Resting-state connectivity in the default mode network and insula during experimental low back pain简

Functional magnetic resonance imaging studies have shown that the insular cortex has a significant role in pain identification and information integration, while the default mode network is associated with cognitive and memory-related aspects of pain perception. However, changes in the functional connectivity between the default mode network and insula during pain remain unclear. This study used 3.0 T functional magnetic resonance imaging scans in 12 healthy subjects aged 24.8 ± 3.3 years to compare the differences in the functional activity and connectivity of the insula and default mode network between the baseline and pain condition induced by intramuscular injection of hypertonic saline. Compared with the baseline, the insula was more functionally connected with the medial prefrontal and lateral temporal cortices, whereas there was lower connectivity with the posterior cingulate cortex, precuneus and inferior parietal lobule in the pain condition. In addition, compared with baseline, the anterior cingulate cortex exhibited greater connectivity with the posterior insula, but lower connectivity with the anterior insula, during the pain condition. These data indicate that experimental low back pain led to dysfunction in the connectivity between the insula and default mode network resulting from an impairment of the regions of the brain related to cognition and emotion, suggesting the importance of the interaction between these regions in pain processing.     

Resting-state functional connectivity abnormalities in first-onset unmedicated depression简

Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Auto- mated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We se- lected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effec- tive feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more sig- nificant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.     

鼻咽癌颅底转移特点的临床研究简

目的探讨鼻咽癌颅底侵犯和转移的发生规律、途径及其临床意义。方法回顾性分析101例经病理确诊并经MRI检查发现有颅底侵犯的鼻咽癌病人的MR／资料,对鼻咽癌颅底侵犯方式、发生率及侵犯和转移的解剖部位进行研究。结果本组发生颅底直接侵犯84例（83．2％）,颅底转移17例（16．8％）。颅底直接侵犯最易受累的部位依次为蝶窦和鞍底、海绵窦、斜坡、颈内动脉管。颅底转移多发生在颈内动脉管和颈静脉孔区域。结论鼻咽癌早期存在颅底淋巴结转移,通过鼻咽癌颅底侵犯和转移的MRI研究,提高鼻咽癌颅底侵犯影像学诊断的准确性,选择合适的靶区和放疗技术,选择恰当的手术入路,以达到提高鼻咽癌病人的生存率,改善其生存质量的目标。     

MRI 导向立体定向活检术在颅内病变诊断中的应用简

目的探讨MR／引导下立体定向活检手术在颅内病灶诊断的准确性、安全性及其临床应用价值。方法回顾性分析52例病变性质不明的颅内病变病人的临床资料,在MRI导向下行立体定向活检术。结果52例病人均取得病理诊断．活检成功率100％。星形细胞瘤20例（38．5％）,脑转移瘤13例（25．0％）,脑非化脓性感染10例（19．2％）,非霍奇金淋巴瘤4例（7．7％）,胶质细胞增生2例（3．8％）,脑真菌病、脑囊尾蚴病及结核性病变各1例（1．9％）。术后无一例出现严重并发症。除l例放弃治疗,其余病人转相应专科治疗。结论对于难以行开颅手术的颅内疑难病变,MRI导向立体定向活检手术可提供一种可靠的诊断手段,为临床进一步诊疗提供理论依据。     

扩散张量成像在周围神经病变的研究进展简

扩散张量成像是fMRI的重要组成部分,是目前唯一可在体显示白质纤维束的无创性检查方法。此外,还可通过测量各向同性和各向异性等参数,对白质纤维束的完整性进行评价。目前,已有研究将扩散张量成像技术应用于周围神经系统疾病,本文拟从急性周围神经损伤、慢性周围神经损伤,以及周围神经系统炎症和肿瘤等方面对其在周围神经系统疾病的研究进展进行概述。     

Changes in brain activation in stroke patients after mental practice and physical exercise： a functional MRI study

Mental practice is a new rehabilitation method that reters to the mental rehearsal ot motor imagery content with the goal of improving motor performance. However, the relationship between activated regions and motor recovery after mental practice training is not well understood. In this study, 15 patients who suffered a firstever subcortical stroke with neurological deficits affecting the right hand, but no significant cognitive impairment were recruited. 10 patients underwent mental practice combined with physical practice training, and 5 patients only underwent physical practice training. We observed brain activation regions after 4 weeks of training, and explored the correlation of activation changes with functional recovery of the affected hands. The results showed that, after 4 weeks of mental practice combined with physical training, the Fugl-Meyer assessment score for the affected right hand was significantly increased than that after 4 weeks of practice training alone. Functional MRI showed enhanced activation in the left primary somatosensory cortex, attenuated activation intensity in the right primary motor cortex, and enhanced right cerebellar activation observed during the motor imagery task using the affected right hand after mental practice training. The changes in brain cortical activity were related to functional recovery of the hand. Experimental findings indicate that cortical and cerebellar functional reorganization following mental practice contributed to the improvement of hand function.     

缺血性卒中患者颈动脉粥样硬化斑块高分辨磁共振成像研究进展简

脑卒中是严重危害人类生命健康的主要疾病之一,而以动脉粥样硬化性病变为病理基础的缺血性卒中为其最常见的临床发病类型。因此,及时识别粥样硬化斑块易损性即显得愈发重要。颈动脉MRI作为一项无创性检查手段,对动脉粥样硬化斑块性质的检测具有独特优势,对显示斑块形态和成分具有较高的敏感性和特异性,可以对粥样硬化斑块破裂的潜在危险进行评价和分层,进而为临床制定有效治疗方案提供影像学诊断依据。     

Hallervorden?Spatz病:三例报告并文献复习简

目的探讨Hallervorden—Spatz病之临床和影像学特点。方法与结果回顾分析3例Hallervorden—Spatz病患者临床资料。其中2例临床表现为锥体外系症状,影像学符合典型“虎眼征”,诊断明确;1例阳性体征为痉挛步态伴严重构音障碍,锥体外系症状不典型,缺乏不自主动作,T2WI显示典型“虎眼征”,20年后复查时双侧苍白球前内侧高信号区明显缩小,符合不典型Hallervorden—Spatz病。结论典型Hallervorden—Spatz病儿童期发病、病程短,可根据以锥体外系症状为主的临床体征和T2WI显示典型“虎眼征”而获得早期诊断;非典型Hallervorden．Spatz病青少年期发病、病程长,锥体外系症状可不典型,T2WI“虎眼征”可随病程出现动态变化。     

贯穿桥脑脊索瘤1例报告简

患者,女,49岁,头晕2年,加重伴走路不稳1年于2011年4月25日入院。查体：神志清,精神欠佳,双侧瞳孔等大同圆,对光反射灵敏,左右视均有水平眼球震颤,颅神经无阳性体征,四肢肌力肌张力正常,共济运动正常,Romberg征阳性,     

Instructions to Authors简

<正>Aims and Scopes Neural Regeneration Research(NRR;ISSN 1673-5374)is an open-access,peer-reviewed only international journal focusing exclusively on the exciting field of neural regeneration.NRR is devoted to publishing basic research,translational medicine and randomized clinical trial articles,as well as prospective reviews written by invited experts in the field of neural regeneration.NRR publishes a diverse array     

Sensory‐motor brain network connectivity for speech comprehension

The act of listening to speech activates a large network of brain areas. In the present work, a novel data‐driven technique (the combination of independent component analysis and Granger causality) was used to extract brain network dynamics from an fMRI study of passive listening to Words, Pseudo‐Words, and Reverse‐played words. Using this method we show the functional connectivity modulations among classical language regions (Broca's and Wernicke's areas) and inferior parietal, somatosensory, and motor areas and right cerebellum. Word listening elicited a compact pattern of connectivity within a parieto‐somato‐motor network and between the superior temporal and inferior frontal gyri. Pseudo‐Word stimuli induced activities similar to the Word condition, which were characterized by a highly recurrent connectivity pattern, mostly driven by the temporal lobe activity. Also the Reversed‐Word condition revealed an important influence of temporal cortices, but no integrated activity of the parieto‐somato‐motor network. In parallel, the right cerebellum lost its functional connection with motor areas, present in both Word and Pseudo‐Word listening. The inability of the participant to produce the Reversed‐Word stimuli also evidenced two separate networks: the first was driven by frontal areas and the right cerebellum toward somatosensory cortices; the second was triggered by temporal and parietal sites towards motor areas. Summing up, our results suggest that semantic content modulates the general compactness of network dynamics as well as the balance between frontal and temporal language areas in driving those dynamics. The degree of reproducibility of auditory speech material modulates the connectivity pattern within and toward somatosensory and motor areas. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.     

Abnormal brain functional network dynamics in sleep-related hypermotor epilepsy

Aims

This study aimed to use resting-state functional magnetic resonance imaging (rs-fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep-related hypermotor epilepsy (SHE).

Methods

High-resolution T1 and rs-fMRI scanning were performed on all the subjects. We used a sliding-window approach to construct a dynamic functional connectivity (dFC) network. The k-means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network-based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed.

Results

After k-means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE.

Conclusion

The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.     

Altered functional organization within and between resting-state networks in chronic subcortical infarction

This study aimed to investigate the changes in functional connectivity (FC) within each resting-state network (RSN) and between RSNs in subcortical stroke patients who were well recovered in global motor function. Eleven meaningful RSNs were identified via functional magnetic resonance imaging data from 25 subcortical stroke patients and 22 normal controls using independent component analysis. Compared with normal controls, stroke patients exhibited increased intranetwork FC in the sensorimotor (SMN), visual (VN), auditory (AN), dorsal attention (DAN), and default mode (DMN) networks; they also exhibited decreased intranetwork FC in the frontoparietal network (FPN) and anterior DMN. Stroke patients displayed a shift from no FC in controls to negative internetwork FC between the VN and AN as well as between the VN and SMN. Stroke patients also exhibited weakened positive (anterior and posterior DMN; posterior DMN and right FPN) or negative (AN and right FPN; posterior DMN and dorsal SMN) internetwork FC when compared with normal controls. We suggest that subcortical stroke may induce connectivity changes in multiple functional networks, affecting not only the intranetwork FC within RSNs but also the internetwork FC between these RSNs.     

Functional network connectivity during rest and task conditions: A comparative study

Functional connectivity (FC) examines temporal statistical dependencies among distant brain regions by means of seed‐based analysis or independent component analysis (ICA). Spatial ICA also makes it possible to investigate FC at the network level, termed functional network connectivity (FNC). The dynamics of each network (ICA component), which may consist of several remote regions is described by the ICA time‐course of that network; hence, FNC studies statistical dependencies among ICA time‐courses. In this article, we compare comprehensively FNC in the resting state and during performance of an auditory oddball (AOD) task in 28 healthy subjects on relevant (nonartifactual) brain networks. The results show global FNC decrease during the performance of the task. In addition, we show that specific networks enlarge and/or demonstrate higher activity during the performance of the task. The results suggest that performing an active task like AOD may be facilitated by recruiting more neurons and higher activation of related networks rather than collaboration among different brain networks. We also evaluated the impact of temporal filtering on FNC analyses. Results showed that the results are not significantly affected by filtering. Hum Brain Mapp 34:2959–2971, 2013. © 2012 Wiley Periodicals, Inc.     

Quantifying functional connectivity in multi‐subject fMRI data using component models

Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between‐voxel interactions in high‐dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi‐subject fMRI datasets. A flexible cross‐validation approach that assesses the ability of the models to predict voxel‐wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting‐state fMRI data. It was demonstrated that highly flexible subject‐specific component subspaces, as well as very constrained average models, are poor predictors of whole‐brain functional connectivity, whereas the best‐generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent‐covariance model that accounts for subject‐specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882–899, 2017. © 2016 Wiley Periodicals, Inc.     

Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke

The aim of the current study was to explore the whole‐brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long‐term stroke severity. We investigated resting‐state functional MRI‐based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re‐occurring dynamic connectivity configurations were obtained using a sliding window approach and k‐means clustering. We evaluated differences in dynamic patterns between three NIHSS‐stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90‐day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post‐stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three‐level ANOVA: p < .05, post hoc t tests: p < .05, FDR‐corrected). Configuration‐specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson''s r = −.68, p = .003, FDR‐corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.     

Nonlinear functional network connectivity in resting functional magnetic resonance imaging data

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting‐state fMRI data included 151 schizophrenia patients and 163 age‐ and gender‐matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a “boosted” approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.     

Drooling disrupts the brain functional connectivity network in Parkinson's disease

Aims

This study aimed to investigate the causal interaction between significant sensorimotor network (SMN) regions and other brain regions in Parkinson's disease patients with drooling (droolers).

Methods

Twenty-one droolers, 22 PD patients without drooling (non-droolers), and 22 matched healthy controls underwent 3T-MRI resting-state scans. We performed independent component analysis and Granger causality analysis to determine whether significant SMN regions help predict other brain areas. Pearson's correlation was computed between imaging characteristics and clinical characteristics. ROC curves were plotted to assess the diagnostic performance of effective connectivity (EC).

Results

Compared with non-droolers and healthy controls, droolers showed abnormal EC of the right caudate nucleus (CAU.R) and right postcentral gyrus to extensive brain regions. In droolers, increased EC from the CAU.R to the right middle temporal gyrus was positively correlated with MDS-UPDRS, MDS-UPDRS II, NMSS, and HAMD scores; increased EC from the right inferior parietal lobe to CAU.R was positively correlated with MDS-UPDRS score. ROC curve analysis showed that these abnormal ECs are of great significance in diagnosing drooling in PD.

Conclusion

This study identified that PD patients with drooling have abnormal EC in the cortico-limbic-striatal-cerebellar and cortio-cortical networks, which could be potential biomarkers for drooling in PD.