Connectivity‐based parcellation: Critique and implications

Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long‐range connections. This notion has prompted the quickly developing family of connectivity‐based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in‐vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies. Hum Brain Mapp 36:4771–4792, 2015. © 2015 Wiley Periodicals, Inc.     

乙型肝炎病毒感染标记家庭分布规律的探讨

本文用SPRIA检测了99个家庭424人血清标本的乙型肝炎病毒(HBV)感染标记,并用二项分布理论分析了HBsAg携带者、抗-HBs和抗-HBc阳性者以及HBV感染者的家庭分布规律。结果显示,HBsAg携带者的家庭分布不符合二项分布,提示有家庭聚集性;而抗-HBs、抗-HBc阳性者以及HBV感染者的家庭分布符合二项分布,无家庭聚集性。HBV感染标记的家庭分布规律不同,提示不同家庭的成员获得HBV持续感染和获得抗HBV感染的免疫力的机理是不同的。作者推测,遗传因素可能是造成HBsAg携带者呈家庭聚集性的主导因素之一。     

Temporal and Spatial Analysis of the New World Screwworm (Cochliomyia hominivorax) in Darien and Embera,Panama (2001–2011)

Larvae (maggots) of Cochliomyia hominivorax, the New World Screwworm fly, are voracious consumers of living flesh that have a negative economic impact by decreasing productivity, predisposing to other pathogens, and, in severe cases, causing death of domestic livestock. Screwworm caused extensive financial losses to the livestock industry in North America prior to its eradication. Sterile insect technique (SIT) was used to eradicate screwworm throughout North and Central America and continues to be the main tool to control it in eastern Panama. The goal of this study was to evaluate the temporal and spatial trends of screwworm myiasis cases reported in the Province of Darien and Comarca Embera (border with Colombia), Panama, from 2001 to 2011. We hypothesized that screwworm cases would vary seasonally and be spatially clustered near Colombia as a result of effective eradication strategies in Panama and the presence of an autochthonous population of flies in western Colombia. Temporal and spatial data were retrieved from COPEG‐USDA records (Panama) and analysed by anova , Ripley's K function, discrete Poisson spatial statistic scan and Getis‐Ord Gi*. No significant temporal trend was found, but cases were spatially distributed in four clusters. One cluster of cases occurred from 2001 to 2003 and was considered a focal temporal and spatial cluster. One cluster occurred in 2001 and 2007 indicating more rare outbreaks in an area with fewer cattle. The two remaining clusters contained cases from 2004 to 2011 and 2001 to 2011 suggesting regular breaks in the control barrier due to occasional failures of the SIT programme, difficulties implementing border quarantine strategies, livestock smuggling or the movement of infested wildlife.     

帕金森病和抑郁症转录组生物信息学分析

目的抑郁症状(MDD)是帕金森病(PD)常见的非运动症状,探讨帕金森病和抑郁症可能具有的共同致病机制。方法通过文本挖掘及转录组数据分析帕金森病与抑郁症共同的致病机制。结果文本挖掘发现63. 8%的MDD基因和32%的PD基因为共有基因及438个共有的生物学过程;转录组筛选出有统计学意义的10个共同差异基因:I类肌球蛋白(MYO1F)、活化免疫球蛋白样受体(LILRA2)、垂体腺苷酸环化酶激活多肽(ADCYAP1)、骨骼肌肌球蛋白轻链激酶(MYLK2)、钙结合蛋白2(CLSTN2)、钙调蛋白依赖性蛋白激酶4(CAMK4)、前蛋白转化酶枯草杆菌蛋白酶1 (PCSK1)、瞬时受体电位阳离子通道5(TRPC5)、钠离子葡萄糖联合转运子(SLC5A1)、酪氨酸酶相关蛋白1 (TYPR1)(P 0. 01);基因功能富集分析发现PD和MDD具有相同的14个生物学过程,6个细胞组成,10个分子功能,并且有3个相同的京都基因与基因组百科全书(KEGG)信号通路(P 0. 05);通过蛋白质网络构建,筛选出4个共同的关键基因(MYO1F、CAMK4、PCSK1、TRPC5);通过对蛋白质网络模块分析后发现关键模块具有共同的生物学过程。结论帕金森病和抑郁症具有共同的致病基因及通路,这为帕金森病和抑郁症伴存现象提供了理论基础。     

Deriving reference values for nerve conduction studies from existing data using mixture model clustering

Objective: to obtain locally valid reference values (RVs) from existing nerve conduction study (NCS) data.Methods: we used age, sex, height and limb temperature-based mixture model clustering (MMC) to identify normal and abnormal measurements on NCS data from two university hospitals. We compared MMC-derived RVs to published data; examined the effect of using different variables; validated MMC-derived RVs using independent data from 26 healthy control subjects and investigated their clinical applicability for the diagnosis of polyneuropathy.Results: MMC-derived RVs were similar to published RVs. Clustering can be achieved using only sex and age as variables. MMC is likely to yield reliable results with fewer abnormal than normal measurements and when the total number of measurements is at least 300. Measurements from healthy controls fell within the 95% MMC-derived prediction interval in 97.4% of cases.Conclusions: MMC can be used to obtain RVs from existing data, providing a locally valid, accurate reflection of the (ab)normality of an NCS result.Significance: MMC can be used to generate locally valid RVs for any test for which sufficient data are available.¹     

Functional organization of the human posterior cingulate cortex,revealed by multiple connectivity‐based parcellation methods

Based on cytoarchitecture, the posterior cingulate cortex (PCC) is thought to be comprised of two distinct functional subregions: the dorsal and ventral PCC (dPCC and vPCC). However, functional subregions do not completely match anatomical boundaries in the human brain. To understand the relationship between the functional organization of regions and anatomical features, it is necessary to apply parcellation algorithms based on functional properties. We therefore defined functionally informed subregions in the human PCC by parcellation of regions with similar patterns of functional connectivity in the resting brain. We used various patterns of functional connectivity, namely local, whole‐brain and diffuse functional connections of the PCC, and various clustering methods, namely hierarchical, spectral, and k‐means clustering to investigate the subregions of the PCC. Overall, the approximate anatomical boundaries and predicted functional regions were highly overlapped to each other. Using hierarchical clustering, the PCC could be clearly separated into two anatomical subregions, namely the dPCC and vPCC, and further divided into four subregions segregated by local functional connectivity patterns. We show that the PCC could be separated into two (dPCC and vPCC) or four subregions based on local functional connections and hierarchical clustering, and that subregions of PCC display differential global functional connectivity, particularly along the dorsal‐ventral axis. These results suggest that differences in functional connectivity between dPCC and vPCC may be due to differences in local connectivity between these functionally hierarchical subregions of the PCC. Hum Brain Mapp 38:2808–2818, 2017. © 2017 Wiley Periodicals, Inc.     

Feature extraction for pulmonary crackle representation via wavelet networks

In this study, wavelet networks have been used to parameterize and quantify pulmonary crackles with an aim to depict the waveform with a small set of meaningful parameters. Complex Morlet wavelets are used at the nodes of both single and double-node networks to model the waveforms with the double-node rendering smaller modeling error. The features extracted from the model parameters have been compared with the conventional time domain features in a two-class clustering experiment with nearly 90% matching between the clusters of different parameter sets and with the model parameters forming clusters more closely distributed around their means and better separated from each other. Moreover, using simulated crackles embedded on real respiratory sounds, features extracted from wavelet networks have been shown to be more robust to background vesicular sounds compared to conventional parameters which are very sensitive to noise.     

A multivariate hypothesis testing framework for tissue clustering and classification of DTI data

The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors belonging to the same distribution is assessed by an F‐test. Tissue type is classified according to one of the four possible diffusion models, the assignment of which is determined by a parsimonious model selection framework based on Schwarz Criterion. Both numerical phantoms and diffusion‐weighted imaging (DWI) data obtained from excised rat and pig spinal cords are used to test and validate these tissue clustering and classification approaches. The unsupervised clustering method effectively identifies distinct regions of interest (ROIs) in phantoms and real experimental DTI data. Copyright © 2009 John Wiley & Sons, Ltd.