Comparison of Gadoterate Meglumine and Gadobutrol in the MRI Diagnosis of Primary Brain Tumors: A Double-Blind Randomized Controlled Intraindividual Crossover Study (the REMIND Study).

BACKGROUND AND PURPOSE: Effective management of patients with brain tumors depends on accurate detection and characterization of lesions. This study aimed to demonstrate the noninferiority of gadoterate meglumine versus gadobutrol for overall visualization and characterization of primary brain tumors. MATERIALS AND METHODS: This multicenter, double-blind, randomized, controlled intraindividual, crossover, noninferiority study included 279 patients. Both contrast agents (dose = 0.1 mmol/kg of body weight) were assessed with 2 identical MRIs at a time interval of 2-14 days. The primary end point was overall lesion visualization and characterization, scored independently by 3 off-site readers on a 4-point scale, ranging from "poor" to "excellent." Secondary end points were qualitative assessments (lesion border delineation, internal morphology, degree of contrast enhancement, diagnostic confidence), quantitative measurements (signal intensity), and safety (adverse events). All qualitative assessments were also performed on-site. RESULTS: For all 3 readers, images of most patients (>90%) were scored good or excellent for overall lesion visualization and characterization with either contrast agent; and the noninferiority of gadoterate meglumine versus gadobutrol was statistically demonstrated. No significant differences were observed between the 2 contrast agents regarding qualitative end points despite quantitative mean lesion percentage enhancement being higher with gadobutrol (P < .001). Diagnostic confidence was high/excellent for all readers in >81% of the patients with both contrast agents. Similar percentages of patients with adverse events related to the contrast agents were observed with gadoterate meglumine (7.8%) and gadobutrol (7.3%), mainly injection site pain. CONCLUSIONS: The noninferiority of gadoterate meglumine versus gadobutrol for overall visualization and characterization of primary brain tumors was demonstrated.

Identification of minimal hepatic encephalopathy in patients with cirrhosis based on white matter imaging and Bayesian data mining.

BACKGROUND AND PURPOSE: White matter abnormalities have been demonstrated to play an important role in minimal hepatic encephalopathy. In this study, we aimed to evaluate whether WM diffusion tensor imaging can be used to identify minimal hepatic encephalopathy among patients with cirrhosis. MATERIALS AND METHODS: Our study included 65 patients with cirrhosis with covert hepatic encephalopathy (29 with minimal hepatic encephalopathy and 36 without hepatic encephalopathy). Participants underwent DTI, from which we generated mean diffusivity and fractional anisotropy maps. We used a Bayesian machine-learning technique, called Graphical-Model-based Multivariate Analysis, to determine WM regions that characterize group differences. To further test the clinical significance of these potential biomarkers, we performed Cox regression analysis to assess the potential of these WM regions in predicting survival. RESULTS: In mean diffusivity or fractional anisotropy maps, 2 spatially distributed WM regions (predominantly located in the bilateral frontal lobes, corpus callosum, and parietal lobes) were consistently identified as differentiating minimal hepatic encephalopathy from no hepatic encephalopathy and yielded 75.4%-81.5% and 83.1%-92.3% classification accuracy, respectively. We were able to follow 55 of 65 patients (median = 18 months), and 15 of these patients eventually died of liver-related causes. Survival analysis indicated that mean diffusivity and fractional anisotropy values in WM regions were predictive of survival, in addition to the Child-Pugh score. CONCLUSIONS: Our findings indicate that WM DTI can provide useful biomarkers differentiating minimal hepatic encephalopathy from no hepatic encephalopathy, which would be helpful for minimal hepatic encephalopathy detection and subsequent treatment.

A prospective longitudinal brain morphometry study of children with sickle cell disease.

BACKGROUND AND PURPOSE: Age-related changes in brain morphology are crucial to understanding the neurobiology of sickle cell disease. We hypothesized that the growth trajectories for total GM volume, total WM volume, and regional GM volumes are altered in children with sickle cell disease compared with controls. MATERIALS AND METHODS: We analyzed T1-weighted images of the brains of 28 children with sickle cell disease (mean baseline age, 98 months; female/male ratio, 15:13) and 28 healthy age- and sex-matched controls (mean baseline age, 99 months; female/male ratio, 16:12). The total number of MR imaging examinations was 141 (2-4 for each subject with sickle cell disease, 2-3 for each control subject). Total GM volume, total WM volume, and regional GM volumes were measured by using an automated method. We used the multilevel-model-for-change approach to model growth trajectories. RESULTS: Total GM volume in subjects with sickle cell disease decreased linearly at a rate of 411 mm(3) per month. For controls, the trajectory of total GM volume was quadratic; we did not observe a significant linear decline. For subjects with sickle cell disease, we found 35 brain structures that demonstrated age-related GM volume reduction. Total WM volume in subjects with sickle cell disease increased at a rate of 452 mm(3) per month, while the trajectory of controls was quadratic. CONCLUSIONS: There was a significant age-related decrease in total GM volume in children with sickle cell disease. The GM volume reduction was spatially distributed widely across the brain, primarily in the frontal, parietal, and occipital lobes. Total WM volume in subjects with sickle cell disease increased at a lower rate than for controls.

Comparative study of pentoxifylline vs antiaggregants in patients with transient ischaemic attacks.

Out of 235 patients with recent cerebral transient ischaemic attacks, 208 subjects were available for final evaluation after 6 months' randomised treatment with either pentoxifylline (PTX 1200 mg/day) or a combination (ASAD) of acetylsalicylic acid (ASA, 1050 mg/day) and dipyridamole (D, 150 mg/day). Prevention of TIA, stroke or death attributable to previous events were endpoint criteria. The pentoxifylline group (n = 100) exhibited no recurrent episodes in 86 patients (86%). TIA occurred in 9 patients, stroke in 5 patients and there was 1 death. In the ASAD group (n = 108) no recurrence of ischaemic episodes was recorded in 82 cases (75.9%). TIA occurred in 20 patients, stroke in 6 patients and there were 3 deaths of vascular origin. Side effects were recorded in 4 ASAD and 1 PTX patients. The total rate of recurrence was 14% with PTX as compared to 24.1% with ASAD treatment.

Quantitative 1H MR spectroscopic imaging in early Rett syndrome.

OBJECTIVE: To determine cerebral regional concentrations of N-acetyl aspartate (NAA), total choline (Cho), and total creatine (Cr) in Rett syndrome (RS) using 1H magnetic resonance spectroscopic imaging (MRSI). BACKGROUND: The biochemical defect underlying RS is unknown. Because in vivo MRSI can detect important cerebral metabolites, MRSI has a potential to reveal impairment of regional cerebral metabolism in RS noninvasively. METHODS: High-resolution, multislice 1H MRSI was carried out in 17 girls with RS. The control group consisted of nine healthy children. RESULTS: In patients with RS, average Cho concentration was 12% higher (p < 0.005) and average NAA concentration 11% lower (p < 0.0001) compared with the control group. Regional metabolic differences included significantly lower NAA concentration in the frontal gray and white matter, insula, and hippocampus in RS; no difference in regional Cho and Cr concentrations were found. A 20 to 38% higher Cho:NAA ratio in frontal and parietal gray and white matter, insular gray matter, and hippocampus (p < 0.05) and a 14 to 47% lower NAA:Cr ratio in frontal cortical gray matter, parietal and temporal white matter, insula, and putamen (p < 0.05) were found in subjects with RS compared with controls. Patients with seizures had higher average concentrations of Cho, Cr, and NAA compared with those without seizures (8-19%, p < 0.05). CONCLUSION: Metabolic impairment in RS involves both gray and white matter and particularly involves frontal and parietal lobes and the insular cortex. Loss of NAA most likely reflects reduced neuronal and dendritic tree size; increased Cho concentration may result from gliosis.

A brain image database for structure/function analysis.

BACKGROUND AND PURPOSE: Lesion-deficit-based structure-function analysis has traditionally been empirical and nonquantitative. Our purpose was to establish a new brain image database (BRAID) that allows the statistical correlation of brain functional measures with anatomic lesions revealed by clinical brain images. METHODS: Data on 303 participants in the MR Feasibility Study of the Cardiovascular Health Study were tested for lesion/deficit correlations. Functional data were derived from a limited neurologic examination performed at the time of the MR examination. Image data included 3D lesion descriptions derived from the MR examinations by hand segmentation. MR images were normalized in-plane using local, linear Talairach normalization. A database was implemented to support spatial data structures and associated geometric and statistical operations. The database stored the segmented lesions, patient functional scores, and several anatomic atlases. Lesion-deficit association was sought by contingency testing (chi2-test) for every possible combination of each neurologic variable and each labeled atlas structure. Significant associations that confirmed accepted lesion-deficit relationships were sought. RESULTS: Two-hundred thirty-five infarctlike lesions in 117 subjects were viewed collectively after mapping into Talairach cartesian coordinates. Anatomic structures most strongly correlated with neurologic deficits tended to be situated in anatomically appropriate areas. For example, infarctlike lesions associated with visual field defects were correlated with structures in contralateral occipital structures, including the optic radiations and occipital gyri. CONCLUSION: Known lesion-deficit correlations can be established by a database using a standard coordinate system for representing spatial data and incorporating functional and structural data together with appropriate query mechanisms. Improvements and further applications of this methodology may provide a powerful technique for uncovering new structure-function relationships.

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures.

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.

An architecture for a brain-image database.

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

Algorithms for Bayesian belief-network precomputation.

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference. We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

A hybrid classifier for automated radiologic diagnosis: preliminary results and clinical applications.

We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the isodata unsupervised-clustering algorithm; and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone.

Struggling over subjectivity: debates about the "self" and Alzheimer's disease.

The current conception of Alzheimer's disease emerged in the 1970s and achieved wide acceptance and popularization because it effectively served political-economic interests, solved pragmatic, clinical, and psychological problems, and met philosophical and ethical concerns. But the very success of this widespread acceptance and popularization has produced a troubling dilemma regarding the subjectivity of the person diagnosed with Alzheimer's disease. A "loss of self" is implicit in the current Alzheimer's construct, and it has been argued that, consequently, the subjective experience of being and becoming old has become increasingly distressing. It has been further suggested that a response to this unintended assault on the self can be seen in the now burgeoning literature offering diverse representations of and debates about the "self" in Alzheimer's. What appears to be at stake in these competing voices is our very notion of what comprises the self and what constitutes subjective experience. Finally, one can speculate why, as a culture, we tell these stories about aging: it could be that, as a society as well as a community of gerontological thinkers and practitioners, our struggle with the nature of the self-in-Alzheimer's reflects our struggle to grapple with what it will be like, and what it will mean, to be and become old.

Hereditary essential tremor in Buenos Aires (Argentina).

A descriptive trial of essential tremor in Buenos Aires city is proposed. It may be considered as the first one like this in Latin America. During a 15 year period, sixteen families with essential tremor diagnosis have been examined, making a total of 39 patients (20 males and 19 females). The disease was basically studied from its semiological, clinico-evolutive and therapeutical view point, following a protocol. Our results indicate -- a. The clinico-evolutive characteristics: its frequency (according to race, sex, social status, age of beginning and physiopathological type), morbidity and mortality, evolution, prognosis, triggering factors, longevity and multiparity. b. The different treatment given to our patients with discussion of their pharmacokinetic and pharmacodynamic basis and evaluation of the best responses. In this matter, it is concluded that both primidone (750 mg/daily) and/or propranolol (160-240 mg/daily) are undeniably the drugs of first choice for the treatment of this disease. A comparison of the clinico-therapeutic features of this tremor in Buenos Aires and other cities is done. The study of this syndrome in Buenos Aires does not give different results from those obtained in Europe and USA.

[Considerations on low pressure hydrocephalus]. / Consideraciones sobre el hidrocéfalo de baja presión

The physiopathological conditions of the normal pressure hydrocephalus syndrome are analysed. Within a group of 21 patients with clinical sintomathology pertaining this entity, only were 10 selected with a clinical, radiological and cisternografic indubitable diagnosis. It has been noticed that in spite of the stricked selection only a 50% of them have been beneficed by the application of a low pressure valve. Then, the hypothesis that the disease is a multicausal syndrome appears, which by an identical physiopathologic process produces or exhibits a similar clinical, radiological and cisternographic picture. Only those patients on whom the mechanical problem is solved would be beneficed by neurosurgery.

[Paralysis of the conjugated lateral gaze of pontile origin: neuroanatomical discussion of the fasciculus longitudinalis medialis]. / Parálisis de la mirada conjugada lateral de origen pontino: discusión neuroanatómica del fascículo longitudinal medio.

The case of a patient with paralysis of the lateral gaze to the left, paresis of both eyes when he tried to look upwards and a disfunction of the cerebellum to the left, is reported. During the evolution the paresia of both eyes when the patient looked upwards and the disfunction of the cerebellum disappeared, but the paralysis of the lateral gaze to the left, remained. Later on the patient went a little confuse, did not understand what other persons spoke to him, and speak badly. The necropsy showed one old infarct in the medial longitudinal fasciculus in the left, the same type of lesion in the ventral part of the upper pons which was also to the left and demyelinization of the motor lemniscus of the same side. It was also seen a more recent infarct in the transverse gyrus of the left cerebral hemisphere and also the white matter below it. The study of the medial longitudinal fasciculus by means of the secondary degeneration showed that it was crossed above the 6 degrees nuclei, but was straight to the contrary of what is said about it. The authors also observe that the cells of the interstitial nucleus of Cajal were in chromatolysis. These findings justify the syndrome and the absence of the internuclear anterior ophthalmoplegia to the right.

[Specular bilateral lenticular hematomas]. / Hematomas lenticulares bilaterales en espejo.

A 49-year-old right-handed hypertensive female developed two consecutive putaminal hemorrhages in the left and right hemispheres within one month. Recurrent specular hypertensive hematomas are rare, even though the underlying pathological changes in the arterial wall occur bilaterally. This may be explained by the spontaneous decrease in blood pressure occurring in patients after an acute stroke. In this patient, a new abrupt increase in blood pressure may account for the second hemorrhage. While lowering the blood pressure during an acute stroke may be in principle unadvisable, it seems reasonable to prevent a new increase in blood pressure with adequate therapy.

Development of parkinsonism after transient ischemic attacks.

In order to correlate the influence of cerebrovascular disorders with the appearance of parkinsonism, 115 patients aged between 32 and 84 years (mean = 65) with transient ischemic attacks were followed up for one year. They were treated with platelet antiaggregant drugs. None of them received neuroleptics, calcium antagonists or other drugs known to induce parkinsonism. During the study, 8 patients (mean = 75 years) developed parkinsonism, bilateral in all but one, who remarkably enough was the only case responding to L-dopa treatment. On comparing recorded with expected incidence, our series showed significantly greater values. Our findings suggest that cerebrovascular disorders are factors contributing to parkinsonism in the elderly.

A Bayesian diagnostic system to differentiate glioblastomas from solitary brain metastases.

This paper aimed to construct a Bayesian network-based decision support system to differentiate glioblastomas from solitary metastases, based on multimodality MR examination. We enrolled 51 patients with solitary brain tumors (26 with glioblastomas and 25 with solitary brain metastases). These patients underwent contrast-enhanced T1-weighted magnetic resonance (MR) examination, diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC) MRI, and fluid-attenuated inversion recovery (FLAIR). We generated a set of MR biomarkers, including relative cerebral blood volume in the enhancing region, and fractional anisotropy measured in the immediate peritumoral area. We then generated a Bayesian network model to represent associations among these imaging-derived predictors, and the group membership variable, (glioblastoma or solitary metastasis). This Bayesian network can be used to classify new patients' tumors based on their MR appearance. The Bayesian network model accurately differentiated glioblastomas from solitary metastases. Prediction accuracy was 0.94 (sensitivity = 0.96, specificity = 0.92) based on leave-one-out cross-validation. The area under the receiver operating characteristic curve was 0.90. A Bayesian network-based decision support system accurately differentiates glioblastomas from solitary metastases, based on MR-derived biomarkers.

Atlas-Based Classification of Hyperintense Regions from MR Diffusion-Weighted Images of the Brain: Preliminary Results.

The study of subjects with acquired brain damage in a specific location is important in exploring human brain function. Description of lesion locations within and across subjects is a crucial methodological component that usually involves the distinction of normal from damaged tissue (lesion segmentation) in relation to lesion locations in terms of a standard anatomical reference space (lesion mapping). Our study provides an atlas-based, computer-aided methodology for classification of hyperintense regions on diffusion-weighted images of the brain, representing either ischemic lesions or susceptibility artifacts. We applied a leave-one-out method of cross-validation that computed probabilistic atlases of true lesions and artifacts, based on training data. Our approach accurately classifies lesions and artifacts, but leaves a significant number of regions unclassified, due to the relatively small number of training samples. An initial segmentation step based on a larger sample of data sets is required to automate discrimination of lesions and artifacts.

Prediction of conversion from mild cognitive impairment to Alzheimer disease based on bayesian data mining with ensemble learning.

Prediction of disease progress is of great importance to Alzheimer disease (AD) researchers and clinicians. Previous attempts at constructing predictive models have been hindered by undersampling, and restriction to linear associations among variables, among other problems. To address these problems, we propose a novel Bayesian data-mining method called Bayesian Outcome Prediction with Ensemble Learning (BOPEL). BOPEL uses a Bayesian-network representation with boosting, to allow the detection of nonlinear multivariate associations, and incorporates resampling-based feature selection to prevent over-fitting caused by undersampling. We demonstrate the use of this approach in predicting conversion to AD in individuals with mild cognitive impairment (MCI), based on structural magnetic-resonance and magnetic-resonance- spectroscopy data. This study includes 26 subjects with amnestic MCI: the converter group (n = 8) met MCI criteria at baseline, but converted to AD within five years, whereas the non-converter group (n = 18) met MCI criteria at baseline and at follow-up. We found that BOPEL accurately differentiates MCI converters from non-converters, based on the baseline volumes of the left hippocampus, the banks of the right superior temporal sulcus, the right entorhinal cortex, the left lingual gyrus, and the rostral aspect of the left middle frontal gyrus. Prediction accuracy was 0.81, sensitivity was 0.63 and specificity was 0.89. We validated the generated predictive model with an independent data set constructed from the Alzheimer Disease Neuroimaging Initiative database, and again found high predictive accuracy (0.75).

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

PURPOSE: Autism spectrum disorder (ASD) is a neurodevelopmental disorder, of which Asperger syndrome and high-functioning autism are subtypes. Our goal is: 1) to determine whether a diagnostic model based on single-nucleotide polymorphisms (SNPs), brain regional thickness measurements, or brain regional volume measurements can distinguish Asperger syndrome from high-functioning autism; and 2) to compare the SNP, thickness, and volume-based diagnostic models. MATERIAL AND METHODS: Our study included 18 children with ASD: 13 subjects with high-functioning autism and 5 subjects with Asperger syndrome. For each child, we obtained 25 SNPs for 8 ASD-related genes; we also computed regional cortical thicknesses and volumes for 66 brain structures, based on structural magnetic resonance (MR) examination. To generate diagnostic models, we employed five machine-learning techniques: decision stump, alternating decision trees, multi-class alternating decision trees, logistic model trees, and support vector machines. RESULTS: For SNP-based classification, three decision-tree-based models performed better than the other two machine-learning models. The performance metrics for three decision-tree-based models were similar: decision stump was modestly better than the other two methods, with accuracy = 90%, sensitivity = 0.95 and specificity = 0.75. All thickness and volume-based diagnostic models performed poorly. The SNP-based diagnostic models were superior to those based on thickness and volume. For SNP-based classification, rs878960 in GABRB3 (gamma-aminobutyric acid A receptor, beta 3) was selected by all tree-based models. CONCLUSION: Our analysis demonstrated that SNP-based classification was more accurate than morphometry-based classification in ASD subtype classification. Also, we found that one SNP--rs878960 in GABRB3--distinguishes Asperger syndrome from high-functioning autism.