Overlapping structural and functional brain changes in patients with long-term exposure to fibromyalgia pain.

OBJECTIVE: There is vast evidence to support the presence of brain aberrations in patients with fibromyalgia (FM), and it is possible that central plasticity is critical for the transition from acute to chronic pain. The aim of the present study was to investigate the relationship between brain structure and function in patients with FM. METHODS: Functional connectivity of the brain during application of intermittent pressure-pain stimuli and measures of brain structure were compared between 26 patients with FM and 13 age- and sex-matched healthy controls. Magnetic resonance imaging (MRI) was performed to obtain high-resolution anatomic images and functional MRI scans of the brain, which were used for measurements of pain-evoked brain activity. RESULTS: FM patients displayed a distinct overlap between decreased cortical thickness, decreased brain volumes, and decreased functional regional coherence in the rostral anterior cingulate cortex. The morphometric changes were more pronounced with longer exposure to FM pain. In addition, there was evidence of an association between structural and functional changes in the mesolimbic areas of the brain and the severity of comorbid depression symptoms in FM patients. CONCLUSION: The combined integration of structural and functional measures allowed for a unique characterization of the impact of FM pain on the brain. These data may lead to the identification of early structural and functional brain alterations in response to pain, which could be used to develop markers for predicting the development of FM and other pain disorders.

Motor "dexterity"?: Evidence that left hemisphere lateralization of motor circuit connectivity is associated with better motor performance in children.

Motor control relies on well-established motor circuits, which are critical for typical child development. Although many imaging studies have examined task activation during motor performance, none have examined the relationship between functional intrinsic connectivity and motor ability. The current study investigated the relationship between resting state functional connectivity within the motor network and motor performance assessment outside of the scanner in 40 typically developing right-handed children. Better motor performance correlated with greater left-lateralized (mean left hemisphere-mean right hemisphere) motor circuit connectivity. Speed, rhythmicity, and control of movements were associated with connectivity within different individual region pairs: faster speed was associated with more left-lateralized putamen-thalamus connectivity, less overflow with more left-lateralized supplementary motor-primary motor connectivity, and less dysrhythmia with more left-lateralized supplementary motor-anterior cerebellar connectivity. These findings suggest that for right-handed children, superior motor development depends on the establishment of left-hemisphere dominance in intrinsic motor network connectivity.

Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy.

We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.

A non-voxel based feature extraction to detect cognitive states in fMRI.

Over the past few decades, Functional Magnetic Resonance Imaging (fMRI) has evolved into an important utilitarian tool for analyzing brain activity and detecting the cognitive states of a subject. The design and development of effective dimensionality reduction schema for the discovery of discriminatory features for cognitive state classification has become an area of vital interest toward enhanced decision support applications for patients with various brain disorders. In this paper, we present a unique non-voxel based approach using wavelet descriptor differentiation and principal components to extract unique features that reduce slice variability in fMRI data for the enhanced classification of cognitive states. The set of cognitive states that we attempt to classify are 'a person reading a sentence' and 'a person reading a picture.' The discovered feature vector is small and achieves significant classification accuracy using different classifiers and under different Regions of Interest (ROI) constraints. Experimental results using this study demonstrate the effectiveness of the approach when compared to previous voxel-based approaches.