Small-world EEG network analysis of functional connectivity in developmental dyslexia after visual training intervention

Dushanova, Juliana; Tsokov, Stefan A

doi:10.31083/j.jin.2020.04.193

Cited by 10 publications

(6 citation statements)

References 90 publications

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“…Our finding that areas FST and PH demonstrated the greatest number of extraoccipital cortical connections and coactivation outside of the occipital lobe supports the “small‐world network” architecture of the lateral visual system (Dushanova & Tsokov, 2020 ; Shi et al., 2015 ). This posits that various nodes connect to hub‐like regions that are designed to integrate information and transmit the input to other hubs throughout the cortex for global processing and higher order tasks such as for reading, semantic processing, movement, and coordination.…”

Section: Discussionsupporting

confidence: 78%

“…However, emerging technologies, analytic assessments, and white matter fiber tracking have facilitated the study of larger data sets with finer precision to characterize the parcellations of the lateral occipital system in more detail (Baker et al, 2018b;Glasser et al, 2016). Our finding that areas FST and PH demonstrated the greatest number of extraoccipital cortical connections and coactivation outside of the occipital lobe supports the "small-world network" architecture of the lateral visual system (Dushanova & Tsokov, 2020;Shi et al, 2015).…”

Section: Discussionsupporting

confidence: 61%

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Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

et al. 2023

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Background and Purpose Mapping the topology of the visual system is critical for understanding how complex cognitive processes like reading can occur. We aim to describe the connectivity of the visual system to understand how the cerebrum accesses visual information in the lateral occipital lobe. Methods Using meta‐analytic software focused on task‐based functional MRI studies, an activation likelihood estimation (ALE) of the visual network was created. Regions of interest corresponding to the cortical parcellation scheme previously published under the Human Connectome Project were co‐registered onto the ALE to identify the hub‐like regions of the visual network. Diffusion Spectrum Imaging‐based fiber tractography was performed to determine the structural connectivity of these regions with extraoccipital cortices. Results The fundus of the superior temporal sulcus (FST) and parietal area H (PH) were identified as hub‐like regions for the visual network. FST and PH demonstrated several areas of coactivation beyond the occipital lobe and visual network. Furthermore, these parcellations were highly interconnected with other cortical regions throughout extraoccipital cortices related to their nonvisual functional roles. A cortical model demonstrating connections to these hub‐like areas was created. Conclusions FST and PH are two hub‐like areas that demonstrate extensive functional coactivation and structural connections to nonvisual cerebrum. Their structural interconnectedness with language cortices along with the abnormal activation of areas commonly located in the temporo‐occipital region in dyslexic individuals suggests possible important roles of FST and PH in the integration of information related to language and reading. Future studies should refine our model by examining the functional roles of these hub areas and their clinical significance.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionsupporting

confidence: 61%

Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

et al. 2023

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“…Additionally, electroencephalography has developed into a crucial non-invasive method for gauging brain activity that can detect anomalies, abnormalities, and mental illnesses [18,19]. The EEG signal has been widely utilized to identify and analyze human stress [20], particularly in the frontal lobe [21]. Many recent studies have focused on using EEG signals to detect and diagnose mental stress, as well as the link between frontal lobe EEG alpha-bands and emotional states activity [22,23].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals

et al. 2023

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Stress has an impact, not only on a person’s physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.

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“…Recently, using psychological signals to investigate Alzheimer, mental and sleep disorders becomes very common [32][33][34][35][36][37][38][39]. Moreover, the EEG has become a crucial non-invasive measure of brain activities, and it has a vital potentiality to diagnose mental disorders, abnormalities, and the state of the brain [40][41][42]. Thus, the desired features are extracted from the signals recorded from the brain's scalp.…”

Section: Introductionmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

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Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical e fects. Moreover, hormones, physiological e fects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using di ferent mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. A ter that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

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Small-world EEG network analysis of functional connectivity in developmental dyslexia after visual training intervention

Cited by 10 publications

References 90 publications

Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

Topology of the lateral visual system: The fundus of the superior temporal sulcus and parietal area H connect nonvisual cerebrum to the lateral occipital lobe

Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

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