Integrated wavelet processing and spatial statistical testing of fMRI data

We introduce an integrated framework for detecting brain activity from fMRI data, which is based on a spatial discrete wavelet transform. Unlike the standard wavelet-based approach for fMRI analysis, we apply the suitable statistical test procedure in the spatial domain. For a desired significance level, this scheme has one remaining degree of freedom, characterizing the wavelet processing, which is optimized according to the principle of minimal approximation error. This allows us to determine the threshold values in a way that does not depend on data. While developing our framework, we make only conservative assumptions. Consequently, the detection of activation is based on strong evidence. We have implemented this framework as a toolbox (WSPM) for the SPM2 software, taking advantage of multiple options and functions of SPM such as the setup of the linear model and the use of the hemodynamic response function. We show by experimental results that our method is able to detect activation patterns; the results are comparable to those obtained by SPM even though statistical assumptions are more conservative.     

Using voxel-specific hemodynamic response function in EEG-fMRI data analysis: An estimation and detection model

Research groups who study epileptic spikes with simultaneous EEG-fMRI have used mostly the general linear model (GLM). A shortcoming of the GLM is that the specification of a simple hemodynamic response function (HRF) may lead to biased results. Other methods, which predict the hemodynamic response from the measured data, have been termed "recognition models". The merit of recognition models lies in the power of estimating the region-specific or voxel-specific HRF. We propose an approach that merges these two models in a general framework: estimate the HRF on the training data sets, and applying the estimated HRF on the other part of the data sets. The merit of this framework is that it can utilize the advantages of both models. A comparison of performance is made between the GLM with three fixed HRFs and the new model with voxel-specific HRFs. The main results are as follows: (1) in 18 of the 21 patients, the new model has a higher adjusted coefficient of multiple determination than the GLM with fixed HRF; (2) in some subjects, with the new model, we found areas of activation that had not been detected with the three fixed HRFs at our threshold of significance. The results suggest that the new model can do better than the fixed HRF GLM for the analysis of epileptic activity with EEG-fMRI.     

Correspondence between fMRI and SNP data by group sparse canonical correlation analysis

Both genetic variants and brain region abnormalities are recognized as important factors for complex diseases (e.g., schizophrenia). In this paper, we investigated the correspondence between single nucleotide polymorphism (SNP) and brain activity measured by functional magnetic resonance imaging (fMRI) to understand how genetic variation influences the brain activity. A group sparse canonical correlation analysis method (group sparse CCA) was developed to explore the correlation between these two datasets which are high dimensional-the number of SNPs/voxels is far greater than the number of samples. Different from the existing sparse CCA methods (sCCA), our approach can exploit structural information in the correlation analysis by introducing group constraints. A simulation study demonstrates that it outperforms the existing sCCA. We applied this method to the real data analysis and identified two pairs of significant canonical variates with average correlations of 0.4527 and 0.4292 respectively, which were used to identify genes and voxels associated with schizophrenia. The selected genes are mostly from 5 schizophrenia (SZ)-related signalling pathways. The brain mappings of the selected voxles also indicate the abnormal brain regions susceptible to schizophrenia. A gene and brain region of interest (ROI) correlation analysis was further performed to confirm the significant correlations between genes and ROIs.     

Parameter estimation in the magnitude-only and complex-valued fMRI data models

In functional magnetic resonance imaging, voxel time courses are complex-valued data but are traditionally converted to real magnitude-only data ones. At a large signal-to-noise ratio (SNR), the magnitude-only data Ricean distribution is approximated by a normal distribution that has been suggested as reasonable in magnitude-only data magnetic resonance images for an SNR of 5 and potentially as low as 3. A complex activation model has been recently introduced by Rowe and Logan [Rowe, D.B., and Logan, B.R. (2004). A complex way to compute fMRI activation. NeuroImage, 23 (3):1078-1092] that is valid for all SNRs. The properties of the parameter estimators and activation statistic for these two models and a more accurate Ricean approximation based on a Taylor series expansion are characterized in terms of bias, variance, and Cramer-Rao lower bound. It was found that the unbiased estimators in the complex model continued to be unbiased for lower SNRs while those of the normal magnitude-only data model became biased as the SNR decreased and at differing levels for the regression coefficients. The unbiased parameter estimators from the approximate magnitude-only Ricean Taylor model were unbiased to lower SNRs than the magnitude-only normal ones. Further, the variances of the parameter estimators achieved their minimum value in the complex data model regardless of SNR while the magnitude-only data normal model and Ricean approximation using a Taylor series did not as the SNR decreased. Finally, the mean activation statistic for the complex data model was higher and not SNR dependent while it decreased with SNR in the magnitude-only data models but less so for the approximate Ricean model. These results indicate that using the complex data model and not approximations to the true magnitude-only Ricean data model is more appropriate at lower SNRs. Therefore, since the computational cost is relatively low for the complex data model and since the SNR is not inherently known a priori for all voxels, the complex data model is recommended at all SNRs.     

SPM与AFNI在功能磁共振成像数据处理中的应用与比较

功能磁共振成像(fMRI)在脑功能研究方面应用广泛.目前用于fMRI数据处理与分析的软件很多,以统计参数图(SPM)与功能神经影像分析(AFNI)软件应用最为广泛.本文就SPM和AFNI在fMRI中的应用及二者间的差异作一综述.     

The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data

In the present study, we compared the effects of temporal compression (averaging across multiple scans) and space selection (i.e. selection of "regions of interest" from the whole brain) on single-subject and multi-subject classification of fMRI data using the support vector machine (SVM). Our aim was to investigate various data transformations that could be applied before training the SVM to retain task discriminatory variance while suppressing irrelevant components of variance. The data were acquired during a blocked experiment design: viewing unpleasant (Class 1), neutral (Class 2) and pleasant pictures (Class 3). In the multi-subject level analysis, we used a "leave-one-subject-out" approach, i.e. in each iteration, we trained the SVM using data from all but one subject and tested its performance in predicting the class label of the this last subject's data. In the single-subject level analysis, we used a "leave-one-block-out" approach, i.e. for each subject, we selected randomly one block per condition to be the test block and trained the SVM using data from the remaining blocks. Our results showed that in a single-subject level both temporal compression and space selection improved the SVM accuracy. However, in a multi-subject level, the temporal compression improved the performance of the SVM, but the space selection had no effect on the classification accuracy.     

fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis.

The Motor-Free Visual Perception Test, revised (MVPT-R), provides a measure of visual perceptual processing. It involves different cognitive elements including visual discrimination, spatial relationships, and mental rotation. We adapted the MVPT-R to an event-related functional MRI (fMRI) environment to investigate the brain regions involved in the interrelation of these cognitive elements. Two complementary analysis methods were employed to characterize the fMRI data: (a) a general linear model SPM approach based upon a model of the time course and a hemodynamic response estimate and (b) independent component analysis (ICA), which does not constrain the specific shape of the time course per se, although we did require it to be at least transiently task-related. Additionally, we implemented ICA in a novel way to create a group average that was compared with the SPM group results. Both methods yielded similar, but not identical, results and detected a network of robustly activated visual, inferior parietal, and frontal eye-field areas as well as thalamus and cerebellum. SPM appeared to be the more sensitive method and has a well-developed theoretical approach to thresholding. The ICA method segregated functional elements into separate maps and identified additional regions with extended activation in response to presented events. The results demonstrate the utility of complementary analyses for fMRI data and suggest that the cerebellum may play a significant role in visual perceptual processing. Additionally, results illustrate functional connectivity between frontal eye fields and prefrontal and parietal regions.     

Anatomic and functional variability: the effects of filter size in group fMRI data analysis

In the analysis of group fMRI scans, an optimal spatial filter should be large enough to accurately blend functionally homologous anatomic regions, yet small enough not to blur the functionally distinct regions. Hanning filters varying from 0.0 to 18.0 mm were evaluated in a group analysis of six healthy controls performing a simple finger-tapping paradigm. Test-retest reliability and Talairach-based measurements of the sensorimotor region were used to explore the optimal filter size. Two distinct regions of functional activation were noted in the sensorimotor cortex in group images (n = 6) at both time 1 and time 2. These regions merge once the filter size exceeds approximately 6.0 mm. The original hypothesis that these represented a motor and sensory activation was rejected on the basis of structural and functional variability. A discussion of the inherent difficulties in choosing an appropriate filter size is presented.     

Accounting for nonlinear BOLD effects in fMRI: parameter estimates and a model for prediction in rapid event-related studies

Nonlinear effects in fMRI BOLD data may substantially influence estimates of task-related activations, particularly in rapid event-related designs. If the BOLD response to each stimulus is assumed to be independent of the stimulation history, nonlinear interactions create a prediction error that may reduce sensitivity. When stimulus density differs among conditions, nonlinear effects can cause artifactual differences in activation. This situation can occur in rapid event-related designs or when comparing blocks of unequal lengths. We present data showing substantial nonlinear history effects for stimuli 1 s apart and use estimates of nonlinearities in response magnitude, onset time, and time to peak to form a low-dimensional parameterization of these nonlinear effects. Our estimates of nonlinearity appear relatively consistent throughout the brain, and these estimates can be used to form adjusted linear predictors for future rapid event-related fMRI studies. Adjusting the linear model for these known nonlinear effects results in a substantially better model fit. The biggest advantages to using predictors adjusted for known nonlinear effects are (1) higher sensitivity at the individual subject level of analysis, (2) better control of confounds related to nonlinear effects, and (3) more accurate estimates of design efficiency in experimental fMRI design.     

Effects of anandamide in migraine: data from an animal model

Systemic nitroglycerin (NTG) produces spontaneous-like migraine attacks in migraine sufferers and induces a condition of hyperalgesia in the rat 4 h after its administration. Endocannabinoid system seems to be involved in the modulation of NTG-induced hyperalgesia, and probably, in the pathophysiological mechanisms of migraine. In this study, the analgesic effect of anandamide (AEA) was evaluated by means of the formalin test, performed in baseline conditions and following NTG-induced hyperalgesia in male Sprague-Dawley rats. AEA was administered 30 min before the formalin injection. In addition, the effect of AEA (administered 30 min before NTG injection) was investigated on NTG-induced Fos expression and evaluated 4 h following NTG injection. AEA induced a significant decrease in the nociceptive behavior during both phases of the formalin test in the animals treated with vehicle, while it abolished NTG-induced hyperalgesia during the phase II. Pre-treatment with AEA significantly reduced the NTG-induced neuronal activation in nucleus trigeminalis caudalis, confirming the results obtained in our previous study, and in area postrema, while the same treatment induced an increase of Fos expression in paraventricular and supraoptic nuclei of the hypothalamus, parabrachial nucleus, and periaqueductal grey. The study confirms that a dysfunction of the endocannabinoid system may contribute to the development of migraine attacks and that a pharmacological modulation of CB receptors can be useful for the treatment of migraine pain.     

Health belief model and reversal theory: a comparative analysis

BACKGROUND: In recent years, an increasing number of nurses have demonstrated interest in health behaviour change interventions and research. Despite this heightened enthusiasm, there appears to have been less interest in exploring new and emerging health behaviour change theories. AIM: The goal of this work is to assist clinicians and researchers to make more informed choices about the use of the Health Belief Model and Reversal Theory in their practice settings and research projects. METHOD: Primary sources were analysed using qualitative data analysis methods in order to compare and contrast the two models. Four comparative categories provided the structure for analysis: origins of models, ways of knowing and behaving, role of health care providers in behaviour change, and desired outcomes. RESULTS: Based, in part, on their historical origins, the Health Belief Model and Reversal Theory offer differing tenets regarding how individuals perceive, understand, and behave. According to Reversal Theory, ways of knowing and behaving are dependent upon innate physiological factors and subjectively structured perceptions. In contrast, the Health Belief Model suggests that health-related behaviours are largely attributable to cognitive decision-making processes. As such, health care providers are directed to approach health behaviour change in different ways. CONCLUSIONS: Although the Health Belief Model has been widely implemented, Reversal Theory may offer a more comprehensive framework for health behaviour change interventions and research. Clinicians and researchers are urged to learn more about this theory and how it may apply to their areas of practice and research.     

Search for patterns of functional specificity in the brain: a nonparametric hierarchical Bayesian model for group fMRI data

Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with previously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli.     

凝聚胺法与微柱凝聚法检测不规则抗体的对比分析

目的探讨凝聚胺法与微柱凝聚法在不规则抗体筛选中的价值对比。方法对2010～2011年江门市新会区人民医院6 720例预约输血患者,用凝聚胺法与微柱凝聚法分别进行不规则抗体筛查,阳性结果再用谱细胞通过抗人球蛋白方法进行特异性检测。结果用微柱凝聚法筛查出不规则抗体阳性28例,阳性率为0.42%,而凝聚胺检测阳性26例,阳性率仅为0.39%,两种方法比较差异无统计学意义（P〉0.05）。其中Rh系统占总不规则抗体比例为57.1%,分别为抗-D 5例,抗-E 7例,抗-C 2例,抗-Ec 2例。结论凝聚胺法与微柱凝聚法都适用于临床筛查不规则抗体,微柱凝聚法比凝聚胺法更有利于大规模自动化筛查,对于有条件的大医院更适合临床输血前的应用和相关疾病的诊断,而凝聚胺法在基层或者临床急诊上也可合理开展。     

Using qualitative interviews in CAM research: a guide to study design, data collection and data analysis

Qualitative methods, such as the in-depth interview, offer a potentially powerful means of uncovering the complex experiences of patients, carers and clinicians within treatment and decision-making processes. In the context of complementary and alternative medicine (CAM), qualitative interview-based projects allow for subjectivity and complexity within human experience, making them a powerful tool for increasing our knowledge of important processes within CAM. However, qualitative interview-based projects are often poorly designed, carried out and analysed. This article provides a guide for CAM researchers on how to use qualitative interviews to produce meaningful, and methodologically sound, research findings.     

血常规检验在常见贫血诊断与鉴别中的应用价值分析

目的：分析血常规检验在常见贫血诊断与鉴别中的应用价值。方法择取该院2014年8月至2015年8月收治的72例贫血患者设置为本次的观察对象，按照贫血类型进行分组，共分为常规组与实验组两组。常规组患者34例，为缺铁性贫血患者，实验组患者38例，为巨幼细胞性贫血患者，对两组患者的血常规检验结果进行分析比较。结果比较两组患者的M C H、RDW、MCV均差异显著，P值小于0．05。结论对贫血患者进行血常规检验的临床意义重大，可帮助临床医生及时判定病情并做出正确的诊疗措施，提高临床诊断正确率。     

Broadband attenuation and nonlinear propagation in biological fluids: an experimental facility and measurements

The design and construction of a versatile experimental facility for making measurements of the frequency-dependence of attenuation coefficient (over the range 1 MHz to 25 MHz) and nonlinear propagation in samples of biological fluids is described. The main feature of the facility is the ability to perform all of the measurements on the same sample of fluid within a short period of time and under temperature control. In particular, the facility allows the axial development of nonlinear waveform distortion to be measured with a wideband bilaminar polyvinylidene difluoride membrane hydrophone to study nonlinear propagation in biological fluids. The system uses a variable length bellows to contain the fluid, with transparent Mylar end-windows to couple the acoustic field into the fluid. Example results for the frequency-dependence of attenuation of Dow Corning 200/350 silicone fluid, used as a standard fluid, are presented and shown to be in good agreement with alternative measurements. Measurements of finite amplitude propagation in amniotic fluid, urine and 4.5% human albumin solutions at physiological temperature (37 °C) are presented and compared with theoretical predictions using existing models. The measurements were made using a 2.25-MHz single-element transducer coupled to a polymethyl methacrylate lens with a focal amplitude gain of 12 in water. The transducer was driven with an eight-cycle tone burst at source pressures up to 0.137 MPa. In general, given an accurate knowledge of the medium parameters and source conditions, the agreement with theoretical prediction is good for the first five harmonics. (E-mail: Prashant.Verma@sth.nhs.uk)     

2007年北京地区在透析血液透析患者进入透析时心脑血管合并症——BJHDQCIC统计结果分析

目的统计并分析2007年北京地区在透析血液透析患者进入透析时心脑血管并发症患病率。方法北京市血液透析质量控制和改进中心(Beijing Hemodialysis Quality ControlImprovement Center,BJHDQCIC)收集北京地区各血液透析中心2007年在透析血液透析患者资料,大多数血液透析中心使用北京市汇百通公司"血液透析管理软件",部分使用access数据库上报。BJHDQCIC将access数据库和汇百通数据库合并形成包含所有上报信息的数据库。结果2007年在透析患者共7795例,92.4%性别资料完整,(男性51.9%,女性48.1%)。2007年在透析患者致病原因前四位依次为慢性肾炎(30.8%),糖尿病肾病(21.0%),高血压(14.7%),慢性间质性肾炎(10.7%),是北京地区维持性血液透析患者的主要病因(共77.2%)。41～70岁是在透患者主要年龄段,共占63.6%。在透析患者首次透析时缺血性心脏病患病率为43.5%;心肌梗死患病率为13.8%;心律失常患病率为30.5%。充血性心力衰竭患病率为48.4%,高血压患病率为87.7%。脑血管病患病率为19.6%。外周血管病患病率为12.9%。结论提示在透析患者心血管疾病(cardiavascular disease,CVD)、脑血管意外(cerebrovascular accidents,CVA)的患病率较高;要充分认识终末期肾病患者合并CVD、CVA的危险性,积极预防和控制CVD、CVA传统的危险因素如高血压、糖尿病等。     

Age differences in orbitofrontal activation: an fMRI investigation of delayed match and nonmatch to sample

Several investigations have suggested that the orbitofrontal cortex (OFC) may be particularly vulnerable to the effects of age-related changes. We recently reported behavioral data indicating greater age differences in orbitofrontal tasks when directly compared to tasks tapping dorsolateral prefrontal functions. The present study was designed to investigate the neural underpinnings of age differences in OFC functioning. Event-related functional magnetic resonance imaging (fMRI) was performed during delayed match and nonmatch to sample tasks, previously shown to differentially activate medial and lateral OFC in young adults. Sixteen healthy younger [age = 26.7(5.6)] and 16 healthy older individuals [age = 69.1 + 5.6] with similar levels of education and general cognitive functioning participated in the experiment. Participants chose the stimulus from a pair of stimuli matching a previously viewed target (match to sample) or chose the nontarget item (nonmatch to sample) depending upon a trial-specific instruction word. Consistent with previous studies, SPM99 analyses of the younger age group revealed activation for medial OFC regions during the match task compared to the nonmatch task and lateral OFC activation during the nonmatch task compared to the match task. In contrast, older adults showed prefrontal activation only during the match relative to the nonmatch task and posterior temporal and limbic involvement during the nonmatch relative to the match task. Between-group analyses confirmed within-group results suggesting differential age-related recruitment of prefrontal regions when performing match and nonmatch tasks. Results suggest that OFC recruitment during these cognitive tasks changes with age and should be evaluated within the context of other prefrontal subregions to further define differential age effects on frontal functions.