scholarly journals Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players

2021 ◽  
Vol 15 ◽  
Author(s):  
Harish RaviPrakash ◽  
Syed Muhammad Anwar ◽  
Nadia M. Biassou ◽  
Ulas Bagci
Keyword(s):  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Spatial Reasoning ◽  
Medical Condition ◽  
Brain Connectivity ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Connectivity Matrix ◽  
Chess Players ◽  
The Brain

A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

scholarly journals Morphometric and Functional Brain Connectivity Differentiates Chess Masters from Amateur Players

2020 ◽  
Author(s):  
Harish RaviPrakash ◽  
Syed Muhammad Anwar ◽  
Nadia M. Biassou ◽  
Ulas Bagci
Keyword(s):  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Spatial Reasoning ◽  
Medical Condition ◽  
Brain Connectivity ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Connectivity Matrix ◽  
Chess Players ◽  
The Brain

ABSTRACTA common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Towards this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

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.

scholarly journals Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Pan Wang ◽  
Zedong Wang ◽  
Jianlin Wang ◽  
Yuan Jiang ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
White Matter ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Parietal Lobe ◽  
Neurodegenerative Disorder ◽  
Magnetic Resonance Images ◽  
Support Vector

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.

scholarly journals Gray Matter Volume and Functional Connectivity in Hypochondriasis: A Magnetic Resonance Imaging and Support Vector Machine Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Zhe Shen ◽  
Liang Yu ◽  
Zhiyong Zhao ◽  
Kangyu Jin ◽  
Fen Pan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Imaging Data ◽  
Matter Volume ◽  
Medial Frontal Gyrus

Objective: Patients with hypochondriasis hold unexplainable beliefs and a fear of having a lethal disease, with poor compliances and treatment response to psychotropic drugs. Although several studies have demonstrated that patients with hypochondriasis demonstrate abnormalities in brain structure and function, gray matter volume (GMV) and functional connectivity (FC) in hypochondriasis still remain unclear.Methods: The present study collected T1-weighted and resting-state functional magnetic resonance images from 21 hypochondriasis patients and 22 well-matched healthy controls (HCs). We first analyzed the difference in the GMV between the two groups. We then used the regions showing a difference in GMV between two groups as seeds to perform functional connectivity (FC) analysis. Finally, a support vector machine (SVM) was applied to the imaging data to distinguish hypochondriasis patients from HCs.Results: Compared with the HCs, the hypochondriasis group showed decreased GMV in the left precuneus, and increased GMV in the left medial frontal gyrus. FC analyses revealed decreased FC between the left medial frontal gyrus and cuneus, and between the left precuneus and cuneus. A combination of both GMV and FC in the left precuneus, medial frontal gyrus, and cuneus was able to discriminate the hypochondriasis patients from HCs with a sensitivity of 0.98, specificity of 0.93, and accuracy of 0.95.Conclusion: Our study suggests that smaller left precuneus volumes and decreased FC between the left precuneus and cuneus seem to play an important role of hypochondriasis. Future studies are needed to confirm whether this finding is generalizable to patients with hypochondriasis.

FUNCTIONAL CONNECTIVITY CHANGES IN MILD TRAUMATIC BRAIN INJURY ASSESSED USING MAGNETOENCEPHALOGRAPHY

2012 ◽  
Vol 12 (02) ◽  
pp. 1240006 ◽  
Author(s):  
GEORGE ZOURIDAKIS ◽  
UDIT PATIDAR ◽  
NING SITU ◽  
ROOZBEH REZAIE ◽  
EDUARDO M. CASTILLO ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Mild Traumatic Brain Injury ◽  
Brain Connectivity ◽  
Support Vector ◽  
Connectivity Matrix ◽  
Vector Machines ◽  
And Gender

In this study, we analyzed brain connectivity profiles from 10 mild traumatic brain injury (mTBI) patients and 10 age- and gender-matched normal controls. We computed Granger causality measures from magnetoencephalographic (MEG) activity obtained at the resting state, in an attempt to understand how the default network is affected by mTBI. A connectivity matrix was computed for each subject individually and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide a minimum of 85% classification accuracy in separating the two groups, with a sensitivity and specificity of 90% and 80%, respectively. These findings suggest that analysis of functional connectivity patterns may provide a valuable tool for early detection of mTBI.

scholarly journals Automatic Intrahepatic Biliary Segmentation Based Image Processing Techniques using Magnetic Resonance Images

2020 ◽  
Author(s):  
Mohammad Atallah AL-Oudat ◽  
Mohammad AL Oudat ◽  
Hazem Migdady ◽  
Tariq AL Munaizel ◽  
Mohammad Awni Mahmoud ◽  
...  
Keyword(s):  
Bile Duct ◽  
Magnetic Resonance ◽  
Bile Ducts ◽  
Medical Condition ◽  
Biliary Tree ◽  
Extraction Process ◽  
Magnetic Resonance Images ◽  
Papilla Of Vater ◽  
Feed Forward Neural Network ◽  
Important Indicator

Abstract A set of tubes known as bile ducts connects the liver to an organ below it directly that is called Gallbladder. The dilation of a bile duct is an important indicator regarding any serious issue in the human body. Number of reasons may cause bile duct dilation, such as: stones, tumors which commonly occur due to pancreas or papilla of vater. In this paper, the main contributions are: 1) a novel framework that consists of three phases to be applied on a set of Magnetic Resonance Imaging (MRI) images 2) an extracted set of features with their accurate values that express the condition of the biliary trees from the MRI images. Such dataset can be used in several applications to determine whether a bile duct is dilated or not. The dataset is organized as the following: half of the MRI images are for normal bile ducts, while the other half is for dilated bile ducts. To extract the useful features to diagnose the medical condition of the bile ducts from the MRI images, we implemented and applied the proposed framework that is started by using the enhanced active contour technique without edges in combination with Denoising Convolutional Neural Networks (DnCNN) to perform the segmentation and features extraction process. After that, the output of the segmentation process is the segmented biliary tree that will be used later to extract the needful features to make a diagnostic decision whether there is a dilation or not by comparing the features values of the normal versus the dilated bile ducts. We applied the feed forward neural network with backpropagation training algorithm for classification purposes. According to the experiments, the overall accuracy of the proposed framework was 90.00%. Such approach improves and increases the accuracy of the physicians’ diagnostic decisions which is considered as of significant importance for treatment and cure.

Resting state functional magnetic resonance imaging processing techniques in stroke studies

2016 ◽  
Vol 27 (8) ◽  
pp. 871-885 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Hojjat Adeli
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Connectivity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Stroke Patients ◽  
Processing Techniques ◽  
The Brain

AbstractIn recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.

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