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Cao B, Guo Y, Lu M, Wu X, Deng F, Wang J, Huang R. The long-term intensive gymnastic training influences functional stability and integration: A resting-state fMRI study. PSYCHOLOGY OF SPORT AND EXERCISE 2024; 74:102678. [PMID: 38821251 DOI: 10.1016/j.psychsport.2024.102678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 03/17/2024] [Accepted: 05/22/2024] [Indexed: 06/02/2024]
Abstract
INTRODUCTION Long-term motor skill training has been shown to induce anatomical and functional neuroplasticity. World class gymnasts (WCGs) provide a unique opportunity to investigate the effect of long-term intensive training on neuroplasticity. Previous resting-state fMRI studies have demonstrated a high efficient information processing related to motor and cognitive functions in gymnasts compared with healthy controls (HCs). However, most research treated brain signals as static, overlooking the fact that the brain is a complex and dynamic system. In this study, we employed functional stability, a new metric based on dynamic functional connectivity (FC), to examine the impact of long-term intensive training on the functional architecture in the WCGs. METHODS We first conducted a voxel-wise analysis of functional stability between the WCGs and HCs. Then, we applied FC density (FCD) to explore whether regions with modified functional stability were also accompanied by changes in connection patterns in the WCGs. We identified overlapping regions showing significant differences in both functional stability and FCD. Finally, we applied seed-based correlation analysis (SCA) to determine the detailed changes in connection patterns between the WCGs and HCs within these overlapping regions. RESULTS Compared with the HCs, the WCGs exhibited higher functional stability in the bilateral angular gyrus (AG), bilateral inferior temporal gyrus (ITG), bilateral precentral gyrus, and right superior frontal gyrus and lower functional stability in the bilateral hippocampus, bilateral caudate, right rolandic operculum, left superior temporal gyrus, right middle frontal gyrus, right middle cingular cortex, and right precuneus than the HCs. We found that the bilateral AG and ITG not only showed higher functional stability but also increased global and long-range FCD in the WCGs relative to the HCs. The right precuneus displayed lower functional stability as well as decreased local, long-range, and global FCD in the WCGs. Both AG and ITG showed higher FC with regions in the default mode network (DMN) in the WCGs than in the HCs. CONCLUSIONS The increased functional stability in the AG and ITG might be associated with enhanced functional integration within the DMN in the WCGs. These findings may offer new spatiotemporal evidence for the impact of long-term intensive training on neuroplasticity.
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Affiliation(s)
- Bolin Cao
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China
| | - Yu Guo
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China
| | - Min Lu
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaoyan Wu
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China
| | - Feng Deng
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China
| | - Jun Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ruiwang Huang
- School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, South China Normal University, Guangzhou, China.
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Gu S, Mattar MG, Tang H, Pan G. Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection. SCIENCE ADVANCES 2024; 10:eadm8430. [PMID: 39058783 PMCID: PMC11277393 DOI: 10.1126/sciadv.adm8430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.
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Affiliation(s)
- Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Marcelo G. Mattar
- Department of Psychology, New York University, New York, NY 10003, USA
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
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Miller S, Cobos KL, Rasic N, Long X, Lebel C, Bar Am N, Noel M, Kopala-Sibley D, Mychasiuk R, Miller JV. Adverse childhood experiences, brain efficiency, and the development of pain symptoms in youth. Eur J Pain 2024. [PMID: 39010829 DOI: 10.1002/ejp.4702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 06/10/2024] [Accepted: 07/04/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND Adverse childhood experiences (ACEs) are often reported by youths with chronic pain, and both ACEs and chronic pain disrupt how information is processed. However, it is unknown whether changes to shared neural networks underlie the relationship between ACEs and the development of pain symptoms. This study explored the relationships between ACEs, brain efficiency, and pain symptomology in youth. METHODS A community sample of youths aged 14-18 years underwent MRIs, answered trauma and pain questionnaires, and underwent pain sensory testing, twice, 3 months apart (Nbaseline = 44; Nfollow-up = 42). Sensory testing determined thresholds for mechanical and thermal stimuli. Global and local network efficiencies were evaluated using graph theory. Generalized estimating equations were applied to examine whether brain efficiency moderated the relationships between ACEs, pain intensity, and pain sensitivity (i.e., mechanical detection, heat pain, and temperature change thresholds). RESULTS There was a significant interaction between ACEs and global brain efficiency in association with pain intensity (β = -0.31, p = 0.02) and heat pain (β = -0.29, p = 0.004). Lower global brain efficiency exacerbated the relationship between ACEs and pain intensity (θX → Y|W = -1.16 = 0.37, p = 0.05), and heat pain sensitivity (θX → Y|W = -1.30 = 0.44, p = 0.05). Higher global brain efficiency ameliorated the relationship between ACEs and pain intensity (θX → Y|W = 1.75 = -0.53, p = 0.05). CONCLUSIONS The relationship between ACEs and pain symptomology was comparable to chronic pain phenotypes (i.e., higher pain intensity and pain thresholds) and may vary as a function of brain efficiency in youth. This stresses the importance of assessing for pain symptoms in trauma-exposed youth, as earlier identification and intervention are critical in preventing the chronification of pain. SIGNIFICANCE This article explores the relationship between ACEs, pain symptomology, and brain efficiency in youth. ACEs may affect how the brain processes information, including pain. Youths with lower brain efficiencies that were exposed to more ACEs have pain symptomology comparable to youths with chronic pain. Understanding this relationship is important for the earlier identification of pain symptoms, particularly in vulnerable populations such as youths exposed to trauma, and is critical for preventing the chronification of pain.
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Affiliation(s)
- Samantha Miller
- Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Karen L Cobos
- Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Nivez Rasic
- Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
| | - Xiangyu Long
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
- Owerko Centre, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- The Mathison Centre for Mental Health and Education, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Neta Bar Am
- Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Melanie Noel
- Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
- Owerko Centre, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- The Mathison Centre for Mental Health and Education, Hotchkiss Brain Institute, Calgary, Alberta, Canada
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Daniel Kopala-Sibley
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
- The Mathison Centre for Mental Health and Education, Hotchkiss Brain Institute, Calgary, Alberta, Canada
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Richelle Mychasiuk
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
- Department of Neuroscience, Monash University, Melbourne, Victoria, Australia
| | - Jillian Vinall Miller
- Department of Anesthesiology, Perioperative, and Pain Medicine, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Calgary, Alberta, Canada
- Owerko Centre, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
- The Mathison Centre for Mental Health and Education, Hotchkiss Brain Institute, Calgary, Alberta, Canada
- O'Brien Center, University of Calgary, Calgary, Alberta, Canada
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Zhang X, Cheng X, Chen J, Sun J, Yang X, Li W, Chen L, Mao Y, Liu Y, Zeng X, Ye B, Yang C, Li X, Cao L. Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort. J Affect Disord 2024; 357:97-106. [PMID: 38657768 DOI: 10.1016/j.jad.2024.04.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear. METHODS The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed. RESULTS Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group. LIMITATIONS Relatively shorter follow-up duration, medications confounding, and modest sample size. CONCLUSION These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.
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Affiliation(s)
- Xiaofei Zhang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong province 510000, PR China
| | - Xiaofang Cheng
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Jianshan Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Jiaqi Sun
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xiaoyong Yang
- Department of Psychiatry, Guangzhou Medical University, Guangdong province 510300, PR China
| | - Weiming Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Lei Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Yimiao Mao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Yutong Liu
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xuanlin Zeng
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Biyu Ye
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Chanjuan Yang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xuan Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China.
| | - Liping Cao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China.
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Wang B, Yuan Y, Yang L, Huang Y, Zhang X, Zhang X, Yan W, Li Y, Li D, Xiang J, Yang J, Liu M. Multi-hierarchy Network Configuration Can Predict Brain States and Performance. J Cogn Neurosci 2024; 36:1695-1714. [PMID: 38579269 DOI: 10.1162/jocn_a_02153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we propose an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.
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Affiliation(s)
- Bin Wang
- Taiyuan University of Technology
| | | | - Lan Yang
- Taiyuan University of Technology
| | | | - Xi Zhang
- Taiyuan University of Technology
| | | | | | - Ying Li
- Taiyuan University of Technology
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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Taylor HP, Thung KH, Huynh KM, Lin W, Ahmad S, Yap PT. Functional Hierarchy of the Human Neocortex from Cradle to Grave. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.599109. [PMID: 38915694 PMCID: PMC11195193 DOI: 10.1101/2024.06.14.599109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial insight into the relationship between the brain's topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their evolution throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing thereafter. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes.
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Affiliation(s)
- Hoyt Patrick Taylor
- Department of Computer Science, University of North Carolina, Chapel Hill, U.S.A
| | - Kim-Han Thung
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, U.S.A
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, U.S.A
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Yu S, Mao B, Zhou Y, Liu Y, Yi C, Li F, Yao D, Xu P, San Liang X, Zhang T. Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2187-2197. [PMID: 38837930 DOI: 10.1109/tnsre.2024.3409872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.
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Wu W, Hoffman P. Verbal semantic expertise is associated with reduced functional connectivity between left and right anterior temporal lobes. Cereb Cortex 2024; 34:bhae256. [PMID: 38897815 PMCID: PMC11186671 DOI: 10.1093/cercor/bhae256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
The left and right anterior temporal lobes (ATLs) encode semantic representations. They show graded hemispheric specialization in function, with the left ATL contributing preferentially to verbal semantic processing. We investigated the cognitive correlates of this organization, using resting-state functional connectivity as a measure of functional segregation between ATLs. We analyzed two independent resting-state fMRI datasets (n = 86 and n = 642) in which participants' verbal semantic expertise was measured using vocabulary tests. In both datasets, people with more advanced verbal semantic knowledge showed weaker functional connectivity between left and right ventral ATLs. This effect was highly specific. It was not observed for within-hemisphere connections between semantic regions (ventral ATL and inferior frontal gyrus (IFG), though it was found for left-right IFG connectivity in one dataset). Effects were not found for tasks probing semantic control, nonsemantic cognition, or face recognition. Our results suggest that hemispheric specialization in the ATLs is not an innate property but rather emerges as people develop highly detailed verbal semantic representations. We speculate that this effect is a consequence of the left ATL's greater connectivity with left-lateralized written word recognition regions, which causes it to preferentially represent meaning for advanced vocabulary acquired primarily through reading.
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Affiliation(s)
- Wei Wu
- School of Philosophy, Psychology & Language Sciences, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
- Department of Music, Durham University, Palace Green, Durham DH1 3RL, United Kingdom
| | - Paul Hoffman
- School of Philosophy, Psychology & Language Sciences, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
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Kuai H, Chen J, Tao X, Cai L, Imamura K, Matsumoto H, Liang P, Zhong N. Never-Ending Learning for Explainable Brain Computing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307647. [PMID: 38602432 PMCID: PMC11200082 DOI: 10.1002/advs.202307647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/24/2024] [Indexed: 04/12/2024]
Abstract
Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.
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Affiliation(s)
- Hongzhi Kuai
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
| | - Jianhui Chen
- Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China
- Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijing100124China
| | - Xiaohui Tao
- School of Mathematics, Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia
| | - Lingyun Cai
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
| | - Kazuyuki Imamura
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
| | - Hiroki Matsumoto
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
| | - Ning Zhong
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
- Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijing100124China
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11
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Wu B, Long X, Cao Y, Xie H, Wang X, Roberts N, Gong Q, Jia Z. Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder. Psychol Med 2024; 54:1758-1767. [PMID: 38173122 DOI: 10.1017/s0033291723003719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
BACKGROUND Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients. METHODS Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls. RESULTS Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks. CONCLUSIONS Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.
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Affiliation(s)
- Baolin Wu
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xipeng Long
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Cao
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Hongsheng Xie
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xiuli Wang
- Department of Clinical Psychology, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Neil Roberts
- The Queens Medical Research Institute (QMRI), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - Qiyong Gong
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
| | - Zhiyun Jia
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, China
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12
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Pigareva Y, Gladkov A, Kolpakov V, Kazantsev VB, Mukhina I, Pimashkin A. The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro. MICROMACHINES 2024; 15:732. [PMID: 38930702 PMCID: PMC11205292 DOI: 10.3390/mi15060732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.
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Affiliation(s)
- Yana Pigareva
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Arseniy Gladkov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Vladimir Kolpakov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Victor B. Kazantsev
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Irina Mukhina
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Alexey Pimashkin
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
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13
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Callow DD, Spira AP, Zipunnikov V, Lu H, Wanigatunga SK, Rabinowitz JA, Albert M, Bakker A, Soldan A. Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults. Neuroimage Clin 2024; 43:103621. [PMID: 38823249 PMCID: PMC11179421 DOI: 10.1016/j.nicl.2024.103621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/03/2024]
Abstract
Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.
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Affiliation(s)
- Daniel D Callow
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD.
| | - Adam P Spira
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, the United States of America; Johns Hopkins Center on Aging and Health, Baltimore, MD, the United States of America
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, the United States of America
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, the United States of America; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, the United States of America
| | - Sarah K Wanigatunga
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, the United States of America
| | - Jill A Rabinowitz
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ US
| | - Marilyn Albert
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, the United States of America
| | - Arnold Bakker
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, the United States of America
| | - Anja Soldan
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, the United States of America
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14
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Maddaluno O, Della Penna S, Pizzuti A, Spezialetti M, Corbetta M, de Pasquale F, Betti V. Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity. J Neurosci 2024; 44:e1766232024. [PMID: 38538141 PMCID: PMC11097277 DOI: 10.1523/jneurosci.1766-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/21/2024] [Accepted: 02/20/2024] [Indexed: 05/18/2024] Open
Abstract
The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.
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Affiliation(s)
- Ottavia Maddaluno
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences and ITAB - Institute of Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti and Pescara, Chieti 66013, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Matteo Spezialetti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua 35131, Italy
- Veneto Institute of Molecular Medicine (VIMM), Padova 35129, Italy
| | | | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
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15
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Razban RM, Antal BB, Dill KA, Mujica-Parodi LR. Brain signaling becomes less integrated and more segregated with age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.17.567376. [PMID: 38014139 PMCID: PMC10680817 DOI: 10.1101/2023.11.17.567376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, P int and P seg , from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.
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Affiliation(s)
- Rostam M Razban
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Botond B Antal
- Dept. of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Dept. of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
- Dept. of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Lilianne R Mujica-Parodi
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Dept. of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Program in Neuroscience, Stony Brook University, Stony Brook, NY, USA
- Dept. of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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16
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Coronel‐Oliveros C, Gómez RG, Ranasinghe K, Sainz‐Ballesteros A, Legaz A, Fittipaldi S, Cruzat J, Herzog R, Yener G, Parra M, Aguillon D, Lopera F, Santamaria‐Garcia H, Moguilner S, Medel V, Orio P, Whelan R, Tagliazucchi E, Prado P, Ibañez A. Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling. Alzheimers Dement 2024; 20:3228-3250. [PMID: 38501336 PMCID: PMC11095480 DOI: 10.1002/alz.13788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.
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Affiliation(s)
- Carlos Coronel‐Oliveros
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)Universidad de ValparaísoValparaísoChile
| | - Raúl Gónzalez Gómez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Center for Social and Cognitive NeuroscienceSchool of Psychology, Universidad Adolfo IbáñezSantiagoChile
| | - Kamalini Ranasinghe
- Memory and Aging CenterDepartment of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
| | - Gorsev Yener
- Izmir University of Economics, Faculty of Medicine, Fevzi Çakmak, Balçova/İzmirSakaryaTurkey
- Dokuz Eylül University, Brain Dynamics Multidisciplinary Research Center, KonakAlsancakTurkey
| | - Mario Parra
- School of Psychological Sciences and HealthUniversity of StrathclydeGlasgowScotland
| | - David Aguillon
- Neuroscience Research Group, University of AntioquiaBogotáColombia
| | - Francisco Lopera
- Neuroscience Research Group, University of AntioquiaBogotáColombia
| | - Hernando Santamaria‐Garcia
- Pontificia Universidad Javeriana, PhD Program of NeuroscienceBogotáColombia
- Hospital Universitario San Ignacio, Center for Memory and Cognition IntellectusBogotáColombia
| | - Sebastián Moguilner
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Brain and Mind Centre, The University of SydneySydneyNew South WalesAustralia
- Department of NeuroscienceUniversidad de Chile, IndependenciaSantiagoChile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)Universidad de ValparaísoValparaísoChile
- Instituto de NeurocienciaFacultad de Ciencias, Universidad de Valparaíso, Playa AnchaValparaísoChile
| | - Robert Whelan
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Buenos Aires Physics Institute and Physics DepartmentUniversity of Buenos Aires, Intendente Güiraldes 2160 – Ciudad UniversitariaBuenos AiresArgentina
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la RehabilitaciónUniversidad San Sebastián, Región MetropolitanaSantiagoChile
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
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17
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Lu J, Zhang X, Shu Z, Han J, Yu N. A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease. J Neural Eng 2024; 21:026047. [PMID: 38621377 DOI: 10.1088/1741-2552/ad3eb6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.
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Affiliation(s)
- Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Xinyuan Zhang
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, People's Republic of China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, People's Republic of China
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, People's Republic of China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, People's Republic of China
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18
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Jang H, Mashour GA, Hudetz AG, Huang Z. Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589265. [PMID: 38659759 PMCID: PMC11042232 DOI: 10.1101/2024.04.12.589265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Consciousness requires a dynamic balance of integration and segregation in functional brain networks. An optimal integration-segregation balance depends on two key aspects of functional connectivity: global efficiency (i.e., integration) and clustering (i.e., segregation). We developed a new fMRI-based measure, termed the integration-segregation difference (ISD), which captures both aspects. We used this metric to quantify changes in brain state from conscious wakefulness to loss of responsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift to segregation in both whole brain and all brain subnetworks during anesthesia. Moreover, brain networks displayed similar sequences of disintegration and subsequent reintegration during, respectively, loss and return of responsiveness. Random forest machine learning models, trained with the integration and segregation of brain networks, identified the awake vs. unresponsive states and their transitions with accuracy up to 93%. We found that metastability (i.e., the dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (i.e., diversity and intricacy of neural activity) is more closely linked with segregation. The analysis of a sleep dataset revealed similar findings. Our results demonstrate that the integration-segregation balance is a useful index that can differentiate among various conscious and unconscious states.
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Affiliation(s)
- Hyunwoo Jang
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - George A. Mashour
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G. Hudetz
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Zirui Huang
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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19
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Shen W, Wang P, Wei G, Yuan S, Chen M, Su Y, Xu B, Li G. SiC@NiO Core-Shell Nanowire Networks-Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2400458. [PMID: 38607289 DOI: 10.1002/smll.202400458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/18/2024] [Indexed: 04/13/2024]
Abstract
1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long-term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long-term memory and "learning-forgetting-relearning" under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high-temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.
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Affiliation(s)
- Weikang Shen
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Pan Wang
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Guodong Wei
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China
| | - Shuai Yuan
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Mi Chen
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Ying Su
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
| | - Bingshe Xu
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan, Shanxi, 030024, P. R. China
| | - Guoqiang Li
- Xi'an Key Laboratory of Compound Semiconductor Materials and Devices, School of Physics & Information Science, Shaanxi University of Science and Technology, Xi'an, Shaanxi, 710021, P. R. China
- The School of Integrated Circuits, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou, 510641, P. R. China
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20
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Faryadras M, Burles F, Iaria G, Davidsen J. Functional brain networks in Developmental Topographical Disorientation. Cereb Cortex 2024; 34:bhae104. [PMID: 38566506 PMCID: PMC10987990 DOI: 10.1093/cercor/bhae104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Despite a decade-long study on Developmental Topographical Disorientation, the underlying mechanism behind this neurological condition remains unknown. This lifelong selective inability in orientation, which causes these individuals to get lost even in familiar surroundings, is present in the absence of any other neurological disorder or acquired brain damage. Herein, we report an analysis of the functional brain network of individuals with Developmental Topographical Disorientation ($n = 19$) compared against that of healthy controls ($n = 21$), all of whom underwent resting-state functional magnetic resonance imaging, to identify if and how their underlying functional brain network is altered. While the established resting-state networks (RSNs) are confirmed in both groups, there is, on average, a greater connectivity and connectivity strength, in addition to increased global and local efficiency in the overall functional network of the Developmental Topographical Disorientation group. In particular, there is an enhanced connectivity between some RSNs facilitated through indirect functional paths. We identify a handful of nodes that encode part of these differences. Overall, our findings provide strong evidence that the brain networks of individuals suffering from Developmental Topographical Disorientation are modified by compensatory mechanisms, which might open the door for new diagnostic tools.
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Affiliation(s)
- Mahsa Faryadras
- Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
| | - Ford Burles
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
| | - Giuseppe Iaria
- Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
- Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1 AB, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, 2500 University Drive NW, Calgary, T2N 1N4 AB, Canada
- Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, T2N 4N1 AB, Canada
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21
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Chan DC, Kim C, Kang RY, Kuhn MK, Beidler LM, Zhang N, Proctor EA. Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585383. [PMID: 38559177 PMCID: PMC10979954 DOI: 10.1101/2024.03.17.585383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.
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Affiliation(s)
- Dennis C Chan
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - ChaeMin Kim
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Rachel Y Kang
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Madison K Kuhn
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lynne M Beidler
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - Elizabeth A Proctor
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, PA, USA
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22
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Luppi AI, Uhrig L, Tasserie J, Signorelli CM, Stamatakis EA, Destexhe A, Jarraya B, Cofre R. Local orchestration of distributed functional patterns supporting loss and restoration of consciousness in the primate brain. Nat Commun 2024; 15:2171. [PMID: 38462641 PMCID: PMC10925605 DOI: 10.1038/s41467-024-46382-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 02/26/2024] [Indexed: 03/12/2024] Open
Abstract
A central challenge of neuroscience is to elucidate how brain function supports consciousness. Here, we combine the specificity of focal deep brain stimulation with fMRI coverage of the entire cortex, in awake and anaesthetised non-human primates. During propofol, sevoflurane, or ketamine anaesthesia, and subsequent restoration of responsiveness by electrical stimulation of the central thalamus, we investigate how loss of consciousness impacts distributed patterns of structure-function organisation across scales. We report that distributed brain activity under anaesthesia is increasingly constrained by brain structure across scales, coinciding with anaesthetic-induced collapse of multiple dimensions of hierarchical cortical organisation. These distributed signatures are observed across different anaesthetics, and they are reversed by electrical stimulation of the central thalamus, coinciding with recovery of behavioural markers of arousal. No such effects were observed upon stimulating the ventral lateral thalamus, demonstrating specificity. Overall, we identify consistent distributed signatures of consciousness that are orchestrated by specific thalamic nuclei.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Lynn Uhrig
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Department of Anesthesiology and Critical Care, Necker Hospital, AP-HP, Université de Paris Cité, Paris, France
| | - Jordy Tasserie
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Camilo M Signorelli
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, 1070, Brussels, Belgium
- Department of Computer Science, University of Oxford, Oxford, 7 Parks Rd, Oxford, OX1 3QG, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
| | - Bechir Jarraya
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191, Gif-sur-Yvette, France
- Department of Neurology, Hopital Foch, 92150, Suresnes, France
| | - Rodrigo Cofre
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France.
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23
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Zheng W, Zhang Q, Zhao Z, Zhang P, Zhao L, Wang X, Yang S, Zhang J, Yao Z, Hu B. Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder. J Zhejiang Univ Sci B 2024:1-21. [PMID: 38423537 DOI: 10.1631/jzus.b2300401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/24/2023] [Indexed: 03/02/2024]
Abstract
Thalamocortical circuitry has a substantial impact on emotion and cognition. Previous studies have demonstrated alterations in thalamocortical functional connectivity (FC), characterized by region-dependent hypo- or hyper-connectivity, among individuals with major depressive disorder (MDD). However, the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear. Hence, we analyzed dynamic FC (dFC) between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls (HCs) to investigate time-varying changes in thalamocortical FC in patients with MDD. Moreover, dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time. Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated. We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks, compared with the HCs. In addition, MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration. The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort. Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms. Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients. The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.
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Affiliation(s)
- Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Qin Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730030, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Songyu Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Jing Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730030, China. ,
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China. ,
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China. ,
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China. ,
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou 730000, China.
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24
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Özalay Ö, Mediavilla T, Giacobbo BL, Pedersen R, Marcellino D, Orädd G, Rieckmann A, Sultan F. Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging. Commun Biol 2024; 7:210. [PMID: 38378942 PMCID: PMC10879497 DOI: 10.1038/s42003-024-05873-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 01/30/2024] [Indexed: 02/22/2024] Open
Abstract
The human aging brain is characterized by changes in network efficiency that are currently best captured through longitudinal resting-state functional MRI (rs-fMRI). These studies however are challenging due to the long human lifespan. Here we show that the mouse animal model with a much shorter lifespan allows us to follow the functional network organization over most of the animal's adult lifetime. We used a longitudinal study of the functional connectivity of different brain regions with rs-fMRI under anesthesia. Our analysis uncovers network modules similar to those reported in younger mice and in humans (i.e., prefrontal/default mode network (DMN), somatomotor and somatosensory networks). Statistical analysis reveals different patterns of network reorganization during aging. Female mice showed a pattern akin to human aging, with de-differentiation of the connectome, mainly due to increases in connectivity of the prefrontal/DMN cortical networks to other modules. Our male cohorts revealed heterogenous aging patterns with only one group confirming the de- differentiation, while the majority showed an increase in connectivity of the somatomotor cortex to the Nucleus accumbens. In summary, in line with human work, our analysis in mice supports the concept of de-differentiation in the aging mammalian brain and reveals additional trajectories in aging mice networks.
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Affiliation(s)
- Özgün Özalay
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
| | - Tomas Mediavilla
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
| | - Bruno Lima Giacobbo
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
| | - Robin Pedersen
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
| | - Daniel Marcellino
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
| | - Greger Orädd
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
| | - Anna Rieckmann
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden
- Department of Diagnostics and Intervention, Radiation Physics, Umeå University, 90 187, Umeå, Sweden
- Institute for Psychology, University of the Bundeswehr Munich, Neubiberg, Germany
| | - Fahad Sultan
- Department of Medical and Translational Biology, Umeå University, 90 187, Umeå, Sweden.
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25
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Fiorenzato E, Moaveninejad S, Weis L, Biundo R, Antonini A, Porcaro C. Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease. Mov Disord 2024; 39:305-317. [PMID: 38054573 DOI: 10.1002/mds.29678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline. OBJECTIVES The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features. METHODS Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models. RESULTS FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity. CONCLUSIONS Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Eleonora Fiorenzato
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Sadaf Moaveninejad
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Luca Weis
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- IRCCS, San Camillo Hospital, Venice, Italy
| | - Roberta Biundo
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
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26
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Wang XK, Yang C, Dong WQ, Zhang QR, Ma SZ, Zang YF, Yuan LX. Impaired segregation of the attention deficit hyperactivity disorder related pattern in children. J Psychiatr Res 2024; 170:111-121. [PMID: 38134720 DOI: 10.1016/j.jpsychires.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/05/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Inattention is a key characteristic of attention deficit hyperactivity disorder (ADHD). Specific brain abnormalities associated with this symptom form a discernible pattern related with ADHD in children (i.e., ADHD related pattern) in our earlier research. The developmental processes of segregation and integration may be crucial to ADHD. However, how brains reconfigure these processes of the ADHD related pattern in different subtypes of ADHD and across sexes remain unclear. METHODS Nested-spectral partition method was applied to identify effects of subtype and sex on segregation and integration of the ADHD related pattern, using 145 ADHD patients and 135 typically developing controls (TDC) aged 7-14. Relationships between the measures and inattention symptoms were also investigated. RESULTS Children with ADHD exhibited lower segregation of the ADHD related pattern (p = 1.17 × 10-8) than TDCs. Only the main effect of subtype was significant (p = 1.14 × 10-5). Both ADHD-C (p = 2.16 × 10-6) and ADHD-I (p = 2.87 × 10-6) patients had lower segregation components relative to the TDC. Moreover, segregation components were negatively correlated with inattention scores. CONCLUSIONS This study identified impaired segregation in the ADHD related pattern of children with ADHD and found shared neural bases among different subtypes and sexes.
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Affiliation(s)
- Xing-Ke Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Chen Yang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Wen-Qiang Dong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Qiu-Rong Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Sheng-Zhi Ma
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China; Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China; TMS Center, Deqing Hospital of Hangzhou Normal University, Deqing, Zhejiang, China
| | - Li-Xia Yuan
- School of Physics, Zhejiang University, Hangzhou, China.
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27
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Li Z, Kang Z, Xia X, Li L, Wu J, Dai J, Liu T, Chen C, Qiu Y, Chen M, Liu Y, Zhang Z, Han Z, Dai Z, Wei Q. Associations of resilience, white matter topological organization, and cognitive functions in first-episode, drug-naïve schizophrenia patients: A moderated mediation analysis. Prog Neuropsychopharmacol Biol Psychiatry 2024; 128:110867. [PMID: 37783265 DOI: 10.1016/j.pnpbp.2023.110867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 07/25/2023] [Accepted: 09/29/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Cognitive deficits are core symptoms of schizophrenia (SZ) and are associated with impaired resilience to stress. Different cognitive functions appeared to be interrelated, and the mechanism may involve neural alterations. The disrupted topological organization indicated abnormalities in the segregation and integration of brain networks that support various cognitive processes in SZ patients. Therefore, this study aimed to assess the direct and indirect effects of resilience on cognitive functions. We hypothesized that topological properties would moderate these associations. METHODS Forty-nine SZ patients and fifty-two healthy controls (HCs) were recruited in this study. The Connor-Davidson Resilience Scale and the MATRICS Consensus Cognitive Battery were used to examine resilience and cognitive functions, respectively, and a graph theory approach was used to assess white matter topological organization. RESULTS Compared to HCs, SZ patients showed lower levels of resilience and cognitive functions in multiple domains as well as abnormal global properties and nodal metrics. In addition, shorter characteristic path length was associated with a stronger indirect effect of resilience on working memory through processing speed in SZ patients. CONCLUSION Characteristic path length might moderate the mediating effects of processing speed in the relationship between resilience and working memory in schizophrenia patients.
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Affiliation(s)
- Zhinan Li
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Zhuang Kang
- Department of Radiology, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiaowei Xia
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Leijun Li
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Junyan Wu
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Jiamin Dai
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Tong Liu
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou; Department of Psychiatry, the First Affiliated Hospital of Xi 'an Jiaotong University, Shaanxi, China
| | - Cai Chen
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Yong Qiu
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Ming Chen
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Yanxi Liu
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Ziyi Zhang
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Zili Han
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| | - Qinling Wei
- Department of Psychiatry, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou; Department of Psychology, Sun Yat-sen University, Guangzhou, China.
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Xu T, Wu Y, Zhang Y, Zuo XN, Chen F, Zhou C. Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development. Neurosci Bull 2024; 40:50-64. [PMID: 37715923 PMCID: PMC10774512 DOI: 10.1007/s12264-023-01108-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 06/01/2023] [Indexed: 09/18/2023] Open
Abstract
The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.
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Affiliation(s)
- Tianyong Xu
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
| | - Yunying Wu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Yi Zhang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Feiyan Chen
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
| | - Changsong Zhou
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, 999077, China.
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29
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Knodt AR, Elliott ML, Whitman ET, Winn A, Addae A, Ireland D, Poulton R, Ramrakha S, Caspi A, Moffitt TE, Hariri AR. Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient. Hum Brain Mapp 2023; 44:6399-6417. [PMID: 37851700 PMCID: PMC10681655 DOI: 10.1002/hbm.26517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/23/2023] [Accepted: 09/30/2023] [Indexed: 10/20/2023] Open
Abstract
Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.
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Affiliation(s)
- Annchen R. Knodt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Maxwell L. Elliott
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
| | - Ethan T. Whitman
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Alex Winn
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - Angela Addae
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of PsychologyUniversity of OtagoDunedinNew Zealand
| | - Avshalom Caspi
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Terrie E. Moffitt
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Ahmad R. Hariri
- Department of Psychology and NeuroscienceDuke UniversityDurhamNorth CarolinaUSA
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Lord LD, Carletti T, Fernandes H, Turkheimer FE, Expert P. Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1279646. [PMID: 38116461 PMCID: PMC10728865 DOI: 10.3389/fnetp.2023.1279646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13-30 Hz) and low-gamma bands (30-80 Hz), and with strongly preserved local synchrony in all bands.
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Affiliation(s)
- Louis-David Lord
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
| | - Timoteo Carletti
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Department of Mathematics and Namur Institute for Complex Systems (naXys), University of Namur, Namur, Belgium
| | - Henrique Fernandes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Centre for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Paul Expert
- Institut Méditerranéen de Recherches Avancées (IMéRA), Aix-Marseille Université, Marseille, France
- Global Business School for Health, University College London, London, United Kingdom
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31
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Yi C, Fan Y, Wu Y. Cross-module switching diversity of brain network nodes in resting and cognitive states. Cogn Neurodyn 2023; 17:1485-1499. [PMID: 37974588 PMCID: PMC10640499 DOI: 10.1007/s11571-022-09894-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 11/03/2022] Open
Abstract
Large-scale brain network dynamics reflect state change in brain activities and have potential effects on cognition. Such dynamics can be described by node temporal switching between modules; however, there are only a few studies on the influence of brain network node switching on brain cognition. Based on the functional magnetic resonance imaging (fMRI) data of resting and task states, we constructed dynamic functional networks using overlap sliding-time windows and applied multilayer network analysis to study the behaviours of nodes across brain modules. We found that (i) nodes with a high level switching rate in the resting-state mainly come from the default network, while nodes with a low level of switching rate mainly come from the visual network, (ii) nodes with a high switching rate have lower clustering coefficients and shorter characteristic path lengths, which are mainly affected by the somatomotor network and dorsal attention network; and (iii) in task states, there is still a negative correlation between switching rate, clustering coefficient and characteristic path length. However, the main subsystems that affect brain functions are regulated by the tasks. Our findings not only reveal the relevant characteristics of network node switching behaviours but also provide new insights for further understanding the complex functions of the brain. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09894-z.
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Affiliation(s)
- Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an, 710049 China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an, 710049 China
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32
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Seshadri NPG, Singh BK, Pachori RB. EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4672-4682. [PMID: 37988207 DOI: 10.1109/tnsre.2023.3335806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.83±1.85 years) and 15 non-dyslexic children (mean age 9.91±1.97 years) were selected for this study. The children were asked to perform a visual continuous performance task (VCPT) while their electroencephalogram (EEG) signals were recorded. In dyslexic children, significant variations in task measurements revealed considerable omission and commission errors. During task performance, the dyslexic group with the absence of a small-world network had a lower clustering coefficient, a longer characteristic pathlength, and lower global and local efficiency than the non-dyslexic group (mainly in theta and alpha bands). When classifying data from the dyslexic and non-dyslexic groups, the current study achieved the maximum classification accuracy of 96.7% using a k-nearest neighbor (KNN) classifier. To summarize, our findings revealed indications of poor functional segregation and disturbed information transfer in dyslexic brain networks during a sustained attention task.
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Geng L, Feng Q, Wang X, Gao Y, Hao L, Qiu J. Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination. Front Psychol 2023; 14:1264221. [PMID: 37965648 PMCID: PMC10642796 DOI: 10.3389/fpsyg.2023.1264221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Background Rumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process. Methods We utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination. Results A positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination. Conclusion The findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.
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Affiliation(s)
- Li Geng
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Qiuyang Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Yixin Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Lei Hao
- College of Teacher Education, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
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Kristanto D, Hildebrandt A, Sommer W, Zhou C. Cognitive abilities are associated with specific conjunctions of structural and functional neural subnetworks. Neuroimage 2023; 279:120304. [PMID: 37536528 DOI: 10.1016/j.neuroimage.2023.120304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
Cognitive neuroscience assumes that different mental abilities correspond to at least partly separable brain subnetworks and strives to understand their relationships. However, single-task approaches typically revealed multiple brain subnetworks to be involved in performance. Here, we chose a bottom-up approach of investigating the association between structural and functional brain subnetworks, on the one hand, and domain-specific cognitive abilities, on the other. Structural network was identified using machine-learning graph neural network by clustering anatomical brain properties measured in 838 individuals enroled in the WU-Minn Young Adult Human Connectome Project. Functional network was adapted from seven Resting State Networks (7-RSN). We then analyzed the results of 15 cognitive tasks and estimated five latent abilities: fluid reasoning (Gf), crystallized intelligence (Gc), memory (Mem), executive functions (EF), and processing speed (Gs). In a final step we determined linear associations between these independently identified ability and brain entities. We found no one-to-one mapping between latent abilities and brain subnetworks. Analyses revealed that abilities are associated with properties of particular combinations of brain subnetworks. While some abilities are more strongly associated to within-subnetwork connections, others are related with connections between multiple subnetworks. Importantly, domain-specific abilities commonly rely on node(s) as hub(s) to connect with other subnetworks. To test the robustness of our findings, we ran the analyses through several defensible analytical decisions. Together, the present findings allow a novel perspective on the distinct nature of domain-specific cognitive abilities building upon unique combinations of associated brain subnetworks.
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Affiliation(s)
- Daniel Kristanto
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany
| | - Werner Sommer
- Department of Psychology, Humboldt University at Berlin, Berlin, Germany; Department of Psychology, Zhejiang Normal University, Jin Hua, China; Department of Physics, Hong Kong Baptist University, Hong Kong, China.
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Life Science Imaging Centre, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China; Department of Physics, Zhejiang University, Hangzhou 310000, China.
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35
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Liu Z, Han F, Wang Q. Task-relevant brain dynamics among cognitive subsystems induced by regional stimulation in a whole-brain computational model. Phys Rev E 2023; 108:044402. [PMID: 37978611 DOI: 10.1103/physreve.108.044402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/11/2023] [Indexed: 11/19/2023]
Abstract
Cognition involves the global integration of distributed brain regions that are known to work cohesively as cognitive subsystems during brain functioning. Empirical evidence has suggested that spatiotemporal phase relationships between brain regions, measured as synchronization and metastability, may encode important task-relevant information. However, it remains largely unknown how phase relationships aggregate at the level of cognitive subsystems under different cognitive processing. Here, we probe this question by simulating task-relevant brain dynamics through regional stimulation of a whole-brain dynamical network model operating in the resting-state dynamical regime. The model is constructed with structurally embedded Stuart-Laudon oscillators and then fitted with human resting-state functional magnetic resonance imaging data. Based on this framework, we first demonstrate the plausibility of introducing the cognitive system partition into the modeling analysis framework by showing that the clustering of regions across functional networks is better circumscribed by the predefined partition. At the cognitive subsystem level, we focus on how task-relevant phase dynamics are organized in terms of synchronization and metastability. We found that patterns of cognitive synchronization are more task specific, whereas patterns of cognitive metastability are more consistent across different states, suggesting it may encode a more task-general property during cognitive processing, an inherent property conferred by brain organization. This consistent network architecture in cognitive metastability may be related to the distinct functional responses of realistic cognitive systems. We also provide empirical evidence to partially support our computational results. Our paper may provide insights for the mechanisms underlying task-relevant brain dynamics, and establish a model-based link between brain structure, dynamics, and cognition, a fundamental step for computationally aided brain interventions.
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Affiliation(s)
- Zilu Liu
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai 200051, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
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Lombardo D, Kaufmann T. Different patterns of intrinsic functional connectivity at the default mode and attentional networks predict crystalized and fluid abilities in childhood. Cereb Cortex Commun 2023; 4:tgad015. [PMID: 37675438 PMCID: PMC10477707 DOI: 10.1093/texcom/tgad015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/01/2023] [Accepted: 08/14/2023] [Indexed: 09/08/2023] Open
Abstract
Crystallized abilities are skills used to solve problems based on experience, while fluid abilities are linked to reasoning without evoke prior knowledge. To what extent crystallized and fluid abilities involve dissociated or overlapping neural systems is debatable. Due to often deployed small sample sizes or different study settings in prior work, the neural basis of crystallized and fluid abilities in childhood remains largely unknown. Here we analyzed within and between network connectivity patterns from resting-state functional MRI of 2707 children between 9 and 10 years from the ABCD study. We hypothesized that differences in functional connectivity at the default mode network (DMN), ventral, and dorsal attentional networks (VAN, DAN) explain differences in fluid and crystallized abilities. We found that stronger between-network connectivity of the DMN and VAN, DMN and DAN, and VAN and DAN predicted crystallized abilities. Within-network connectivity of the DAN predicted both crystallized and fluid abilities. Our findings reveal that crystallized abilities rely on the functional coupling between attentional networks and the DMN, whereas fluid abilities are associated with a focal connectivity configuration at the DAN. Our study provides new evidence into the neural basis of child intelligence and calls for future comparative research in adulthood during neuropsychiatric diseases.
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Affiliation(s)
- Diego Lombardo
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
- German Center for Mental Health (DZPG), Partner Site Tübingen, Calwerstraße 14, 72076 Tübingen, Germany
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0450 Oslo, Norway
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37
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Argiris G, Stern Y, Lee S, Ryu H, Habeck C. Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort. Neuroimage 2023; 277:120237. [PMID: 37343735 PMCID: PMC10999229 DOI: 10.1016/j.neuroimage.2023.120237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 06/18/2023] [Indexed: 06/23/2023] Open
Abstract
Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.
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Affiliation(s)
- Georgette Argiris
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States
| | - Seonjoo Lee
- Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States; Department of Biostatistics, Mailman School of Public Health, New York, NY, United States; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
| | - Hyunnam Ryu
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States; Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States; Taub Institute, Columbia University, New York, NY, United States.
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Yang Y, Dai Y, He Q, Wang S, Chen X, Geng X, He J, Duan F. Altered brain functional connectivity in vegetative state and minimally conscious state. Front Aging Neurosci 2023; 15:1213904. [PMID: 37469954 PMCID: PMC10352323 DOI: 10.3389/fnagi.2023.1213904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/06/2023] [Indexed: 07/21/2023] Open
Abstract
Objectives The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there is a high rate of misdiagnosis for subtypes of DoC. We aimed to explore whether topological characterization may explain the pathological mechanisms of DoC and be effective in diagnosing the subtypes of DoC. Methods Using resting-state functional magnetic resonance imaging data, the weighted brain functional networks for normal control subjects and patients with vegetative state (VS) and minimally conscious state (MCS) were constructed. Global and local network characteristics of each group were analyzed. A support vector machine was employed to identify MCS and VS patients. Results The average connection strength was reduced in DoC patients and roughly equivalent in MCS and VS groups. Global efficiency, local efficiency, and clustering coefficients were reduced, and characteristic path length was increased in DoC patients (p < 0.05). For patients of both groups, global network measures were not significantly different (p > 0.05). Nodal efficiency, nodal local efficiency, and nodal clustering coefficient were reduced in frontoparietal brain areas, limbic structures, and occipital and temporal brain areas (p < 0.05). The comparison of nodal centrality suggested that DoC causes reorganization of the network structure on a large scale, especially the thalamus. Lobal network measures emphasized that the differences between the two groups of patients mainly involved frontoparietal brain areas. The accuracy, sensitivity, and specificity of the classifier for identifying MCS and VS patients were 89.83, 78.95, and 95%, respectively. Conclusion There is an association between altered network structures and clinical symptoms of DoC. With the help of network metrics, it is feasible to differentiate MCS and VS patients.
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Affiliation(s)
- Yi Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Beijing Institute of Brain Disorders, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yangyang Dai
- Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Qiheng He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shan Wang
- Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
| | - Xueling Chen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Geng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Feng Duan
- Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, College of Artificial Intelligence, Nankai University, Tianjin, China
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田 树, 徐 桂, 杨 新, Paul BF, Alan W. [Effect of electroconvulsive therapy on brain functional network in major depressive disorder]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:426-433. [PMID: 37380380 PMCID: PMC10307598 DOI: 10.7507/1001-5515.202201032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/30/2023] [Indexed: 06/30/2023]
Abstract
Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.
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Affiliation(s)
- 树香 田
- 河北工业大学 河北省生物电磁与神经工程重点实验室(天津 300401)Key Laboratory of Bioelectromagnetic and Neural Engineering of Hebei Province, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300401)Tianjin Key Laboratory of Bioelectricity and Intelligent Health, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130 )State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
- 北京大学 中国药物依赖性研究所(北京 100191)National Institute on Drug Dependence, Peking University, Beijing 100191, P.R. China
| | - 桂芝 徐
- 河北工业大学 河北省生物电磁与神经工程重点实验室(天津 300401)Key Laboratory of Bioelectromagnetic and Neural Engineering of Hebei Province, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300401)Tianjin Key Laboratory of Bioelectricity and Intelligent Health, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130 )State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
| | - 新生 杨
- 河北工业大学 河北省生物电磁与神经工程重点实验室(天津 300401)Key Laboratory of Bioelectromagnetic and Neural Engineering of Hebei Province, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300401)Tianjin Key Laboratory of Bioelectricity and Intelligent Health, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130 )State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
| | - B Fitzgerald Paul
- 河北工业大学 河北省生物电磁与神经工程重点实验室(天津 300401)Key Laboratory of Bioelectromagnetic and Neural Engineering of Hebei Province, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Wang Alan
- 河北工业大学 河北省生物电磁与神经工程重点实验室(天津 300401)Key Laboratory of Bioelectromagnetic and Neural Engineering of Hebei Province, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 天津市生物电工与智能健康重点实验室(天津 300401)Tianjin Key Laboratory of Bioelectricity and Intelligent Health, Hebei University of Technology, Tianjin 300401, P. R. China
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130 )State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P.R.China
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Wang Y, Lu M, Liu R, Wang L, Wang Y, Xu L, Wu K, Chen C, Chen T, Shi X, Li K, Zou Y. Acupuncture Alters Brain's Dynamic Functional Network Connectivity in Stroke Patients with Motor Dysfunction: A Randomised Controlled Neuroimaging Trial. Neural Plast 2023; 2023:8510213. [PMID: 37383656 PMCID: PMC10299883 DOI: 10.1155/2023/8510213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/19/2023] [Accepted: 05/25/2023] [Indexed: 06/30/2023] Open
Abstract
Objectives Neuroimaging studies have confirmed that acupuncture can promote static functional reorganization in poststroke patients with motor dysfunction. But its effect on dynamic brain networks remains unclear. This study is aimed at investigating how acupuncture affected the brain's dynamic functional network connectivity (dFNC) after ischemic stroke. Methods We conducted a single-center, randomised controlled neuroimaging study in ischemic stroke patients. A total of 53 patients were randomly divided into the true acupoint treatment group (TATG) and the sham acupoint treatment group (SATG) at a ratio of 2 : 1. Clinical assessments and magnetic resonance imaging (MRI) scans were performed on subjects before and after treatment. We used dFNC analysis to estimate distinct dynamic connectivity states. Then, the temporal properties and strength of functional connectivity (FC) matrix were compared within and between the two groups. The correlation analysis between dynamic characteristics and clinical scales was also calculated. Results All functional network connectivity (FNC) matrices were clustered into 3 connectivity states. After treatment, the TATG group showed a reduced mean dwell time and found attenuated FC between the sensorimotor network (SMN) and the frontoparietal network (FPN) in state 3, which was a sparsely connected state. The FC between the dorsal attention network (DAN) and the default mode network (DMN) was higher after treatment in the TATG group in state 1, which was a relative segregated state. The SATG group preferred to increase the mean dwell time and FC within FPN in state 2, which displayed a local tightly connected state. In addition, we found that the FC value increased between DAN and right frontoparietal network (RFPN) in state 1 in the TATG group after treatment compared to the SATG group. Correlation analyses before treatment showed that the Fugl-Meyer Assessment (FMA) lower score was negatively correlated with the mean dwell time in state 3. FMA score showed positive correlation with FC in RFPN-SMN in state 3. FMA-lower score was positively correlated with FC in DAN-DMN and DAN-RFPN in state 1. Conclusions Acupuncture has the potential to modulate abnormal temporal properties and promote the balance of separation and integration of brain function. True acupoint stimulation may have a more positive effect on regulating the brain's dynamic function. Clinical Trial Registration. This trial is registered with Chinese Clinical Trials Registry (ChiCTR1800016263).
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Affiliation(s)
- Yahui Wang
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Mengxin Lu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Ruoyi Liu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Liping Wang
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yue Wang
- China-Japan Friendship Hospital, Beijing, China
| | - Lingling Xu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Kang Wu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Chen Chen
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Tianzhu Chen
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xinyue Shi
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Kuangshi Li
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yihuai Zou
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
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Mijalkov M, Veréb D, Canal-Garcia A, Hinault T, Volpe G, Pereira JB. Nonlinear changes in delayed functional network topology in Alzheimer's disease: relationship with amyloid and tau pathology. Alzheimers Res Ther 2023; 15:112. [PMID: 37328909 DOI: 10.1186/s13195-023-01252-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Alzheimer's disease is a neurodegenerative disorder associated with the abnormal deposition of pathological processes, such as amyloid-ß and tau, which produces nonlinear changes in the functional connectivity patterns between different brain regions across the Alzheimer's disease continuum. However, the mechanisms underlying these nonlinear changes remain largely unknown. Here, we address this question using a novel method based on temporal or delayed correlations and calculate new whole-brain functional networks to tackle these mechanisms. METHODS To assess our method, we evaluated 166 individuals from the ADNI database, including amyloid-beta negative and positive cognitively normal subjects, patients with mild cognitive impairment, and patients with Alzheimer's disease dementia. We used the clustering coefficient and the global efficiency to measure the functional network topology and assessed their relationship with amyloid and tau pathology measured by positron emission tomography, as well as cognitive performance using tests measuring memory, executive function, attention, and global cognition. RESULTS Our study found nonlinear changes in the global efficiency, but not in the clustering coefficient, showing that the nonlinear changes in functional connectivity are due to an altered ability of brain regions to communicate with each other through direct paths. These changes in global efficiency were most prominent in early disease stages. However, later stages of Alzheimer's disease were associated with widespread network disruptions characterized by changes in both network measures. The temporal delays required for the detection of these changes varied across the Alzheimer's disease continuum, with shorter delays necessary to detect changes in early stages and longer delays necessary to detect changes in late stages. Both global efficiency and clustering coefficient showed quadratic associations with pathological amyloid and tau burden as well as cognitive decline. CONCLUSIONS This study suggests that global efficiency is a more sensitive indicator of network changes in Alzheimer's disease when compared to clustering coefficient. Both network properties were associated with pathology and cognitive performance, demonstrating their relevance in clinical settings. Our findings provide an insight into the mechanisms underlying nonlinear changes in functional network organization in Alzheimer's disease, suggesting that it is the lack of direct connections that drives these functional changes.
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Affiliation(s)
- Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Dániel Veréb
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Thomas Hinault
- Normandie Univ, Unicaen, PSL, Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, 14000, Caen, France
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana B Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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Yurchenko SB. A systematic approach to brain dynamics: cognitive evolution theory of consciousness. Cogn Neurodyn 2023; 17:575-603. [PMID: 37265655 PMCID: PMC10229528 DOI: 10.1007/s11571-022-09863-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/29/2022] [Accepted: 07/21/2022] [Indexed: 12/18/2022] Open
Abstract
The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.
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Zhou L, Xie Y, Wang R, Fan Y, Wu Y. Dynamic segregation and integration of brain functional networks associated with emotional arousal. iScience 2023; 26:106609. [PMID: 37250309 PMCID: PMC10214403 DOI: 10.1016/j.isci.2023.106609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/12/2023] [Accepted: 03/31/2023] [Indexed: 05/31/2023] Open
Abstract
The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.
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Affiliation(s)
- Lv Zhou
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Yong Xie
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Rong Wang
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Yongchen Fan
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Ying Wu
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
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Fan Y, Wang R, Yi C, Zhou L, Wu Y. Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification. iScience 2023; 26:106575. [PMID: 37250302 PMCID: PMC10214405 DOI: 10.1016/j.isci.2023.106575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 08/23/2022] [Accepted: 03/29/2023] [Indexed: 05/31/2023] Open
Abstract
The idea that brain networks have a hierarchical modular organization is pervasive. Increasing evidence suggests that brain modules overlap. However, little is known about the hierarchical overlapping modular structure in the brain. In this study, we developed a framework to uncover brain hierarchical overlapping modular structures based on a nested-spectral partition algorithm and an edge-centric network model. Overlap degree between brain modules is symmetrical across hemispheres, with highest overlap observed in the control and salience/ventral attention networks. Furthermore, brain edges are clustered into two groups: intrasystem and intersystem edges, to form hierarchical overlapping modules. At different levels, modules are self-similar in the degree of overlap. Additionally, the brain's hierarchical structure contains more individual identifiable information than a single-level structure, particularly in the control and salience/ventral attention networks. Our results offer pathways for future studies aimed at relating the organization of hierarchical overlapping modules to brain cognitive behavior and disorders.
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Affiliation(s)
- Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Rong Wang
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710049, China
| | - Chao Yi
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Lv Zhou
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures and School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
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Lucas A, Cornblath EJ, Sinha N, Hadar P, Caciagli L, Keller SS, Bonilha L, Shinohara RT, Stein JM, Das S, Gleichgerrcht E, Davis KA. Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy. Epilepsia 2023; 64:1305-1317. [PMID: 36855286 DOI: 10.1111/epi.17565] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 03/02/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). METHODS In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction. RESULTS Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. SIGNIFICANCE Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eli J Cornblath
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nishant Sinha
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Peter Hadar
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, Georgia, USA
| | - Russell T Shinohara
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joel M Stein
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu Das
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ezequiel Gleichgerrcht
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Zúñiga RG, Davis JRC, Boyle R, De Looze C, Meaney JF, Whelan R, Kenny RA, Knight SP, Ortuño RR. Brain connectivity in frailty: Insights from The Irish Longitudinal Study on Ageing (TILDA). Neurobiol Aging 2023; 124:1-10. [PMID: 36680853 DOI: 10.1016/j.neurobiolaging.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia).
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Affiliation(s)
- Raquel Gutiérrez Zúñiga
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland.
| | - James R C Davis
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Rory Boyle
- Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Céline De Looze
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - James F Meaney
- Centre for Advanced Medical Imaging (CAMI), St James's Hospital, Dublin, Ireland
| | - Robert Whelan
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Rose Anne Kenny
- The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Mercer's Institute for Successful Ageing (MISA), St James's Hospital, Dublin, Ireland
| | - Silvin P Knight
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Román Romero Ortuño
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; The Irish Longitudinal Study on Ageing (TILDA), Trinity College Dublin, Dublin, Ireland; Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Ireland; Mercer's Institute for Successful Ageing (MISA), St James's Hospital, Dublin, Ireland
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47
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Aydın S. Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level. Cogn Neurodyn 2023; 17:331-344. [PMID: 37007189 PMCID: PMC10050309 DOI: 10.1007/s11571-022-09843-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/03/2022] [Accepted: 07/01/2022] [Indexed: 11/26/2022] Open
Abstract
In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.
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Affiliation(s)
- Serap Aydın
- Medical Faculty, Biophysics Department, Hacettepe University, Ankara, Turkey
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48
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Ngo GN, Hori Y, Everling S, Menon RS. Joint-embeddings reveal functional differences in default-mode network architecture between marmosets and humans. Neuroimage 2023; 272:120035. [PMID: 36948281 DOI: 10.1016/j.neuroimage.2023.120035] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/30/2022] [Accepted: 03/14/2023] [Indexed: 03/24/2023] Open
Abstract
The default-mode network (DMN) is a distributed functional brain system integral for social and higher-order cognition in humans with implications in a myriad of neuropsychological disorders. In this study, we compared the functional architecture of the DMN between humans and marmosets to assess their similarities and differences using joint gradients. This approach permits simultaneous large-scale mapping of functional systems across the cortex of humans and marmosets, revealing evidence of putative homologies between them. In doing so, we find that the DMN architecture of the marmoset exhibits differences along its anterolateral-posterior axis. Specifically, the anterolateral node of the DMN (dorsolateral prefrontal cortex) displayed weak connections and inconsistent connection topographies as compared to its posterior DMN-nodes (posterior cingulate and posterior parietal cortices). We also present evidence that the marmoset medial prefrontal cortex and temporal lobe areas correspond to other macroscopical distributed functional systems that are not part of the DMN. Given the importance of the marmoset as a pre-clinical primate model for higher-order cognitive functioning and the DMN's relevance to cognition, our results suggest that the marmoset may lack the capacity to integrate neural information to subserve cortical dynamics that is necessary for supporting diverse cognitive demands.
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Affiliation(s)
- Geoffrey N Ngo
- Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 5C1, Canada
| | - Yuki Hori
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B7, Canada; Department of Functional Brain Imaging, National Institutes of Quantum and Radiological Science and Technology, Chiba 263-8555, Japan
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B7, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario N6A 5C1, Canada
| | - Ravi S Menon
- Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 5C1, Canada.; Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario N6A 5B7, Canada.
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Wang X, Chang Z, Wang R. Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia. Commun Biol 2023; 6:279. [PMID: 36932140 PMCID: PMC10023794 DOI: 10.1038/s42003-023-04637-0] [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: 10/21/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023] Open
Abstract
Schizophrenia is a severe psychotic disorder characterized by positive and negative symptoms, but their neural bases remain poorly understood. Here, we utilized a nested-spectral partition (NSP) approach to detect hierarchical modules in resting-state brain functional networks in schizophrenia patients and healthy controls, and we studied dynamic transitions of segregation and integration as well as their relationships with clinical symptoms. Schizophrenia brains showed a more stable integrating process and a more variable segregating process, thus maintaining higher segregation, especially in the limbic system. Hallucinations were associated with higher integration in attention systems, and avolition was related to a more variable segregating process in default-mode network (DMN) and control systems. In a machine-learning model, NSP-based features outperformed graph measures at predicting positive and negative symptoms. Multivariate analysis confirmed that positive and negative symptoms had opposite effects on dynamic segregation and integration of brain networks. Gene ontology analysis revealed that the effect of negative symptoms was related to autistic, aggressive and violent behavior; the effect of positive symptoms was associated with hyperammonemia and acidosis; and the interaction effect was correlated with abnormal motor function. Our findings could contribute to the development of more accurate diagnostic criteria for positive and negative symptoms in schizophrenia.
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Affiliation(s)
- Xinrui Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Zhao Chang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China
| | - Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
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50
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Altered white matter functional network in nicotine addiction. Psychiatry Res 2023; 321:115073. [PMID: 36716553 DOI: 10.1016/j.psychres.2023.115073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.
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