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Neural imaging studies have found the connection between strabismus and brain cortex. However, the pathological mechanisms of intermittent exotropia are still not fully understood. In the present study, changes of binocular fusion related cortices in intermittent exotropia were investigated with blood oxygen level dependent functional magnetic resonance imaging. Activated cortices induced by fusion stimulus were found to be distributed in several regions such as bilateral middle occipital gyrus, bilateral middle temporal gyrus, left superior parietal lobule and so on. Compared with normal subjects, the increased activation intensity was observed in bilateral superior parietal lobule and inferior parietal lobule in subjects with intermittent exotropia. These findings indicate that binocular fusion involves a complicated brain network including several regions. And cortical activities of bilateral superior parietal lobule and inferior parietal lobule compensate for the binocular fusion dysfunction in intermittent exotropia.

Deception is not a rare occurrence among human behaviors; however, the present brain mapping techniques are insufficient to reveal the neural mechanism of deception under spontaneous or controlled conditions. Interestingly, functional near-infrared spectroscopy (fNIRS) has emerged as a highly promising neuroimaging technique that enables continuous and noninvasive monitoring of changes in blood oxygenation and blood volume in the human brain. In this study, fNIRS was used in combination with complex network theory to extract the attribute features of the functional brain networks underling deception in subjects exhibiting spontaneous or controlled behaviors. Our findings revealed that the small-world networks of the subjects engaged in spontaneous behaviors exhibited greater clustering coefficients, shorter average path lengths, greater average node degrees, and stronger randomness compared with those of subjects engaged in control behaviors. Consequently, we suggest that small-world network topology is capable of distinguishing well between spontaneous and controlled deceptions.

This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.

Background: Neuroimaging studies have shown that major depressive disorder (MDD) is accompanied by structural and functional abnormalities in specific brain regions and connections; yet, little is known about alterations of the topological organization of whole-brain networks in MDD patients., Methods: Thirty drug-naive, first-episode MDD patients and 63 healthy control subjects underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and their topological properties (e.g., small-world, efficiency, and nodal centrality) were analyzed using graph theory-based approaches. Nonparametric permutation tests were further used for group comparisons of topological metrics., Results: Both the MDD and control groups showed small-world architecture in brain functional networks, suggesting a balance between functional segregation and integration. However, compared with control subjects, the MDD patients showed altered quantitative values in the global properties, characterized by lower path length and higher global efficiency, implying a shift toward randomization in their brain networks. The MDD patients exhibited increased nodal centralities, predominately in the caudate nucleus and default-mode regions, including the hippocampus, inferior parietal, medial frontal, and parietal regions, and reduced nodal centralities in the occipital, frontal (orbital part), and temporal regions. The altered nodal centralities in the left hippocampus and the left caudate nucleus were correlated with disease duration and severity., Conclusions: These results suggest that depressive disorder is associated with disruptions in the topological organization of functional brain networks and that this disruption may contribute to disturbances in mood and cognition in MDD patients., (Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features. [ABSTRACT FROM AUTHOR]