The generalized gradient vector flow (GGVF) calculated from the edge map drives the evolution of the boundary of each nucleus. Region based force, balloon force, and curvature force are also employed to refine the boundaries. Experiments have been carried out on five real subjects. Quantitative measures show that the automated parcellation agrees with the manual delineation of an expert under a published protocol.

The cerebellum is instrumental in coordinating many vital functions ranging from speech and balance to eye movement. The effect of cerebellar pathology on these functions is frequently examined using volumetric studies that depend on consistent and accurate delineation, however, no existing automated methods adequately delineate the cerebellar lobules. In this work, we describe a method we call the Automatic Classification of Cerebellar Lobules Algorithm using Implicit Multi-boundary evolution (ACCLAIM). A multiple object geometric deformable model (MGDM) enables each boundary surface of each individual lobule to be evolved under different level set speeds. An important innovation described in this work is that the speed for each lobule boundary is derived from a classifier trained specifically to identify that boundary. We compared our method to segmentations obtained using the atlas-based and multi-atlas fusion techniques, and demonstrate ACCLAIM's superior performance.

The issues in socially responsible supply chain coordination from brand equity perspective are discussed in the current study. Socially responsible supply chain coordination that integrates all efforts of CSR initiatives from brand distributors to upstream partner-suppliers shall be surely needed within the uncertainly globalizing and networklizing knowledge economy era. This paper develops a five-step CSR performance evaluation from brand equity perspective for modeling and analysis of socially responsible supply chain coordination and shows relevant optimal coordination strategies to improve and reconcile economic, social and environmental benefits of all partners in the socially responsible supply chain system.

We propose an experiment in Linear Optical Quantum Computing (LOQC) in the style rst suggested by Knill, La amme, and Milburn. This experiment is intended to test the theories proposed in the authors' previous work on imperfect LOQC gates using number-resolving photon-detectors. We develop a simple physical apparatus capable of producing controllable delity less than 1 and success rates higher than the current theoretical maximum (S=2/27) for perfect delity. This experimental setup is within the reach of many experimental groups and would provide an interesting experiment in photon-based quantum computing.

We propose a fully automatic method to segment the dentate nucleus using diffusion weighted images (DWI). Initialization of the dentate nucleus is produced by combining the information from tractography results on the diffusion tensor images (reconstructed from DWI) and b0 images. A geometric de-formable model (GDM) with generalized gradient vector flow (GGVF) is then applied on the b0 image to generate the segmentation by evolving the level set function. Experiments have been carried out on real data and quantitative comparison shows that our segmentation results agree well with expert manual delineations and produce accurate results.

Volumetric measurements obtained from image parcellation have been instrumental in uncovering structure–function relationships. However, anatomical study of the cerebellum is a challenging task. Because of its complex structure, expert human raters have been necessary for reliable and accurate segmentation and parcellation. Such delineations are time-consuming and prohibitively expensive for large studies. Therefore, we present a three-part cerebellar parcellation system that utilizes multiple inexpert human raters that can efficiently and expediently produce results nearly on par with those of experts. This system includes a hierarchical delineation protocol, a rapid verification and evaluation process, and statistical fusion of the inexpert rater parcellations. The quality of the raters’ and fused parcellations was established by examining their Dice similarity coefficient, region of interest (ROI) volumes, and the intraclass correlation coefficient of region volume. The intra-rater ICC was found to be 0.93 at the finest level of parcellation.

The cerebellar peduncles are white matter tracts that play an important role in the communication of the cerebellum with other regions of the brain. They can be grouped into three fiber bundles: inferior cerebellar peduncle middle cerebellar peduncle, and superior cerebellar peduncle. Their automatic segmentation on diffusion tensor images would enable a better understanding of the cerebellum and would be less time-consuming and more reproducible than manual delineation. This paper presents a method that automatically labels the three fiber bundles based on the segmentatin results from the diffusion oriented tract segmentation (DOTS) algorithm, which achieves volume segmentation of white matter tracts using a Markov random field (MRF) framework. We use the DOTS labeling result as a guide to determine the classification of fibers produced by wild bootstrap probabilistic tractography. Mean distances from each fiber to each DOTS volume label are defined and then used as features that contribute to classification. A supervised Gaussian classifier is employed to label the fibers. Manually delineated cerebellar peduncles serve as training data to determine the parameters of class probabilities for each label. Fibers are labeled ad the class that has the highest posterior probability. An outlier detection ste[ re,pves fober tracts that belong to noise of that are not modeled by DOTS. Experiments show a successful classification of the cerebellar peduncles. We have also compared results between successive scans to demonstrate the reproducibility of the proposed method.