NeuroImage. Clinical
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NeuroImage. Clinical · Jan 2019
Differences in brain processing of proprioception related to postural control in patients with recurrent non-specific low back pain and healthy controls.
Patients with non-specific low back pain (NSLBP) show an impaired postural control during standing and a slower performance of sit-to-stand-to-sit (STSTS) movements. Research suggests that these impairments could be due to an altered use of ankle compared to back proprioception. However, the neural correlates of these postural control impairments in NSLBP remain unclear. ⋯ Activity in the right amygdala during ankle proprioceptive processing correlated with an impaired proprioceptive use in the patients with NSLBP, but not in healthy controls. Moreover, while activity in the left superior parietal lobule, a sensory processing region, during back proprioceptive processing correlated with a better use of proprioception in the NSLBP group, it was associated with a less optimal use of proprioception in the control group. These findings suggest that functional brain changes during proprioceptive processing in patients with NSLBP may contribute to their postural control impairments.
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NeuroImage. Clinical · Jan 2019
Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer's disease.
Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy. ⋯ Effects of time, aging and MMSE all explained variance in GM atrophy slopes within individuals. Associations with MMSE were weaker than those for time or age, but specific for amyloid pathology. This suggests that at least some of the atrophy observed in time or age models may not be specific to AD.
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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.