NeuroImage. Clinical
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NeuroImage. Clinical · Jan 2019
Comparative StudyComparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.
Accurate lesion segmentation is important for measurements of lesion load and atrophy in subjects with multiple sclerosis (MS). International MS lesion challenges show a preference of convolutional neural networks (CNN) strategies, such as nicMSlesions. However, since the software is trained on fairly homogenous training data, we aimed to test the performance of nicMSlesions in an independent dataset with manual and other automatic lesion segmentations to determine whether this method is suitable for larger, multi-center studies. ⋯ Input from only one subject to re-train the deep learning CNN nicMSlesions is sufficient for adequate lesion segmentation, with on average higher volumetric and spatial agreement with manual than obtained with the untrained methods LesionTOADS and LST-LPA.
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NeuroImage. Clinical · Jan 2019
Multiparameter MRI quantification of microstructural tissue alterations in multiple sclerosis.
Conventional MRI is not sensitive to many pathological processes underpinning multiple sclerosis (MS) ongoing in normal appearing brain tissue (NABT). Quantitative MRI (qMRI) and a multiparameter mapping (MPM) protocol are used to simultaneously quantify magnetization transfer (MT) saturation, transverse relaxation rate R2* (1/T2*) and longitudinal relaxation rate R1 (1/T1), and assess differences in NABT microstructure between MS patients and healthy controls (HC). ⋯ Multiparametric data of brain microstructure concord with the literature, predict clinical performance and suggest a diffuse reduction in myelin and/or iron content within NABT of MS patients.
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NeuroImage. Clinical · Jan 2019
Brain function, structure and genomic data are linked but show different sensitivity to duration of illness and disease stage in schizophrenia.
The progress of schizophrenia at various stages is an intriguing question, which has been explored to some degree using single-modality brain imaging data, e.g. gray matter (GM) or functional connectivity (FC). However it remains unclear how those changes from different modalities are correlated with each other and if the sensitivity to duration of illness and disease stages across modalities is different. ⋯ Our results suggested: 1) both GM and FC highlighted impairments in hippocampal, temporal gyrus and cerebellum in schizophrenia, which were significantly correlated with genes like SATB2, GABBR2, PDE4B, CACNA1C etc. 2) GM and FC presented gradually decrease trend (HC > FESZ>CSZ), while SNP indicated a non-gradual variation trend with un-significant group difference observed between FESZ and CSZ; 3) Group difference between HC and FESZ of FC was more remarkable than GM, and FC presented a stronger negative correlation with duration of illness than GM (p = 0.0006). Collectively, these results highlight the benefit of leveraging multimodal data and provide additional clues regarding the impact of mental illness at various disease stages.
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NeuroImage. Clinical · Jan 2019
Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study.
Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP (n = 90) and control individuals (n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. ⋯ Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.
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NeuroImage. Clinical · Jan 2019
Visual responsiveness in sensorimotor cortex is increased following amputation and reduced after mirror therapy.
Phantom limb pain (PLP) following amputation, which is experienced by the vast majority of amputees, has been reported to be relieved with daily sessions of mirror therapy. During each session, a mirror is used to view the reflected image of the intact limb moving, providing visual feedback consistent with the movement of the missing/phantom limb. To investigate potential neural correlates of the treatment effect, we measured brain responses in volunteers with unilateral leg amputation using functional magnetic resonance imaging (fMRI) during a four-week course of mirror therapy. ⋯ A similar pattern of results was also observed in extrastriate and parietal regions typically responsive to viewing hand actions, but not in regions corresponding to secondary somatosensory cortex. Finally, there was a significant correlation between initial visual responsiveness in sensorimotor cortex and reduction in PLP suggesting a potential marker for predicting efficacy of mirror therapy. Thus, enhanced visual responsiveness in sensorimotor cortex is associated with PLP and modulated over the course of mirror therapy.