The use of reticulated foam in texture test objects for magnetic resonance imaging

The use of texture analysis in magnetic resonance imaging requires the availability of texture test objects for use in standardisation of in vivo measurement. A series of such objects of varying texture has been developed using reticulated foam, which has an open pore structure. The texture properties of these foams have been compared with in vivo brain and the ability of texture analysis to discriminate the different porosities of foam and brain (white matter) demonstrated.     

Nonlinear Dynamics of Firing Activity Patterns of Nociceptive Neurons during Management of Damaging Pain Stimulation

Technical Physics - A bifurcation analysis of a nociceptive neuron model was performed to study how the firing activity pattern changes when an antinociceptive response to damaging pain stimulation...     

Percept-related activity in the human somatosensory system: functional magnetic resonance imaging studies

In this paper, we review blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies addressing the neural correlates of touch, thermosensation, pain and the mechanisms of their cognitive modulation in healthy human subjects. There is evidence that fMRI signal changes can be elicited in the parietal cortex by stimulation of single mechanoceptive afferent fibers at suprathreshold intensities for conscious perception. Positive linear relationships between the amplitude or the spatial extents of BOLD fMRI signal changes, stimulus intensity and the perceived touch or pain intensity have been described in different brain areas. Some recent fMRI studies addressed the role of cortical areas in somatosensory perception by comparing the time course of cortical activity evoked by different kinds of stimuli with the temporal features of touch, heat or pain perception. Moreover, parametric single-trial functional MRI designs have been adopted in order to disentangle subprocesses within the nociceptive system.

Available evidence suggest that studies that combine fMRI with psychophysical methods may provide a valuable approach for understanding complex perceptual mechanisms and top-down modulation of the somatosensory system by cognitive factors specifically related to selective attention and to anticipation. The brain networks underlying somatosensory perception are complex and highly distributed. A deeper understanding of perceptual-related brain mechanisms therefore requires new approaches suited to investigate the spatial and temporal dynamics of activation in different brain regions and their functional interaction.     

Evaluation of discrete orthogonal versus polar Stockwell Transform for local multi-resolution texture analysis using brain MRI of multiple sclerosis patients

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification.     

MRI texture analysis on texture test objects, normal brain and intracranial tumors

Texture analysis was performed in three different MRI units on T1 and T2-weighted MR images from 10 healthy volunteers and 63 patients with histologically confirmed intracranial tumors. The goal of this study was a multicenter evaluation of the usefulness of this quantitative approach for the characterization of healthy and pathologic human brain tissues (white matter, gray matter, cerebrospinal fluid, tumors and edema). Each selected brain region of interest was characterized with both its mean gray level values and several texture parameters. Multivariate statistical analyses were then applied in order to discriminate each brain tissue type represented by its own set of texture parameters. Texture analysis was previously performed on test objects to evaluate the method dependence on acquisition parameters and consequently the interest of a multicenter evaluation. Even obtained on different sites with their own acquisition routine protocol, MR brain images contain textural features that can reveal discriminant factors for tissue classification and image segmentation. It can also offer additional information in case of undetermined diagnosis or to develop a more accurate tumor grading.     

Remyelination assessment by MRI texture analysis in a cuprizone mouse model

The purpose of this study was to perform serial texture analysis of brain MRI of cuprizone-treated mice for the assessment of regional demyelination and remyelination. Cuprizone-fed mice undergo a brain demyelination process. This process was followed over 56 days by MRI in the olfactory bulbs, cerebellum, putamen and brain stem. The texture of T2-weighted images has been analyzed at two levels: (1) with the average intensity as first order parameter and (2) with several higher order parameters for the best differentiation between myelinated (controls) and demyelinated brains. The most pertinent of these parameters, called horizontal gray level nonuniformity (HGLNU), has been selected by stepwise discriminant analysis. The time evolution of the average value of HGLNU not only confirmed the overall demyelination tendency followed by the average intensity, but also more precisely characterized a transitory remyelination on day 41 in the olfactory bulbs and cerebellum, in agreement with already published immunohistochemical destructive studies.     

Detection of late epilepsy by the texture analysis of MR brain images in the lithium-pilocarpine rat model

The aim of the study was to detect by texture analysis non easily visible anomalies of magnetic resonance (MR) images of piriform and entorhinal cortices relevant to the lithium-pilocarpine (Li-Pilo) model of temporal lobe epilepsy in rats. Status epilepticus was induced by Li-Pilo in twenty male rats 21 day-old. T₂-weighted MR images of their brain, were obtained before injection of Li-Pilo and one day after status epilepticus. An hyperintense signal was found in the piriform and entorhinal cortices of six rats, which developed chronic epilepsy after a latent period of one to three months. Among the 14 other rats which displayed images similar to those obtained before injection, four remained healthy but 10 rats developed late epileptic symptoms, raising the problem of hidden cortical damage which may be too subtle to be detected by classic MRI examination. A numeric treatment of digital images was then undertaken by texture analysis, to derive image information from a purely computational point of view. The combined texture and discriminant analyses based on pixels pattern anomalies, selected 3 texture parameters derived from co-occurrence matrix which characterized structural abnormalities relevant to the hyperintense signal, not only in the modified images of 6 rats but also in images of 10 rats with apparently non modified images. These three texture’s parameters allowed to classify the twenty rats of our experiment as follows: sixteen epileptic rats were effectively classified with cortical lesions, two non epileptic were correctly classified with healthy cortex, but two healthy rats were not correctly classified. This misclassification is discussed on the basis of the time dependence of the onset of seizure in the Li-Pilo model. These promising results suggest to apply this method to MRI examinations for an improvement of the early diagnostic of human epilepsy.     

Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas

The discrimination of tumor boundaries from normal tissue, as well as the evaluation of tissue heterogeneity and tumor grading often continue to pose a challenge in MRI. Although yielding promising results in various fields of medical imaging, two- dimensional (2D) texture analysis in MRI has, until now, demonstrated a lack of specificity in brain tumor classification. A new three-dimensional (3D) approach using Cooccurrence Matrix analysis is proposed to increase the sensitivity and specificity of brain tumor characterization. A preliminary comparative evaluation of 2D and 3D texture analysis was performed on T(1)-weighted MRI of seven gliomas for characterization of solid tumor, necrosis, edema and surrounding white matter. With 3D compared to 2D method, a better discrimination is obtained between necrosis and solid tumor as well as between edema and solid tumor. Using both methods, peritumoral white matter overlaps with edema, but is completely separated from far homo-lateral matter. This latter shows a complete overlapping with contra-lateral matter. The 3D texture analysis approach could provide a new tool for tumor grading and treatment follow-up, as well as for surgery or radiation therapy planning.     

磁刺激穴位复杂脑功能网络构建与分析

本文采用互信息方法对磁刺激内关穴过程中的脑电信 号进行了两两通道间非线性时域关联特性分析, 构建了不同频率刺激前、刺激中、刺激后的脑功能网络, 并基于复杂网络理论对脑功能网络的特征进行了深入研究. 结果表明, 磁刺激频率为3 Hz 时, 大脑功能网络的平均度、平均聚类系数和全局效率与刺激前相比均有显著升高, 平均路径长度显著降低, 并且相应脑功能网络的"小世界"属性有所增强, 信息在大脑各区域间的传递更加高效. 本研究首次开展了磁刺激穴位复杂脑功能网络的构建与分析, 为探索磁刺激穴位对大脑神经调节的作用和机理提供新思路和新方法. 关键词： 复杂网络 磁刺激 脑功能网络 互信息     

Exploring vision-related acupuncture point specificity with multivoxel pattern analysis

Acupoint specificity is one of the central issues of functional magnetic resonance imaging (fMRI) studies of acupuncture and has been under discussed. However, strong and consistent proof has not been provided for the existence of acupoint specificity, and unsuitable analysis approach applied could be the reason. We observed that previous researches of acupoint specificity were mostly based on model-based methods which were limited to make exploration of acupoint specificity because of the inaccurate specified prior. Here we applied multi-voxel pattern analysis (MVPA) to investigate the specificity of brain activation patterns induced by acupuncture stimulations at a vision-related acupoint (GB37) and a nearby nonacupoint (NAP). Results showed that multiple brain areas could differentiate the central neural response patterns induced by acupuncture stimulation at these two sites with higher accuracy above the chance level. These regions included occipital cortex, limbic-cerebellar areas and somatosensory cortex. Our results support that the characteristic neural response patterns of brain cortex to the acupuncture stimulation at GB37 and a nearby NAP could differ from each other effectively with the application of MVPA approach.     

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.     

Texture discrimination and multi-unit recording in the rat vibrissal nerve

Background  

Rats distinguish objects differing in surface texture by actively moving their vibrissae. In this paper we characterized some aspects of texture sensing in anesthetized rats during active touch. We analyzed the multifiber discharge from a deep vibrissal nerve when the vibrissa sweeps materials (wood, metal, acrylic, sandpaper) having different textures. We polished these surfaces with sandpaper (P1000) to obtain close degrees of roughness and we induced vibrissal movement with two-branch facial nerve stimulation. We also consider the change in pressure against the vibrissa as a way to improve the tactile information acquisition. The signals were compared with a reference signal (control) – vibrissa sweeping the air – and were analyzed with the Root Mean Square (RMS) and the Power Spectrum Density (PSD).     

经颅超声刺激人脑海马的数值仿真研究

颞叶癫痫、阿尔兹海默症等神经系统疾病与大脑海马的异常放电相关。近年兴起的经颅超声刺激治疗脑疾病具有无创、能深入脑组织和可调控海马神经元放电的特点。该文基于128阵元相控阵换能器、人头颅CT数据、水体建立超声经颅刺激海马数值仿真模型,数值仿真优选适应于海马刺激的换能器结构参数,并结合电容模型,探究超声声学参数对海马神经元放电的影响。结果表明曲率半径90 mm、开口半径56 mm、阵元半径2.0 mm、频率0.9 MHz的128阵元相控超声换能器可形成适应于刺激人脑海马大小的焦域;且在超声频率为0.9 MHz时对海马神经元放电有较好的抑制作用;在较小的占空比和较低的空间峰值时间平均声强条件下对神经元放电有较好的抑制作用。     

Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle

Automated magnetic resonance imaging (MRI) texture analysis was compared with visual MRI analysis for the diagnosis of skeletal muscle dystrophy in 14 healthy and 17 diseased subjects. MRI texture analysis was performed on 8 muscle regions of interest (ROI) using four statistical methods (histogram, co-occurrence matrix, gradient matrix, runlength matrix) and one structural (mathematical morphology) method. Nine senior radiologists assessed full leg transverse slice images and proposed a diagnosis. The 59 extracted texture parameters for each ROI were statistically analyzed by Correspondence Factorial Analysis. Non-parametric tests were used to compare diagnoses based on automated texture analysis and visual analysis. Texture analysis methods discriminated between healthy volunteers and patients with a sensitivity of 70%, and a specificity of 86%. Comparison with visual analysis of MR images suggests that texture analysis can provide useful information contributing to the diagnosis of skeletal muscle disease.     

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer’s disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a “transitional change” between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.     

An improved temporal clustering analysis method applied to whole-brain data in fMRI study

Temporal clustering analysis (TCA) has been proposed as a method to detect the brain responses of an fMRI time series when the time and location of the activation are completely unknown. But TCA is still incompetent in dealing with the time series of the whole brain due to the existence of many inactive pixels. If only active pixels are considered, the sensitivity of TCA will be improved greatly and it could be applied to the whole brain. In this study, some modifications were made to TCA to remove inactive pixels, and the applicability of the modified TCA to the whole brain was validated with a set of visual fMRI data. Based on the time series of the modified TCA, activations of the whole brain corresponding to the visual stimulation were detected. Compared with the previous TCA, the modified TCA method shows a significant improvement in the sensitivity to detect activation peaks of the whole brain.     

Investigation of the properties of filled polyethylene by the texture pattern of macrostructure images

The results of an investigation of the structure of polymer composite materials are presented. The materials were produced with the use of the texture analysis and mathematical methods of processing of macrostructure images. The approach proposed to measure texture parameters allows one to investigate the effect of the type and concentration of the filler on the properties of the material.     

The neutron texture diffractometer at the China Advanced Research Reactor

The first neutron texture diffractometer in China has been built at the China Advanced Research Reactor,due to strong demand for texture measurement with neutrons from the domestic user community. This neutron texture diffractometer has high neutron intensity, moderate resolution and is mainly applied to study texture in commonly used industrial materials and engineering components. In this paper, the design and characteristics of this instrument are described. The results for calibration with neutrons and quantitative texture analysis of zirconium alloy plate are presented. The comparison of texture measurements with the results obtained in HIPPO at LANSCE and Kowari at ANSTO illustrates the reliability of the texture diffractometer.     

Effects of Bilateral Subthalamic Nucleus Stimulation and Medication on Parkinsonian Speech Impairment

This study aimed to assess quantitatively the effect of bilateral subthalamic nucleus (STN) stimulation and medication on hypokinetic parkinsonian dysarthria. Twelve Italian patients (11 males and 1 female) with idiopathic Parkinson's disease (mean age 60.29+/-7.50 years) and bilateral STN implantation were studied. Neurological assessments and acoustic recordings were performed in four clinical conditions combining stimulation and medication to assess the degree of motor disabilities and speech impairment. Acoustic analysis was performed by means of the Multidimensional Voice Program and the Advanced Motor Speech Profile (Kay Elemetrics, Lincoln Park, NJ). None of the evaluated parameters deteriorated after STN deep brain stimulation. STN stimulation significantly improved motor performances and vocal tremor and provided a major stability to glottal vibration. Effect of stimulation on these parameters was superior to that of levodopa. No significant variations were observed in perceptual evaluation and in acoustic parameters related to prosody, articulation, and intensity after either stimulation or medication. The improvement of acoustic parameters related to glottal vibration and voice tremor was not accompanied by a substantial effect on speech intelligibility. STN stimulation was more effective on global motor limb dysfunctions than on dysarthria, but we did not report negative consequences on speech.