Decreased functional connectivity between the mediodorsal thalamus and default mode network in patients with disorders of consciousness.

BACKGROUND: Interactions between the thalamus and the cortex play an important role in consciousness. The thalamus as a homogenous structure is less strongly connected with the default mode network (DMN) in patients with disorders of consciousness (DOC), but the roles of specific thalamic nuclei are not clear. The purpose of this study was to investigate the functional connectivity between individual thalamic nuclei and the DMN in DOC patients. METHODS: Nine DOC patients and nine age-matched healthy controls were scanned with functional magnetic resonance imaging (fMRI) at resting state. Data-driven independent component analysis and hypothesis-driven region of interest-based correlation analysis were performed. RESULTS: In comparison with healthy controls, DOC patients had significantly decreased functional connectivity between the mediodorsal thalamus and brain areas within the DMN, including the medial prefrontal cortex and posterior cingulate cortex/precuneus. Patients and controls did not show significant differences in functional connectivity in other thalamic nuclei. CONCLUSION: Our results suggest that functional connections between the mediodorsal thalamus and the DMN may play important roles in the pathogenesis of DOC.

Diffusion tensor imaging in the assessment of normal-appearing brain tissue damage in relapsing neuromyelitis optica.

BACKGROUND AND PURPOSE: Normal-appearing brain tissue (NABT) damage was established in multiple sclerosis by histology, MR spectroscopy, magnetization transfer imaging and diffusion tensor imaging (DTI). However, whether this phenomenon can be detected in relapsing neuromyelitis optica (RNMO) remains unclear. The aim of this study was to use DTI to investigate the presence of NABT damage in RNMO patients and its possible mechanism. METHODS: Conventional MR imaging and DTI scans were performed in 16 patients with RNMO without visible lesions on brain MR imaging and in 16 sex- and age-matched healthy control subjects. Histogram analysis of mean diffusivity (MD) and fractional anisotropy (FA) was performed in the entire brain tissue (BT), white matter (WM), and gray matter (GM). Region of interest (ROI) analysis of MD and FA was also performed in WM regions connected with the spinal white matter tracts or optic nerve (including medulla oblongata, cerebral peduncle, internal capsule, and optic radiation), in corpus callosum without direct connection with them, and in some GM regions. RESULTS: From histogram analysis, we found the RNMO group had a higher average MD of the BT, WM, and GM, a lower average MD peak height and a higher average MD peak location of the GM, and a higher average FA peak height of the WM than did the control group. From ROI analysis, compared with control subjects, RNMO patients had a higher average MD and a lower average FA in ROIs of WM connected with the spinal white matter tracts or optic nerve and a normal average MD and FA in corpus callosum without direct connection with them. In addition, a high average MD was found in parietal GM in these patients. CONCLUSIONS: Our findings confirm the presence of abnormal diffusion in brain tissue in patients with RNMO and suggest that secondary degeneration caused by lesions in the spinal cord and optic nerve might be an important mechanism for this abnormality.

Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder.

In this work, a discriminative model of attention deficit hyperactivity disorder (ADHD) is presented on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model consists of two parts, a classifier and an intuitive representation of discriminative pattern of brain function between patients and normal controls. Regional homogeneity (ReHo), a measure of brain function at resting-state, is used here as a feature of classification. Fisher discriminative analysis (FDA) is performed on the features of training samples and a linear classifier is generated. Our initial experimental results show a successful classification rate of 85%, using leave-one-out cross validation. The classifier is also compared with linear support vector machine (SVM) and Batch Perceptron. Our classifier outperforms the alternatives significantly. Fisher brain, the optimal projective-direction vector in FDA, is used to represent the discriminative pattern. Some abnormal brain regions identified by Fisher brain, like prefrontal cortex and anterior cingulate cortex, are well consistent with that reported in neuroimaging studies on ADHD. Moreover, some less reported but highly discriminative regions are also identified. We conclude that the discriminative model has potential ability to improve current diagnosis and treatment evaluation of ADHD.