Intermediate cervical plexus block for cervical esophagus diverticulectomy.

We report the case of a 97 year old woman suffering from a voluminous diverticle of the cervical esophagus with important comorbidities and a very poor quality of life. A diverticulectomy under general anesthesia implied a high level of risk. Regional anesthesia was chosen, i.e. an intermediate cervical plexus block, with mild sedation allowing to maintain contact with the patient. The procedure was carried out without complications and the patient's and surgical staff satisfaction were optimal.

Severity of depression and anxiety are predictors of response to antidepressant treatment in Parkinson's disease.

BACKGROUND: Antidepressants have appeared to be more effective than placebo treatment in treating depressive syndromes in patients with Parkinson's disease (PD). OBJECTIVE: To identify factors that predict improvement in depressive symptoms during antidepressant treatment in depressed PD patients. METHODS: A secondary analysis was performed on the dataset of the Randomized Placebo-controlled Study of Antidepressants in PD (SAD-PD), in which 76 patients received active treatment with either paroxetine or venlafaxine extended release (XR), and 39 patients received placebo treatment. Backward stepwise regression analyses were conducted with change in 24-item Hamilton Depression Rating Scale (HAMD-24) score between assessments at baseline and week 12 as the main outcome measure, and sex, age, baseline HAMD-24 score, Unified Parkinson's Disease Rating Scale section III (UPDRS-III) score, Mini-Mental State Examination (MMSE), and the Clinical Anxiety Scale (CAS) as independent variables. RESULTS: In both the active treatment and placebo groups, higher baseline HAMD-24 score and lower UPDRS-III score were associated with greater reduction in HAMD-24 score. Higher anxiety scores predicted less response in the active treatment group. Higher MMSE scores predicted greater response only in the placebo-treated group. Sex and age were no predictors of response. CONCLUSIONS: Higher pre-treatment depression scores and lower pre-treatment anxiety scores are the two most important predictors for improvement during antidepressant treatment in depressed PD patients, which is in line with those found in treatment studies of depressed non-PD patients. Furthermore, our results indicate the requirement for different or more intensive treatment for depressed PD patients with more severe anxiety symptoms.

A randomized, double-blind, placebo-controlled trial of antidepressants in Parkinson disease.

OBJECTIVE: To evaluate the efficacy and safety of a selective serotonin reuptake inhibitor (SSRI) and a serotonin and norepinephrine reuptake inhibitor (SNRI) in the treatment of depression in Parkinson disease (PD). METHODS: A total of 115 subjects with PD were enrolled at 20 sites. Subjects were randomized to receive an SSRI (paroxetine; n = 42), an SNRI (venlafaxine extended release [XR]; n = 34), or placebo (n = 39). Subjects met DSM-IV criteria for a depressive disorder, or operationally defined subsyndromal depression, and scored >12 on the first 17 items of the Hamilton Rating Scale for Depression (HAM-D). Subjects were followed for 12 weeks (6-week dosage adjustment, 6-week maintenance). Maximum daily dosages were 40 mg for paroxetine and 225 mg for venlafaxine XR. The primary outcome measure was change in the HAM-D score from baseline to week 12. RESULTS: Treatment effects (relative to placebo), expressed as mean 12-week reductions in HAM-D score, were 6.2 points (97.5% confidence interval [CI] 2.2 to 10.3, p = 0.0007) in the paroxetine group and 4.2 points (97.5% CI 0.1 to 8.4, p = 0.02) in the venlafaxine XR group. No treatment effects were seen on motor function. CONCLUSIONS: Both paroxetine and venlafaxine XR significantly improved depression in subjects with PD. Both medications were generally safe and well tolerated and did not worsen motor function. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that paroxetine and venlafaxine XR are effective in treating depression in patients with PD.

Setting up robotic surgery in gynaecology: the experience of the Strasbourg teaching hospital.

Teleoperated surgical robots could provide a genuine breakthrough in laparoscopy and it is for this reason that the development of robot-assisted laparoscopy is one of the priorities of the Strasbourg University Hospitals' strategic plan. The hospitals purchased a da Vinci S(®) robot in June 2006 and Strasbourg has, in IRCAD, one of the few robotic surgery training centres in the world. Our experience has, however, revealed the difficulties involved in setting up robotic surgery, the first of which are organizational issues. This prospective work was carried out between December 2007 and September 2008, primarily to examine the possibility of setting up robotic surgery on a regular basis for gynaecological surgical procedures at the Strasbourg University Hospitals. We maintained a "logbook" in which we prospectively noted all the resources implemented in setting up the robotic surgery service. The project was divided into two phases: the preparatory phase up until the first hysterectomy and then the second phase with the organization of subsequent hysterectomies. The first surgical procedure took 5 months to organize, and followed 25 interviews, 10 meetings, 53 telephone conversations and 48 e-mails with a total of 40 correspondents. The project was presented to seven separate groups, including the hospital medical commission, the gynaecology unit committee and the surgical staff. Fifteen members of the medical and paramedical team attended a two-day training course. Preparing the gynaecology department for robotic surgery required freeing up 8.5 days of "physician time" and 12.5 days of "nurse time". In the following five months, we performed five hysterectomies. Preparation for each procedure involved on average 5 interviews, 19 telephone conversations and 11 e-mails. The biggest obstacle was obtaining an operating slot, as on average it required 18 days, four telephone calls and four e-mails to be assigned a slot in the operating theatre schedule, which is prepared on average 28 days in advance. It is extremely important for organising robotic surgery and assembling the surgical teams to have a series of operating slots allocated a sufficiently long time in advance. Considerable benefits would be had by setting up a team of anaesthetists and especially perioperative nurses dedicated to robotic surgery.

The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves.

Learning curves are presented as an unbiased means for evaluating the performance of models for neuroimaging data analysis. The learning curve measures the predictive performance in terms of the generalization or prediction error as a function of the number of independent examples (e.g., subjects) used to determine the parameters in the model. Cross-validation resampling is used to obtain unbiased estimates of a generic multivariate Gaussian classifier, for training set sizes from 2 to 16 subjects. We apply the framework to four different activation experiments, in this case [(15)O]water data sets, although the framework is equally valid for multisubject fMRI studies. We demonstrate how the prediction error can be expressed as the mutual information between the scan and the scan label, measured in units of bits. The mutual information learning curve can be used to evaluate the impact of different methodological choices, e.g., classification label schemes, preprocessing choices. Another application for the learning curve is to examine the model performance using bias/variance considerations enabling the researcher to determine if the model performance is limited by statistical bias or variance. We furthermore present the sensitivity map as a general method for extracting activation maps from statistical models within the probabilistic framework and illustrate relationships between mutual information and pattern reproducibility as derived in the NPAIRS framework described in a companion paper.

A novel miniature ventricular assist device for hemodynamic support.

The HemoDynamics Systems enabler is a new cardiac assist pump that can expel blood from the left ventricle and provide pulsatile flow in the aorta. We evaluated the efficacy of the 18 Fr enabler. The enabler was inserted from the left ventricular apex into the ascending aorta in eight sheep. Heart failure (mild, moderate, and severe) was induced by microsphere injection into the coronary arteries to reduce cardiac output by 10-30%, 31-50%, and more than 50% from baseline, respectively. The enabler was activated, and its flow was increased to approximately 2.0 L/min. Hemodynamic variables were recorded before and after activation. In moderate heart failure, cardiac output and mean aortic pressure increased from 2.3 +/- 0.6 L/min and 59 +/- 12 mm Hg before assist to 2.8 +/- 0.6 L/min and 70 +/- 8 mm Hg at 30 minutes after activation, respectively (p < 0.01). Left atrial pressure decreased from 17 +/- 3 to 13 +/- 4 mm Hg (p < 0.05). Similar findings were observed in mild and severe heart failure. Despite its small diameter, the enabler significantly improved the hemodynamics of failing hearts and may potentially serve as a means of peripheral left ventricular support. Further study is warranted.

Magnetic resonance image tissue classification using a partial volume model.

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.

Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects.

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.

Effects of changes in experimental design on PET studies of isometric force.

Based on single-cell recordings in primates, the relationship between neuronal activity and force magnitude is thought to be monotonic, at least for a subset of pyramidal cells in the motor cortex. Functional neuroimaging studies have also suggested a monotonic relationship between cerebral activation and force magnitude. In order to more precisely define this relationship and to characterize the activation pattern(s) associated with the modulation of static force, we studied 40 normal subjects using [(15)O]water PET and a simple visuomotor task-application of static force on a micro force sensor with the thumb and index finger of the right hand. When our experimental design did not produce the expected result (evidence of a relationship between cerebral activation and force magnitude in ten subjects), we made serial changes in the experimental protocol, including the addition of control (baseline) trials, and increased the number of subjects in an effort to increase our sensitivity to variations in force magnitude. We compared univariate and multivariate data-analytic strategies, but we relied on our multivariate results to elucidate the interaction of attentional and motor networks. We found that increasing the number of subjects from 10 to 20 resulted in an increase in statistical power and a more stable (i.e., more replicable) but qualitatively similar result, and that the inclusion of control trials in a 10-subject group did not enhance our ability to discern significant brain-behavior relationships. Our results suggest that sample sizes greater than 20 may be required to detect parametric variation in some instances and that failure to detect such variation may result from unanticipated neurobehavioral effects.

Multivariate predictive relationship between kinematic and functional activation patterns in a PET study of visuomotor learning.

Imaging studies of visuomotor learning have reported practice-related activation in brain regions mediating sensorimotor functions. However, development and testing of functional motor learning models, based on the relationship between imaging and behavioral measures, is complicated by the multidimensional nature of motoric control. In the present study, multivariate techniques were used to analyze [15O]water PET and kinematic correlates of learning in a visuomotor tracing task. Fourteen subjects traced a geometric form over a series of eight tracing trials, preceded and followed by baseline trials in which they passively viewed the geometric form. Simultaneous evaluation of multiple behavioral measures indicated that performance improvement was most strongly associated with a global performance measure and least strongly associated with measures of fine motor control. Results of three independent analytic techniques (i.e., intertrial correlation matrices, power function modeling, iterative canonical variate analysis) indicated that imaging and behavioral measures were most closely related on early learning trials. Performance improvement was associated with covarying increases in normalized activity among superior parietal, postcentral gyrus, and premotor regions and covarying decreases in normalized activity among cerebellar, inferior parietal, pallidal, and medial occipital regions. These findings suggest that performance improvement may be associated with increased activation in neural systems previously implicated in visually guided reaching and decreased activation in neural systems previously implicated in attentive visuospatial processing.

Comparison of voxel- and volume-of-interest-based analyses in FDG PET scans of HIV positive and healthy individuals.

UNLABELLED: Abnormal glucose metabolic patterns in the brain have been reported for HIV-1 seropositive (HIV+) patients when compared with seronegative healthy individuals. The metabolic covariance pattern obtained from voxel- or volume-of-interest (VOI)-based multivariate data analysis techniques can be used to characterize disease and potentially to detect and monitor disease progression in the early stage of AIDS dementia complex. However, the arbitrary smoothing typically applied to PET data during reconstruction and processing to reduce noise has an unknown effect on the data, especially for the voxel-based analysis. METHODS: To investigate the impact of smoothing on a discrimination task, we applied principal component analysis with scaled subprofile-model preprocessing (SSM/PCA) followed by Fisher discriminant analysis to FDG PET data that were reconstructed and processed with different degrees of smoothing. Receiver operating characteristic curves were used to compare the ability of derived metabolic covariance patterns to discriminate HIV+ patients from healthy volunteers. RESULTS: For the voxel-based analysis, we found that the spatial distribution of voxel weights from the SSM/PCA analysis suggested edge effects along major tissue and cerebrospinal fluid boundaries, indicative of a disease-specific pattern of cerebral atrophy for the HIV+ patients. In terms of its discrimination performance, this metabolic covariance pattern is stable and insensitive to a wide range of smoothing kernels, except for ramp reconstruction and Hanning reconstruction with 7 x 7 x 7 block smoothing. In these reconstructions, the discrimination performance decreased as a result of high image noise and excessive smoothing, respectively. Our results also indicated that if sufficient variance from the VOI measurements is included, the overall performance of a conventional VOI-based analysis can be similar to that of the voxel-based analysis for the same discrimination task. However, the VOI-based analysis performed poorly at low false-positive fraction and is less tolerant to noise in the metabolic covariance pattern than the voxel-based analysis. CONCLUSION: We have obtained a unique covariance pattern of brain glucose metabolism for HIV+ patients compared with healthy volunteers. Discrimination based on this covariance pattern was found to be insensitive to a wide range of image smoothness.

Are brain functions really additive?

Although Positron Emission Tomography (PET) and functional magnetic resonance imaging (fMRI) studies commonly subtract data obtained during two or more experimental conditions to decompose a complex task, there have been few opportunities to evaluate this approach directly. In the present study, PET was used to study three motor speech tasks selected such that two were constituent components of the third, making possible a direct examination of decomposition by subtraction. In Experiment 1, a group of 13 right-handed normal volunteers participated in three activation studies: syllable repetition; phonation; and repetitive lip closure. A scanning session was devoted to a single task, repeated four times. In Experiment 2, six of the original subjects performed the same three activation studies during a single scanning session. Whether tasks were studied in separate scanning sessions or combined within a single session, the results of decomposition by compound subtraction differed significantly from the results obtained when individual tasks were compared to a simple baseline condition. These data failed to demonstrate task additivity, a necessary property if decomposition by subtraction is to provide an accurate characterization of the brain activity accompanying complex behavior.

A symbolic environment for visualizing activated foci in functional neuroimaging datasets.

This paper presents a symbolic visualization environment known as the Corner Cube environment, which was developed to facilitate rapid examination and comparison of activated foci defined by analyses of functional neuroimaging datasets. We have performed a comparative evaluation of this environment against maximum-intensity projection and 'gallery of slices' displays, and the results suggest that the Corner Cube environment has definite advantages over both conventional display techniques. We conclude that the Corner Cube is an effective tool for summarizing the spatial characteristics of activated foci within an easily understood visual context and is especially useful for displaying the similarities and differences in functional neuroimaging datasets.

Improved resolution for PET volume imaging through three-dimensional iterative reconstruction.

UNLABELLED: It has been demonstrated that in two-dimensional iterative reconstruction, a resolution model can improve image resolution while controlling noise. With the lower noise levels of three-dimensional PET volume imaging, these iterative reconstruction advantages may be extended to three dimensions to further improve the reconstructed image resolution. METHODS: We have implemented three-dimensional versions of iterative filtered backprojection (IFBP) and the maximum likelihood by expectation maximization (ML-EM) reconstruction algorithms and applied them to three-dimensional PET volume datasets. The results were compared to images obtained using the standard three-dimensional reprojection reconstruction (3DRP) algorithm. RESULTS: For IFBP with 15 iterations and no regularization compared to 3DRP, both using a ramp filter, the transaxial resolution improved 52%, and the axial resolution improved 39%. With a strong regularization, the transaxial and axial resolution improvements were reduced to 6% and 5%, respectively. If a Hanning roll-off is applied to the ramp filter in the transaxial direction, the transaxial resolution for IFBP without regularization improved 35% compared to 3DRP; with regularization the improvement dropped to 19%. The axial resolution for IFBP and 3DRP was unaffected by this transaxial smoothing in the reconstruction filter. With the same Hanning roll-off, the noise for IFBP without regularization increased by a factor of 6 compared to 3DRP; with regularization the noise was increased only by a factor of 3. Compared to IFBP, the three-dimensional ML-EM reconstruction produced similar resolution improvements with a much smaller increase in noise and slower convergence. Resolution improvements from both IFBP and ML-EM reconstructions are visually apparent in three-dimensional FDG brain images and result in increased activation signals in a three-dimensional [15O]water functional activation study. CONCLUSION: Our results demonstrate that resolution improvement is possible for IFBP and ML-EM compared to 3DRP with or without noise increase.

Activation pattern reproducibility: measuring the effects of group size and data analysis models.

The reproducibility of patterns from brain activation experiments has been examined only for suprathreshold spatially localized foci. Scatter plots comparing signal levels across all pairs of Talairach voxels for pairs of functional activation images provide an alternative approach for assessing reproducibility. Image-wide, signal-level reproducibility may be quantitatively summarized using pattern similarity measures such as the Pearson product-moment correlation, rho. Empirical population distributions of rho for many pair-wise image comparisons, generated using statistical resampling techniques, may be used to examine the impact of a wide range of experimental variables. We demonstrate the use of such empirical rho-histograms to measure changes in reproducibility for [(15)O]-water PET scans of a simple motor task as a function of group size and data analysis model.

Abnormal cerebral glucose metabolism in HIV-1 seropositive subjects with and without dementia.

UNLABELLED: This study was undertaken in order to extend our previous finding of relative basal ganglia hypermetabolism in AIDS dementia complex (ADC) and to develop clinically useful metabolic indices of CNS involvement in HIV-seropositive (HIV+) subjects. METHODS: Twenty-one HIV+ subjects (11 with AIDS) underwent FDG-PET scanning; 12 had a follow-up scan at 6 mo and 4 had a third scan at 12 mo. Forty-three age-matched heterosexual volunteers served as controls. FDG-PET scanning was performed with arterial blood sampling, and scan data were analyzed using the Scaled Subprofile Model (SSM) with principal component analysis. RESULTS: SSM/principal component analysis of the combined (HIV+ and controls) FDG-PET dataset extracted two major disease-related metabolic components: (a) a nonspecific indicator of cerebral dysfunction, which was significantly correlated with age, cerebral atrophy and ADC stage and (b) the striatum, which was heavily weighted (relatively hypermetabolic) and appeared to provide a disease-specific measure of early CNS involvement. CONCLUSION: FDG-PET scans provide quantitative measures of abnormal functional connectivity in HIV-seropositives-with or without AIDS or ADC. These measures, which are robust across centers with respect to instrumentation, scanning technique and disease severity, appear to track the progression of CNS involvement in patients with subclinical neurologic or neuropsychologic dysfunction.

Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. "Functional connectivity" of the human motor system studied with [15O]water PET.

Using [15O]water PET and a previously well studied motor activation task, repetitive finger-to-thumb opposition, we compared the spatial activation patterns produced by (1) global normalization and intersubject averaging of paired-image subtractions, (2) the mean differences of ANCOVA-adjusted voxels in Statistical Parametric Mapping, (3) ANCOVA-adjusted voxels followed by principal component analysis (PCA), (4) ANCOVA-adjustment of mean image volumes (mean over subjects at each time point) followed by F-masking and PCA, and (5) PCA with Scaled Subprofile Model pre- and postprocessing. All data analysis techniques identified large positive focal activations in the contralateral sensorimotor cortex and ipsilateral cerebellar cortex, with varying levels of activation in other parts of the motor system, e.g., supplementary motor area, thalamus, putamen; techniques 1-4 also produced extensive negative areas. The activation signal of interest constitutes a very small fraction of the total nonrandom signal in the original dataset, and the exact choice of data preprocessing steps together with a particular analysis procedure have a significant impact on the identification and relative levels of activated regions. The challenge for the future is to identify those preprocessing algorithms and data analysis models that reproducibly optimize the identification and quantification of higher-order sensorimotor and cognitive responses.