Tumor Classification Based on Regional Heterogeneity Using Pixel Level Feature Descriptors

2021 ◽  
Vol 11 (5) ◽  
pp. 1481-1488
Author(s):  
C. Gunasundari ◽  
K. Selva Bhuvaneswari
Keyword(s):  
Texture Analysis ◽  
Early Stage ◽  
Pituitary Tumors ◽  
Brain Regions ◽  
Regional Heterogeneity ◽  
Gradient Based ◽  
Classification Of Tumors ◽  
Effective Diagnosis ◽  
The Brain

Brain tumor is considered to be widely analyzed disease for effective diagnosis and treatment planning. Several approaches were framed to detect and diagnose tumor at early stage. In this work, texture analysis is carried out to analyze the nature of tumor and categorize it. Around 3064 images were analyzed during this study consisting of meningioma, glioma and pituitary tumors. Intensity and gradient pixel based texture analysis is carried out in this analysis. Results confirm that the tumors can be classified and categorized based on the intensity and gradient pixel information. A total of 2216 feature vector is extracted it is observed that the gradient based information aids for better classification of tumors. Localized binary patterns are found to provide detailed information about the subtle variation in the brain regions due to the presence of abnormality in brain tissues. It is further observed that the normalized feature vectors show better differentiation between tumor categories. The ROC and PRC curves exhibit the high classification ability using the extracted features to differentiate tumor grades.

scholarly journals Location Matters: Navigating Regional Heterogeneity of the Neurovascular Unit

2021 ◽  
Vol 15 ◽  
Author(s):  
Louis-Philippe Bernier ◽  
Clément Brunner ◽  
Azzurra Cottarelli ◽  
Matilde Balbi
Keyword(s):  
Brain Function ◽  
Energy Demand ◽  
Neurovascular Unit ◽  
Cell Types ◽  
Brain Regions ◽  
Regional Heterogeneity ◽  
Regional Specialization ◽  
Multiple Cell ◽  
Cellular Components ◽  
The Brain

The neurovascular unit (NVU) of the brain is composed of multiple cell types that act synergistically to modify blood flow to locally match the energy demand of neural activity, as well as to maintain the integrity of the blood-brain barrier (BBB). It is becoming increasingly recognized that the functional specialization, as well as the cellular composition of the NVU varies spatially. This heterogeneity is encountered as variations in vascular and perivascular cells along the arteriole-capillary-venule axis, as well as through differences in NVU composition throughout anatomical regions of the brain. Given the wide variations in metabolic demands between brain regions, especially those of gray vs. white matter, the spatial heterogeneity of the NVU is critical to brain function. Here we review recent evidence demonstrating regional specialization of the NVU between brain regions, by focusing on the heterogeneity of its individual cellular components and briefly discussing novel approaches to investigate NVU diversity.

scholarly journals Identification and Research of Adhd and Healthy Controls using Fmri

2019 ◽  
Vol 8 (6S) ◽  
pp. 370-372
Keyword(s):  
Brain Regions ◽  
Healthy Controls ◽  
Hyperactivity Disorder ◽  
Network Activation ◽  
Healthy Control ◽  
Temporal And Spatial ◽  
The Individual ◽  
The Brain ◽  
Coronal View

Analyzing the brain regions for different activations corresponding to the activation input for an experimental setup of task functional MRI or a resting state functional Magnetic Resonance Imaging(fMRI) for a diagnosed or healthy control is a challenging issue as the processing data is voluminous 4D data with nearly 1,51,552 voxels for a single volume of 261 scans fMRI. The data considered for analysis consists of 10 healthy controls and 10 Attention Deficit Hyperactivity Disorder(ADHD) fMRI. The workflow starts with preprocessing the individual scan for realignment, coregistration and Normalisation to Montreal Neurological Institute (MNI) space. Single site scan visit consists of 64x64x37 voxels. Seventy independent components are obtained from processed data by data reduction, Independent Component Analysis (ICA) calculation, Back reconstruction and Component Calibration. ICA performs satisfactorily well on temporal and spatial localization. Visual medial network activation is pronounced in ADHD Controls than in healthy people. Sagittal, Axial and Coronal view of ADHD controls is obtained as component number 42.The analysis is further used for the automatic classification of healthy controls and ADHD people.

scholarly journals Mutant huntingtin reduces vesicular zinc level by inhibiting the binding of Sp1 to ZnT3 promoter

2020 ◽  
Author(s):  
Li Niu ◽  
Shiming Yang ◽  
Weixi Wang ◽  
Cui-fang Ye ◽  
He Li
Keyword(s):  
Molecular Mechanisms ◽  
Early Stage ◽  
Brain Regions ◽  
Zinc Level ◽  
Significant Loss ◽  
Zinc Homeostasis ◽  
Synaptic Dysfunction ◽  
Mutant Huntingtin ◽  
Bhk Cells ◽  
The Brain

Abstract Background Synaptic dysfunction caused by mutant huntingtin greatly contributes to Huntington’s disease (HD) pathogenesis. HD patients show cognitive impairment as well as uncontrolled movements. Vesicular zinc is closely linked to modulating synaptic transmission and maintaining cognitive ability. However, whether does mutant huntingtin affect zinc homeostasis in the brain or not? This will be of great significance for further revealing the pathogenesis of HD. Methods N171-HD82Q transgenic mice and cultured BHK cells expressing N-terminal mutant huntingtin fragment containing 160 glutamines (160Q BHK cells) were used to investigate the effect of mutant huntingtin on zinc homeostasis and its molecular mechanisms. Results Herein, we have demonstrated that the density of synaptic vesicular zinc decreases in the cortex, striatum and hippocampus of N171-82Q mice. Given that vesicular zinc concentration depends on the abundance of zinc transporter 3 (ZnT3) on the membrane of synaptic vesicles, ZnT3 expression is detected in the brain of N171-82Q mice and 160Q BHK cells. Mutant huntingtin leads to a dramatical decrease in ZnT3 mRNA and protein levels in the three brain regions of these mice aged from 14 to 20 weeks. Significantly, Sp1 activates ZnT3 transcription via its binding to the GC boxes in ZnT3 promoter. Nevertheless, mutant huntingtin inhibits the binding of Sp1 to the promoter of ZnT3 gene and down-regulates ZnT3 expression. Furthermore, the overexpression of Sp1 ameliorates inhibition of ZnT3 gene transcription by mutant huntingtin. Conclusions Collectively, this first study to reveal a significant loss of synaptic vesicular zinc and ZnT3 expression caused by mutant huntingtin in the early stage of HD. Our findings have revealed the molecular mechanism underlying this change. Mutant huntingtin inhibits the binding of Sp1 to ZnT3 gene promoter to reduce ZnT3 expression. The imbalance of vesicular zinc homeostasis may be closely associated with synaptic dysfunction and cognitive deficits in HD. This work sheds novel mechanistic insights into the pathogenesis of HD and promises a potential therapeutic strategy for HD.

scholarly journals The Occurrence of Pain-Induced Depression Is Different between Rat Models of Inflammatory and Neuropathic Pain

2021 ◽  
Vol 10 (17) ◽  
pp. 4016
Author(s):  
Yung-Chi Hsu ◽  
Kuo-Hsing Ma ◽  
Shu-Lin Guo ◽  
Bo-Feng Lin ◽  
Chien-Sung Tsai ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Early Stage ◽  
Preference Test ◽  
Time Frame ◽  
Brain Regions ◽  
Rat Models ◽  
Positron Emission ◽  
Initial Injury ◽  
Sucrose Preference Test ◽  
The Brain

Various pain conditions may be associated with depressed mood. However, the effect of inflammatory or neuropathic pain on depression-like behavior and its associated time frame has not been well established in rat models. This frontward study investigated the differences in pain behavior, depression-like behavior, and serotonin transporter (SERT) distribution in the brain between rats subjected to spared nerve injury (SNI)-induced neuropathic pain or complete Freund’s adjuvant (CFA)-induced inflammatory pain. A dynamic plantar aesthesiometer and an acetone spray test were used to evaluate mechanical and cold allodynia responses, and depression-like behavior was examined using a forced swimming test and sucrose preference test. We also investigated SERT expression by using positron emission tomography. We found that the inflammation-induced pain was less severe than neuropathic pain from days 3 to 28 after induced pain; however, the CFA-injected rats exhibited more noticeable depression-like behavior and had significantly reduced SERT expression in the brain regions (thalamus and striatum) at an early stage (on days 14, 21, and 28 in two groups of CFA-injected rats versus day 28 in SNI rats). We speculated that not only the pain response after initial injury but also the subsequent neuroinflammation may have been the crucial factors influencing depression-like behavior in rats.

scholarly journals Improved Classification of Brain Tumor in MR Images using RNN Classification Framework

2020 ◽  
Vol 9 (3) ◽  
pp. 1098-1101
Keyword(s):  
Brain Tumor ◽  
Pituitary Tumors ◽  
Classification Problem ◽  
Mr Images ◽  
Mri Brain ◽  
Important Constraint ◽  
Classification Framework ◽  
The Brain ◽  
Aided Diagnosis

Classification of brain tumor for medical applications is considered as an important constraint in computer-aided diagnosis (CAD). In this paper, we study the classification of brain tumor by considering the constraint as a classification problem in order to segregate the tumors among pituitary tumors, gliomatumorand meningioma tumor. This method adopts deep learning principle to extract the brain features from the MRI images. In this study, Recurrent Neural Network is used to classify the extracted features from brain. The experiments are carried out in terms of three fold crossvalidation process over MRI brain image dataset. The results show that the proposed RNN classifier classifies the brain tumors effectively with 98% of mean classification accuracy than other existing methods.

scholarly journals Shared and modality-specific brain regions that mediate auditory and visual word comprehension

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Anne Keitel ◽  
Joachim Gross ◽  
Christoph Kayser
Keyword(s):  
Brain Regions ◽  
Visual Word ◽  
Visual Speech ◽  
Speech Comprehension ◽  
Multivariate Classification ◽  
Word Comprehension ◽  
The Brain

Visual speech carried by lip movements is an integral part of communication. Yet, it remains unclear in how far visual and acoustic speech comprehension are mediated by the same brain regions. Using multivariate classification of full-brain MEG data, we first probed where the brain represents acoustically and visually conveyed word identities. We then tested where these sensory-driven representations are predictive of participants’ trial-wise comprehension. The comprehension-relevant representations of auditory and visual speech converged only in anterior angular and inferior frontal regions and were spatially dissociated from those representations that best reflected the sensory-driven word identity. These results provide a neural explanation for the behavioural dissociation of acoustic and visual speech comprehension and suggest that cerebral representations encoding word identities may be more modality-specific than often upheld.

scholarly journals Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 465
Author(s):  
Manuel Curado ◽  
Francisco Escolano ◽  
Miguel A. Lozano ◽  
Edwin R. Hancock
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Random Walk ◽  
Early Stage ◽  
Directed Graphs ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Left And Right ◽  
The Brain ◽  
Analyze Data

Alzheimer’s disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture asymmetries in the interactions between different anatomical brain regions. The detection of these asymmetries is relevant to detect the disease in an early stage. For this reason, in this paper, we analyze data extracted from fMRI images using the net4Lap algorithm to infer a directed graph from the available BOLD signals, and then seek to determine asymmetries between the left and right hemispheres of the brain using a directed version of the Return Random Walk (RRW). Experimental evaluation of this method reveals that it leads to the identification of anatomical brain regions known to be implicated in the early development of Alzheimer’s disease in clinical studies.

scholarly journals MIC-MAC: High-throughput analysis of three-dimensional microglia morphology in mammalian brains

2018 ◽  
Author(s):  
Luis Salamanca ◽  
Naguib Mechawar ◽  
Keith K. Murai ◽  
Rudi Balling ◽  
David S. Bouvier ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Brain Regions ◽  
High Throughput Analysis ◽  
Disease States ◽  
Single Cell Rna Sequencing ◽  
Morphology Characterization ◽  
The Brain ◽  
Microglia Morphology

ABSTRACTMicroglia, the resident immune cells of the brain, exhibit complex and diverse phenotypes depending on their physiological context including brain regions and disease states. While single-cell RNA-sequencing has recently resolved this heterogeneity, it does not capture tissue and intercellular mechanisms. Our complementary imaging-based approach enables automatic 3D-morphology characterization and classification of thousands of individual microglia in situ and revealed species- and disease-specific morphological phenotypes in mouse and human brain samples.

scholarly journals Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

2018 ◽  
Vol 8 (2) ◽  
pp. 111-120 ◽  
Author(s):  
Alhadi Bustamam ◽  
Devvi Sarwinda ◽  
Gianinna Ardenaswari
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Texture Analysis ◽  
Microarray Data ◽  
Human Memory ◽  
Nerve Tissue ◽  
Whole Brain ◽  
The Brain

Abstract Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

scholarly journals CLASSIFICATION OF BRAIN CANCER TYPE USING MACHINE LEARNING

2019 ◽  
Vol 2019 (2) ◽  
Author(s):  
Dr. T. Vijaya kumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Cancer ◽  
Early Stage ◽  
Learning System ◽  
Cancer Type ◽  
Convolutional Networks ◽  
Testing Accuracy ◽  
The Brain

The Brain cancer is the most dangerous and found commonly in multitude of people in the younger stage and the adolescent stages. The early stage identification about the tumors in the brain and the appropriate type of the cancer would help the physicians in deciding the accurate treatments and further analyzing based on the responses from the patients to the treatment done. The paper puts forth the capsule neural network, the machine learning system that can be trained using a less number of dataset unlike convolutional neural network and is sturdy against the rotation or the affine conversions, to identify the type of cancerous tumors in brain at its early stage. The evaluation of the training and the testing accuracy of the proposed method for classification of the brain cancer type using the capsule neural network proves that Caps Net based classification have outperformed the convolutional networks.

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