基于数字化可视人数据集的医学图像配准研究

目的:采用医学图像配准技术,对中国数字化可视人数据集进行准确、高效的配准。方法:①图像Lab色度空间变换和二值化;②图像分割和特征量提取;③构造坐标变换矩阵;④图像序列配准变换。结果:配准后的图像序列中,人体解剖结构在空间和结构上实现了精确的匹配对应。进行三维重建后,几何模型外表面具有较高的光滑度,说明方法有效、可靠。结论:采用特征提取和快速坐标变换的配准方法可较好地应用于数字化可视人数据集的图像配准。     

线性系统的特征结构配置

本文讨论线性系统状态反馈的特征结构配置问题,给出可配置根系的必要充分条件,指出其结构特征,并用例子说明根系的选择过程及反馈矩阵的求法。     

Flexible connectivity in the aging brain revealed by task modulations

Recent studies have shown that aging has a large impact on connectivity within and between functional networks. An open question is whether elderly still have the flexibility to adapt functional network connectivity (FNC) to the demands of the task at hand. To study this, we collected fMRI data in younger and older participants during resting state, a selective attention (SA) task and an n‐back working memory task with varying levels of difficulty. Spatial independent component (IC) analysis was used to identify functional networks over all participants and all conditions. Dual regression was used to obtain participant and task specific time‐courses per IC. Subsequently, functional connectivity was computed between all ICs in each of the tasks. Based on these functional connectivity matrices, a scaled version of the eigenvector centrality (SEC) was used to measure the total influence of each IC in the complete graph of ICs. The results demonstrated that elderly remain able to adapt FNC to task demands. However, there was an age‐related shift in the impetus for FNC change. Older participants showed the maximal change in SEC patterns between resting state and the SA task. Young participants, showed the largest shift in SEC patterns between the less demanding SA task and the more demanding 2‐back task. Our results suggest that increased FNC changes from resting state to low demanding tasks in elderly reflect recruitment of additional resources, compared with young adults. The lack of change between the low and high demanding tasks suggests that elderly reach a resource ceiling. Hum Brain Mapp 35:3788–3804, 2014. © 2013 Wiley Periodicals, Inc.     

状态反馈系统的新特征

本文研究了状态反馈设计方法的机理,给出了状态反馈系统等价类的充分必要条件,以及状态反馈方法在配置系统极点时,对应的闭环特征向量的解析型式。指出了状态反馈设计方法中存在的设计自由度的型式,以及利用特征向量设计状态反馈系统的可行性。     

Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T

PURPOSE: To develop an experimental protocol to calculate the precision and accuracy of fractional anisotropy (FA), mean diffusivity (MD), and the orientation of the principal eigenvector (PEV) as a function of the signal-to-noise ratio (SNR) in vivo. MATERIALS AND METHODS: A healthy male volunteer was scanned in three separate scanning sessions, yielding a total of 45 diffusion tensor imaging (DTI) scans. To provide FA, MD, and PEV as a function of SNR, sequential scans from a scan session were grouped into nonintersecting sets. Analysis of the accuracy and precision of the DTI-derived contrasts was done in both a voxel-wise and region of interest (ROI)-based manner. RESULTS: An upward bias of FA and no significant bias in MD were present as SNR decreased, confirming results from simulation-based studies. Notably, while the precision of the PEV became worse at low SNR, no bias in the PEV orientation was observed. Overall, an accurate and precise quantification of FA values in GM requires substantially more SNR than the quantification of white matter (WM) FA values CONCLUSION: This study provides guidance for FA, MD, and PEV quantification and a means to investigate the minimal detectable differences within and across scan sessions as a function of SNR.     

Network dynamics of the brain and influence of the epileptic seizure onset zone

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.Epilepsy affects over 60 million people worldwide, and approximately 40% of patients have drug-resistant epilepsy (DRE) with recurrent seizures that are not controlled by available medications (1–3). It is now routine to consider drug-resistant partial epilepsy patients, who represent the largest cohort of patients with uncontrolled seizures, for possible resective seizure surgery (4). Successful seizure surgery is predicated upon the ability to localize the seizure onset zone. Although some patients (e.g., those with mesial temporal sclerosis or lesional epilepsy) can proceed to surgery following scalp recordings of seizures delineating a seizure onset zone (5), a significant number of patients have seizures that are challenging to localize with scalp ictal (i.e., seizure) recordings. In this case, ictal recordings using intracranial electrodes (e.g., subdural strips, grids, or depth electrode arrays) are necessary. The purpose of these intracranial recording arrays is to provide information about seizure onset and propagation, representing spatiotemporal changes in cerebral function.Using intracranial electrocorticographic (ECoG) recordings taken over several days to capture ictal events, clinicians visually inspect the ECoG recordings at the onset of the seizures and look for signatures on individual channels (e.g., rhythmic spiking, low-voltage fast activity, etc.) that might be characteristic of the seizure onset zone (6). With the large numbers of implanted electrodes (typically more than 100 contacts), this can be time-consuming even for epileptologists who are quick and experienced. Moreover, the activity on individual channels is affected by functional networks that may involve many channels in the surrounding regions, and this is not always easily detected through visual inspection. Hence, seizure onset zone localization can be challenging in a significant number of patients (e.g., nonlesional) (7, 8).In particular, it is unknown whether or not a consistent structure emerges over time in brain functional networks during interictal and ictal periods. To date, brain connectivity has been studied by using intracranial ECoG recordings from brief temporal intervals only (tens to a few hundreds of seconds) in either interictal periods or ictal periods (9–19), or neuronal ensembles have been studied in vitro (20–22). Only a handful of studies have examined the role of the clinically annotated focus (i.e., the region that is clinically identified as the seizure onset zone and subsequently surgically resected) in brain networks over time (23–28).To further investigate the spatiotemporal mechanisms of cerebral function and to investigate whether a consistent structure emerges over time in brain functional networks, we used a network-based analysis and ECoG recordings from subdural and depth electrodes in 12 patients with DRE undergoing presurgical evaluations. We measured brain connectivity continuously (i.e., every second) during interictal, periictal, and ictal periods spanning several days (5.4 ± 1.7 d per patient, mean ± SD), and, for each patient, we used unsupervised clustering to group all of the networks computed over time into a finite set of distinct networks. Finally, if a robust set of clusters emerged, we examined how the brain transitions between these network clusters (brain states).Across all patients, we found that the interictal activity enters only a small set of distinct states (two to five), whereas there are 2–11 states during seizures. We also found that, during seizures, the brain transitions through a finite set of network states in a reproducible manner, i.e., the pattern is the same across different seizures in the same patient, with characteristic onset and termination. These findings suggest that the brain connectivity may be described in a low-dimensional state space.Moreover, the uncovered brain states allowed us to characterize the activity in the seizure onset zone before, during, and after ictal events, which may help understand the role of the zone in network connectivity dynamics. In particular, for each brain state revealed by our analysis, we studied the connectivity of the clinically annotated seizure onset zone to the remaining brain network and we found that there is a specific state during seizures that consistently occurs shortly after the seizure begins (especially in patients with successful surgeries that likely had adequate electrode coverage of the seizure onset zone). In this state, the seizure onset zone is significantly isolated from the rest of the network.In Discussion, we describe how the isolated state of the seizure onset zone may be used to assist in the development of a method to localize the seizure onset zone from intracranial ECoG recordings. Characterization and localization of the seizure onset zone are of utmost importance to guide subsequent surgical resection, which is still the best therapeutic alternative for patients with DRE (4). However, failure to optimally identify the seizure onset zone can occur even with intracranial ECoG recordings, particularly in patients with nonlesional epilepsy, and this can result in suboptimal surgical outcomes and a high chance (∼40%) of seizure recurrence within one year postsurgery (7, 8). Preliminary results of this study were presented in (29).     

Pathophysiology of LV Remodeling Following STEMI: A Longitudinal Diffusion Tensor CMR Study

BackgroundAdverse LV remodeling post–ST-segment elevation myocardial infarction (STEMI) is associated with a poor prognosis, but the underlying mechanisms are not fully understood. Diffusion tensor (DT)-cardiac magnetic resonance (CMR) allows in vivo characterization of myocardial architecture and provides unique mechanistic insight into pathophysiologic changes following myocardial infarction.ObjectivesThis study evaluated the potential associations between DT-CMR performed soon after STEMI and long-term adverse left ventricular (LV) remodeling following STEMI.MethodsA total of 100 patients with STEMI underwent CMR at 5 days and 12 months post-reperfusion. The protocol included DT-CMR for assessing fractional anisotropy (FA), secondary eigenvector angle (E2A) and helix angle (HA), cine imaging for assessing LV volumes, and late gadolinium enhancement for calculating infarct and microvascular obstruction size. Adverse remodeling was defined as a 20% increase in LV end-diastolic volume at 12 months.ResultsA total of 32 patients experienced adverse remodeling at 12 months. Compared with patients without adverse remodeling, they had lower FA (0.23 ± 0.03 vs 0.27 ± 0.04; P < 0.001), lower E2A (37 ± 6° vs 51 ± 7°; P < 0.001), and, on HA maps, a lower proportion of myocytes with right-handed orientation (RHM) (8% ± 5% vs 17% ± 9%; P < 0.001) in their acutely infarcted myocardium. On multivariable logistic regression analysis, infarct FA (odds ratio [OR]: <0.01; P = 0.014) and E2A (OR: 0.77; P = 0.001) were independent predictors of adverse LV remodeling after adjusting for left ventricular ejection fraction (LVEF) and infarct size. There were no significant changes in infarct FA, E2A, or RHM between the 2 scans.ConclusionsExtensive cardiomyocyte disorganization (evidenced by low FA), acute loss of sheetlet angularity (evidenced by low E2A), and a greater loss of organization among cardiomyocytes with RHM, corresponding to the subendocardium, can be detected within 5 days post-STEMI. These changes persist post-injury, and low FA and E2A are independently associated with long-term adverse remodeling.