The automated preprocessing pipe-line for the estimation of scale-wise entropy from EEG data (APPLESEED): Development and validation for use in pediatric populations

It is increasingly understood that moment-to-moment brain signal variability – traditionally modeled out of analyses as mere “noise” – serves a valuable functional role related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) – a measure of signal irregularity across temporal scales – is an increasingly popular analytic technique in human neuroscience calculated from time series such as electroencephalography (EEG) signals. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain’s moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that adapts a critical modification to the original MSE algorithm, and generates reliable scale-wise entropy estimates capable of differentiating developmental stages and cognitive states. This novel pipeline – the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED.     

Benchmarking common preprocessing strategies in early childhood functional connectivity and intersubject correlation fMRI

Preprocessing choices present a particular challenge for researchers working with functional magnetic resonance imaging (fMRI) data from young children. Steps which have been shown to be important for mitigating head motion, such as censoring and global signal regression (GSR), remain controversial, and benchmarking studies comparing preprocessing pipelines have been conducted using resting data from older participants who tend to move less than young children. Here, we conducted benchmarking of fMRI preprocessing steps in a population with high head-motion, children aged 4–8 years, leveraging a unique longitudinal, passive viewing fMRI dataset. We systematically investigated combinations of global signal regression (GSR), volume censoring, and ICA-AROMA. Pipelines were compared using previously established metrics of noise removal as well as metrics sensitive to recovery of individual differences (i.e., connectome fingerprinting), and stimulus-evoked responses (i.e., intersubject correlations; ISC). We found that: 1) the most efficacious pipeline for both noise removal and information recovery included censoring, GSR, bandpass filtering, and head motion parameter (HMP) regression, 2) ICA-AROMA performed similarly to HMP regression and did not obviate the need for censoring, 3) GSR had a minimal impact on connectome fingerprinting but improved ISC, and 4) the strictest censoring approaches reduced motion correlated edges but negatively impacted identifiability.     

微机医学图象预处理

本文介绍微机数字图象预处理技术,包括图象信息采集、锐化、滤波,边缘检测,对比度增强等技术,为医学数字图象处理作必要的技术准备,程序用C语言编制     

Optimization of region of interest luminances may enhance radiologists' light adaptation

RATIONALE AND OBJECTIVES: Radiologic image details are best discriminated at luminance levels to which the eye is adapted. Recommendations that ambient light conditions are matched to overall monitor luminance to encourage appropriate adaptation are based on an assumption that clinically significant regions within the image match average monitor luminance. The current work examines this assumption. MATERIALS AND METHODS: Three image types were considered: posteroanterior (PA) chest; PA wrist; and computed tomography (CT) head. Luminance at clinically significant regions was measured at hilar region and peripheral lung (chest), distal radius (wrist), and supraventricular white matter (head). Average monitor luminances were calculated from measurements at 16 regions of the display face plate. Three ambient light levels-30, 100 and 400 lux-were employed. Thirty samples of each image type were used. RESULTS: Statistically significant differences were noted between average monitor luminances and clinically important regions of interest of up to a factor of 3.8, 2, and 6.3 for chest, wrist, and CT head images respectively (P < .0001). Values for the hilum of the chest and distal radius were higher than average monitor levels, whereas the reverse was observed for the peripheral lung and CT brain. Increasing ambient light had no impact on results. CONCLUSIONS: Clinically important radiologic information for common radiologic examinations is not being presented to observers in a way that facilitates optimized adaptation. This may have a significant impact on the ability of the observer to identify details with low contrast discriminability. The importance of image-processing algorithms focussing on clinically significant abnormalities rather than anatomic regions is highlighted.     

基于LADT—BP算法的心电图快速分析

本文提出了一种应用LADT（Linear Approximation Distance Thresholding)压缩算法进行预处理的BP（Backpropagation)网络算法（我们称为LADT-BP算法）。实验主日新月异该算法与现有的算法相比,在运算速度及正确识别率等方面,均有大幅度的提高。     

两种不同多酶清洗剂对器械预处理的方法研究

目的选择更适合用于器械预处理浸泡的多酶清洗剂。方法两种不同多酶清洗剂按照说明书进行配置,分别浸泡相同数量、类型的器械,浸泡时间相同,使用三磷酸腺苷(ATP)生物荧光检测法测定不同时间的浸泡液中的相对光单位(RLU),以及浸泡过后的器械上的RLU,并进行对比。结果两种浸泡液中的RLU检测值差异明显,普通多酶浸泡液中的RLU随着时间的延长迅速增长,60min内检测ATP得到的RLU<600,但是90min后检测得到的RLU数值迅速增加至>1 000,240min时RLU数值已经>2 000;ANIOSYME DD1多酶浸泡液的增长缓慢,240min内检测ATP得到的RLU值均<600;ANIOSYME DD1多酶浸泡液使用240min内,浸泡的器械RLU均<2 000。结论 ANIOSYME DD1多酶清洗剂是一种理想的预处理清洗剂,既能提高最终的清洗效果,有效避免器械间的交叉污染,又能保障人员的安全。     

Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets

A new approach using a similarity measure based on Yu's norms is presented for the detection of erythemato-squamous diseases, diabetes, breast cancer, lung cancer and lymphography. The domain contains records of patients with known diagnoses. The results are very promising with all data sets and (in conclusion, can be drawn that) a similarity model derived from Yu's norms could be used for the diagnosis of patients taking into consideration the error rate. A similarity classifier derived from Yu's norms was used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The results confirmed that the proposed model has potential in detecting the erythemato-squamous diseases. The similarity model derived from Yu's norms achieved an accuracy rate (97.8%) which was higher than that of the stand-alone neural network model or the ANFIS model suggested in Ubeyli and Güler [Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems, Comput. Biol. Med. 35 (2005) 421-433] or the similarity model based on ?ukasiewicz similarity [Luukka and Lepp?lampi, Similarity classifier with generalized mean applied to medical data, Comput. Biol. Med. 36 (2006) 1026-1040]. With PIMA Indian diabetes, the detection model has an error rate of about 24% which is much better than the overall rate of 33% for diabetes. Also, a classifier was applied to the lung cancer data set and the results were to my knowledge better than before. When the lung cancer data were preprocessed with an entropy minimization technique and the classifier with similarity based on Yu's norm was applied, 99.99% accuracy was achieved. The use of this preprocessing method enhanced the results over 30%. In lymphography, entropy minimization also enhanced the results remarkably and 86.2% accuracy was achieved.     

fMRI of the brainstem using dual-echo EPI

The brainstem is the part of the human brain that plays a pivotal role in the maintenance of many critical body functions. Due to the elevated level of cardiogenic noise, few fMRI studies have investigated the brainstem so far. Cardiac-gated echo-planar imaging with acquisition of two echoes per excitation (dual-echo EPI) is one method that significantly reduces cardiogenic noise and, thus, allows for fMRI measurements of the brainstem. As information on optimal preprocessing approaches for brainstem-fMRI data is still scarce, the goal of this study was to compare different combinations of normalization and smoothing procedures as implemented in standard fMRI software packages and to identify the combinations yielding optimal results for dual-echo EPI. 21 healthy subjects were measured while executing a simple motor paradigm to activate the facial and trigeminal motor nucleus in the brainstem. After motion correction and calculation of T(2)*-maps the data were preprocessed with 24 combinations of standard normalization (SPM classic, SPM unified, FSL, ABC) and smoothing procedures (pre-/post-smoothing with 3mm-, 4.5mm- and 6mm-kernel) before undergoing first- and second-level statistical analysis. Activation results were compared for first-level and second-level statistics using two anatomically defined regions of interest. Five methods were found to be sensitive for activation of both nuclei. These included FSL normalization with 3mm and 4.5mm pre-smoothing as well as 3mm post-smoothing, SPM unified normalization with 3mm pre-smoothing and ABC normalization with 4.5mm pre-smoothing. All these methods can be recommended for normalization and smoothing when analyzing fMRI data of the brainstem acquired by cardiac-gated dual-echo EPI.