Wilson`s disease: two treatment modalities. Correlations to pretreatment and posttreatment brain MRI

Brain magnetic resonance imaging (MRI) studies on Wilson`s disease (WD) show lack of correlations between neurological and neuroimaging features. Long-term follow-up reports with sequential brain MRI in patients with neurological WD comparing different modalities of treatment are scarce. Eighteen patients with neurological WD underwent pretreatment and posttreatment brain MRI scans to evaluate the range of abnormalities and the evolution along these different periods. All patients underwent at least two MRI scans at different intervals, up to 11 years after the beginning of treatment. MRI findings were correlated with clinical picture, clinical severity, duration of neurological symptoms, and treatment with two different drugs. Patients were divided into two groups according to treatment: d-penicillamine (D-P), zinc (Zn), and Zn after the onset of severe intolerance to D-P. MRI scans before treatment showed, in all patients, hypersignal intensity lesions on T2- and proton-density-weighted images bilaterally and symmetrically at basal nuclei, thalamus, brain stem, cerebellum, brain cortex, and brain white matter. The most common neurological symptoms were: dysarthria, parkinsonism, dystonia, tremor, psychiatric disturbances, dysphagia, risus sardonicus, ataxia, chorea, and athetosis. From the neurological point of view, there was no difference on the evolution between the group treated exclusively with D-P and the one treated with Zn. Analysis of MRI scans with longer intervals after the beginning of treatment depicted a trend for neuroimaging worsening, without neurological correspondence, among patients treated with Zn. Neuroimaging pattern of evolution was more favorable for the group that received exclusively D-P.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.

Posterior reversible encephalopathy syndrome after liver transplantation in children: A rare complication related to calcineurin inhibitor effects

PRES is a neuroclinical and radiological syndrome that results from treatment with calcineurin inhibitor immunosuppressives. Severe hypertension is commonly present, but some patients may be normotensive. We report herein two children who received liver transplants, as treatment for biliary atresia in the first case and for Alagille`s syndrome in the second one. In the early postoperative, both patients presented hypertension and seizures. In both cases, the image findings suggested the diagnosis of PRES. The CT scan showed alterations in the posterior area of the brain, and brain MRI demonstrated parietal and occipital areas of high signal intensity. Both children were treated by switching the immunosuppressive regimen and controlling arterial blood pressure. They displayed full recuperation without any neurologic sequelae. Probably, the pathophysiology of PRES results from sparse sympathetic innervation of the vertebrobasilar circulation, which is responsible for supplying blood to the posterior areas of the brain. In conclusion, all liver-transplanted children who present with neurological symptoms PRES should be considered in the differential diagnosis, although this is a rare complication. As treatment, we recommend rigorous control of arterial blood pressure and switching the immunosuppressive regimen.

Leigh-like syndrome with the T8993G mutation in the mitochondrial ATPase 6 gene: long-term follow-up discloses a slowly progressive course

We describe the long-term clinical outcome of a patient with Leigh-like syndrome presenting as an early onset encephalopathy and peripheral neuropathy caused by the T8993G mutation in the mitochondrial DNA (mtDNA). Clinical follow-up for 20 years revealed a peculiar pattern of slow disease progression, characterized by the addition of new minor deficits, while worsening of previous symptoms was mild. Brain MRI revealed cerebellar atrophy, diffuse demyelination of corona radiata and parietal white matter, and bilateral and symmetrical putaminal lesions. The proportion of mutant mtDNAs in blood was 72% (+/- 0.02%) and in skeletal muscle was 81% (+/- 0.4%). Leigh-like syndrome caused by the T8993G mtDNA mutation is a progressive disease, although not necessarily associated with an aggressive clinical course. (C) 2009 Elsevier B.V. All rights reserved.

Characterizing the phenotypic manifestations of MFN2 R104W mutation in Charcot-Marie-Tooth type 2

Mutations of the mitofusin 2 (MFN2) gene have been reported to be the most common cause of the axonal form of Charcot Marie Tooth disease (CMT). The aim of this study was to describe a de novo MFN2 p.R104W mutation and characterize the associated phenotype. We screened the entire coding region of MFN2 gene and characterized its clinical phenotype, nerve conduction studies and sural nerve biopsy. Neuropsychological tests and brain MRI were also performed. A de nova mutation was found in exon 4 (c.310C > T; p.R104W). In addition to a severe and early onset axonal neuropathy, the patient presented learning problems, obesity, glucose intolerance, leukoencephalopathy, brain atrophy and evidence of myelin involvement and mitochondrial structural changes on sural nerve biopsy. These results suggest that MFN2 p.R104W mutation is as a hot-spot for MFN2 gene associated to a large and complex range of phenotypes. (C) 2011 Elsevier B.V. All rights reserved.

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

Assessment of factors that confound MRI and neuropathological correlation of human postmortem brain tissue

In spite of considerable technical advance in MRI techniques, the optical resolution of these methods are still limited. Consequently, the delineation of cytoarchitectonic fields based on probabilistic maps and brain volume changes, as well as small-scale changes seen in MRI scans need to be verified by neuronanatomical/neuropathological diagnostic tools. To attend the current interdisciplinary needs of the scientific community, brain banks have to broaden their scope in order to provide high quality tissue suitable for neuroimaging- neuropathology/anatomy correlation studies. The Brain Bank of the Brazilian Aging Brain Research Group (BBBABSG) of the University of Sao Paulo Medical School (USPMS) collaborates with researchers interested in neuroimaging-neuropathological correlation studies providing brains submitted to postmortem MRI in-situ. In this paper we describe and discuss the parameters established by the BBBABSG to select and to handle brains for fine-scale neuroimaging-neuropathological correlation studies, and to exclude inappropriate/unsuitable autopsy brains. We tried to assess the impact of the postmortem time and storage of the corpse on the quality of the MRI scans and to establish fixation protocols that are the most appropriate to these correlation studies. After investigation of a total of 36 brains, postmortem interval and low body temperature proved to be the main factors determining the quality of routine MRI protocols. Perfusion fixation of the brains after autopsy by mannitol 20% followed by formalin 20% was the best method for preserving the original brain shape and volume, and for allowing further routine and immunohistochemical staining. Taken to together, these parameters offer a methodological progress in screening and processing of human postmortem tissue in order to guarantee high quality material for unbiased correlation studies and to avoid expenditures by post-imaging analyses and histological processing of brain tissue.

Determination of trace elements in human brain tissues using neutron activation analysis

Neutron activation analysis was applied to assess trace element concentrations in brain tissues from normal (n = 21) and demented individuals (n = 21) of both genders aged more than 50 years. Concentrations of the elements Br, Fe, K, Na, Rb, Se and Zn were determined. Comparisons were made between the results obtained for the hippocampus and frontal cortex tissues, as well as, those obtained in brains of normal and demented individuals. Certified reference materials, NIST 1566b Oyster Tissue and NIST 1577b Bovine Liver were analyzed for quality of the analytical results.

Illness duration and total brain gray matter in bipolar disorder: Evidence for neurodegeneration?

Previous studies have suggested that bipolar disorder (BD) is associated with alterations in neuronal plasticity, but the effects of the progression of illness on brain anatomy have been poorly investigated. We studied the correlation between length of illness, age, age at onset, and the number of previous episodes and total brain, total gray, and total white matter volumes in BD, unipolar (UP) and healthy control (HC) subjects. Thirty-six BD, 31 UP and 55 HCs underwent a 1.5 T brain magnetic resonance imaging scan, and gray and white matter volumes were manually traced blinded to the subjects` diagnosis. Partial correlation analysis showed that length of illness was inversely correlated with total gray matter volume after adjusting for total intracranial volume in BD (r(p)=-0.51; p=0.003) but not in UP subjects (r(p)=-0.23; p=0.21). Age at illness onset and the number of previous episodes were not significantly correlated with gray matter volumes in BD or UP subjects. No significant correlation with total white matter volume was observed. These results suggest that the progression of illness may be associated with abnormal cellular plasticity. Prospective longitudinal studies are necessary to elucidate the long-term effects of illness progression on brain structure in major mood disorders. (C) 2008 Published by Elsevier B.V.

Gene expression profile analysis of primary glioblastomas and non-neoplastic brain tissue: identification of potential target genes by oligonucleotide microarray and real-time quantitative PCR

The prognosis of glioblastomas is still extremely poor and the discovery of novel molecular therapeutic targets can be important to optimize treatment strategies. Gene expression analyses comparing normal and neoplastic tissues have been used to identify genes associated with tumorigenesis and potential therapeutic targets. We have used this approach to identify differentially expressed genes between primary glioblastomas and non-neoplastic brain tissues. We selected 20 overexpressed genes related to cell cycle, cellular movement and growth, proliferation and cell-to-cell signaling and analyzed their expression levels by real time quantitative PCR in cDNA obtained from microdissected fresh tumor tissue from 20 patients with primary glioblastomas and from 10 samples of non-neoplastic white matter tissue. The gene expression levels were significantly higher in glioblastomas than in non-neoplastic white matter in 18 out of 20 genes analyzed: P < 0.00001 for CDKN2C, CKS2, EEF1A1, EMP3, PDPN, BNIP2, CA12, CD34, CDC42EP4, PPIE, SNAI2, GDF15 and MMP23b; and NFIA (P: 0.0001), GPS1 (P: 0.0003), LAMA1 (P: 0.002), STIM1 (P: 0.006), and TASP1 (P: 0.01). Five of these genes are located in contiguous loci at 1p31-36 and 2 at 17q24-25 and 8 of them encode surface membrane proteins. PDPN and CD34 protein expression were evaluated by immunohistochemistry and they showed concordance with the PCR results. The present results indicate the presence of 18 overexpressed genes in human primary glioblastomas that may play a significant role in the pathogenesis of these tumors and that deserve further functional investigation as attractive candidates for new therapeutic targets.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

The Stability of the Blood Oxygenation Level-Dependent Functional MRI Response to Motor Tasks Is Altered in Patients With Chronic Ischemic Stroke

Background and Purpose-Functional MRI is a powerful tool to investigate recovery of brain function in patients with stroke. An inherent assumption in functional MRI data analysis is that the blood oxygenation level-dependent (BOLD) signal is stable over the course of the examination. In this study, we evaluated the validity of such assumption in patients with chronic stroke. Methods-Fifteen patients performed a simple motor task with repeated epochs using the paretic and the unaffected hand in separate runs. The corresponding BOLD signal time courses were extracted from the primary and supplementary motor areas of both hemispheres. Statistical maps were obtained by the conventional General Linear Model and by a parametric General Linear Model. Results-Stable BOLD amplitude was observed when the task was executed with the unaffected hand. Conversely, the BOLD signal amplitude in both primary and supplementary motor areas was progressively attenuated in every patient when the task was executed with the paretic hand. The conventional General Linear Model analysis failed to detect brain activation during movement of the paretic hand. However, the proposed parametric General Linear Model corrected the misdetection problem and showed robust activation in both primary and supplementary motor areas. Conclusions-The use of data analysis tools that are built on the premise of a stable BOLD signal may lead to misdetection of functional regions and underestimation of brain activity in patients with stroke. The present data urge the use of caution when relying on the BOLD response as a marker of brain reorganization in patients with stroke. (Stroke. 2010; 41:1921-1926.)

Morphometric analysis of the AMPA-type neurons in the Deiters vestibular complex of the chick brain

Chicken (Gallus gallus) brains were used to investigate the typology and the immunolabel pattern for the subunits composing the AMPA-type glutamate receptors (GluR) of hindbrain neurons of the dorsal (dND) and ventral nuclei (vND) of the Deiter`s vestibular complex (CD), which is the avian correspondent of the lateral vestibular nucleus (LVN) of mammals. Our results revealed that neurons of both divisions were poor in GluR1. The vND, the GluR2/3+ and GluR4+ label presented no area or neuronal size preference, although most neurons were around 75%. The dND neurons expressing GluR2/3 are primarily around 85%, medium to large-sized 85%, and predominantly 60% located in the medial portion of the rostral pole and in the lateral portion of the caudal pole. The majority of dND neurons containing GluR4 are also around 75%, larger (70% are large and giant), exhibiting a distribution that seems to be complementary to that of GluR2/3+ neurons. This distinct arrangement indicates functional differences into and between the DC nuclei, also signaling that such variation could be attributed to the diverse nature of the subunit composition of the GluRs. Discussion addresses the morphological and functional correlation of the avian DC with the LVN of mammals in addition to the high morphological correspondence, To include these data into the modern comparative approach we propose to adopt a similar nomenclature for the avian divisions dND and vND that could be referred as dLVN and vLVN. (C) 2008 Elsevier B.V. All rights reserved.