960 resultados para MUSCLE FUNCTIONAL MAGNETIC RESONANCE


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Functional magnetic resonance imaging (FMRI) analysis methods can be quite generally divided into hypothesis-driven and data-driven approaches. The former are utilised in the majority of FMRI studies, where a specific haemodynamic response is modelled utilising knowledge of event timing during the scan, and is tested against the data using a t test or a correlation analysis. These approaches often lack the flexibility to account for variability in haemodynamic response across subjects and brain regions which is of specific interest in high-temporal resolution event-related studies. Current data-driven approaches attempt to identify components of interest in the data, but currently do not utilise any physiological information for the discrimination of these components. Here we present a hypothesis-driven approach that is an extension of Friman's maximum correlation modelling method (Neurolmage 16, 454-464, 2002) specifically focused on discriminating the temporal characteristics of event-related haemodynamic activity. Test analyses, on both simulated and real event-related FMRI data, will be presented.

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In primates, the observation of meaningful, goaldirected actions engages a network of cortical areas located within the premotor and inferior parietal lobules. Current models suggest that activity within these regions arises relatively automatically during passive action observation without the need for topdown control. Here we used functional magnetic resonance imaging to determine whether cortical activit)' associated with action observation is modulated by the strategic allocation of selective attention. Normal observers viewed movie clips of reach-to-grasp actions while performing an easy or difficult visual discrimination at the fovea. A wholebrain analysis was performed to determine the effects of attentional load on neural responses to observed hand actions. Our results suggest that cortical areas involved in action observation are significantiy modulated by attentional load. These findings have important implications for recent attempts to link the human action-observation system to response properties of "mirror neurons" in monkeys.

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A polymorphism of the dopamine transporter gene (DAT1, 10-repeat) is associated with attention-deficit hyperactivity disorder (ADHD) and has been linked to an enhanced response to methylphenidate (MPH). One aspect of the attention deficit in ADHD includes a subtle inattention to left space, resembling that seen after right cerebral hemisphere damage. Since left-sided inattention in ADHD may resolve when treated with MPH, we asked whether left-sided inattention in ADHD was related to DAT1 genotype and the therapeutic efficacy of MPH. A total of 43 ADHD children and their parents were genotyped for the DAT1 30 variable number of tandem repeats polymorphism. The children performed the Landmark Test, a well-validated measure yielding a spatial attentional asymmetry index ( leftward to rightward attentional bias). Parents rated their child's response to MPH retrospectively using a three-point scale ( no, mediocre or very good response). Additionally, parents used a symptom checklist to rate behavior while on and off medication. A within-family control design determined whether asymmetry indices predicted biased transmission of 10-repeat parental DAT1 alleles and/or response to MPH. It was found that left-sided inattention predicted transmission of the 10-repeat allele from parents to probands and was associated with the severity of ADHD symptomatology. Children rated as achieving a very good response to MPH displayed left-sided inattention, while those rated as achieving a poorer response did not. Our results suggest a subgroup of children with ADHD for whom the 10-repeat DAT1 allele is associated with left-sided inattention. MPH may be most efficacious in this group because it ameliorates a DAT1-mediated hypodopaminergic state.

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Pathological inattention following parietal damage causes perceptual impairments for visual stimuli in the contralesional hemifield. Here we used functional magnetic resonance imaging (fMRI) to examine visual cortex activity in parietal patients as they performed a spatial attention task. Righthemisphere patients and healthy controls viewed counterphasing checkerboards in which coloured targets appeared briefly within the contralesional and ipsilesional hemifields. In separate fMRI runs participants focused their attention covertiy on the left or right hemifield, or on both hemifields concurrentiy. They were required to detect coloured targets that appeared briefly within the attended hemifield(s), and to withhold responses to distractor stimuli. Neural activit}' was significantly attenuated in early visual areas within the damaged hemisphere. Crucially, although attention significantiy modulated early visual activit}' within the intact (left) hemisphere, there was relatively littie modulation of activity within the affected hemisphere. Our findings suggest that parietal lesions alter early cortical responses to contralesional visual inputs.

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The mechanisms underlying the effects of antidepressant treatment in patients with Parkinson`s disease (PD) are unclear. The neural changes after successful therapy investigated by neuroimaging methods can give insights into the mechanisms of action related to a specific treatment choice. To study the mechanisms of neural modulation of repetitive transcranial magnetic Stimulation (rTMS) and fluoxetine, 21 PD depressed patients were randomized into only two active treatment groups for 4 wk: active rTMS over left dorsolateral prefrontal cortex (DLPFC) (5 Hz rTMS; 120% motor threshold) with placebo pill and sham rTMS with fluoxetine 20mg/d. Event-related functional magnetic resonance imaging (fMRI) with emotional stimuli was performed before and after treatment - in two sessions (test and re-test) at each time-point. The two groups of treatment had a significant, similar mood improvement. After rTMS treatment, there were brain activity decreases in left fusiform gyrus, cerebellum and right DLPFC and brain activity increases in left DLPFC and anterior cingulate gyrus compared to baseline. In contrast, after fluoxetine treatment, there were brain activity increases in right premotor and right medial prefrontal cortex. There was a significant interaction effect between groups vs. time in the left medial prefrontal cortex, suggesting that the activity in this area changed differently in the two treatment groups. Our findings show that antidepressant effects of rTMS and fluoxetine in PD are associated with changes in different areas of the depression-related neural network.

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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.

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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.

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Previous functional magnetic resonance imaging (fMRI) studies examined neural activity responses to emotive stimuli in healthy individuals after acute/subacute administration of antidepressants. We now report the effects of repeated use of the antidepressant clomipramine on fMRI data acquired during presentation of emotion-provoking and neutral stimuli on healthy volunteers. A total of 12 volunteers were evaluated with fMRI after receiving low doses of clomipramine for 4 weeks and again after 4 weeks of washout. Fear-, happiness-, anger-provoking and neutral pictures from the International Affective Picture System (IAPS) were used. Data analysis was performed with statistical parametric mapping (P < 0.05). Paired t-test comparisons for each condition between medicated and unmedicated states showed, to negative valence paradigms, decrease in brain activity in the amygdala when participants were medicated. We also demonstrated, across both positive and negative valence paradigms, consistent decreases in brain activity in the medicated state in the anterior cingulate gyrus and insula. This is the first report of modulatory effects of repeated antidepressant use on the central representation of somatic states in response to emotions of both negative and positive valences in healthy individuals. Also, our results corroborate findings of antidepressant-induced temporolimbic activity changes to emotion-provoking stimuli obtained in studies of subjects treated acutely with such agents.

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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.

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Resting state functional magnetic resonance imaging (fMRI) reveals a distinct network of correlated brain function representing a default mode state of the human brain The underlying structural basis of this functional connectivity pattern is still widely unexplored We combined fractional anisotropy measures of fiber tract integrity derived from diffusion tensor imaging (DTI) and resting state fMRI data obtained at 3 Tesla from 20 healthy elderly subjects (56 to 83 years of age) to determine white matter microstructure e 7 underlying default mode connectivity We hypothesized that the functional connectivity between the posterior cingulate and hippocampus from resting state fMRI data Would be associated with the white matter microstructure in the cingulate bundle and fiber tracts connecting posterior cingulate gyrus With lateral temporal lobes, medial temporal lobes, and precuneus This was demonstrated at the p<0001 level using a voxel-based multivariate analysis of covariance (MANCOVA) approach In addition, we used a data-driven technique of joint independent component analysis (ICA) that uncovers spatial pattern that are linked across modalities. It revealed a pattern of white matter tracts including cingulate bundle and associated fiber tracts resembling the findings from the hypothesis-driven analysis and was linked to the pattern of default mode network (DMN) connectivity in the resting state fMRI data Out findings support the notion that the functional connectivity between the posterior cingulate and hippocampus and the functional connectivity across the entire DMN is based oil distinct pattern of anatomical connectivity within the cerebral white matter (C) 2009 Elsevier Inc All rights reserved

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The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

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Emotional liability and mood dysregulation characterize bipolar disorder (BID), yet no study has examined effective connectivity between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems subserving mood regulation in BD. Participants comprised 46 individuals (age range: 18-56 years): 21 with a DSM-IV diagnosis of BID, type I currently remitted; and 25 age- and gender-matched healthy controls (HC). Participants performed an event-related functional magnetic resonance imaging paradigm, viewing mild and intense happy and neutral faces. We employed dynamic causal modeling (I)CM) to identify significant alterations in effective connectivity between BD and HC. Bayes model selection was used to determine the best model. The right parahippocampal gyrus (PHG) and right subgenual cingulate gyrus (sgCG) were included as representative regions of the ventromedial neural system. The right dorsolateral prefrontal cortex (DLPFC) region was included as representative of the dorsal/lateral neural system. Right PHG-sgCG effective connectivity was significantly greater in BD than HC, reflecting more rapid, forward PHG-sgCG signaling in BD than HC. There was no between-group difference in sgCG-DLPFC effective connectivity. In BD, abnormally increased right PHG-sgCG effective connectivity and reduced right PHG activity to emotional stimuli suggest a dysfunctional ventromedial neural system implicated in early stimulus appraisal, encoding and automatic regulation of emotion that may represent a pathophysiological functional neural mechanism for mood dysregulation in BD. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

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Background: In view of conflicting neuroimaging results regarding autonomic-specific activity within the anterior cingulate cortex (ACC), we investigated autonomic responses to direct brain stimulation during sterecitactic limbic surgery. Methods: Skin conductance activity and accelerative heart rate responses to multi-voltage stimulation of the ACC (n = 7) and paralimbic subcauclate (n = 5) regions were recorded during bilateral anterior cingulotomy and bilateral subcauclate tractotomy (in patients that had previously received an adequate lesion in the ACC), respectively. Results: Stimulations in both groups were accompanied by increased autonomic arousal. Skin conductance activity was significantly increased during ACC stimulations compared with paralimbic targets at 2 V (2.34 +/- .68 [score in microSiemens +/- SE] vs. .34 +/- .09, p = .013) and 3 V (3.52 +/- .86 vs. 1.12 +/- .37, p = .036), exhibiting a strong ""voltage-response"" relationship between stimulus magnitude and response amplitude (difference from 1 to 3 V = 1.15 +/- .90 vs. 3.52 +/- .86, p = .041). Heart rate response was less indicative of between-group differences. Conclusions: This is the first study of its kind aiming at seeking novel insights into the mechanisms responsible for central autonomic modulation. It supports a concept that interregional interactions account for the coordination of autonomic arousal.

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The amygdala has a key role in automatic non-conscious processing of emotions. Highly salient emotional stimuli elicit amygdala activity, and happy faces are among the most rapidly perceived facial expressions. In backward masking paradigms, an image is presented briefly and then masked by another stimulus. However, reports of amygdala responses to masked happy faces have been mixed. In the present Study, we used functional magnetic resonance imaging (fMRI) to examine amygdala activation to masked happy, sad, and neutral facial expressions. Masked happy faces elicited greater amygdala activation bilaterally as compared to masked sad faces. Our findings indicate that the amygdala is highly responsive to non-consciously perceived happy facial expressions. (JINS, 2010, 16, 383-387.)

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Delta-9-tetrahydrocannabinol (Delta-9-THC) and Cannabidiol (CBD), the two main ingredients of the Cannabis sativa plant have distinct symptomatic and behavioral effects. We used functional magnetic resonance imaging (fMRI) in healthy volunteers to examine whether Delta-9-THC and CBD had opposite effects on regional brain function. We then assessed whether pretreatment with CBD can prevent the acute psychotic symptoms induced by Delta-9-THC. Fifteen healthy men with minimal earlier exposure to cannabis were scanned while performing a verbal memory task, a response inhibition task, a sensory processing task, and when viewing fearful faces. Subjects were scanned on three occasions, each preceded by oral administration of Delta-9-THC, CBD, or placebo. BOLD responses were measured using fMRI. In a second experiment, six healthy volunteers were administered Delta-9-THC intravenously on two occasions, after placebo or CBD pretreatment to examine whether CBD could block the psychotic symptoms induced by Delta-9-THC. Delta-9-THC and CBD had opposite effects on activation relative to placebo in the striatum during verbal recall, in the hippocampus during the response inhibition task, in the amygdala when subjects viewed fearful faces, in the superior temporal cortex when subjects listened to speech, and in the occipital cortex during visual processing. In the second experiment, pretreatment with CBD prevented the acute induction of psychotic symptoms by Delta-9-tetrahydrocannabinol. Delta-9-THC and CBD can have opposite effects on regional brain function, which may underlie their different symptomatic and behavioral effects, and CBD`s ability to block the psychotogenic effects of Delta-9-THC. Neuropsychopharmacology (2010) 35, 764-774; doi:10.1038/npp.2009.184; published online 18 November 2009