The fMRI response of the LGN and V1 to cardinal red-green, blue-yellow, and achromatic visual stimuli

We compared the responsiveness of the LGN and the early retinotopic cortical areas to stimulation of the two cone-opponent systems (red - green and blue - yellow) and the achromatic system. This was done at two contrast levels to control for any effect of contrast. MR images were acquired on seven subjects with a 4T Bruker MedSpec scanner. The early visual cortical areas were localised by phase encoded retinotopic mapping with a volumetric analysis (Dumoulin et al, 2003 NeuroImage 18 576 - 587). We initially located the LGN in four subjects by using flickering stimuli in a separate scanning session, but subsequently identified it using the experimental stimuli. Experimental stimuli were sine-wave counterphasing rings (2 Hz, 0.5 cycle deg-1), cardinal for the selective activation of the L/M cone-opponent (RG), S cone-opponent (BY), and achromatic (Ach) systems. A region of interest analysis was performed. When presented at equivalent absolute contrasts (cone contrast = 5% - 6%), the BOLD response of the LGN is strongest to isoluminant red - green stimuli and weakest to blue - yellow stimuli, with the achromatic response falling in between. Area V1, on the other hand, responds best to both chromatic stimuli, with the achromatic response falling below. The key change from the LGN to V1 is a dramatic boost in the relative blue - yellow response, which occurred at both contrast levels used. This greatly enhanced cortical response to blue - yellow relative to the red - green and achromatic responses may be due to an increase in cell number and/or cell response between the LGN and V1. We speculate that the effect might reflect the operation of contrast constancy across colour mechanisms at the cortical level.

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.

White matter microstructure underlying default mode network connectivity in the human brain

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

Depression in Parkinson`s disease: Convergence from voxel-based morphometry and functional magnetic resonance imaging in the limbic thalamus

Depression is the most frequent psychiatric disorder in Parkinson`s disease (PD). Although evidence Suggests that depression in PD is related to the degenerative process that underlies the disease, further studies are necessary to better understand the neural basis of depression in this population of patients. In order to investigate neuronal alterations underlying the depression in PD, we studied thirty-six patients with idiopathic PD. Twenty of these patients had the diagnosis of major depression disorder and sixteen did not. The two groups were matched for PD motor severity according to Unified Parkinson Disease Rating Scale (UPDRS). First we conducted a functional magnetic resonance imaging (fMRI) using an event-related parametric emotional perception paradigm with test retest design. Our results showed decreased activation in the left mediodorsal (MD) thalamus and in medial prefrontall cortex in PD patients with depression compared to those without depression. Based upon these results and the increased neuron count in MD thalamus found in previous studies, we conducted a region of interest (ROI) guided voxel-based morphometry (VBM) study comparing the thalamic volume. Our results showed an increased volume in mediodorsal thalamic nuclei bilaterally. Converging morphological changes and functional emotional processing in mediodorsal thalamus highlight the importance of limbic thalamus in PD depression. In addition this data supports the link between neurodegenerative alterations and mood regulation. (C) 2009 Elsevier Inc. All rights reserved.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Objective: The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models. Methods: A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas. Results: In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set. Conclusions: We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard. (C) 2010 Elsevier B.V. All rights reserved.

Increased Brain Membrane Fluidity in Schizophrenia

Recent findings showing significant correlations between phospholipase A2 (PLA2) activity and structural changes in schizophrenic brains contribute to the membrane hypothesis of schizophrenia, which was hampered because a clean functional link between elevated PLA2 activity and brain structure was missing (Neuroimage, 2010; 52: 1314-1327). We measured membrane fluidity parameters and found that brain membranes isolated from the prefrontal cortex of schizophrenic patients showed significantly increased flexibility of fatty acid chains. Our findings support a possible link between elevated PLA2 activity in cortical areas of schizophrenic patients and subsequent alterations of the biophysical parameters of neuronal membranes leading to structural changes in these areas.

Reduced gray matter volume in ventral prefrontal cortex but not amygdala in bipolar disorder: Significant effects of gender and trait anxiety

Neuroimaging studies in bipolar disorder report gray matter volume (GMV) abnormalities in neural regions implicated in emotion regulation. This includes a reduction in ventral/orbital medial prefrontal cortex (OMPFC) GMV and, inconsistently, increases in amygdala GMV. We aimed to examine OMPFC and amygdala GMV in bipolar disorder type 1 patients (BPI) versus healthy control participants (HC), and the potential confounding effects of gender, clinical and illness history variables and psychotropic medication upon any group differences that were demonstrated in OMPFC and amygdala GMV Images were acquired from 27 BPI (17 euthymic, 10 depressed) and 28 age- and gender-matched HC in a 3T Siemens scanner. Data were analyzed with SPM5 using voxel-based morphometry (VBM) to assess main effects of diagnostic group and gender upon whole brain (WB) GMV. Post-hoc analyses were subsequently performed using SPSS to examine the extent to which clinical and illness history variables and psychotropic medication contributed to GMV abnormalities in BPI in a priori and non-a priori regions has demonstrated by the above VBM analyses. BPI showed reduced GMV in bilateral posteromedial rectal gyrus (PMRG), but no abnormalities in amygdala GMV. BPI also showed reduced GMV in two non-a priori regions: left parahippocampal gyrus and left putamen. For left PMRG GMV, there was a significant group by gender by trait anxiety interaction. GMV was significantly reduced in male low-trait anxiety BPI versus male low-trait anxiety HC, and in high-versus low-trait anxiety male BPI. Our results show that in BPI there were significant effects of gender and trait-anxiety, with male BPI and those high in trait-anxiety showing reduced left PMRG GMV. PMRG is part of medial prefrontal network implicated in visceromotor and emotion regulation. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

Magnetic resonance imaging quantification of regional cerebral blood flow and cerebrovascular reactivity to carbon dioxide in normotensive and hypertensive rats

Hypertension afflicts 25% of the general population and over 50% of the elderly. In the present work, arterial spin labeling MRI was used to non-invasively quantify regional cerebral blood flow (CBE), cerebrovascular resistance and CO(2) reactivity in spontaneously hypertensive rats (SHR) and in normotensive Wistar Kyoto rats (WKY), at two different ages (3 months and 10 months) and under the effects of two anesthetics, alpha-chloralose and 2% isoflurane (1.5 MAC). Repeated CBE measurements were highly consistent, differing by less than 10% and 18% within and across animals, respectively. Under alpha-chloralose, whole brain CBE at normocapnia did not differ between groups (young WKY: 61 3 ml/100 g/min; adult WKY: 62 +/- 4 ml/100 g/min; young SHR: 70 +/- 9 ml/100 g/min: adult SHR: 69 8 ml/100 g/min), indicating normal cerebral autoregulation in SHR. At hypercapnia, CBE values increased significantly, and a linear relationship between CBE and PaCO(2) levels was observed. In contrast, 2% isoflurane impaired cerebral autoregulation. Whole brain CBE in SHR was significantly higher than in WKY rats at normocapnia (young SHR: 139 +/- 25 ml/100 g/min; adult SHR: 104 +/- 23 ml/100 g/min; young WKY: 55 +/- 9 ml/100 g/min; adult WKY: 71 +/- 19 ml/100 g/min). CBE values increased significantly with increasing CO(2): however, there was a clear saturation of CBF at PaCO(2) levels greater than 70 mm Hg in both young and adult rats, regardless of absolute CBE values, suggesting that isoflurane interferes with the vasoclilatory mechanisms of CO(2). This behavior was observed for both cortical and subcortical structures. Under either anesthetic, CO(2) reactivity values in adult SHR were decreased, confirming that hypertension, when combined with age, increases cerebrovascular resistance and reduces cerebrovascular compliance. Published by Elsevier Inc.

Effects of bariatric surgery on the central nervous system and eating behavior in humans: a systematic review on the neuroimaging studies

INTRODUCTION: Neuroimaging studies suggest that obese people might show hyperactivity of brain areas regarding reward processing, and hypoactivity of brain areas concerning cognitive control, when exposed to food cues. Although the effects of bariatric surgery on the central nervous system and eating behavior are well known, few studies have used neuroimage techniques with the aim of investigating the central effects of bariatric surgery in humans. OBJECTIVES: This paper systematically and critically reviews studies using functional neuroimaging to investigate changes on the patterns of activation of central areas related to the regulation of eating behavior after bariatric surgery. METHOD: A search on the databases Medline, Web of Science, Lilacs and Science Direct on Line, was conducted in February 2013, using the keywords "Neuroimaging", "Positron-Emission Tomography", "Magnetic Resonance Imaging", "Gastric Bypass", "Gastroplasty", "Jejunoileal Bypass", "Bariatric Surgery". RESULTS: Seven manuscripts were included; the great majority studied the central effects of Roux en Y gastric bypass, using positron emission tomography or functional magnetic resonance. CONCLUSIONS: Bariatric surgery might normalize the activity of central areas concerned with reward and incentive salience processing, as the nucleus accumbens and mesencephalic tegmental ventral area, as well as circuitries processing behavioral inhibition, as the dorsolateral prefrontal cortex.

Getting in touch: segregated somatosensory what and where pathways in humans revealed by electrical neuroimaging.

Whether the somatosensory system, like its visual and auditory counterparts, is comprised of parallel functional pathways for processing identity and spatial attributes (so-called what and where pathways, respectively) has hitherto been studied in humans using neuropsychological and hemodynamic methods. Here, electrical neuroimaging of somatosensory evoked potentials (SEPs) identified the spatio-temporal mechanisms subserving vibrotactile processing during two types of blocks of trials. What blocks varied stimuli in their frequency (22.5 Hz vs. 110 Hz) independently of their location (left vs. right hand). Where blocks varied the same stimuli in their location independently of their frequency. In this way, there was a 2x2 within-subjects factorial design, counterbalancing the hand stimulated (left/right) and trial type (what/where). Responses to physically identical somatosensory stimuli differed within 200 ms post-stimulus onset, which is within the same timeframe we previously identified for audition (De Santis, L., Clarke, S., Murray, M.M., 2007. Automatic and intrinsic auditory "what" and "where" processing in humans revealed by electrical neuroimaging. Cereb Cortex 17, 9-17.). Initially (100-147 ms), responses to each hand were stronger to the what than where condition in a statistically indistinguishable network within the hemisphere contralateral to the stimulated hand, arguing against hemispheric specialization as the principal basis for somatosensory what and where pathways. Later (149-189 ms) responses differed topographically, indicative of the engagement of distinct configurations of brain networks. A common topography described responses to the where condition irrespective of the hand stimulated. By contrast, different topographies accounted for the what condition and also as a function of the hand stimulated. Parallel, functionally specialized pathways are observed across sensory systems and may be indicative of a computationally advantageous organization for processing spatial and identity information.

Discrimination of cortical laminae using MEG.

Typically MEG source reconstruction is used to estimate the distribution of current flow on a single anatomically derived cortical surface model. In this study we use two such models representing superficial and deep cortical laminae. We establish how well we can discriminate between these two different cortical layer models based on the same MEG data in the presence of different levels of co-registration noise, Signal-to-Noise Ratio (SNR) and cortical patch size. We demonstrate that it is possible to make a distinction between superficial and deep cortical laminae for levels of co-registration noise of less than 2mm translation and 2° rotation at SNR>11dB. We also show that an incorrect estimate of cortical patch size will tend to bias layer estimates. We then use a 3D printed head-cast (Troebinger et al., 2014) to achieve comparable levels of co-registration noise, in an auditory evoked response paradigm, and show that it is possible to discriminate between these cortical layer models in real data.

Estudi GPGPU dedicat al processament de neuroimatges

La tecnologia GPGPU permet paral∙lelitzar càlculs executant operacions aritmètiques en els múltiples processadors de que disposen els xips gràfics. S'ha fet servir l'entorn de desenvolupament CUDA de la companyia NVIDIA, que actualment és la solució GPGPU més avançada del mercat. L'algorisme de neuroimatge implementat pertany a un estudi VBM desenvolupat amb l'eina SPM. Es tracta concretament del procés de segmentació d'imatges de ressonància magnètica cerebrals, en els diferents teixits dels quals es composa el cervell: matèria blanca, matèria grisa i líquid cefaloraquidi. S'han implementat models en els llenguatges Matlab, C i CUDA, i s'ha fet un estudi comparatiu per plataformes hardware diferents.