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17 pages, 4977 KiB

Open AccessArticle

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment

by Rupu Zhang, Xidong Fu, Chaofan Song, Haifeng Shi and Zhuqing Jiao

Brain Sci. 2023, 13(8), 1187; https://doi.org/10.3390/brainsci13081187 - 10 Aug 2023

Cited by 1 | Viewed by 1116

Abstract

Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain [...] Read more.

Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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15 pages, 4707 KiB

Open AccessArticle

Classification of Alzheimer’s Disease Using Maximal Information Coefficient-Based Functional Connectivity with an Extreme Learning Machine

by Nishant Chauhan and Byung-Jae Choi

Brain Sci. 2023, 13(7), 1046; https://doi.org/10.3390/brainsci13071046 - 8 Jul 2023

Cited by 8 | Viewed by 2405

Abstract

Alzheimer’s disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based [...] Read more.

Alzheimer’s disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based functional connectivity (FC) measures, including the Pearson correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC), combined with extreme learning machines (ELM) for AD classification. Our findings demonstrate that employing non-linear techniques, such as MIC and eMIC, as features for classification yields accurate results. Specifically, eMIC-based features achieve a high accuracy of 94% for classifying cognitively normal (CN) and mild cognitive impairment (MCI) individuals, outperforming PCC (81%) and MIC (85%). For MCI and AD classification, MIC achieves higher accuracy (81%) compared to PCC (58%) and eMIC (78%). In CN and AD classification, eMIC exhibits the best accuracy of 95% compared to MIC (90%) and PCC (87%). These results underscore the effectiveness of fMRI-based features derived from non-linear techniques in accurately differentiating AD and MCI individuals from CN individuals, emphasizing the potential of neuroimaging and machine learning methods for improving AD diagnosis and classification. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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21 pages, 4435 KiB

Open AccessArticle

Audiovisual n-Back Training Alters the Neural Processes of Working Memory and Audiovisual Integration: Evidence of Changes in ERPs

by Ao Guo, Weiping Yang, Xiangfu Yang, Jinfei Lin, Zimo Li, Yanna Ren, Jiajia Yang and Jinglong Wu

Brain Sci. 2023, 13(7), 992; https://doi.org/10.3390/brainsci13070992 - 24 Jun 2023

Cited by 2 | Viewed by 2944

Abstract

(1) Background: This study investigates whether audiovisual n-back training leads to training effects on working memory and transfer effects on perceptual processing. (2) Methods: Before and after training, the participants were tested using the audiovisual n-back task (1-, 2-, or 3-back), to detect [...] Read more.

(1) Background: This study investigates whether audiovisual n-back training leads to training effects on working memory and transfer effects on perceptual processing. (2) Methods: Before and after training, the participants were tested using the audiovisual n-back task (1-, 2-, or 3-back), to detect training effects, and the audiovisual discrimination task, to detect transfer effects. (3) Results: For the training effect, the behavioral results show that training leads to greater accuracy and faster response times. Stronger training gains in accuracy and response time using 3- and 2-back tasks, compared to 1-back, were observed in the training group. Event-related potentials (ERPs) data revealed an enhancement of P300 in the frontal and central regions across all working memory levels after training. Training also led to the enhancement of N200 in the central region in the 3-back condition. For the transfer effect, greater audiovisual integration in the frontal and central regions during the post-test rather than pre-test was observed at an early stage (80–120 ms) in the training group. (4) Conclusion: Our findings provide evidence that audiovisual n-back training enhances neural processes underlying a working memory and demonstrate a positive influence of higher cognitive functions on lower cognitive functions. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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16 pages, 2743 KiB

Open AccessArticle

Changes in Functional Connectivity of Hippocampal Subregions in Patients with Obstructive Sleep Apnea after Six Months of Continuous Positive Airway Pressure Treatment

by Ling Huang, Haijun Li, Yongqiang Shu, Kunyao Li, Wei Xie, Yaping Zeng, Ting Long, Li Zeng, Xiang Liu and Dechang Peng

Brain Sci. 2023, 13(5), 838; https://doi.org/10.3390/brainsci13050838 - 22 May 2023

Cited by 3 | Viewed by 1859

Abstract

Previous studies have shown that the structural and functional impairments of hippocampal subregions in patients with obstructive sleep apnea (OSA) are related to cognitive impairment. Continuous positive airway pressure (CPAP) treatment can improve the clinical symptoms of OSA. Therefore, this study aimed to [...] Read more.

Previous studies have shown that the structural and functional impairments of hippocampal subregions in patients with obstructive sleep apnea (OSA) are related to cognitive impairment. Continuous positive airway pressure (CPAP) treatment can improve the clinical symptoms of OSA. Therefore, this study aimed to investigate functional connectivity (FC) changes in hippocampal subregions of patients with OSA after six months of CPAP treatment (post-CPAP) and its relationship with neurocognitive function. We collected and analyzed baseline (pre-CPAP) and post-CPAP data from 20 patients with OSA, including sleep monitoring, clinical evaluation, and resting-state functional magnetic resonance imaging. The results showed that compared with pre-CPAP OSA patients, the FC between the right anterior hippocampal gyrus and multiple brain regions, and between the left anterior hippocampal gyrus and posterior central gyrus were reduced in post-CPAP OSA patients. By contrast, the FC between the left middle hippocampus and the left precentral gyrus was increased. The changes in FC in these brain regions were closely related to cognitive dysfunction. Therefore, our findings suggest that CPAP treatment can effectively change the FC patterns of hippocampal subregions in patients with OSA, facilitating a better understanding of the neural mechanisms of cognitive function improvement, and emphasizing the importance of early diagnosis and timely treatment of OSA. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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16 pages, 4171 KiB

Open AccessArticle

Local Brain Network Alterations and Olfactory Impairment in Alzheimer’s Disease: An fMRI and Graph-Based Study

by Bing Zhu, Qi Li, Yang Xi, Xiujun Li, Yu Yang and Chunjie Guo

Brain Sci. 2023, 13(4), 631; https://doi.org/10.3390/brainsci13040631 - 7 Apr 2023

Cited by 5 | Viewed by 2215

Abstract

Alzheimer’s disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance [...] Read more.

Alzheimer’s disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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14 pages, 2461 KiB

Open AccessArticle

Articulation-Function-Associated Cortical Developmental Changes in Patients with Cleft Lip and Palate

by Wenjing Zhang, Cui Zhao, Liwei Sun, Xintao Yang, Linrui Yang, Ying Liang, Xu Zhang, Xiaoxia Du, Renji Chen and Chunlin Li

Brain Sci. 2023, 13(4), 550; https://doi.org/10.3390/brainsci13040550 - 25 Mar 2023

Cited by 1 | Viewed by 1771

Abstract

Cleft lip and palate (CLP) is one of the most common craniofacial malformations. Overall, 40–80% of CLP patients have varying degrees of articulation problems after palatoplasty. Previous studies revealed abnormal articulation-related brain function in CLP patients. However, the association between articulation disorders and [...] Read more.

Cleft lip and palate (CLP) is one of the most common craniofacial malformations. Overall, 40–80% of CLP patients have varying degrees of articulation problems after palatoplasty. Previous studies revealed abnormal articulation-related brain function in CLP patients. However, the association between articulation disorders and cortical structure development in CLP patients remains unclear. Twenty-six CLP adolescents (aged 5–14 years; mean 8.88 years; female/male 8/18), twenty-three CLP adults (aged 18–35 years; mean 23.35 years; female/male 6/17), thirty-seven healthy adolescents (aged 5–16 years; mean 9.89 years; female/male 5/16), and twenty-two healthy adults (aged 19–37 years; mean 24.41 years; female/male 19/37) took part in the experiment. The current study aims to investigate developmental changes in cortical structures in CLP patients with articulation disorders using both structural and functional magnetic resonance imaging (MRI). Our results reveal the distinct distribution of abnormal cortical structures in adolescent and adult CLP patients. We also found that the developmental pattern of cortical structures in CLP patients differed from the pattern in healthy controls (delayed cortical development in the left lingual gyrus (t = 4.02, cluster-wise p < 0.05), inferior temporal cortex (z = −4.36, cluster-wise p < 0.05) and right precentral cortex (t = 4.19, cluster-wise p < 0.05)). Mediation analysis identified the cortical thickness of the left pericalcarine cortex as the mediator between age and articulation function (partial mediation effect (a*b = −0.48), 95% confident interval (−0.75, −0.26)). In conclusion, our results demonstrate an abnormal developmental pattern of cortical structures in CLP patients, which is directly related to their articulation disorders. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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19 pages, 6479 KiB

Open AccessArticle

Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG–fMRI Investigation

by Yuchao Liu, Yin Zhang, Zhongyi Jiang, Wanzeng Kong and Ling Zou

Brain Sci. 2023, 13(3), 485; https://doi.org/10.3390/brainsci13030485 - 13 Mar 2023

Cited by 1 | Viewed by 2470

Abstract

Background: It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)–functional magnetic resonance imaging (fMRI) study in a gambling task by [...] Read more.

Background: It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)–functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition. Methods: First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices. Results: The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli. Conclusion: This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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13 pages, 1301 KiB

Open AccessSystematic Review

Alzheimer’s Disease: Insights from Large-Scale Brain Dynamics Models

by Lan Yang, Jiayu Lu, Dandan Li, Jie Xiang, Ting Yan, Jie Sun and Bin Wang

Brain Sci. 2023, 13(8), 1133; https://doi.org/10.3390/brainsci13081133 - 28 Jul 2023

Viewed by 2037

Abstract

Alzheimer’s disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models [...] Read more.

Alzheimer’s disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience. Full article

(This article belongs to the Special Issue Deep Research of EEG/fMRI Application in Cognition and Consciousness)

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