Brain Mechanisms of Exercise-Induced Hypoalgesia: To Find a Way Out from “Fear-Avoidance Belief”

It is well known that exercise produces analgesic effects (exercise-induced hypoalgesia (EIH)) in animal models and chronic pain patients, but the brain mechanisms underlying these EIH effects, especially concerning the emotional aspects of pain, are not yet fully understood. In this review, we describe drastic changes in the mesocorticolimbic system of the brain which permit the induction of EIH effects. The amygdala (Amyg) is a critical node for the regulation of emotions, such as fear and anxiety, which are closely associated with chronic pain. In our recent studies using neuropathic pain (NPP) model mice, we extensively examined the association between the Amyg and EIH effects. We found that voluntary exercise (VE) activated glutamate (Glu) neurons in the medial basal Amyg projecting to the nucleus accumbens (NAc) lateral shell, while it almost completely suppressed NPP-induced activation of GABA neurons in the central nucleus of the Amyg (CeA). Furthermore, VE significantly inhibited activation of pyramidal neurons in the ventral hippocampus-CA1 region, which play important roles in contextual fear conditioning and the retrieval of fear memory. This review describes novel information concerning the brain mechanisms underlying EIH effects as a result of overcoming the fear-avoidance belief of chronic pain.     

Pediatric Brain Tumors: Signatures from the Intact Proteome

The present investigation aimed to explore the intact proteome of tissues of pediatric brain tumors of different WHO grades and localizations, including medulloblastoma, pilocytic astrocytoma, and glioblastoma, in comparison with the available data on ependymoma, to contribute to the understanding of the molecular mechanisms underlying the onset and progression of these pathologies. Tissues have been homogenized in acidic water–acetonitrile solutions containing proteases inhibitors and analyzed by LC–high resolution MS for proteomic characterization and label-free relative quantitation. Tandem MS spectra have been analyzed by either manual inspection or software elaboration, followed by experimental/theoretical MS fragmentation data comparison by bioinformatic tools. Statistically significant differences in protein/peptide levels between the different tumor histotypes have been evaluated by ANOVA test and Tukey’s post-hoc test, considering a p-value > 0.05 as significant. Together with intact protein and peptide chains, in the range of molecular mass of 1.3–22.8 kDa, several naturally occurring fragments from major proteins, peptides, and proteoforms have been also identified, some exhibiting proper biological activities. Protein and peptide sequencing allowed for the identification of different post-translational modifications, with acetylations, oxidations, citrullinations, deamidations, and C-terminal truncations being the most frequently characterized. C-terminal truncations, lacking from two to four amino acid residues, particularly characterizing the β-thymosin peptides and ubiquitin, showed a different modulation in the diverse tumors studied. With respect to the other tumors, medulloblastoma, the most frequent malignant brain tumor of the pediatric age, was characterized by higher levels of thymosin β4 and β10 peptides, the latter and its des-IS form particularly marking this histotype. The distribution pattern of the C-terminal truncated forms was also different in glioblastoma, particularly underlying gender differences, according to the definition of male and female glioblastoma as biologically distinct diseases. Glioblastoma was also distinguished for the peculiar identification of the truncated form of the α-hemoglobin chain, lacking the C-terminal arginine, and exhibiting oxygen-binding and vasoconstrictive properties different from the intact form. The proteomic characterization of the undigested proteome, following the top-down approach, was challenging to originally investigate the post-translational events that differently characterize pediatric brain tumors. This study provides a contribution to elucidate the molecular profiles of the solid tumors most frequently affecting the pediatric age, and which are characterized by different grades of aggressiveness and localization.     

Neuroelectric Mechanisms of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) remains a challenging but very important condition, because DCI is preventable and treatable for improving functional outcomes after aneurysmal subarachnoid hemorrhage (SAH). The pathologies underlying DCI are multifactorial. Classical approaches to DCI focus exclusively on preventing and treating the reduction of blood flow supply. However, recently, glutamate-mediated neuroelectric disruptions, such as excitotoxicity, cortical spreading depolarization and seizures, and epileptiform discharges, have been reported to occur in high frequencies in association with DCI development after SAH. Each of the neuroelectric disruptions can trigger the other, which augments metabolic demand. If increased metabolic demand exceeds the impaired blood supply, the mismatch leads to relative ischemia, resulting in DCI. The neuroelectric disruption also induces inverted vasoconstrictive neurovascular coupling in compromised brain tissues after SAH, causing DCI. Although glutamates and the receptors may play central roles in the development of excitotoxicity, cortical spreading ischemia and epileptic activity-related events, more studies are needed to clarify the pathophysiology and to develop novel therapeutic strategies for preventing or treating neuroelectric disruption-related DCI after SAH. This article reviews the recent advancement in research on neuroelectric disruption after SAH.     

The Role of Plasma Extracellular Vesicles in Remote Ischemic Conditioning and Exercise-Induced Ischemic Tolerance

Ischemic conditioning and exercise have been suggested for protecting against brain ischemia-reperfusion injury. However, the endogenous protective mechanisms stimulated by these interventions remain unclear. Here, in a comprehensive translational study, we investigated the protective role of extracellular vesicles (EVs) released after remote ischemic conditioning (RIC), blood flow restricted resistance exercise (BFRRE), or high-load resistance exercise (HLRE). Blood samples were collected from human participants before and at serial time points after intervention. RIC and BFRRE plasma EVs released early after stimulation improved viability of endothelial cells subjected to oxygen-glucose deprivation. Furthermore, post-RIC EVs accumulated in the ischemic area of a stroke mouse model, and a mean decrease in infarct volume was observed for post-RIC EVs, although not reaching statistical significance. Thus, circulating EVs induced by RIC and BFRRE can mediate protection, but the in vivo and translational effects of conditioned EVs require further experimental verification.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain-related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas-Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary results on different orientations, sequences of both of these datasets as per accuracy (up to 99.6%), sensitivity (up to 100%), specificity (up to 100%), precision (up to 100%), and AUC value (up to 1.0) have been achieved. The tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.