Yuanyuan Qin

Exploring the Functional Brain Network of Alzheimer’s Disease: Based on the Computational Experiment

The purpose of this study is to explore the changes in functional brain networks of AD patients using complex network theory. In this study, resting-state fMRI datasets of 10 AD patients and 11 healthy controls were collected. Time series of 90 brain regions were extracted from the fMRI datasets after preprocessing. Pearson correlation method was used to calculate the correlation coefficient between any two time series. Then, a wide threshold range was selected to transform the adjacency matrix to a binary matrix under a different threshold. The topology parameters of each binary network were calculated, and all of them were then averaged within a group. During the evolution, node betweenness and the Euclidean distance between the nodes were set as control factors. Each binary network of healthy controls underwent evolution of 100 steps in accordance with the evolution rules. Then, the topology parameters of the evolution network were calculated. Finally, support vector machine (SVM) was used to classify the network topology parameters of the evolution network and to determine whether evolution results matched the datasets from AD patients. We found there were differing degrees of decline in global efficiency, clustering coefficient, number of edges and transitivity in AD patients compared with healthy controls. The topology parameters of the evolution network tended toward those of the AD group. The results of SVM classification of the evolution network show that the evolution network had a greater probability to be classified as an AD patients group. A new biological marker for diagnosis of AD was provided through comparison of topology parameters between AD patients and healthy controls. The study of network evolution strategies enriched the method of brain network evolution. The use of SVM to classify the results of network evolution provides an objective criteria for determining evolution results.

Discriminative analysis of multivariate features from structural MRI and diffusion tensor images

Imaging markers derived from magnetic resonance images, together with machine learning techniques allow for the recognition of unique anatomical patterns and further differentiating Alzheimers disease (AD) from normal states. T1-based imaging markers, especially volumetric patterns have demonstrated their discriminative potential, however, rely on the tissue abnormalities of gray matter alone. White matter abnormalities and their contribution to AD discrimination have been studied by measuring voxel-based intensities in diffusion tensor images (DTI); however, no systematic study has been done on the discriminative power of either region-of-interest (ROI)-based features from DTI or the combined features extracted from both T1 images and DTI. ROI-based analysis could potentially reduce the feature dimensionality of DTI indices, usually from more than 10e+5, to 10-150 which is almost equal to the order of magnitude with respect to volumetric features from T1. Therefore it allows for straight forward combination of intensity based landmarks of DTI indices and volumetric features of T1. In the present study, the feasibility of tract-based features related to Alzheimers disease was first evaluated by measuring its discriminative capability using support vector machine on fractional anisotropy (FA) maps collected from 21 subjects with Alzheimers disease and 15 normal controls. Then the performance of the tract-based FA+gray matter volumes-combined feature was evaluated by cross-validation. The combined feature yielded good classification result with 94.3% accuracy, 95.0% sensitivity, 93.3% specificity, and 0.96 area under the receiver operating characteristic curve. The tract-based FA and the tract-based FA+gray matter volumes-combined features are certified their feasibilities for the recognition of anatomical features and may serve to complement classification methods based on other imaging markers.

Altered Intranetwork and Internetwork Functional Connectivity in Type 2 Diabetes Mellitus With and Without Cognitive Impairment

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment. We investigated whether alterations of intranetwork and internetwork functional connectivity with T2DM progression exist, by using resting-state functional MRI. MRI data were analysed from 19 T2DM patients with normal cognition (DMCN) and 19 T2DM patients with cognitive impairment (DMCI), 19 healthy controls (HC). Functional connectivity among 36 previously well-defined brain regions which consisted of 5 resting-state network (RSN) systems [default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL) and sensorimotor network (SMN)] was investigated at 3 levels (integrity, network and connectivity). Impaired intranetwork and internetwork connectivity were found in T2DM, especially in DMCI, on the basis of the three levels of analysis. The bilateral posterior cerebellum, the right insula, the DMN and the CON were mainly involved in these changes. The functional connectivity strength of specific brain architectures in T2DM was found to be associated with haemoglobin A1c (HbA1c), cognitive score and illness duration. These network alterations in intergroup differences, which were associated with brain functional impairment due to T2DM, indicate that network organizations might be potential biomarkers for predicting the clinical progression, evaluating the cognitive impairment, and further understanding the pathophysiology of T2DM.

Brain white matter plasticity and functional reorganization underlying the central pathogenesis of trigeminal neuralgia

Peripheral nerve damage does not fully explain the pathogenesis of trigeminal neuralgia (TN). Central nervous system changes can follow trigeminal nerve dysfunction. We hypothesized that brain white matter and functional connectivity changes in TN patients were involved in pain perception, modulation, the cognitive-affective system, and motor function; moreover, changes in functional reorganization were correlated with white matter alterations. Twenty left TN patients and twenty-two healthy controls were studied. Diffusion kurtosis imaging was analyzed to extract diffusion and kurtosis parameters, and functional connectivity density (FCD) mapping was used to explore the functional reorganization in the brain. In the patient group, we found lower axial kurtosis and higher axial diffusivity in tracts participated in sensory, cognitive-affective, and modulatory aspects of pain, such as the corticospinal tract, superior longitudinal fasciculus, anterior thalamic radiation, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, cingulated gyrus, forceps major and uncinate fasciculus. Patients exhibited complex FCD reorganization of hippocampus, striatum, thalamus, precentral gyrus, precuneus, prefrontal cortex and inferior parietal lobule in multiple modulatory networks that played crucial roles in pain perception, modulation, cognitive-affective system, and motor function. Further, the correlated structural-functional changes may be responsible for the persistence of long-term recurrent pain and sensory-related dysfunction in TN.

Comparative study of DSC-PWI and 3D-ASL in ischemic stroke patients

The purpose of this study was to quantitatively analyze the relationship between three dimensional arterial spin labeling (3D-ASL) and dynamic susceptibility contrast-enhanced perfusion weighted imaging (DSC-PWI) in ischemic stroke patients. Thirty patients with ischemic stroke were included in this study. All subjects underwent routine magnetic resonance imaging scanning, diffusion weighted imaging (DWI), magnetic resonance angiography (MRA), 3D-ASL and DSC-PWI on a 3.0T MR scanner. Regions of interest (ROIs) were drawn on the cerebral blood flow (CBF) maps (derived from ASL) and multi-parametric DSC perfusion maps, and then, the absolute and relative values of ASL-CBF, DSC-derived CBF, and DSC-derived mean transit time (MTT) were calculated. The relationships between ASL and DSC parameters were analyzed using Pearson’s correlation analysis. Receiver operative characteristic (ROC) curves were performed to define the thresholds of relative value of ASL-CBF (rASL) that could best predict DSC-CBF reduction and MTT prolongation. Relative ASL better correlated with CBF and MTT in the anterior circulation with the Pearson correlation coefficients (R) values being 0.611 (P<0.001) and–0.610 (P<0.001) respectively. ROC curves demonstrated that when rASL ≤0.585, the sensitivity, specificity and accuracy for predicting ROIs with rCBF<0.9 were 92.3%, 63.6% and 76.6% respectively. When rASL ≤0.952, the sensitivity, specificity and accuracy for predicting ROIs rMTT>1.0 were 75.7%, 89.2% and 87.8% respectively. ASL-CBF map has better linear correlations with DSC-derived parameters (DSC-CBF and MTT) in anterior circulation in ischemic stroke patients. Additionally, when rASL is lower than 0.585, it could predict DSC-CBF decrease with moderate accuracy. If rASL values range from 0.585 to 0.952, we just speculate the prolonged MTT.The purpose of this study was to quantitatively analyze the relationship between three dimensional arterial spin labeling (3D-ASL) and dynamic susceptibility contrast-enhanced perfusion weighted imaging (DSC-PWI) in ischemic stroke patients. Thirty patients with ischemic stroke were included in this study. All subjects underwent routine magnetic resonance imaging scanning, diffusion weighted imaging (DWI), magnetic resonance angiography (MRA), 3D-ASL and DSC-PWI on a 3.0T MR scanner. Regions of interest (ROIs) were drawn on the cerebral blood flow (CBF) maps (derived from ASL) and multi-parametric DSC perfusion maps, and then, the absolute and relative values of ASL-CBF, DSC-derived CBF, and DSC-derived mean transit time (MTT) were calculated. The relationships between ASL and DSC parameters were analyzed using Pearson’s correlation analysis. Receiver operative characteristic (ROC) curves were performed to define the thresholds of relative value of ASL-CBF (rASL) that could best predict DSC-CBF reduction and MTT prolongation. Relative ASL better correlated with CBF and MTT in the anterior circulation with the Pearson correlation coefficients (R) values being 0.611 (P<0.001) and–0.610 (P<0.001) respectively. ROC curves demonstrated that when rASL ≤0.585, the sensitivity, specificity and accuracy for predicting ROIs with rCBF<0.9 were 92.3%, 63.6% and 76.6% respectively. When rASL ≤0.952, the sensitivity, specificity and accuracy for predicting ROIs rMTT>1.0 were 75.7%, 89.2% and 87.8% respectively. ASL-CBF map has better linear correlations with DSC-derived parameters (DSC-CBF and MTT) in anterior circulation in ischemic stroke patients. Additionally, when rASL is lower than 0.585, it could predict DSC-CBF decrease with moderate accuracy. If rASL values range from 0.585 to 0.952, we just speculate the prolonged MTT.

An efficient approach for differentiating Alzheimer's disease from normal elderly based on multicenter MRI using gray-level invariant features.

Machine learning techniques, along with imaging markers extracted from structural magnetic resonance images, have been shown to increase the accuracy to differentiate patients with Alzheimers disease (AD) from normal elderly controls. Several forms of anatomical features, such as cortical volume, shape, and thickness, have demonstrated discriminative capability. These approaches rely on accurate non-linear image transformation, which could invite several nuisance factors, such as dependency on transformation parameters and the degree of anatomical abnormality, and an unpredictable influence of residual registration errors. In this study, we tested a simple method to extract disease-related anatomical features, which is suitable for initial stratification of the heterogeneous patient populations often encountered in clinical data. The method employed gray-level invariant features, which were extracted from linearly transformed images, to characterize AD-specific anatomical features. The intensity information from a disease-specific spatial masking, which was linearly registered to each patient, was used to capture the anatomical features. We implemented a two-step feature selection for anatomic recognition. First, a statistic-based feature selection was implemented to extract AD-related anatomical features while excluding non-significant features. Then, seven knowledge-based ROIs were used to capture the local discriminative powers of selected voxels within areas that were sensitive to AD or mild cognitive impairment (MCI). The discriminative capability of the proposed feature was measured by its performance in differentiating AD or MCI from normal elderly controls (NC) using a support vector machine. The statistic-based feature selection, together with the knowledge-based masks, provided a promising solution for capturing anatomical features of the brain efficiently. For the analysis of clinical populations, which are inherently heterogeneous, this approach could stratify the large amount of data rapidly and could be combined with more detailed subsequent analyses based on non-linear transformation.

Simulating the Evolution of Functional Brain Networks in Alzheimer's Disease: Exploring Disease Dynamics from the Perspective of Global Activity.

Functional brain connectivity is altered during the pathological processes of Alzheimer’s disease (AD), but the specific evolutional rules are insufficiently understood. Resting-state functional magnetic resonance imaging indicates that the functional brain networks of individuals with AD tend to be disrupted in hub-like nodes, shifting from a small world architecture to a random profile. Here, we proposed a novel evolution model based on computational experiments to simulate the transition of functional brain networks from normal to AD. Specifically, we simulated the rearrangement of edges in a pathological process by a high probability of disconnecting edges between hub-like nodes, and by generating edges between random pair of nodes. Subsequently, four topological properties and a nodal distribution were used to evaluate our model. Compared with random evolution as a null model, our model captured well the topological alteration of functional brain networks during the pathological process. Moreover, we implemented two kinds of network attack to imitate the damage incurred by the brain in AD. Topological changes were better explained by ‘hub attacks’ than by ‘random attacks’, indicating the fragility of hubs in individuals with AD. This model clarifies the disruption of functional brain networks in AD, providing a new perspective on topological alterations.

Region-specific atrophy of precentral gyrus in patients with amyotrophic lateral sclerosis

To assess the region‐specific atrophy of precentral gyrus (PrCG) and its correlation to clinical function score in amyotrophic lateral sclerosis (ALS).

Amid proton transfer (APT) and magnetization transfer (MT) MRI contrasts provide complimentary assessment of brain tumors similarly to proton magnetic resonance spectroscopy imaging (MRSI)

ObjectivesUsing MRSI as comparison, we aimed to explore the difference between amide proton transfer (APT) MRI and conventional semi-solid magnetization transfer ratio (MTR) MRI, and to investigate if molecular APT and structural MTR can provide complimentary information in assessing brain tumors.MethodsSeventeen brain tumor patients and 17 age- and gender-matched volunteers were included and scanned with anatomical MRI, APT and MT-weighted MRI, and MRSI. Multi-voxel choline (Cho) and N-acetylaspartic acid (NAA) signals were quantified from MRSI and compared with MTR and MTRasym(3.5ppm) contrasts averaged from corresponding voxels. Correlations between contrasts were explored voxel-by-voxel by pooling values from all voxels into Pearson’s correlation analysis. Differences in correlation coefficients were tested with the Z-test (set at p<0.05).ResultsAPT and MT provide good contrast and quantitative parameters in tumor imaging, as do the metabolite (Cho and NAA) maps. MTRasym(3.5ppm) significantly correlated with MTR (R=-0.61, p<0.0001), Cho (R=0.568, p<0.0001) and NAA (R=-0.619, p<0.0001) in tumors, and MTR also significantly correlated with Cho (R=-0.346, p<0.0001) and NAA (R=0.624, p<0.0001). In healthy volunteers, MTRasym(3.5ppm) was non-significantly correlated with MTR (R=-0.049, p=0.239), Cho (R=0.030, p=0.478) and NAA (R=-0.083, p=0.046). Significant correlations were found among MTR with Cho (R=0.199, p<0.0001) and NAA (R=0.263, p<0.0001) in the group of healthy volunteers with lower correlation R values than those in tumor patients.ConclusionsAPT and MT could provide independent and supplementary information for the comprehensive assessment of molecular and structural changes due to brain tumor cancerogenesis.Key Points• MTRasym(3.5ppm)positively correlated with Cho while negatively with NAA in tumors.• MTR positively correlated with NAA while negatively with Cho in tumors.• Combining APT/MT provides molecular and structural information similarly to MRSI.

Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer's Disease

Background: Previous studies have indicated that the cognitive deficits in patients with Alzheimers disease (AD) may be due to topological deteriorations of the brain network. However, whether the selection of a specific frequency band could impact the topological properties is still not clear. Our hypothesis is that the topological properties of AD patients are also frequency-specific. Methods: Resting state functional magnetic resonance imaging data from 10 right-handed moderate AD patients (mean age: 64.3 years; mean mini mental state examination [MMSE]: 18.0) and 10 age and gender-matched healthy controls (mean age: 63.6 years; mean MMSE: 28.2) were enrolled in this study. The global efficiency, the clustering coefficient (CC), the characteristic path length (CpL), and “small-world” property were calculated in a wide range of thresholds and averaged within each group, at three different frequency bands (0.01–0.06 Hz, 0.06–0.11 Hz, and 0.11–0.25 Hz). Results: At lower-frequency bands (0.01–0.06 Hz, 0.06–0.11 Hz), the global efficiency, the CC and the “small-world” properties of AD patients decreased compared to controls. While at higher-frequency bands (0.11–0.25 Hz), the CpL was much longer, and the “small-world” property was disrupted in AD, particularly at a higher threshold. The topological properties changed with different frequency bands, suggesting the existence of disrupted global and local functional organization associated with AD. Conclusions: This study demonstrates that the topological alterations of large-scale functional brain networks in AD patients are frequency dependent, thus providing fundamental support for optimal frequency selection in future related research.