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Tang Y, Cao M, Li Y, Lin Y, Wu X, Chen M. Altered structural covariance of locus coeruleus in individuals with significant memory concern and patients with mild cognitive impairment. Cereb Cortex 2023; 33:8523-8533. [PMID: 37130822 PMCID: PMC10321106 DOI: 10.1093/cercor/bhad137] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/31/2023] [Accepted: 04/02/2023] [Indexed: 05/04/2023] Open
Abstract
The locus coeruleus (LC) is the site where tau accumulation is preferentially observed pathologically in Alzheimer's disease (AD) patients, but the changes in gray matter co-alteration patterns between the LC and the whole brain in the predementia phase of AD remain unclear. In this study, we estimated and compared the gray matter volume of the LC and its structural covariance (SC) with the whole brain among 161 normal healthy controls (HCs), 99 individuals with significant memory concern (SMC) and 131 patients with mild cognitive impairment (MCI). We found that SC decreased in MCI groups, which mainly involved the salience network and default mode network. These results imply that seeding from LC, the gray matter network disruption and disconnection appears early in the MCI group. The altered SC network seeding from the LC can serve as an imaging biomarker for discriminating the patients in the potential predementia phase of AD from the normal subjects.
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Affiliation(s)
- Yingmei Tang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Minghui Cao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Yunhua Li
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Yuting Lin
- School of Psychology, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, No.55 Zhongshan Avenue West, Guangzhou 510631, Guangdong, China
| | - Xiaoyan Wu
- School of Psychology, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, No.55 Zhongshan Avenue West, Guangzhou 510631, Guangdong, China
| | - Meiwei Chen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
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Xiao Y, Lin Y, Ma J, Qian J, Ke Z, Li L, Yi Y, Zhang J, Dai Z. Predicting visual working memory with multimodal magnetic resonance imaging. Hum Brain Mapp 2021; 42:1446-1462. [PMID: 33277955 PMCID: PMC7927291 DOI: 10.1002/hbm.25305] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/18/2020] [Accepted: 11/21/2020] [Indexed: 12/15/2022] Open
Abstract
The indispensability of visual working memory (VWM) in human daily life suggests its importance in higher cognitive functions and neurological diseases. However, despite the extensive research efforts, most findings on the neural basis of VWM are limited to a unimodal context (either structure or function) and have low generalization. To address the above issues, this study proposed the usage of multimodal neuroimaging in combination with machine learning to reveal the neural mechanism of VWM across a large cohort (N = 547). Specifically, multimodal magnetic resonance imaging features extracted from voxel-wise amplitude of low-frequency fluctuations, gray matter volume, and fractional anisotropy were used to build an individual VWM capacity prediction model through a machine learning pipeline, including the steps of feature selection, relevance vector regression, cross-validation, and model fusion. The resulting model exhibited promising predictive performance on VWM (r = .402, p < .001), and identified features within the subcortical-cerebellum network, default mode network, motor network, corpus callosum, anterior corona radiata, and external capsule as significant predictors. The main results were then compared with those obtained on emotional regulation and fluid intelligence using the same pipeline, confirming the specificity of our findings. Moreover, the main results maintained well under different cross-validation regimes and preprocess strategies. These findings, while providing richer evidence for the importance of multimodality in understanding cognitive functions, offer a solid and general foundation for comprehensively understanding the VWM process from the top down.
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Affiliation(s)
- Yu Xiao
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Ying Lin
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Junji Ma
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Jiehui Qian
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Zijun Ke
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Liangfang Li
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Yangyang Yi
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Jinbo Zhang
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN)University of Cambridge and MRC Cognition and Brain Sciences UnitCambridgeUK
| | - Zhengjia Dai
- Department of PsychologySun Yat‐sen UniversityGuangzhouChina
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Huang W, Zhang P, Yu T, Gu Z, Guo Q, Li Y. A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study. IEEE Trans Biomed Eng 2020; 68:2509-2519. [PMID: 33373294 DOI: 10.1109/tbme.2020.3047812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.
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Xue C, Sun H, Hu G, Qi W, Yue Y, Rao J, Yang W, Xiao C, Chen J. Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. Front Neurosci 2020; 14:575652. [PMID: 33177982 PMCID: PMC7593791 DOI: 10.3389/fnins.2020.575652] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/25/2020] [Indexed: 01/06/2023] Open
Abstract
Background Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI. Methods We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously. Results Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (Lp) between groups. Notably, the Lp values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory. Conclusion The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University (Air Force Medical University), Xi'an, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenjie Yang
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
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Guo Z, Fan C, Li T, Gesang L, Yin W, Wang N, Weng X, Gong Q, Zhang J, Wang J. Neural network correlates of high-altitude adaptive genetic variants in Tibetans: A pilot, exploratory study. Hum Brain Mapp 2020; 41:2406-2430. [PMID: 32128935 PMCID: PMC7267913 DOI: 10.1002/hbm.24954] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/16/2020] [Accepted: 02/09/2020] [Indexed: 02/05/2023] Open
Abstract
Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype–phenotype associations remain largely unknown in the context of high‐altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci (EGLN1, EPAS1, and PPARA) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large‐scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single‐nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype–phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases.
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Affiliation(s)
- Zhiyue Guo
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Cunxiu Fan
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China.,Department of Neurology, Shanghai Changhai Hospital, Navy Medical University, Shanghai, China
| | - Ting Li
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Luobu Gesang
- Institute of High Altitude Medicine, Tibet Autonomous Region People's Hospital, Lhasa, Tibet Autonomous Region, China
| | - Wu Yin
- Department of Radiology, Tibet Autonomous Region People's Hospital, Lhasa, Tibet Autonomous Region, China
| | - Ningkai Wang
- Department of Psychology, Hangzhou Normal University, Hangzhou, China
| | - Xuchu Weng
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jiaxing Zhang
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Jinhui Wang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou, China
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Ahmed-Popova F, Sivkov S, Topolov M, Beshkov A. An fMRI Study of Adult Brain Cortical Activation Following Intensive Learning. Front Psychiatry 2020; 11:115. [PMID: 32194456 PMCID: PMC7063975 DOI: 10.3389/fpsyt.2020.00115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 02/10/2020] [Indexed: 11/13/2022] Open
Abstract
Functional imaging techniques, fMRI in particular, has given the possibility to investigate non-invasively the cognitive processes in healthy populations and different disorders concerning neuro-psychiatry, thus unfolding the concepts guiding diagnosis and patient management. Different brain structures seem to support different types of cognitive functions in particular learning and memory thus the neurobiological explanation of the retrieval of information is associated with knowledge of brain plasticity, memory circuits, synaptic neurotransmission and the modulation of glial cells. Consistent with fMRI investigations of memory systems we tested the dependability of a memory paradigm using heterogeneous memory stimuli in order to find the neurobiological basis that correlates with memory task performance. Our study resulted with statistical significant differences in brain activations across the block design contrasts in both occipital and temporal regions in 29 mentally healthy students during a memory paradigm performance after intensive learning. As functional magnetic resonance imaging has become an important and reliable tool for investigation of brain anatomy and its function in health and disease, it becomes clear that further research of neurobiological basis of cognitive and memory domains can clarify different diagnostic prototypes and thus explain the human brain impairments in neuropsychological patients, since these are characterized by various cognitive dysfunctions.
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Affiliation(s)
- Ferihan Ahmed-Popova
- Department of Anatomy, Histology and Embryology, Faculty of Medicine, Medical University - Plovdiv, Plovdiv, Bulgaria.,Research Institute, Medical University - Plovdiv, Plovdiv, Bulgaria
| | - Stefan Sivkov
- Department of Anatomy, Histology and Embryology, Faculty of Medicine, Medical University - Plovdiv, Plovdiv, Bulgaria.,Research Institute, Medical University - Plovdiv, Plovdiv, Bulgaria
| | - Mariyan Topolov
- Department of Pharmacology and Drug Toxicology, Faculty of Pharmacy, Medical University - Plovdiv, Plovdiv, Bulgaria
| | - Asen Beshkov
- Department of Psychiatry and Medical Psychology, Faculty of Medicine, Medical University - Plovdiv, Plovdiv, Bulgaria
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