scholarly journals The Proliferation of Dentate Gyrus Progenitors in the Ferret Hippocampus by Neonatal Exposure to Valproic Acid

2021 ◽  
Vol 15 ◽  
Author(s):  
Kazuhiko Sawada ◽  
Shiori Kamiya ◽  
Ichio Aoki
Keyword(s):  
Valproic Acid ◽  
Cerebral Cortex ◽  
Magnetic Resonance ◽  
Dentate Gyrus ◽  
Structural Changes ◽  
S100 Protein ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Magnetic Resonance Images ◽  
Neonatal Exposure

Prenatal and neonatal exposure to valproic acid (VPA) is associated with human autism spectrum disorder (ASD) and can alter the development of several brain regions, such as the cerebral cortex, cerebellum, and amygdala. Neonatal VPA exposure induces ASD-like behavioral abnormalities in a gyrencephalic mammal, ferret, but it has not been evaluated in brain regions other than the cerebral cortex in this animal. This study aimed to facilitate a comprehensive understanding of brain abnormalities induced by developmental VPA exposure in ferrets. We examined gross structural changes in the hippocampus and tracked proliferative cells by 5-bromo-2-deoxyuridine (BrdU) labeling following VPA administration to ferret infants on postnatal days (PDs) 6 and 7 at 200 μg/g of body weight. Ex vivo short repetition time/time to echo magnetic resonance imaging (MRI) with high spatial resolution at 7-T was obtained from the fixed brain of PD 20 ferrets. The hippocampal volume estimated using MRI-based volumetry was not significantly different between the two groups of ferrets, and optical comparisons on coronal magnetic resonance images revealed no differences in gross structures of the hippocampus between VPA-treated and control ferrets. BrdU-labeled cells were observed throughout the hippocampus of both two groups at PD 20. BrdU-labeled cells were immunopositive for Sox2 (>70%) and almost immunonegative for NeuN, S100 protein, and glial fibrillary acidic protein. BrdU-labeled Sox2-positive progenitors were abundant, particularly in the subgranular layer of the dentate gyrus (DG), and were denser in VPA-treated ferrets. When BrdU-labeled Sox2-positive progenitors were examined at 2 h after the second VPA administration on PD 7, their density in the granular/subgranular layer and hilus of the DG was significantly greater in VPA-treated ferrets compared to controls. The findings suggest that VPA exposure to ferret infants facilitates the proliferation of DG progenitors, supplying excessive progenitors for hippocampal adult neurogenesis to the subgranular layer.

scholarly journals Musical skills can be decoded from magnetic resonance images

2020 ◽  
Author(s):  
Tuomas Puoliväli ◽  
Tuomo Sipola ◽  
Anja Thiede ◽  
Marina Kliuchko ◽  
Brigitte Bogert ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Structure ◽  
Structural Changes ◽  
Musical Instrument ◽  
Structural Features ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Brain

AbstractLearning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions encompassing the whole cortex. Using a supervised machine learning technique called support vector machines, we achieved significant (κ = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding an acquired skill from magnetic resonance images of brain structure is feasible to some extent. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely overlapped with earlier findings, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude.

Semiautomatic brain region extraction: a method of parcellating brain regions from structural magnetic resonance images

NeuroImage ◽  
2004 ◽  
Vol 22 (4) ◽  
pp. 1492-1502 ◽  
Author(s):  
L.A Dade ◽  
F.Q Gao ◽  
N Kovacevic ◽  
P Roy ◽  
C Rockel ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Region ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Region Extraction

scholarly journals DENTATE GYRUS MOLECULAR LAYER MICROGLIA CHANGES, IL1-β AND BEHAVIOR IN MOUSE VALPROIC ACID MODEL OF AUTISM SPECTRUM DISORDERS

2019 ◽  
Author(s):  
Alinne Lorrany Gomes Dos Santos ◽  
Ellen Rose Leandro Ponce de Leão ◽  
Larissa Victória Barra de Moura ◽  
Dilza Souza ◽  
Daniel Guerreiro Diniz ◽  
...  
Keyword(s):  
Valproic Acid ◽  
Autism Spectrum Disorders ◽  
Dentate Gyrus ◽  
Molecular Layer ◽  
Autism Spectrum ◽  
Spectrum Disorders ◽  
And Behavior

Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach

2014 ◽  
Vol 221 ◽  
pp. 22-31 ◽  
Author(s):  
Ye Chen ◽  
Judd Storrs ◽  
Lirong Tan ◽  
Lawrence J. Mazlack ◽  
Jing-Huei Lee ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Structural Changes ◽  
Magnetic Resonance Images ◽  
Local Feature ◽  
Feature Based

scholarly journals Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1402
Author(s):  
Zhao Zhang ◽  
Guangfei Li ◽  
Yong Xu ◽  
Xiaoying Tang
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Human Brain ◽  
Autism Spectrum ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Psychiatric Diseases ◽  
Hyperactivity Disorder ◽  
Future Research Directions ◽  
Magnetic Resonance Imaging Mri

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.

Automatic Diagnosis of Brain Magnetic Resonance Images based on Riemannian Geometry

2015 ◽  
Vol 13 (2) ◽  
pp. 30-40
Author(s):  
Mohamed Gouskir ◽  
Belaid Bouikhalene ◽  
Hicham Aissaoui ◽  
Benachir Elhadadi
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Riemannian Manifolds ◽  
Tumor Detection ◽  
Brain Regions ◽  
Magnetic Resonance Images ◽  
Automatic Diagnosis ◽  
Brain Extraction ◽  
Brain Magnetic Resonance Image ◽  
Brain Image Analysis

Automated brain tumor detection and segmentation, from medical images, is one of the most challenging. The authors present, in this paper, an automatic diagnosis of brain magnetic resonance image. The goal is to prepare the image of the human brain to locate the existence of abnormal tissues in multi-modal brain magnetic resonance images. The authors start from the image acquisition, reduce information, brain extraction, and then brain region diagnosis. Brain extraction is the most important preprocessing step for automatic brain image analysis. The authors consider the image as residing in a Riemannian space and they based on Riemannian manifold to develop an algorithm to extract brain regions, these regions used in other algorithm to brain tumor detection, segmentation and classification. Riemannian Manifolds show the efficient results to brain extraction and brain analysis for multi-modal resonance magnetic images.

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