Texture analysis of brain MRI evidences the amygdala activation by nociceptive stimuli under deep anesthesia in the propofol–formalin rat model

Simultaneous CMOS-Based Imaging of Calcium Signaling of the Central Amygdala and the Dorsal Raphe Nucleus During Nociception in Freely Moving Mice

Fluorescence imaging devices have been indispensable in elucidating the workings of the brain in living animals, including unrestrained, active ones. Various devices are available, each with their own strengths and weaknesses in terms of many factors. We have developed CMOS-based needle-type imaging devices that are small and lightweight enough to be doubly implanted in freely moving mice. The design also allowed angled implantations to avoid critical areas. We demonstrated the utility of the devices by using them on GCaMP6 mice in a formalin test experiment. Simultaneous implantations to the capsular-lateral central amygdala (CeLC) and dorsal raphe nucleus (DRN) were proven to be safe and did not hinder the execution of the study. Analysis of the collected calcium signaling data, supported by behavior data, showed increased activity in both regions as a result of pain stimulation. Thus, we have successfully demonstrated the various advantages of the device in its application in the pain experiment.

Texture analysis of T1 - and T2 -weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children

Brain tumours are the most common solid tumours in children, representing 20% of all cancers. The most frequent posterior fossa tumours are medulloblastomas, pilocytic astrocytomas and ependymomas. Texture analysis (TA) of MR images can be used to support the diagnosis of these tumours by providing additional quantitative information. MaZda software was used to perform TA on T1 - and T2 -weighted images of children with pilocytic astrocytomas, medulloblastomas and ependymomas of the posterior fossa, who had MRI at Birmingham Children's Hospital prior to treatment. The region of interest was selected on three slices per patient in Image J, using thresholding and manual outlining. TA produced 279 features, which were reduced using principal component analysis (PCA). The principal components (PCs) explaining 95% of the variance were used in a linear discriminant analysis (LDA) and a probabilistic neural network (PNN) to classify the cases, using DTREG statistics software. PCA of texture features from both T1 - and T2 -weighted images yielded 13 PCs to explain >95% of the variance. The PNN classifier for T1 -weighted images achieved 100% accuracy on training the data and 90% on leave-one-out cross-validation (LOOCV); for T2 -weighted images, the accuracy was 100% on training the data and 93.3% on LOOCV. A PNN classifier with T1 and T2 PCs achieved 100% accuracy on training the data and 85.8% on LOOCV. LDA classification accuracies were noticeably poorer. The features found to hold the highest discriminating potential were all co-occurrence matrix derived, where adjacent pixels had highly correlated intensities. This study shows that TA can be performed on standard T1 - and T2 -weighted images of childhood posterior fossa tumours using readily available software to provide high diagnostic accuracy. Discriminatory features do not correspond to those used in the clinical interpretation of the images and therefore provide novel tumour information.

Common brain activations for painful and non-painful aversive stimuli

BACKGROUND

Identification of potentially harmful stimuli is necessary for the well-being and self-preservation of all organisms. However, the neural substrates involved in the processing of aversive stimuli are not well understood. For instance, painful and non-painful aversive stimuli are largely thought to activate different neural networks. However, it is presently unclear whether there is a common aversion-related network of brain regions responsible for the basic processing of aversive stimuli. To help clarify this issue, this report used a cross-species translational approach in humans (i.e. meta-analysis) and rodents (i.e. systematic review of functional neuroanatomy).

RESULTS

Animal and human data combined to show a core aversion-related network, consisting of similar cortical (i.e. MCC, PCC, AI, DMPFC, RTG, SMA, VLOFC; see results section or abbreviation section for full names) and subcortical (i.e. Amyg, BNST, DS, Hab, Hipp/Parahipp, Hyp, NAc, NTS, PAG, PBN, raphe, septal nuclei, Thal, LC, midbrain) regions. In addition, a number of regions appeared to be more involved in pain-related (e.g. sensory cortex) or non-pain-related (e.g. amygdala) aversive processing.

CONCLUSIONS

This investigation suggests that aversive processing, at the most basic level, relies on similar neural substrates, and that differential responses may be due, in part, to the recruitment of additional structures as well as the spatio-temporal dynamic activity of the network. This network perspective may provide a clearer understanding of why components of this circuit appear dysfunctional in some psychiatric and pain-related disorders.

Altered thalamocortical functional connectivity by propofol anesthesia in rats

Anesthesia, a state of profound central nervous system suppression, involves a sequence of events that is still not well understood. In the present study, we examined the action of propofol (a sedative-hypnotic drug commonly used as anesthetic) on thalamocortical functional connectivity in rats by using functional connectivity magnetic resonance imaging (fcMRI) with a 3.0-tesla MR scanner. Intraperitoneal injections of propofol (80 or 160 mg/kg) were administered to Sprague-Dawley rats. Synchronized low-frequency fluctuations (LFF) of blood oxygen level-dependent (BOLD) signals were found between the thalamic and somatosensory cortices (S1/S2) after administration of 80 mg/kg propofol. However, after application of 160 mg/kg propofol, synchronized LFF of BOLD signals disappeared. These observations indicate that thalamocortical connectivity may play an important role in propofol anesthesia. We also observed that regionally specific long-range correlations of spontaneous low-frequency physiological fluctuations in BOLD signals may be present across somatosensory networks of the brain in the absence of external stimulation. However, our experiment suggests that fcMRI can be used to investigate brain networks that exhibit correlated fluctuations.

MRI texture analysis in multiple sclerosis: toward a clinical analysis protocol

RATIONALE AND OBJECTIVES

Magnetic resonance imaging (MRI)-based texture analysis has been shown to be effective in classifying multiple sclerosis lesions. Regarding the clinical use of texture analysis in multiple sclerosis, our intention was to show which parts of the analysis are sensitive to slight changes in textural data acquisition and which steps tolerate interference.

MATERIALS AND METHODS

The MRI datasets of 38 multiple sclerosis patients were used in this study. Three imaging sequences were compared in quantitative analyses, including a comparison of anatomical levels of interest, variance between sequential slices and two methods of region of interest drawing. We focused on the classification of white matter and multiple sclerosis lesions in determining the discriminatory power of textural parameters. Analyses were run with MaZda software for texture analysis, and statistical tests were performed for raw parameters.

RESULTS

MRI texture analysis based on statistical, autoregressive-model and wavelet-derived texture parameters provided an excellent distinction between the image regions corresponding to multiple sclerosis plaques and white matter or normal-appearing white matter with high accuracy (nonlinear discriminant analysis 96%-100%). There were no significant differences in the classification results between imaging sequences or between anatomical levels. Standardized regions of interest were tolerant of changes within an anatomical level when intra-tissue variance was tested.

CONCLUSION

The MRI texture analysis protocol with fixed imaging sequence and anatomical levels of interest shows promise as a robust quantitative clinical means for evaluating multiple sclerosis lesions.

Non-Hodgkin lymphoma response evaluation with MRI texture classification

BACKGROUND

To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.

METHODS

A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.

RESULTS

NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.

CONCLUSION

Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.

MaZda--a software package for image texture analysis

MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage.