Brain Network for the Core Deficits of Semantic Dementia: A Neural Network Connectivity-Behavior Mapping Study

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

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IDENTIFICATION OF COHERENT STRUCTURES IN TURBULENT SHEAR FLOWS WITH A FUZZY ARTMAP NEURAL NETWORK

International Journal of Neural Systems ◽

10.1142/s0129065796000555 ◽

1996 ◽

Vol 07 (05) ◽

pp. 559-568 ◽

Cited By ~ 10

Author(s):

J. FERRE-GINE ◽

R. RALLO ◽

A. ARENAS ◽

FRANCE GIRALT

Keyword(s):

Neural Network ◽

Template Matching ◽

Phase Relationship ◽

Fuzzy Artmap ◽

Turbulent Shear ◽

Training Procedure ◽

Full Data ◽

The Core ◽

Data Files ◽

Number Of Classes

An implementation of a Fuzzy Artmap neural network is used to detect and to identify (recognise) structures (patterns) embedded in the velocity field of a turbulent wake behind a circular cylinder. The net is trained to recognise both clockwise and anticlockwise eddies present in the u and v velocity fields at 420 diameters downstream of the cylinder that generates the wake, using a pre-processed part of the recorded velocity data. The phase relationship that exists between the angles of the velocity vectors of an eddy pattern is used to reduce the number of classes contained in the data, before the start of the training procedure. The net was made stricter by increasing the vigilance parameter within the interval [0.90, 0.95] and a set of net-weights were obtained for each value. Full data files were scanned with the net classifying patterns according to their phase characteristics. The net classifies about 27% of the recorded signals as eddy motions, with the strictest vigilance parameter and without the need to impose external initial templates. Spanwise distances (homogeneous direction of the flow) within the centres of the eddies identified suggest that they form pairs of counter-rotating vortices (double rollers). The number of patterns selected with Fuzzy Artmap is lower than that reported for template matching because the net classifies eddies according to the recirculating pattern present at the core or central region, while template matching extends the region over which correlation between data and template is performed. In both cases, the topology of educed patterns is in agreement.

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Core-brain-network based muti-layer convolutional neural network for emotion recognition

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3090164 ◽

2021 ◽

pp. 1-1

Author(s):

Zhongke Gao ◽

Rumei Li ◽

Chao Ma ◽

Linge Rui ◽

Xinlin Sun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Brain Network

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Altered Brain Network Connectivity as a Potential Endophenotype of Schizophrenia

Scientific Reports ◽

10.1038/s41598-017-05774-3 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 20

Author(s):

Peng Li ◽

Teng-Teng Fan ◽

Rong-Jiang Zhao ◽

Ying Han ◽

Le Shi ◽

...

Keyword(s):

Network Connectivity ◽

Brain Network ◽

Brain Network Connectivity

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The Research of Temperature Compensation for Thermopile Sensor Based on Improved PSO-BP Algorithm

Mathematical Problems in Engineering ◽

10.1155/2015/854945 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Yuanjiang Li ◽

Yuehua Li ◽

Feng Li ◽

Bin Zhao ◽

QingQing Li

Keyword(s):

Neural Network ◽

Thermal Radiation ◽

Bp Neural Network ◽

Temperature Compensation ◽

Safety Monitoring ◽

Bp Algorithm ◽

Bp Network ◽

The Core ◽

Thermopile Sensor ◽

Improved Pso

When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usually affected by ambient temperature due to its unique structure. An improved PSO-BP algorithm is proposed for temperature compensation of thermopile sensor and correcting the error in the condition of the system accuracy requirements reduced by temperature. The core of improved PSO-BP algorithm is to improve the certainty of initial weights and thresholds that belonged to BP neural network and then train the samples by using BP neural network for enhancing the generalization ability and stability of system. The experimental results show that the proposed PSO-BP network outperforms other similar algorithms with faster convergence speed, lower errors, and higher accuracy.

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Mechanisms of cognitive-behavioral therapy for obsessive-compulsive disorder involve robust and extensive increases in brain network connectivity

Translational Psychiatry ◽

10.1038/tp.2017.192 ◽

2017 ◽

Vol 7 (9) ◽

pp. e1230-e1230 ◽

Cited By ~ 11

Author(s):

T D Moody ◽

F Morfini ◽

G Cheng ◽

C Sheen ◽

R Tadayonnejad ◽

...

Keyword(s):

Cognitive Behavioral Therapy ◽

Obsessive Compulsive Disorder ◽

Behavioral Therapy ◽

Network Connectivity ◽

Brain Network ◽

Cognitive Behavioral ◽

Obsessive Compulsive ◽

Compulsive Disorder ◽

Brain Network Connectivity

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Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

Frontiers in Neuroscience ◽

10.3389/fnins.2020.629630 ◽

2021 ◽

Vol 14 ◽

Author(s):

Jingjing Gao ◽

Mingren Chen ◽

Yuanyuan Li ◽

Yachun Gao ◽

Yanling Li ◽

...

Keyword(s):

Neural Network ◽

Autism Spectrum Disorder ◽

Convolutional Neural Network ◽

Structural Mri ◽

Autism Spectrum ◽

Brain Network ◽

Spectrum Disorder ◽

Structural Covariance ◽

Almost All ◽

New Framework

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

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Childhood Trauma Associated With Abnormal Resting-State Brain Network Connectivity in First-Episode Schizophrenia Patients

10.21203/rs.3.rs-73545/v1 ◽

2020 ◽

Author(s):

Xiangyun Long ◽

Jiaxin Wu ◽

Fei Liu ◽

Ansi Qi ◽

Nan Huang ◽

...

Keyword(s):

Childhood Trauma ◽

Resting State ◽

Network Connectivity ◽

Brain Network ◽

Traumatic Experiences ◽

First Episode ◽

Resting State Networks ◽

Attention Network ◽

Brain Network Connectivity ◽

First Episode Schizophrenia

Abstract Childhood trauma is a central risk factor for schizophrenia. We explored the correlation between early traumatic experiences and the functional connectivity of resting-state networks. This fMRI study included 28 first-episode schizophrenia patients and 27 healthy controls. In first-episode schizophrenia patients, higher levels of childhood trauma associated with abnormal connections of resting-state networks, and these anomalies distributed among task-positive networks (i.e., ventral attention network, dorsal-ventral attention network and frontal-parietal network), and sensory networks (i.e., visual network and auditory network). These findings mentioned that childhood traumatic experiences may impact resting-state network connectivity in adulthood, mainly involving systems related to attention and execution control.

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Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

International Journal of Image and Graphics ◽

10.1142/s021946782240006x ◽

2021 ◽

Author(s):

A. Thushara ◽

C. Ushadevi Amma ◽

Ansamma John

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Graph Theory ◽

Neurodegenerative Disorder ◽

Brain Networks ◽

Network Connectivity ◽

Brain Regions ◽

Brain Network ◽

Fiber Tracts ◽

The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

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