Yunyun Duan

Diffusion Tensor Tractography Reveals Disrupted Topological Efficiency in White Matter Structural Networks in Multiple Sclerosis

Little is currently known about the alterations in the topological organization of the white matter (WM) structural networks in patients with multiple sclerosis (MS). In the present study, we used diffusion tensor imaging and deterministic tractography to map the WM structural networks in 39 MS patients and 39 age- and gender-matched healthy controls. Graph theoretical methods were applied to investigate alterations in the network efficiency in these patients. The MS patients and the controls exhibited efficient small-world properties in their WM structural networks. However, the global and local network efficiencies were significantly decreased in the MS patients compared with the controls, with the most pronounced changes observed in the sensorimotor, visual, default-mode, and language areas. Furthermore, the decreased network efficiencies were significantly correlated with the expanded disability status scale scores, the disease durations, and the total WM lesion loads. Together, the results suggest a disrupted integrity in the large-scale brain systems in MS, thus providing new insights into the understanding of MS connectome. Our data also suggest that a topology-based brain network analysis can provide potential biomarkers for disease diagnosis and for monitoring the progression and treatment effects for patients with MS.

Impaired Long Distance Functional Connectivity and Weighted Network Architecture in Alzheimer's Disease

Alzheimers disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD.

Comparison of grey matter atrophy between patients with neuromyelitis optica and multiple sclerosis: a voxel-based morphometry study.

PURPOSE Previous studies have established regional grey matter (GM) loss in multiple sclerosis (MS). However, whether there is any regional GM atrophy in neuromyelitis optica (NMO) and the difference between NMO and MS is unclear. The present study addresses this issue by voxel-based morphometry (VBM). METHODS Conventional magnetic resonance imaging (MRI) and T1-weighted three-dimensional MRI were obtained from 26 NMO patients, 26 relapsing-remitting MS (RRMS) patients, and 26 normal controls. An analysis of covariance model assessed with cluster size inference was used to compare GM volume among three groups. The correlations of GM volume changes with disease duration, expanded disability status scale (EDSS) and brain T2 lesion volume (LV) were analyzed. RESULTS GM atrophy was found in NMO patients in several regions of frontal, temporal, parietal lobes and insula (uncorrected, p < 0.001). While extensive GM atrophy was found in RRMS patients, including most cortical regions and the deep grey matter (corrected for multiple comparisons, p < 0.01). Compared with NMO, those with RRMS had significant GM loss in bilateral thalami, caudate, left parahippocampal gyrus, right hippocampus and insula (corrected, p < 0.01). In RRMS group, regional GM loss in right caudate and bilateral thalami were strongly correlated with brain T2LV. CONCLUSIONS Our study found the difference of GM atrophy between NMO and RRMS patients mainly in deep grey matter. The correlational results suggested axonal degeneration from lesions on T2WI may be a key pathogenesis of atrophy in deep grey matter in RRMS.

Abnormal salience network in normal aging and in amnestic mild cognitive impairment and Alzheimer's disease

The salience network (SN) serves to identify salient stimuli and to switch between the central executive network (CEN) and the default‐mode network (DMN), both of which are impaired in Alzheimers disease (AD)/amnestic mild cognitive impairment (aMCI). We hypothesized that both the structural and functional organization of the SN and functional interactions between the SN and CEN/DMN are altered in normal aging and in AD/aMCI. Gray matter volume (GMV) and resting‐state functional connectivity (FC) were analyzed from healthy younger (HYC) to older controls (HOC) and from HOC to aMCI and AD patients. All the SN components showed significant differences in the GMV, intranetwork FC, and internetwork FC between the HYC and HOC. Most of the SN components showed differences in the GMV between the HOC and AD and between the aMCI and AD. Compared with the HOC, AD patients exhibited significant differences in intra‐ and internetwork FCs of the SN, whereas aMCI patients demonstrated differences in internetwork FC of the SN. Most of the GMVs and internetwork FCs of the SN and part of the intranetwork FC of the SN were correlated with cognitive differences in older subjects. Our findings suggested that structural and functional impairments of the SN may occur as early as in normal aging and that functional disconnection between the SN and CEN/ DMN may also be associated with both normal aging and disease progression. Hum Brain Mapp 35:3446–3464, 2014.

Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation

Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi‐atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi‐atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target images segmentation label using statistical machine learning techniques. In particular, we use a L1‐regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state‐of‐the‐art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in‐house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimers disease. Hum Brain Mapp 35:2674–2697, 2014.

A tract-based diffusion study of cerebral white matter in neuromyelitis optica reveals widespread pathological alterations

Background: It remains uncertain whether neuromyelitis optica (NMO) exhibits diffuse cerebral abnormalities or whether the pathology is truly restricted to optic nerves and spinal cord in the majority of cases. We examined NMO patients with diffusion tensor imaging (DTI) and utilized a tract-based spatial statistics (TBSS) method to analyze the data. Methods: Twenty-seven NMO patients (25 females, age mean ± SD: 35.1 ± 12 years) and 27 age- and sex-matched normal controls were included in this study. Voxel-wise analyses were performed with TBSS using multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ1) and radial diffusivity (λ23). Results: The NMO patients had significantly increased MD (3.6%), λ1 (2.6%) and λ23 (4.6%) in their white matter (WM) skeletons compared with the controls. Furthermore, TBSS analyses revealed significantly (p < 0.05, corrected for multiple comparisons) increased diffusivities (MD, λ1 and λ23) in many cerebral WM tracts in the patients with NMO, including the superior and inferior longitudinal fasciculi, inferior fronto-occipital fasciculi, corpus callosum, cingulum bundles, corticospinal tracts, optic radiation, uncinate fasciculi, fornices, internal capsules, external capsules and cerebral peduncles. Exploratory analyses also revealed the possible associations between WM diffusion changes (MD, λ1 and λ23) and clinical variables (Expanded Disability Status Scale and disease duration) in the patients. Conclusions: This study provided imaging evidence for widespread cerebral WM abnormalities. While these findings require independent replication, they potentially signify the presence of widespread, low-grade cerebral pathology in NMO.

Abnormal baseline brain activity in patients with neuromyelitis optica: a resting-state fMRI study.

PURPOSE Recent immunopathologic and MRI findings suggest that tissue damage in neuromyelitis optica (NMO) is not limited to spinal cord and optic nerve, but also in brain. Baseline brain activity can reveal the brain functional changes to the tissue damages and give clues to the pathophysiology of NMO, however, it has never been explored by resting-state functional MRI (fMRI). We used regional amplitude of low frequency fluctuation (ALFF) as an index in resting-state fMRI to investigate how baseline brain activity changes in patients with NMO. METHODS Resting-state fMRIs collected from seventeen NMO patients and seventeen age- and sex-matched normal controls were compared to investigate the ALFF difference between the two groups. The relationships between ALFF in regions with significant group differences and the EDSS (Expanded Disability Status Scale), disease duration were further explored. RESULTS Our results showed that NMO patients had significantly decreased ALFF in precuneus, posterior cingulate cortex (PCC) and lingual gyrus; and increased ALFF in middle frontal gyrus, caudate nucleus and thalamus, compared to normal controls. Moderate negative correlations were found between the EDSS and ALFF in the left middle frontal gyrus (r=-0.436, p=0.040) and the left caudate (r=-0.542, p=0.012). CONCLUSION The abnormal baseline brain activity shown by resting-state fMRI in NMO is relevant to cognition, visual and motor systems. It implicates a complex baseline brain status of both functional impairments and adaptations caused by tissue damages in these systems, which gives clues to the pathophysiology of NMO.

Brain MRI abnormalities in neuromyelitis optica

OBJECTIVE The purpose of this study was to explore brain MRI findings in neuromyelitis optica (NMO) and to investigate specific brain lesions with respect to the localization of aquaporin-4 (AQP-4). MATERIALS AND METHODS Forty admitted patients (36 women) who satisfied the 2006 criteria of Wingerchuk et al. for NMO were included in this study. All patients received a neurological examination and MRI scanning including brain and spinal cord. MRIs were classified as normal, nonspecific, multiple sclerosis-like, typical abnormalities. MS-like lesions were too few to satisfy the Barkhof et al. criteria for MS. Confluent lesions involving high AQP-4 regions were considered typical. Non-enhancing deep white matter lesions other than MS-like lesions or typical lesions were classified as nonspecific. RESULTS Brain MRI lesions were delineated in 12 patients (25%). Four patients (10%) had hypothalamus, brainstem or periventricle lesions. Six (15%) patients were nonspecific, and 2 (5%) patients had multiple sclerosis-like lesions. CONCLUSION Brain MRIs are negative in most NMO, and brain lesions do not exclude the diagnosis of NMO. Hypothalamus, brainstem or periventricle lesions, corresponding to high sites of AQP-4 in the brain, are indicative of lesions of NMO.

Microstructural abnormalities in the trigeminal nerves of patients with trigeminal neuralgia revealed by multiple diffusion metrics

OBJECTIVE To investigate microstructural tissue changes of trigeminal nerve (TGN) in patients with unilateral trigeminal neuralgia (TN) by multiple diffusion metrics, and correlate the diffusion indexes with the clinical variables. METHODS 16 patients with TN and 6 healthy controls (HC) were recruited into our study. All participants were imaged with a 3.0 T system with three-dimension time-of-flight (TOF) magnetic resonance angiography and fluid attenuated inversion recovery (FLAIR) DTI-sequence. We placed regions of interest over the root entry zone of the TGN and measured fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). The mean values of FA, MD, AD and RD were compared between the affected and unaffected sides in the same patient, and to HC values. The correlation between the side-to-side diffusion metric difference and clinical variables (disease duration and visual analogy scale, VAS) was further explored. RESULTS Compared with the unaffected side and HC, the affected side showed significantly decreased FA and increased RD; however, no significant changes of AD were found. A trend toward significantly increased MD was identified on the affected side comparing with the unaffected side. We also found the significant correlation between the FA reduction and VAS of pain (r=-0.55, p=0.03). CONCLUSION DTI can quantitatively assess the microstructural abnormalities of the affected TGN in patients with TN. Our results suggest demyelination without significant axonal injury is the essential pathological basis of the affected TGN by multiple diffusion metrics. The correlation between FA reduction and VAS suggests FA as a potential objective MRI biomarker to correlate with clinical severity.

Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

OBJECTIVE To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. METHODS Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. RESULTS The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. CONCLUSION Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS.