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Kim M, Seo JW, Kim MS, Lee KH, Kim M. White matter tract density index is associated with disability in multiple sclerosis. Neurobiol Dis 2024; 198:106548. [PMID: 38825050 DOI: 10.1016/j.nbd.2024.106548] [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: 04/03/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 06/04/2024] Open
Abstract
BACKGROUND The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak. Given that the disability in patients with MS may depend on the underlying structural connectivity of the brain, our study aimed to examine the association between white matter tracts affected by MS and the patients' disability using a new tract density index (TDI). METHOD This study included 53 patients diagnosed with MS, examined between 2019 and 2020. Manual lesion segmentation was performed on fluid-attenuated inversion recovery (FLAIR) images, and the density of white matter tracts encompassing the lesion (i.e., TDI) was calculated. Correlation analysis was employed to assess the association between TDI and disability. Additionally, the relationship between disability, TDI, and lesion-derived network metrics was examined by computing a partial correlation network. RESULTS The TDI significantly correlated with the expanded disability status scale (EDSS) (r = 0.30, p = 0.03). Furthermore, the patient's disability is linked solely through TDI to lesion-derived network metrics -a key metric that 'bridges' the gap between the brain lesion and disability. CONCLUSIONS In this study, MS lesions encompassing regions with high white matter tract density were associated and linked with severe physical disability. These findings indicate that TDI may be an outcome predictor that may connect radiologic findings to clinical practice.
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Affiliation(s)
- Minhoe Kim
- Computer Convergence Software Department, Korea University, Sejong, Republic of Korea
| | - Ji Won Seo
- Department of Radiology, Research Institute and Hospital of National Cancer Center, Goyang-si, Republic of Korea
| | - Myung Sub Kim
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyung Hoon Lee
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Minchul Kim
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Hechler A, Kuchling J, Müller-Jensen L, Klag J, Paul F, Prüss H, Finke C. Hippocampal hub failure is linked to long-term memory impairment in anti-NMDA-receptor encephalitis: insights from structural connectome graph theoretical network analysis. J Neurol 2024:10.1007/s00415-024-12545-4. [PMID: 38977462 DOI: 10.1007/s00415-024-12545-4] [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: 03/12/2024] [Revised: 06/22/2024] [Accepted: 06/26/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. METHODS Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome. RESULTS Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001). CONCLUSIONS Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.
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Affiliation(s)
- André Hechler
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- TUM-Neuroimaging Center, Technische Universitaet Muenchen, Munich, Germany
| | - Joseph Kuchling
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Leonie Müller-Jensen
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Johanna Klag
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
- Neurocure Cluster of Excellence, NeuroCure Clinical Research Center, Charité, Berlin Institute of Health, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Harald Prüss
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Berlin, Germany
| | - Carsten Finke
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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Tang Z, Zhao Y, Sun X, Liu Y, Su W, Liu T, Zhang X, Zhang H. Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis. Brain Res 2024:149113. [PMID: 38972627 DOI: 10.1016/j.brainres.2024.149113] [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: 05/03/2024] [Revised: 06/10/2024] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
OBJECTIVES To investigate alterations of whole-brain network after stroke and therapeutic mechanisms of robot-assisted gait training (RAGT). METHODS 21 S patients and 20 healthy subjects were enrolled, with the stroke patients randomized to either control group (n = 11) or robot group (n = 10), and resting-state functional magnetic resonance imaging data were collected. The global network metrics were obtained using graph theory analysis and compared between stroke patients and healthy subjects, and the effect of the RAGT on the whole-brain networks was explored. RESULTS Compared to healthy subjects, area under the curve (AUC) for small-worldness (σ), clustering coefficient (Cp), global efficiency (Eg) and mean local efficiency (Eloc) were significantly lower in stroke patients, whereas AUC for characteristic path length (Lp) were significantly higher. Compared with the control group, patients in robot group showed significant improvement in lower limb motor function, balance function and walking function after intervention, with a significant reduction in the AUC of Cp. Moreover, the improvement of walking function was positively correlated with the changes of AUC of σ and Eg, and negatively correlated with the changes of AUC of Cp. CONCLUSIONS Small-worldness and network efficiency were significantly reduced after stroke, whereas RAGT decreased characteristic path length and promoted normalization of the whole-brain network, and this change was associated with improvement in walking function. Our findings reveal the mechanism by which RAGT regulates network reorganization and neuroplasticity after stroke.
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Affiliation(s)
- Zhiqing Tang
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Yaxian Zhao
- Department of Cardiac Surgery, Peking University International Hospital, Beijing, China
| | - Xinting Sun
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Ying Liu
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Wenlong Su
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China; University of Health and Rehabilitation Sciences, Shandong Province, China
| | - Tianhao Liu
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Xiaonian Zhang
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China
| | - Hao Zhang
- School of Rehabilitation, Capital Medical University, Beijing, China; Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing, China; Cheeloo College of Medicine, Shandong University, Shandong Province, China; University of Health and Rehabilitation Sciences, Shandong Province, China.
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Nusbaum F, Hannoun S, Barile B, Suprano I, Mouchet S, Sappey-Marinier D. Personal Income Performance Correlates with Brain Structural Network Modularity but Not Intelligence Quotient. Brain Connect 2024; 14:284-293. [PMID: 38848246 DOI: 10.1089/brain.2023.0077] [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] [Indexed: 06/09/2024] Open
Abstract
Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score (p = 0.003) and negatively with FSIQ (p = 0.04) and processing speed index (p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical (p = 0.001) and frontal (p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.
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Affiliation(s)
- Fanny Nusbaum
- Health Systemic Process (P2S), UR 4129, Université Claude Bernard-Lyon 1, Université de Lyon, Lyon, France
| | - Salem Hannoun
- Medical Imaging Sciences Program, Division of Health Professions, Faculty of Health Sciences, American University of Beirut, Beirut, Lebanon
| | - Berardino Barile
- CREATIS, CNRS UMR 5220, INSERM U1294, Université Claude Bernard-Lyon1, INSA-Lyon, Université de Lyon, Villeurbanne, France
| | - Ilaria Suprano
- CREATIS, CNRS UMR 5220, INSERM U1294, Université Claude Bernard-Lyon1, INSA-Lyon, Université de Lyon, Villeurbanne, France
| | - Sabine Mouchet
- Service de Psychiatrie Légale - Pôle Santé Mentale des Détenus et Psychiatrie Légale, Centre Hospitalier le Vinatier, Bron, France
| | - Dominique Sappey-Marinier
- CREATIS, CNRS UMR 5220, INSERM U1294, Université Claude Bernard-Lyon1, INSA-Lyon, Université de Lyon, Villeurbanne, France
- CERMEP-Imagerie du Vivant, Université de Lyon, Bron, France
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Ke M, Hou Y, Zhang L, Liu G. Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis. Front Neurosci 2024; 18:1363255. [PMID: 38774788 PMCID: PMC11106382 DOI: 10.3389/fnins.2024.1363255] [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: 12/30/2023] [Accepted: 04/04/2024] [Indexed: 05/24/2024] Open
Abstract
Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Yaru Hou
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Li Zhang
- Hospital of Lanzhou University of Technology, Lanzhou University of Technology, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
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Wang Z, Zhao Z, Song Z, Xu J, Wang Y, Zhao Z, Li Y. Functional alterations of the brain default mode network and somatosensory system in trigeminal neuralgia. Sci Rep 2024; 14:10205. [PMID: 38702383 PMCID: PMC11068897 DOI: 10.1038/s41598-024-60273-6] [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: 01/02/2024] [Accepted: 04/21/2024] [Indexed: 05/06/2024] Open
Abstract
Mapping the localization of the functional brain regions in trigeminal neuralgia (TN) patients is still lacking. The study aimed to explore the functional brain alterations and influencing factors in TN patients using functional brain imaging techniques. All participants underwent functional brain imaging to collect resting-state brain activity. The significant differences in regional homogeneity (ReHo) and amplitude of low frequency (ALFF) between the TN and control groups were calculated. After familywise error (FWE) correction, the differential brain regions in ReHo values between the two groups were mainly located in bilateral middle frontal gyrus, bilateral inferior cerebellum, right superior orbital frontal gyrus, right postcentral gyrus, left inferior temporal gyrus, left middle temporal gyrus, and left gyrus rectus. The differential brain regions in ALFF values between the two groups were mainly located in the left triangular inferior frontal gyrus, left supplementary motor area, right supramarginal gyrus, and right middle frontal gyrus. With the functional impairment of the central pain area, the active areas controlling memory and emotion also change during the progression of TN. There may be different central mechanisms in TN patients of different sexes, affected sides, and degrees of nerve damage. The exact central mechanisms remain to be elucidated.
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Affiliation(s)
- Zairan Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, China
| | - Zijun Zhao
- Spine Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Zihan Song
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jiayi Xu
- Medical Records Room, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yizheng Wang
- Department of Pain Rehabilitation, The Forth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zongmao Zhao
- Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
- Department of Neurosurgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
| | - Yongning Li
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No.1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing, China.
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Qin K, Lei D, Zhu Z, Li W, Tallman MJ, Rodrigo Patino L, Fleck DE, Aghera V, Gong Q, Sweeney JA, McNamara RK, DelBello MP. Different brain functional network abnormalities between attention-deficit/hyperactivity disorder youth with and without familial risk for bipolar disorder. Eur Child Adolesc Psychiatry 2024; 33:1395-1405. [PMID: 37336861 DOI: 10.1007/s00787-023-02245-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) commonly precedes the initial onset of mania in youth with familial risk for bipolar disorder (BD). Although ADHD youth with and without BD familial risk exhibit different clinical features, associated neuropathophysiological mechanisms remain poorly understood. This study aimed to identify brain functional network abnormalities associated with ADHD in youth with and without familial risk for BD. Resting-state functional magnetic resonance imaging scans were acquired from 37 ADHD youth with a family history of BD (high-risk), 45 ADHD youth without a family history of BD (low-risk), and 32 healthy controls (HC). Individual whole-brain functional networks were constructed, and graph theory analysis was applied to estimate network topological metrics. Topological metrics, including network efficiency, small-worldness and nodal centrality, were compared across groups, and associations between topological metrics and clinical ratings were evaluated. Compared to HC, low-risk ADHD youth exhibited weaker global integration (i.e., decreased global efficiency and increased characteristic path length), while high-risk ADHD youth showed a disruption of localized network components with decreased frontoparietal and frontolimbic connectivity. Common topological deficits were observed in the medial superior frontal gyrus between low- and high-risk ADHD. Distinct network deficits were found in the inferior parietal lobule and corticostriatal circuitry. Associations between global topological metrics and externalizing symptoms differed significantly between the two ADHD groups. Different patterns of functional network topological abnormalities were found in high- as compared to low-risk ADHD, suggesting that ADHD in youth with BD familial risk may represent a phenotype that is different from ADHD alone.
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Affiliation(s)
- Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
| | - Ziyu Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Wenbin Li
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, Henan, China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Veronica Aghera
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China.
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Robert K McNamara
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, 45219, USA
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Leong C, Zhao Z, Yuan Z, Liu B. Distinct brain network organizations between club players and novices under different difficulty levels. Brain Behav 2024; 14:e3488. [PMID: 38641879 PMCID: PMC11031636 DOI: 10.1002/brb3.3488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/17/2024] [Accepted: 03/31/2024] [Indexed: 04/21/2024] Open
Abstract
SIGNIFICANT Chunk memory is one of the essential cognitive functions for high-expertise (HE) player to make efficient decisions. However, it remains unknown how the neural mechanisms of chunk memory processes mediate or alter chess players' performance when facing different opponents. AIM This study aimed at inspecting the significant brain networks associated with chunk memory, which would vary between club players and novices. APPROACH Functional networks and topological features of 20 club players (HE) and 20 novice players (LE) were compared at different levels of difficulty by means of functional near-infrared spectroscopy. RESULTS Behavioral performance indicated that the club player group was unaffected by differences in difficulty. Furthermore, the club player group demonstrated functional connectivity among the dorsolateral prefrontal cortex, the frontopolar cortex, the supramarginal gyrus, and the subcentral gyrus, as well as higher clustering coefficients and lower path lengths in the high-difficulty task. CONCLUSIONS The club player group illustrated significant frontal-parietal functional connectivity patterns and topological characteristics, suggesting enhanced chunking processes for improved chess performance.
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Affiliation(s)
- Chantat Leong
- Centre for Cognitive and Brain SciencesUniversity of MacauMacau SARChina
- Faculty of Health SciencesUniversity of MacauMacau SARChina
| | - Zhiying Zhao
- Centre for Cognitive and Brain SciencesUniversity of MacauMacau SARChina
| | - Zhen Yuan
- Centre for Cognitive and Brain SciencesUniversity of MacauMacau SARChina
- Faculty of Health SciencesUniversity of MacauMacau SARChina
| | - Bin Liu
- Department of EmergencyZhujiang Hospital, Southern Medical UniversityGuangzhouChina
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9
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Xiao P, Li Q, Gui H, Xu B, Zhao X, Wang H, Tao L, Chen H, Wang H, Lv F, Luo T, Cheng O, Luo J, Man Y, Xiao Z, Fang W. Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease. Neurol Sci 2024:10.1007/s10072-024-07472-1. [PMID: 38528280 DOI: 10.1007/s10072-024-07472-1] [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: 12/22/2023] [Accepted: 03/14/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.
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Affiliation(s)
- Pan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Honge Gui
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Bintao Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xiaole Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hongyu Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Oumei Cheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Huang Y, Zhang J, He K, Mo X, Yu R, Min J, Zhu T, Ma Y, He X, Lv F, Lei D, Liu M. Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models. Diagnostics (Basel) 2024; 14:389. [PMID: 38396428 PMCID: PMC10888009 DOI: 10.3390/diagnostics14040389] [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: 01/10/2024] [Revised: 02/03/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders.
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Affiliation(s)
- Yang Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jingbo Zhang
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Kewei He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xue Mo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jing Min
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Tong Zhu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Yunfeng Ma
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Xiangqian He
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Du Lei
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China (J.M.)
| | - Mengqi Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
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11
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Wang Q, Qi L, He C, Feng H, Xie C. Age- and gender-related dispersion of brain networks across the lifespan. GeroScience 2024; 46:1303-1318. [PMID: 37542582 PMCID: PMC10828139 DOI: 10.1007/s11357-023-00900-8] [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: 11/11/2022] [Accepted: 07/30/2023] [Indexed: 08/07/2023] Open
Abstract
The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.
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Affiliation(s)
- Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Lingyu Qi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Haixia Feng
- Department of Nursing, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China.
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210096, China.
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12
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Huang Y, Zhang X, Cheng M, Yang Z, Liu W, Ai K, Tang M, Zhang X, Lei X, Zhang D. Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus. Front Neurosci 2024; 18:1327061. [PMID: 38332862 PMCID: PMC10851426 DOI: 10.3389/fnins.2024.1327061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/11/2024] [Indexed: 02/10/2024] Open
Abstract
Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.
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Affiliation(s)
- Yang Huang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xin Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Miao Cheng
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Zhen Yang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Wanting Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Kai Ai
- Department of Clinical and Technical Support, Philips Healthcare, Xi’an, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
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13
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Li X, Lei D, Qin K, Li L, Zhang Y, Zhou D, Kemp GJ, Gong Q. Effects of PRRT2 mutation on brain gray matter networks in paroxysmal kinesigenic dyskinesia. Cereb Cortex 2024; 34:bhad418. [PMID: 37955636 DOI: 10.1093/cercor/bhad418] [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: 08/26/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
Although proline-rich transmembrane protein 2 is the primary causative gene of paroxysmal kinesigenic dyskinesia, its effects on the brain structure of paroxysmal kinesigenic dyskinesia patients are not yet clear. Here, we explored the influence of proline-rich transmembrane protein 2 mutations on similarity-based gray matter morphological networks in individuals with paroxysmal kinesigenic dyskinesia. A total of 51 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations, 55 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, and 80 healthy controls participated in the study. We analyzed the structural connectome characteristics across groups by graph theory approaches. Relative to paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation and healthy controls, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations exhibited a notable increase in characteristic path length and a reduction in both global and local efficiency. Relative to healthy controls, both patient groups showed reduced nodal metrics in right postcentral gyrus, right angular, and bilateral thalamus; Relative to healthy controls and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations showed almost all reduced nodal centralities and structural connections in cortico-basal ganglia-thalamo-cortical circuit including bilateral supplementary motor area, bilateral pallidum, and right caudate nucleus. Finally, we used support vector machine by gray matter network matrices to classify paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, achieving an accuracy of 73%. These results show that proline-rich transmembrane protein 2 related gray matter network deficits may contribute to paroxysmal kinesigenic dyskinesia, offering new insights into its pathophysiological mechanisms.
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Affiliation(s)
- Xiuli Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Du Lei
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, 260 Stetson St., Suite 3326, Cincinnati, Ohio, 45219, United States
| | - Kun Qin
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Lei Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Yingying Zhang
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, L69 3BX, Liverpool, L3 5TR, United Kingdom
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Lane, Wuhou District, Chengdu, 610041, China
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14
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Fleischer V, Gonzalez-Escamilla G, Pareto D, Rovira A, Sastre-Garriga J, Sowa P, Høgestøl EA, Harbo HF, Bellenberg B, Lukas C, Ruggieri S, Gasperini C, Uher T, Vaneckova M, Bittner S, Othman AE, Collorone S, Toosy AT, Meuth SG, Zipp F, Barkhof F, Ciccarelli O, Groppa S. Prognostic value of single-subject grey matter networks in early multiple sclerosis. Brain 2024; 147:135-146. [PMID: 37642541 PMCID: PMC10766234 DOI: 10.1093/brain/awad288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/17/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.
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Affiliation(s)
- Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology (IDI), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia, Hospital Universitari Vall d'Hebron, 08035 Barcelona, Spain
| | - Piotr Sowa
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Einar A Høgestøl
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Hanne F Harbo
- Institute of Clinical Medicine, University of Oslo, NO-0316 Oslo, Norway
- Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - Barbara Bellenberg
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St Josef Hospital, Ruhr-University Bochum, 44791 Bochum, Germany
| | - Serena Ruggieri
- Department of Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | - Tomas Uher
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Manuela Vaneckova
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, 121 08 Prague, Czech Republic
| | - Stefan Bittner
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Sara Collorone
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Ahmed T Toosy
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
| | - Frauke Zipp
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Frederik Barkhof
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, 1100 DD Amsterdam, Netherlands
| | - Olga Ciccarelli
- Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, WC1E 6BT London, UK
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN) and Immunotherapy (FZI), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
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15
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Lin S, Wu P, Duan S, Du Q, Guo S, Chen Z, Wu N, Chen X, Xie T, Han Y, Zhao H. Altered functional brain networks in coronary heart disease: independent component analysis and graph theoretical analysis. Brain Struct Funct 2024; 229:133-142. [PMID: 37943310 DOI: 10.1007/s00429-023-02724-w] [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: 06/28/2023] [Accepted: 10/14/2023] [Indexed: 11/10/2023]
Abstract
Coronary heart disease (CHD) confers a high risk of cognitive and mental impairments in patients. This study aimed to explore the association of CHD with functional connectivity and topological properties of brain networks. A total of 27 patients with CHD and 44 healthy controls (HCs) participated in this study and underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Intra- and internetwork functional connectivity alterations were explored using independent component analysis in CHD patients. Furthermore, graph theoretical analysis was adopted to assess abnormalities in small-world properties and network efficiency metrics of brain networks. Compared to HCs, CHD patients exhibited increased functional connectivity between the posterior default mode network and posterior visual network, as well as decreased functional connectivity between the left frontoparietal network and auditory network. In terms of graph theoretical analysis, small-world network topology was identified in both CHD patients and HCs. Furthermore, the nodal local efficiency of the left putamen was significantly decreased in CHD patients compared to HCs. This study revealed alterations in brain functional connectivity and topological properties in CHD patients, shedding light on the potential neurological mechanism underlying cognitive and mental impairments in these patients and suggesting unexplored connections between CHD and higher order cognitive processing.
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Affiliation(s)
- Simin Lin
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China
| | - Puyeh Wu
- GE Healthcare, Beijing, 102600, China
| | - Shaoyin Duan
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361001, Fujian, China
| | - Qianni Du
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China
| | - Shujia Guo
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China
| | - Zhishang Chen
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China
| | - Naiming Wu
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China
| | - Xiaoyan Chen
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China
| | - Ting Xie
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China
| | - Yi Han
- Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
- Department of Ophthalmology, The First Affiliated Hospital, Postdoctoral Mobile Station of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
| | - Hengyu Zhao
- Department of Radiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361006, Fujian, China.
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16
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Shahbodaghy F, Shafaghi L, Rostampour M, Rostampour A, Kolivand P, Gharaylou Z. Symmetry differences of structural connectivity in multiple sclerosis and healthy state. Brain Res Bull 2023; 205:110816. [PMID: 37972899 DOI: 10.1016/j.brainresbull.2023.110816] [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: 04/13/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/19/2023]
Abstract
Focal and diffuse cerebral damages occur in Multiple Sclerosis (MS) that promotes profound shifts in local and global structural connectivity parameters, mainly derived from diffusion tensor imaging. Most of the reconstruction analyses have applied conventional tracking algorithms largely based on the controversial streamline count. For a more credible explanation of the diffusion MRI signal, we used convex optimization modeling for the microstructure-informed tractography2 (COMMIT2) framework. All multi-shell diffusion data from 40 healthy controls (HCs) and 40 relapsing-remitting MS (RRMS) patients were transformed into COMMIT2-weighted matrices based on the Schefer-200 parcels atlas (7 networks) and 14 bilateral subcortical regions. The success of the classification process between MS and healthy state was efficiently predicted by the left DMN-related structures and visual network-associated pathways. Additionally, the lesion volume and age of onset were remarkably correlated with the components of the left DMN. Using complementary approaches such as global metrics revealed differences in WM microstructural integrity between MS and HCs (efficiency, strength). Our findings demonstrated that the cutting-edge diffusion MRI biomarkers could hold the potential for interpreting brain abnormalities in a more distinctive way.
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Affiliation(s)
- Fatemeh Shahbodaghy
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Lida Shafaghi
- Department of Neuroscience, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Massoumeh Rostampour
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ali Rostampour
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
| | - Pirhossein Kolivand
- Department of Health Economics, School of Medicine, Shahed University, Tehran, Iran
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17
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Martinez-Heras E, Solana E, Vivó F, Lopez-Soley E, Calvi A, Alba-Arbalat S, Schoonheim MM, Strijbis EM, Vrenken H, Barkhof F, Rocca MA, Filippi M, Pagani E, Groppa S, Fleischer V, Dineen RA, Bellenberg B, Lukas C, Pareto D, Rovira A, Sastre-Garriga J, Collorone S, Prados F, Toosy A, Ciccarelli O, Saiz A, Blanco Y, Llufriu S. Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes. J Neurol Neurosurg Psychiatry 2023; 94:916-923. [PMID: 37321841 DOI: 10.1136/jnnp-2023-331531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
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Affiliation(s)
- Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Francesc Vivó
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Alberto Calvi
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Salut Alba-Arbalat
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Eva M Strijbis
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sergiu Groppa
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Robert A Dineen
- Mental Health and Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK; and NIHR Nottingham Biomedical Research Centre, Nottingham, UK
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Jaume Sastre-Garriga
- Neurology-Neuroimmunology Department, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Sara Collorone
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ahmed Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Albert Saiz
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
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18
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Zhao G, Zhan Y, Zha J, Cao Y, Zhou F, He L. Abnormal intrinsic brain functional network dynamics in patients with cervical spondylotic myelopathy. Cogn Neurodyn 2023; 17:1201-1211. [PMID: 37786665 PMCID: PMC10542087 DOI: 10.1007/s11571-022-09807-0] [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: 10/17/2021] [Revised: 03/15/2022] [Accepted: 04/01/2022] [Indexed: 11/03/2022] Open
Abstract
The specific topological changes in dynamic functional networks and their role in cervical spondylotic myelopathy (CSM) brain function reorganization remain unclear. This study aimed to investigate the dynamic functional connection (dFC) of patients with CSM, focusing on the temporal characteristics of the functional connection state patterns and the variability of network topological organization. Eighty-eight patients with CSM and 77 healthy controls (HCs) were recruited for resting-state functional magnetic resonance imaging. We applied the sliding time window analysis method and K-means clustering analysis to capture the dFC variability patterns of the two groups. The graph-theoretical approach was used to investigate the variance in the topological organization of whole-brain functional networks. All participants showed four types of dynamic functional connection states. The mean dwell time in state 2 was significantly different between the two groups. Particularly, the mean dwell time in state 2 was significantly longer in the CSM group than in the healthy control group. Among the four states, switching of relative brain networks mainly included the executive control network (ECN), salience network (SN), default mode network (DMN), language network (LN), visual network (VN), auditory network (AN), precuneus network (PN), and sensorimotor network (SMN). Additionally, the topological properties of the dynamic network were variable in patients with CSM. Dynamic functional connection states may offer new insights into intrinsic functional activities in CSM brain networks. The variance of topological organization may suggest instability of the brain networks in patients with CSM.
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Affiliation(s)
- Guoshu Zhao
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Yaru Zhan
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Jing Zha
- The 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force, Fuzhou, 330006 People’s Republic of China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041 People’s Republic of China
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Fuqing Zhou
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
| | - Laichang He
- Department of Radiology, the First Affiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi 330006 People’s Republic of China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006 People’s Republic of China
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19
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Zhu Z, Lei D, Qin K, Li X, Li W, Tallman MJ, Patino LR, Fleck DE, Aghera V, Gong Q, Sweeney JA, McNamara RK, DelBello MP. Brain network structural connectome abnormalities among youth with attention-deficit/hyperactivity disorder at varying risk for bipolar I disorder: a cross-sectional graph-based magnetic resonance imaging study. J Psychiatry Neurosci 2023; 48:E315-E324. [PMID: 37643802 PMCID: PMC10473038 DOI: 10.1503/jpn.220209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/10/2023] [Accepted: 05/30/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is highly prevalent among youth with or at familial risk for bipolar-I disorder (BD-I), and ADHD symptoms commonly precede and may increase the risk for BD-I; however, associated neuropathophysiological mechanisms are not known. In this cross-sectional study, we sought to investigate brain structural network topology among youth with ADHD, with and without familial risk of BD-I. METHODS We recruited 3 groups of psychostimulant-free youth (aged 10-18 yr), namely youth with ADHD and at least 1 biological parent or sibling with BD-I (high-risk group), youth with ADHD who did not have a first- or second-degree relative with a mood or psychotic disorder (low-risk group) and healthy controls. We used graph-based network analysis of structural magnetic resonance imaging data to investigate topological properties of brain networks. We also evaluated relationships between topological metrics and mood and ADHD symptom ratings. RESULTS A total of 149 youth were included in the analysis (49 healthy controls, 50 low-risk youth, 50 high-risk youth). Low-risk and high-risk ADHD groups exhibited similar differences from healthy controls, mainly in the default mode network and central executive network. We found topological alterations in the salience network of the high-risk group, relative to both low-risk and control groups. We found significant abnormalities in global network properties in the high-risk group only, compared with healthy controls. Among both low-risk and high-risk ADHD groups, nodal metrics in the right triangular inferior frontal gyrus correlated positively with ADHD total and hyperactivity/impulsivity subscale scores. LIMITATIONS The cross-sectional design of this study could not determine the relevance of these findings to BD-I risk progression. CONCLUSION Youth with ADHD, with and without familial risk for BD-I, exhibit common regional abnormalities in the brain connectome compared with healthy youth, whereas alterations in the salience network distinguish these groups and may represent a prodromal feature relevant to BD-I risk.
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Affiliation(s)
- Ziyu Zhu
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Du Lei
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Kun Qin
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Xiuli Li
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Wenbin Li
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Maxwell J Tallman
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - L Rodrigo Patino
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - David E Fleck
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Veronica Aghera
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Qiyong Gong
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - John A Sweeney
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Robert K McNamara
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
| | - Melissa P DelBello
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China (Zhu, Qin, X. Li, Gong); the Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH (Zhu, Qin, Tallman, Patino, Fleck, Aghera, Sweeney, McNamara, DelBello); the College of Medical Informatics, Chongqing Medical University, Chongqing, China (Lei); the Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China (X. Li); the Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (W. Li); the Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Gong); the Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China (Gong)
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Ma K, Zhang X, Song C, Han S, Li W, Wang K, Mao X, Zhang Y, Cheng J. Altered topological properties and their relationship to cognitive functions in unilateral temporal lobe epilepsy. Epilepsy Behav 2023; 144:109247. [PMID: 37267843 DOI: 10.1016/j.yebeh.2023.109247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 06/04/2023]
Abstract
OBJECTIVE To investigate abnormalities in topological properties in unilateral temporal lobe epilepsy (TLE) with hippocampal sclerosis and their correlations with cognitive functions. METHODS Thirty-eight patients with TLE and 19 age- and sex-matched healthy controls (HCs) were enrolled in this research and underwent resting-state functional magnetic resonance imaging (fMRI) examinations. Whole-brain functional networks of participants were constructed based on the fMRI data. Topological characteristics of the functional network were compared between patients with left and right TLE and HCs. Correlations between altered topological properties and cognitive measurements were explored. RESULTS Compared with the HCs, patients with left TLE showed decreased clustering coefficient, global efficiency, and local efficiency (Eloc), and patients with right TLE showed decreased Eloc. We found altered nodal centralities in six regions related to the basal ganglia (BG) network or default mode network (DMN) in patients with left TLE and those in three regions related to reward/emotion network or ventral attention network in patients with right TLE. Patients with right TLE showed higher integration (reduced nodal shortest path length) in four regions related to the DMN and lower segregation (reduced nodal local efficiency and nodal clustering coefficient) in the right middle temporal gyrus. When comparing left TLE with right TLE, no significant differences were detected in global parameters, but the nodal centralities in the left parahippocampal gyrus and the left pallidum were decreased in left TLE. The Eloc and several nodal parameters were significantly correlated with memory functions, duration, national hospital seizure severity scale (NHS3), or antiseizure medications (ASMs) in patients with TLE. CONCLUSIONS The topological properties of whole-brain functional networks were disrupted in TLE. Networks of left TLE were characterized by lower efficiency; right TLE was preserved in global efficiency but disrupted in fault tolerance. Several nodes with abnormal topological centrality in the basal ganglia network beyond the epileptogenic focus in the left TLE were not found in the right TLE. Right TLE had some nodes with reduced shortest path length in regions of the DMN as compensation. These findings provide new insights into the effect of lateralization on TLE and help us to understand the cognitive impairment of patients with TLE.
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Affiliation(s)
- Keran Ma
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Xiaonan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Chengru Song
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Wenbin Li
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Kefan Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Xinyue Mao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China; Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China; Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China; Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China; Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China; Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China.
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21
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Yin G, Li T, Jin S, Wang N, Li J, Wu C, He H, Wang J. A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects. Cereb Cortex 2023:7169131. [PMID: 37197789 DOI: 10.1093/cercor/bhad178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/29/2023] [Accepted: 04/30/2023] [Indexed: 05/19/2023] Open
Abstract
Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.
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Affiliation(s)
- Guole Yin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Changwen Wu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310058, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
- Key Laboratory of Cognition and Education Sciences, Ministry of Education, Beijing 100816, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510000, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510000, China
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22
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Song L, Liu X, Yang W, Chen Q, Lv H, Yang Z, Liu W, Wang H, Wang Z. Altered Resting-State Functional Networks in Nondialysis Patients with Stage 5 Chronic Kidney Disease: A Graph-Theoretical Analysis. Brain Sci 2023; 13:brainsci13040628. [PMID: 37190593 DOI: 10.3390/brainsci13040628] [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: 02/21/2023] [Revised: 03/21/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
This study aimed to investigate the topological characteristics of the resting-state functional network and the underlying pathological mechanism in nondialysis patients with stage 5 chronic kidney disease (CKD5 ND). Eighty-five subjects (21 patients with CKD5 ND, 32 patients with CKD on maintenance hemodialysis (HD), and 32 healthy controls (HCs)) underwent laboratory examinations, neuropsychological tests, and brain magnetic resonance imaging. The topological characteristics of networks were compared with a graph-theoretical approach, and correlations between neuropsychological scores and network properties were analyzed. All participants exhibited networks with small-world attributes, and global topological attributes were impaired in both groups of patients with CKD 5 (ND and HD) compared with HCs (p < 0.05); these impairments were more severe in the CKD5 ND group than in the HD group (p < 0.05). Compared with the HC group, the degree centrality of the CKD5 ND group decreased mainly in the basal ganglia and increased in the bilateral orbitofrontal gyrus, bilateral precuneus, and right cuneus. Correlation analysis showed that the degree of small-worldness, normalized clustering coefficients, and Montreal Cognitive Assessment (MoCA) scores were positively correlated and that characteristic path length was negatively correlated with these variables in patients with CKD5 ND. The nodal efficiency of the bilateral putamen (r = 0.53, p < 0.001 and r = 0.47, p < 0.001), left thalamus (r = 0.37, p < 0.001), and right caudate nucleus (r = 0.28, p = 0.01) was positively correlated with MoCA scores. In conclusion, all CKD5 ND patients exhibited changes in functional network topological properties and were closely associated with mild cognitive impairment. More interestingly, the topological property changes in CKD5 ND patients were dominated by basal ganglia areas, which may be more helpful to understand and possibly reveal the underlying pathological mechanisms of cognitive impairment in CKD5 ND.
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Affiliation(s)
- Lijun Song
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Xu Liu
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Wenbo Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Wenhu Liu
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Hao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Beijing 100050, China
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23
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Gao J, Chen M, Xiao D, Li Y, Zhu S, Li Y, Dai X, Lu F, Wang Z, Cai S, Wang J. Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network. Cereb Cortex 2023; 33:2415-2425. [PMID: 35641181 DOI: 10.1093/cercor/bhac217] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.
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Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mingren Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Die Xiao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yue Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shunli Zhu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
| | - Xin Dai
- School of Automation, Chongqing University, Chongqing 400044, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shimin Cai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jiaojian Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
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24
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Rispoli MG, D'Apolito M, Pozzilli V, Tomassini V. Lessons from immunotherapies in multiple sclerosis. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:293-311. [PMID: 36803817 DOI: 10.1016/b978-0-323-85555-6.00013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.
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Affiliation(s)
- Marianna G Rispoli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Maria D'Apolito
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valeria Pozzilli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy.
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25
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Zhou Y, Müller HG, Zhu C, Chen Y, Wang JL, O'Muircheartaigh J, Bruchhage M, Deoni S, Bruchhage M, Carnell S, Deoni S, D’Sa V, Huentelman M, Klepac-Ceraj V, LeBourgeois M, Müller HG, O’Muircheartaigh J, Wang JL. Network evolution of regional brain volumes in young children reflects neurocognitive scores and mother's education. Sci Rep 2023; 13:2984. [PMID: 36804963 PMCID: PMC9941570 DOI: 10.1038/s41598-023-29797-1] [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: 10/19/2022] [Accepted: 02/10/2023] [Indexed: 02/22/2023] Open
Abstract
The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother's education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.
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Affiliation(s)
- Yidong Zhou
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA.
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Changbo Zhu
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Yaqing Chen
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, CA, 95616, USA
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Muriel Bruchhage
- Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, USA.,Department of Diagnostic Imaging, Rhode Island Hospital, Providence, USA.,Institute of Social Sciences, Stavanger University, Stavanger, 4021, Norway
| | - Sean Deoni
- Maternal, Newborn, and Child Health Discovery and Tools, Bill and Melinda Gates Foundation, Seattle, WA, USA
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26
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Yang J, Deng Y, Liu D, Tan Y, Lin M, Zhou X, Zhang J, Yu H, Hu Y, Tang Y, Jiang S, Zhang J. Brain network deficits in breast cancer patients after early neoadjuvant chemotherapy: A longitudinal MRI study. J Neurosci Res 2023; 101:1138-1153. [PMID: 36791216 DOI: 10.1002/jnr.25178] [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: 08/28/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/17/2023]
Abstract
Breast cancer (BC) patients who undergo chemotherapy are likely to develop chemotherapy-related cognitive impairment (CRCI). Recent studies of BC patients after chemotherapy have used graph theory to investigate the topological properties of the brain functional connectome. However, little is known about structural morphological networks in BC patients after early neoadjuvant chemotherapy (NAC). Brain morphological network organization in 47 female participants with BC was investigated before and after NAC. Topological properties of brain networks were ascertained based on morphological similarities in regional gray matter using a graph theory approach based on 3D T1-weighted MRI data. Nonparametric permutation testing was used to assess longitudinal-group differences in topological metrics. Compared with BC patients before NAC, BC patients after early NAC showed significantly increased global efficiency (p = .048), decreased path length (p = .033), and abnormal nodal properties and connectivity, mainly located in the central executive network (CEN). The change in the network efficiency of the right caudate was negatively correlated with the change in the Self-Rating Anxiety Scale score (r = -.435, p = .008), and the change in the nodal degree of the left superior frontal gyrus (dorsolateral part) was positively correlated with the change in the Functional Assessment of Cancer Therapy score (r = .547, p = .002). BC participants showed randomization in global properties and dysconnectivity in the CEN after early NAC. NAC may disrupt the cognitive balance of the brain morphological network in individuals with BC.
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Affiliation(s)
- Jing Yang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Yongchun Deng
- Department of Breast Cancer Center, Chongqing University Cancer Hospital, School of Medicine, Chongqing, China.,Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, School of Medicine, Chongqing, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Yong Tan
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Meng Lin
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Xiaoyu Zhou
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jing Zhang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Hong Yu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Yixin Hu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Yu Tang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Shixi Jiang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
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27
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Hejazi S, Karwowski W, Farahani FV, Marek T, Hancock PA. Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review. Brain Sci 2023; 13:brainsci13020246. [PMID: 36831789 PMCID: PMC9953947 DOI: 10.3390/brainsci13020246] [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: 12/06/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.
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Affiliation(s)
- Sara Hejazi
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
- Correspondence:
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
| | - Farzad V. Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland
| | - P. A. Hancock
- Department of Psychology, University of Central Florida, Orlando, FL 32816, USA
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28
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Lau CI, Yeh JH, Tsai YF, Hsiao CY, Wu YT, Jao CW. Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis. Brain Sci 2023; 13:brainsci13010093. [PMID: 36672073 PMCID: PMC9856782 DOI: 10.3390/brainsci13010093] [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: 10/17/2022] [Revised: 12/18/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023] Open
Abstract
Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.
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Affiliation(s)
- Chi Ieong Lau
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan
- Dementia Center, Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan
- Applied Cognitive Neuroscience Group, Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK
- Department of Neurology, University Hospital, Taipa 999078, Macau
| | - Jiann-Horng Yeh
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan
- Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan
| | - Yuh-Feng Tsai
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei 242, Taiwan
- Department of Diagnostic Radiology, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111, Taiwan
| | - Chen-Yu Hsiao
- Department of Diagnostic Radiology, Shin Kong Wu Ho Su Memorial Hospital, Taipei 111, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (Y.-T.W.); (C.-W.J.); Tel.: +886-02-28267169 (Y.-T.W.); +886-02-28267394 (C.-W.J.)
| | - Chi-Wen Jao
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Research, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111, Taiwan
- Correspondence: (Y.-T.W.); (C.-W.J.); Tel.: +886-02-28267169 (Y.-T.W.); +886-02-28267394 (C.-W.J.)
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Zhang X, Pan J, Lin Y, Fu G, Xu P, Liang J, Ye C, Peng J, Lv X, Yang Y, Feng Y. Structural network alterations in patients with nasopharyngeal carcinoma after radiotherapy: A 1-year longitudinal study. Front Neurosci 2022; 16:1059320. [DOI: 10.3389/fnins.2022.1059320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
This longitudinal study explored the changed patterns of structural brain network after radiotherapy (RT) in patients with nasopharyngeal carcinoma (NPC). Diffusion tensor imaging (DTI) data were gathered from 35 patients with NPC at four time points: before RT (baseline), 0∼3 (acute), 6 (early delayed), and 12 months (late-delayed) after RT. The graph theory was used to characterize the dynamic topological properties after RT and the significant changes were detected over time at the global, regional and modular levels. Significantly altered regional metrics (nodal efficiency and degree centrality) were distributed in the prefrontal, temporal, parietal, frontal, and subcortical regions. The module, that exhibited a significantly altered within-module connectivity, had a high overlap with the default mode network (DMN). In addition, the global, regional and modular metrics showed a tendency of progressive decrease at the acute and early delayed stages, and a partial/full recovery at the late-delayed stage. This changed pattern illustrated that the radiation-induced brain damage began at the acute reaction stage and were aggravated at the early-delayed stage, and then partially recovered at the late-delayed stage. Furthermore, the spearman’s correlations between the abnormal nodal metrics and temporal dose were calculated and high correlations were found at the temporal (MTG.R and HES.L), subcortical (INS.R), prefrontal (ORBinf.L and ACG.L), and parietal (IPL.R) indicating that these regions were more sensitive to dose and should be mainly considered in radiotherapy treatment plan.
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Reaction-diffusion models in weighted and directed connectomes. PLoS Comput Biol 2022; 18:e1010507. [DOI: 10.1371/journal.pcbi.1010507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 11/23/2022] [Accepted: 08/22/2022] [Indexed: 11/07/2022] Open
Abstract
Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.
Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.
In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.
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Barile B, Ashtari P, Stamile C, Marzullo A, Maes F, Durand-Dubief F, Van Huffel S, Sappey-Marinier D. Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome. Front Robot AI 2022; 9:926255. [PMID: 36313252 PMCID: PMC9608344 DOI: 10.3389/frobt.2022.926255] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. Materials and Methods: A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS). Each patient was classified in one of these profiles by our neurologist and underwent longitudinal MRI examinations including T1-weighted image acquisition at each examination, from which the GM tissue was segmented and the cortical GM thickness measured. Following the GM parcellation using two different atlases (FSAverage and Glasser 2016), the morphological connectome was built and six global metrics (Betweenness Centrality (BC), Assortativity (r), Transitivity (T), Efficiency (Eg), Modularity (Q) and Density (D)) were extracted. Based on their connectivity metrics, MS profiles were first statistically compared and second, classified using four different learning machines (Logistic Regression, Random Forest, Support Vector Machine and AdaBoost), combined in a higher level ensemble model by majority voting. Finally, the impact of the GM spatial resolution on the MS clinical profiles classification was analyzed. Results: Using binary comparisons between the four MS clinical profiles, statistical differences and classification performances higher than 0.7 were observed. Good performances were obtained when comparing the two early clinical forms, RRMS and PPMS (F1 score of 0.86), and the two neurodegenerative profiles, PPMS and SPMS (F1 score of 0.72). When comparing the two atlases, slightly better performances were obtained with the Glasser 2016 atlas, especially between RRMS with PPMS (F1 score of 0.83), compared to the FSAverage atlas (F1 score of 0.69). Also, the thresholding value for graph binarization was investigated suggesting more informative graph properties in the percentile range between 0.6 and 0.8. Conclusion: An automated pipeline was proposed for the classification of MS clinical profiles using six global graph metrics extracted from the GM morphological connectome of MS patients. This work demonstrated that GM morphological connectivity data could provide good classification performances by combining four simple ML models, without the cost of long and complex MR techniques, such as MR diffusion, and/or deep learning architectures.
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Affiliation(s)
- Berardino Barile
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Pooya Ashtari
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | | | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende, Italy
| | - Frederik Maes
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - Françoise Durand-Dubief
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- Hôpital Neurologique, Service de Neurologie, Hospices Civils de Lyon, Bron, France
| | | | - Dominique Sappey-Marinier
- CREATIS (UMR 5220 CNRS & U1294 INSERM), Université Claude Bernard Lyon1, INSA-Lyon, Université de Lyon, Lyon, France
- CERMEP–Imagerie du Vivant, Université de Lyon, Lyon, France
- *Correspondence: Dominique Sappey-Marinier,
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32
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Lei D, Li W, Tallman MJ, Strakowski SM, DelBello MP, Rodrigo Patino L, Fleck DE, Lui S, Gong Q, Sweeney JA, Strawn JR, Nery FG, Welge JA, Rummelhoff E, Adler CM. Changes in the structural brain connectome over the course of a nonrandomized clinical trial for acute mania. Neuropsychopharmacology 2022; 47:1961-1968. [PMID: 35585125 PMCID: PMC9485114 DOI: 10.1038/s41386-022-01328-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/17/2022] [Accepted: 04/11/2022] [Indexed: 02/05/2023]
Abstract
Disrupted topological organization of brain functional networks has been widely reported in bipolar disorder. However, the potential clinical implications of structural connectome abnormalities have not been systematically investigated. The present study included 109 unmedicated subjects with acute mania who were assigned to 8 weeks of treatment with quetiapine or lithium and 60 healthy controls. High resolution 3D-T1 weighted magnetic resonance images (MRI) were collected from both groups at baseline, week 1 and week 8. Brain networks were constructed based on the similarity of morphological features across brain regions and analyzed using graph theory approaches. At baseline, individuals with bipolar disorder illness showed significantly lower clustering coefficient (Cp) (p = 0.012) and normalized characteristic path length (λ) (p = 0.004) compared to healthy individuals, as well as differences in nodal centralities across multiple brain regions. No baseline or post-treatment differences were identified between drug treatment conditions, so change after treatment were considered in the combined treatment groups. Relative to healthy individuals, differences in Cp, λ and cingulate gyrus nodal centrality were significantly reduced with treatment; changes in these parameters correlated with changes in Young Mania Rating Scale scores. Baseline structural connectome matrices significantly differentiated responder and non-responder groups at 8 weeks with 74% accuracy. Global and nodal network alterations evident at baseline were normalized with treatment and these changes associated with symptomatic improvement. Further, baseline structural connectome matrices predicted treatment response. These findings suggest that structural connectome abnormalities are clinically significant and may be useful for predicting clinical outcome of treatment and tracking drug effects on brain anatomy in bipolar disorder. CLINICAL TRIALS REGISTRATION Name: Functional and Neurochemical Brain Changes in First-episode Bipolar Mania Following Successful Treatment with Lithium or Quetiapine. URL: https://clinicaltrials.gov/ . REGISTRATION NUMBER NCT00609193. Name: Neurofunctional and Neurochemical Markers of Treatment Response in Bipolar Disorder. URL: https://clinicaltrials.gov/ . REGISTRATION NUMBER NCT00608075.
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Affiliation(s)
- Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA.
| | - Wenbin Li
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, P.R. China
- Department of the Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, P.R. China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Stephen M Strakowski
- Department of Psychiatry & Behavioral Sciences, Dell Medical School of The University of Texas at Austin, Austin, 78712, TX, USA
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - L Rodrigo Patino
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - David E Fleck
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, P.R. China
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
- Huaxi MR Research Center (HMRRC), Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, P.R. China
| | - Jeffrey R Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Fabiano G Nery
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Jeffrey A Welge
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Emily Rummelhoff
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
| | - Caleb M Adler
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA
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Wang Y, Li Y, Yang L, Huang W. Altered topological organization of resting-state functional networks in children with infantile spasms. Front Neurosci 2022; 16:952940. [PMID: 36248635 PMCID: PMC9562010 DOI: 10.3389/fnins.2022.952940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/14/2022] [Indexed: 11/15/2022] Open
Abstract
Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers.
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Affiliation(s)
- Ya Wang
- School of Basic Medical Sciences, Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, National Key Discipline of Human Anatomy, Southern Medical University, Guangzhou, China
| | - Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Lin Yang
- Department of Anesthesiology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- School of Basic Medical Sciences, Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, National Key Discipline of Human Anatomy, Southern Medical University, Guangzhou, China
- Wenhua Huang,
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34
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Shirani S, Mohebbi M. Brain functional connectivity analysis in patients with relapsing-remitting multiple sclerosis: A graph theory approach of EEG resting state. Front Neurosci 2022; 16:801774. [PMID: 36161167 PMCID: PMC9500502 DOI: 10.3389/fnins.2022.801774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease related to the central nervous system (CNS). This study aims to investigate the effects of MS on the brain's functional connectivity network using the electroencephalogram (EEG) resting-state signals and graph theory approach. Resting-state eyes-closed EEG signals were recorded from 20 patients with relapsing-remitting MS (RRMS) and 18 healthy cases. In this study, the prime objective is to calculate the connectivity between EEG channels to assess the differences in brain functional network global features. The results demonstrated lower cortical activity in the alpha frequency bands and higher activity for the gamma frequency bands in patients with RRMS compared to the healthy group. In this study, graph metric calculations revealed a significant difference in the diameter of the functional brain network based on the directed transfer function (DTF) measure between the two groups, indicating a higher diameter in RRMS cases for the alpha frequency band. A higher diameter for the functional brain network in MS cases can result from anatomical damage. In addition, considerable differences between the networks' global efficiency and transitivity based on the imaginary part of the coherence (iCoh) measure were observed, indicating higher global efficiency and transitivity in the delta, theta, and beta frequency bands for RRMS cases, which can be related to the compensatory functional reaction from the brain. This study indicated that in RRMS cases, some of the global characteristics of the brain's functional network, such as diameter and global efficiency, change and can be illustrated even in the resting-state condition when the brain is not under cognitive load.
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Affiliation(s)
- Sepehr Shirani
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
- Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom
| | - Maryam Mohebbi
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
- *Correspondence: Maryam Mohebbi
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35
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Ashtiani SNM, Behnam H, Daliri MR. Diagnosis of Multiple Sclerosis Using Graph-Theoretic Measures of Cognitive-Task-Based Functional Connectivity Networks. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3081605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Seyedeh Naghmeh Miri Ashtiani
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Hamid Behnam
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Mohammad Reza Daliri
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
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36
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Yang J, Lei D, Suo X, Tallman MJ, Qin K, Li W, Bruns KM, Blom TJ, Duran LRP, Cotton S, Sweeney JA, Gong Q, DelBello MP. A preliminary study of the effects of mindfulness-based cognitive therapy on structural brain networks in mood-dysregulated youth with a familial risk for bipolar disorder. Early Interv Psychiatry 2022; 16:1011-1019. [PMID: 34808702 DOI: 10.1111/eip.13245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/17/2021] [Accepted: 11/07/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mindfulness-based cognitive therapy for children (MBCT-C), as a psychotherapeutic intervention, has been shown to be effective for treating mood dysregulation (MD). While previous neuroimaging studies of MD have reported both pre-treatment structural and functional alterations, the effects of MBCT-C on brain morphological network organisation has not been investigated. METHODS We investigated brain morphological network organisation in 10 mood-dysregulated youth with familial risk for bipolar disorder and 15 matched healthy comparison youth (HC). Effects of 12 weeks of MBCT-C were examined in the mood-dysregulated youth. Topological properties of brain networks used for analyses were constructed based on morphological similarities in regional grey matter using a graph-theory approach using MRI data. RESULTS At baseline, compared with the HC group, the mood-dysregulated group exhibited increased global efficiency (Eglob ), decreased path length (Lp ), and abnormal nodal properties, mainly in the limbic system. Right temporal pole alterations at baseline predicted change in Child and Adolescent Mindfulness Measure scores after treatment. The mood-dysregulated group showed significant decreases in both the Eglob and Lp metrics after MBCT-C, suggesting an improved capacity for optimal information processing. Changes in Lp were correlated with changes in Emotion Regulation Checklist scores. Our results show significant topological alterations in the mood-dysregulated group as compared to controls at baseline. After MBCT-C, disrupted topological properties in the mood-dysregulated group were significantly reduced. CONCLUSION MBCT-C may facilitate clinically meaningful changes in the brain structural network in mood-dysregulated individuals.
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Affiliation(s)
- Jing Yang
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Maxwell J Tallman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Kaitlyn M Bruns
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Thomas J Blom
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Luis Rodrigo Patino Duran
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Sian Cotton
- Department of Family and Community Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Departments of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi Xiamen Hospital of Sichuan University, Xiamen, China
| | - Melissa P DelBello
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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37
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Kato S, Bagarinao E, Isoda H, Koyama S, Watanabe H, Maesawa S, Hara K, Katsuno M, Naganawa S, Ozaki N, Sobue G. Reproducibility of functional connectivity metrics estimated from resting-state functional MRI with differences in days, coils, and global signal regression. Radiol Phys Technol 2022; 15:298-310. [PMID: 35960494 DOI: 10.1007/s12194-022-00670-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 08/03/2022] [Accepted: 08/04/2022] [Indexed: 11/26/2022]
Abstract
In multisite studies, differences in imaging acquisition systems could affect the reproducibility of the results when examining changes in brain function using resting-state functional magnetic resonance imaging (rs-fMRI). This is also important for longitudinal studies, in which changes in equipment settings can occur. This study examined the reproducibility of functional connectivity (FC) metrics estimated from rs-fMRI data acquired using scanner receiver coils with different numbers of channels. This study involved 80 rs-fMRI datasets from 20 healthy volunteers scanned in two independent imaging sessions using both 12- and 32-channel coils for each session. We used independent component analysis (ICA) to evaluate the FC of canonical resting-state networks (RSNs) and graph theory to calculate several whole-brain network metrics. The effect of global signal regression (GSR) as a preprocessing step was also considered. Comparisons within and between receiver coils were performed. Irrespective of the GSR, RSNs derived from rs-fMRI data acquired using the same receiver coil were reproducible, but not from different receiver coils. However, both the GSR and the channel count of the receiver coil have discernible effects on the reproducibility of network metrics estimated using whole-brain network analysis. The data acquired using the 32-channel coil tended to have better reproducibility than those acquired using the 12-channel coil. Our findings suggest that the reproducibility of FC metrics estimated from rs-fMRI data acquired using different receiver coils showed some level of dependence on the preprocessing method and the type of analysis performed.
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Affiliation(s)
- Sanae Kato
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Epifanio Bagarinao
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan.
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
| | - Haruo Isoda
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Shuji Koyama
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko Minami, Higashi-ku, Nagoya, Aichi, 461-8673, Japan
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Satoshi Maesawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Masahisa Katsuno
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Norio Ozaki
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Neurology, Aichi Medical University, Nagakute, Aichi, Japan
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38
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Wang X, Lu K, He Y, Gao Z, Hao N. Close spatial distance and direct gaze bring better communication outcomes and more intertwined neural networks. Neuroimage 2022; 261:119515. [PMID: 35932994 DOI: 10.1016/j.neuroimage.2022.119515] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/31/2022] [Accepted: 07/24/2022] [Indexed: 10/31/2022] Open
Abstract
Non-verbal cues tone our communication. Previous studies found that non-verbal factors, such as spatial distance and gaze direction, significantly impact interpersonal communication. However, little is known about the behind multi-brain neural correlates and whether it could affect high-level creative group communication. Here, we provided a new, scalable, and neuro-based approach to explore the effects of non-verbal factors on different communication tasks, and revealed the underlying multi-brain neural correlates using fNIRS-based hyperscanning technique. Across two experiments, we found that closer spatial distance and more direct gaze angle could promote collaborative behaviors, improve both creative and non-creative communication outcomes, and enhance inter-brain neural synchronization. Moreover, compared to the non-creative communication task, participants' inter-brain network was more intertwined when performing the creative communication task. These findings suggest that close spatial distance and direct gaze serve as positive social cues, bringing interacting brains into alignment and optimizing inter-brain information transfer, thus improving communication outcomes.
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Affiliation(s)
- Xinyue Wang
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China, 200062
| | - Kelong Lu
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China, 200062
| | - Yingyao He
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China, 200062
| | - Zhenni Gao
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China, 200062
| | - Ning Hao
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China, 200062.
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Chu C, He N, Zeljic K, Zhang Z, Wang J, Li J, Liu Y, Zhang Y, Sun B, Li D, Yan F, Zhang C, Liu C. Subthalamic and pallidal stimulation in Parkinson's disease induce distinct brain topological reconstruction. Neuroimage 2022; 255:119196. [PMID: 35413446 DOI: 10.1016/j.neuroimage.2022.119196] [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: 12/08/2021] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 10/18/2022] Open
Abstract
The subthalamic nucleus (STN) and globus pallidus internus (GPi) are the two most common and effective target brain areas for deep brain stimulation (DBS) treatment of advanced Parkinson's disease. Although DBS has been shown to restore functional neural circuits of this disorder, the changes in topological organization associated with active DBS of each target remain unknown. To investigate this, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 34 medication-free patients with Parkinson's disease that had DBS electrodes implanted in either the subthalamic nucleus or internal globus pallidus (n = 17 each), in both ON and OFF DBS states. Sixteen age-matched healthy individuals were used as a control group. We evaluated the regional information processing capacity and transmission efficiency of brain networks with and without stimulation, and recorded how stimulation restructured the brain network topology of patients with Parkinson's disease. For both targets, the variation of local efficiency in motor brain regions was significantly correlated (p < 0.05) with improvement rate of the Uniform Parkinson's Disease Rating Scale-III scores, with comparable improvements in motor function for the two targets. However, non-motor brain regions showed changes in topological organization during active stimulation that were target-specific. Namely, targeting the STN decreased the information transmission of association, limbic and paralimbic regions, including the inferior frontal gyrus angle, insula, temporal pole, superior occipital gyri, and posterior cingulate, as evidenced by the simultaneous decrease of clustering coefficient and local efficiency. GPi-DBS had a similar effect on the caudate and lenticular nuclei, but enhanced information transmission in the cingulate gyrus. These effects were not present in the DBS-OFF state for GPi-DBS, but persisted for STN-DBS. Our results demonstrate that DBS to the STN and GPi induce distinct brain network topology reconstruction patterns, providing innovative theoretical evidence for deciphering the mechanism through which DBS affects disparate targets in the human brain.
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Affiliation(s)
- Chunguang Chu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kristina Zeljic
- School of Health Sciences, City, University of London, London, EC1V 0HB, UK
| | - Zhen Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jun Li
- School of Information Science and Technology, Shanghai Tech University, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dianyou Li
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Research Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
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Lapucci C, Schiavi S, Signori A, Sbragia E, Bommarito G, Cellerino M, Uccelli A, Inglese M, Roccatagliata L, Pardini M. The role of disconnection in explaining disability in multiple sclerosis. Eur Radiol Exp 2022; 6:23. [PMID: 35672589 PMCID: PMC9174414 DOI: 10.1186/s41747-022-00277-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 04/14/2022] [Indexed: 11/25/2022] Open
Abstract
Background In multiple sclerosis, the correlation between white matter lesion volumes (LV) and expanded disability status scale (EDSS) is at best moderate, leading to the “clinico-radiological paradox”, influenced by many factors, including the lack of information on the spatial localisation of each lesion on synthetic metrics such as LV. We used a probabilistic approach to provide the volume of WM tracts that may be disconnected by lesions and to evaluate its correlation with EDSS. Methods Forty-five patients (aged 37.4 ± 6.8 years, mean ± standard deviation; 30 females; 29 relapsing-remitting, 16 progressive) underwent 3-T magnetic resonance imaging. Both LV and the volume of the tracts crossing the lesioned regions (disconnectome volume, DV) were calculated using BCBtoolkit and correlated with EDSS. Results T1-weighted LV and DV significantly correlated with EDSS (p ≤ 0.006 r ≥ 0.413) as it was for T2-weighted LV and T2-weighted DV (p ≤ 0.004 r ≥ 0.430), but only T1-weighetd and T2-weighted DVs were EDSS significant predictors (p ≤ 0.001). The correlations of T1-weighted and T2-weighted LV with EDSS were significantly mediated by DV, while no effect of LV on the EDSS-DV correlation was observed. Conclusion The volume of disconnected WM bundles mediates the LV-EDSS correlation, representing the lonely EDSS predictor.
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Affiliation(s)
- Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy. .,IRRCS Ospedale Policlinico San Martino, Largo P. Daneo, 3, 16132, Genoa, Italy.
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Alessio Signori
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Elvira Sbragia
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Giulia Bommarito
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maria Cellerino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy
| | - Antonio Uccelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,IRRCS Ospedale Policlinico San Martino, Largo P. Daneo, 3, 16132, Genoa, Italy
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,IRRCS Ospedale Policlinico San Martino, Largo P. Daneo, 3, 16132, Genoa, Italy
| | - Luca Roccatagliata
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy. .,Department of Neuroradiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
| | - Matteo Pardini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy.,IRRCS Ospedale Policlinico San Martino, Largo P. Daneo, 3, 16132, Genoa, Italy
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Wang X, Zhang Y, He Y, Lu K, Hao N. Dynamic Inter-Brain Networks Correspond With Specific Communication Behaviors: Using Functional Near-Infrared Spectroscopy Hyperscanning During Creative and Non-creative Communication. Front Hum Neurosci 2022; 16:907332. [PMID: 35721354 PMCID: PMC9201441 DOI: 10.3389/fnhum.2022.907332] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/16/2022] [Indexed: 11/30/2022] Open
Abstract
Social interaction is a dynamic and variable process. However, most hyperscanning studies implicitly assume that inter-brain synchrony (IBS) is constant and rarely investigate the temporal variability of the multi-brain networks. In this study, we used sliding windows and k-mean clustering to obtain a set of representative inter-brain network states during different group communication tasks. By calculating the network parameters and temporal occurrence of the inter-brain states, we found that dense efficient interbrain states and sparse inefficient interbrain states appeared alternately and periodically, and the occurrence of efficient interbrain states was positively correlated with collaborative behaviors and group performance. Moreover, compared to common communication, the occurrence of efficient interbrain states and state transitions were significantly higher during creative communication, indicating a more active and intertwined neural network. These findings may indicate that there is a close correspondence between inter-brain network states and social behaviors, contributing to the flourishing literature on group communication.
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Liao Y, Li X, Jia F, Jiang Y, Ning G, Li X, Fu C, Zhou H, He X, Cai X, Qu H. The Alternation of Gray Matter Morphological Topology in Drug-Naïve Tourette's Syndrome in Children. Front Aging Neurosci 2022; 14:873148. [PMID: 35693336 PMCID: PMC9184754 DOI: 10.3389/fnagi.2022.873148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022] Open
Abstract
Tourette syndrome (TS) is a neurodevelopment disorder characterized by motor and phonic tics. We investigated the topological alterations in pediatric TS using morphological topological analysis of brain structures. We obtained three-dimensional T1-weighted magnetic resonance imaging (MRI) sequences from 59 drug-naïve pediatric patients with TS and 87 healthy controls. We identified morphological topographical alterations in the brains of patients with TS compared to those of the healthy controls via GRETNA software. At the global level, patients with TS exhibited increased global efficiency (E glob ) (p = 0.012) and decreased normalized characteristic path length (λ) (p = 0.027), and characteristic path length (Lp) (p = 0.025) compared to healthy controls. At the nodal level, we detected significant changes in the nodal betweenness, nodal degree, and nodal efficiency in the cerebral cortex-striatum-thalamus-cortex circuit. These changes mainly involved the bilateral caudate nucleus, left thalamus, and gyri related to tics. Nodal betweenness, nodal degree, and nodal efficiency in the right superior parietal gyrus were negatively correlated with the motor tic scores of the Yale Global Tic Severity Scale (YGTSS) (r = -0.328, p = 0.011; r = -0.310, p = 0.017; and r = -0.291, and p = 0.025, respectively). In contrast, nodal betweenness, nodal degree, and nodal efficiency in the right posterior cingulate gyrus were positively correlated with the YGTSS phonic tic scores (r = 0.353, p = 0.006; r = 0.300, p = 0.021; r = 0.290, and p = 0.026, respectively). Nodal betweenness in the right supplementary motor area was positively correlated with the YGTSS phonic tic scores (r = 0.348, p = 0.007). The nodal degree in the right supplementary motor area was positively correlated with the YGTSS phonic tic scores (r = 0.259, p = 0.048). Diagnosis by age interactions did not display a significant effect on brain network properties at either the global or nodal level. Overall, our findings showed alterations in the gray matter morphological networks in drug-naïve children with TS. These findings enhance our understanding of the structural topology of the brain in patients with TS and provide useful clues for exploring imaging biomarkers of TS.
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Affiliation(s)
- Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Xiuli Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Yuexin Jiang
- Department of Radiology, Chengdu Office Hospital of People’s Government of Tibet Autonomous Region, Chengdu, China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Chuan Fu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Hui Zhou
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
- Department of Rehabilitation, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Xuejia He
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Xiaotang Cai
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
- Department of Rehabilitation, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
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Hua JC, Xu XM, Xu ZG, Xu JJ, Hu JH, Xue Y, Wu Y. Aberrant Functional Network of Small-World in Sudden Sensorineural Hearing Loss With Tinnitus. Front Neurosci 2022; 16:898902. [PMID: 35663555 PMCID: PMC9160300 DOI: 10.3389/fnins.2022.898902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022] Open
Abstract
Few researchers investigated the topological properties and relationships with cognitive deficits in sudden sensorineural hearing loss (SNHL) with tinnitus. To explore the topological characteristics of the brain connectome following SNHL from the global level and nodal level, we recruited 36 bilateral SNHL patients with tinnitus and 37 well-matched healthy controls. Every subject underwent pure tone audiometry tests, neuropsychological assessments, and MRI scanning. AAL atlas was employed to divide a brain into 90 cortical and subcortical regions of interest, then investigated the global and nodal properties of “small world” network in SNHL and control groups using a graph-theory analysis. The global characteristics include small worldness, cluster coefficient, characteristic path length, local efficiency, and global efficiency. Node properties include degree centrality, betweenness centrality, nodal efficiency, and nodal clustering coefficient. Interregional connectivity analysis was also computed among 90 nodes. We found that the SNHL group had significantly higher hearing thresholds and cognitive impairments, as well as disrupted internal connections among 90 nodes. SNHL group displayed lower AUC of cluster coefficient and path length lambda, but increased global efficiency. The opercular and triangular parts of the inferior frontal gyrus, rectus gyrus, parahippocampal gyrus, precuneus, and amygdala showed abnormal local features. Some of these connectome alterations were correlated with cognitive ability and the duration of SNHL. This study may prove potential imaging biomarkers and treatment targets for future studies.
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Affiliation(s)
- Jin-Chao Hua
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
| | - Xiao-Min Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Zhen-Gui Xu
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jing-Hua Hu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuan Xue
- Department of Otolaryngology, Nanjing Pukou Central Hospital, Pukou Branch Hospital of Jiangsu Province Hospital, Nanjing, China
- *Correspondence: Yuan Xue,
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- Yuanqing Wu,
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Li YL, Wu JJ, Ma J, Li SS, Xue X, Wei D, Shan CL, Hua XY, Zheng MX, Xu JG. Alteration of the Individual Metabolic Network of the Brain Based on Jensen-Shannon Divergence Similarity Estimation in Elderly Patients With Type 2 Diabetes Mellitus. Diabetes 2022; 71:894-905. [PMID: 35133397 DOI: 10.2337/db21-0600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 02/03/2022] [Indexed: 11/13/2022]
Abstract
The aim of this study was to investigate the interactive effect between aging and type 2 diabetes mellitus (T2DM) on brain glucose metabolism, individual metabolic connectivity, and network properties. Using a 2 × 2 factorial design, 83 patients with T2DM (40 elderly and 43 middle-aged) and 69 sex-matched healthy control subjects (HCs) (34 elderly and 35 middle-aged) underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance scanning. Jensen-Shannon divergence was applied to construct individual metabolic connectivity and networks. The topological properties of the networks were quantified using graph theoretical analysis. The general linear model was used to mainly estimate the interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and network. There was an interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and regional metabolic network properties (all P < 0.05). The post hoc analyses showed that compared with elderly HCs and middle-aged patients with T2DM, elderly patients with T2DM had decreased glucose metabolism, increased metabolic connectivity, and regional metabolic network properties in cognition-related brain regions (all P < 0.05). Age and fasting plasma glucose had negative correlations with glucose metabolism and positive correlations with metabolic connectivity. Elderly patients with T2DM had glucose hypometabolism, strengthened functional integration, and increased efficiency of information communication mainly located in cognition-related brain regions. Metabolic connectivity pattern changes might be compensatory changes for glucose hypometabolism.
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Affiliation(s)
- Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Si-Si Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Chao CC, Hsieh PC, Janice Lin CH, Huang SL, Hsieh ST, Chiang MC. Impaired brain network architecture as neuroimaging evidence of pain in diabetic neuropathy. Diabetes Res Clin Pract 2022; 186:109833. [PMID: 35314258 DOI: 10.1016/j.diabres.2022.109833] [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: 10/26/2021] [Revised: 02/14/2022] [Accepted: 03/16/2022] [Indexed: 11/17/2022]
Abstract
AIMS To investigate alterations in structural brain networks due to chronic diabetic neuropathic pain. METHODS The current study recruited 24 patients with painful diabetic neuropathy (PDN) to investigate the influences of chronic pain on the brain. Thirteen patients with painless diabetic neuropathy (PLDN) and 24 healthy adults were recruited as disease and healthy controls. White matter connectivity of the brain networks constructed by diffusion tractography was compared across groups using the Network-based statistic (NBS) method. Graph theoretical analysis was further applied to assess topological changes of the brain networks. RESULTS The PDN patients had a significant reduction in white matter connectivity compared with PLDN and controls in the limbic and temporal regions, particularly the insula, hippocampus and parahippocampus, the amygdala, and the middle temporal gyrus. The PDN patients also exhibited an altered topology of the brain networks with reduced global efficiency and betweenness centrality. CONCLUSION The current findings indicate that topological alterations of brain networks may serve as a biomarker for pain-induced maladaptive reorganization of the brain in PDN. Given the high prevalence of diabetes worldwide, novel insights from network sciences to investigate the central mechanisms of diabetic neuropathic pain are warranted.
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Affiliation(s)
- Chi-Chao Chao
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
| | - Paul-Chen Hsieh
- Department of Dermatology, National Taiwan University Hospital, Taipei, Taiwan
| | - Chien-Ho Janice Lin
- Department of Physical Therapy and Assistive Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan; Yeong-An Orthopedic and Physical Therapy Clinic, Taipei, Taiwan
| | - Shin-Leh Huang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan.
| | - Sung-Tsang Hsieh
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan; Department of Anatomy and Cell Biology, National Taiwan University College of Medicine, Taipei, Taiwan; Center of Precision Medicine, National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Ming-Chang Chiang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Zhang S, Xu X, Li Q, Chen J, Liu S, Zhao W, Cai H, Zhu J, Yu Y. Brain Network Topology and Structural–Functional Connectivity Coupling Mediate the Association Between Gut Microbiota and Cognition. Front Neurosci 2022; 16:814477. [PMID: 35422686 PMCID: PMC9002058 DOI: 10.3389/fnins.2022.814477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Increasing evidence indicates that gut microbiota can influence cognition via the gut–brain axis, and brain networks play a critical role during the process. However, little is known about how brain network topology and structural–functional connectivity (SC–FC) coupling contribute to gut microbiota-related cognition. Fecal samples were collected from 157 healthy young adults, and 16S amplicon sequencing was used to assess gut diversity and enterotypes. Topological properties of brain structural and functional networks were acquired by diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI data), and SC–FC coupling was further calculated. 3-Back, digit span, and Go/No-Go tasks were employed to assess cognition. Then, we tested for potential associations between gut microbiota, complex brain networks, and cognition. The results showed that gut microbiota could affect the global and regional topological properties of structural networks as well as node properties of functional networks. It is worthy of note that causal mediation analysis further validated that gut microbial diversity and enterotypes indirectly influence cognitive performance by mediating the small-worldness (Gamma and Sigma) of structural networks and some nodal metrics of functional networks (mainly distributed in the cingulate gyri and temporal lobe). Moreover, gut microbes could affect the degree of SC–FC coupling in the inferior occipital gyrus, fusiform gyrus, and medial superior frontal gyrus, which in turn influence cognition. Our findings revealed novel insights, which are essential to provide the foundation for previously unexplored network mechanisms in understanding cognitive impairment, particularly with respect to how brain connectivity participates in the complex crosstalk between gut microbiota and cognition.
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Affiliation(s)
- Shujun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Xiaotao Xu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qian Li
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- *Correspondence: Jiajia Zhu,
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Research Center of Clinical Medical Imaging, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Hefei, China
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei, China
- Yongqiang Yu,
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van der Weijden CWJ, Pitombeira MS, Haveman YRA, Sanchez-Catasus CA, Campanholo KR, Kolinger GD, Rimkus CM, Buchpiguel CA, Dierckx RAJO, Renken RJ, Meilof JF, de Vries EFJ, de Paula Faria D. The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging. Insights Imaging 2022; 13:63. [PMID: 35347460 PMCID: PMC8960512 DOI: 10.1186/s13244-022-01198-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/22/2022] [Indexed: 12/03/2022] Open
Abstract
Background Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. Methods The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. Results Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. Conclusion Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01198-4.
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Zaccaro A, Piarulli A, Melosini L, Menicucci D, Gemignani A. Neural Correlates of Non-ordinary States of Consciousness in Pranayama Practitioners: The Role of Slow Nasal Breathing. Front Syst Neurosci 2022; 16:803904. [PMID: 35387390 PMCID: PMC8977447 DOI: 10.3389/fnsys.2022.803904] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/17/2022] [Indexed: 12/24/2022] Open
Abstract
The modulatory effect of nasal respiration on integrative brain functions and hence consciousness has recently been unambiguously demonstrated. This effect is sustained by the olfactory epithelium mechanical sensitivity complemented by the existence of massive projections between the olfactory bulb and the prefrontal cortex. However, studies on slow nasal breathing (SNB) in the context of contemplative practices have sustained the fundamental role of respiratory vagal stimulation, with little attention to the contribution of the olfactory epithelium mechanical stimulation. This study aims at disentangling the effects of olfactory epithelium stimulation (proper of nasal breathing) from those related to respiratory vagal stimulation (common to slow nasal and mouth breathing). We investigated the psychophysiological (cardio-respiratory and electroencephalographic parameters) and phenomenological (perceived state of consciousness) aftereffects of SNB (epithelium mechanical – 2.5 breaths/min) in 12 experienced meditators. We compared the nasal breathing aftereffects with those observed after a session of mouth breathing at the same respiratory rate and with those related to a resting state condition. SNB induced (1) slowing of electroencephalography (EEG) activities (delta-theta bands) in prefrontal regions, (2) a widespread increase of theta and high-beta connectivity complemented by an increase of phase-amplitude coupling between the two bands in prefrontal and posterior regions belonging to the Default Mode Network, (3) an increase of high-beta networks small-worldness. (4) a higher perception of being in a non-ordinary state of consciousness. The emerging scenario strongly suggests that the effects of SNB, beyond the relative contribution of vagal stimulation, are mainly ascribable to olfactory epithelium stimulation. In conclusion, slow Pranayama breathing modulates brain activity and hence subjective experience up to the point of inducing a non-ordinary state of consciousness.
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Affiliation(s)
- Andrea Zaccaro
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
- Department of Neuroscience, Imaging and Clinical Sciences, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Andrea Piarulli
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
- Giga Consciousness, Coma Science Group, University of Liège, Liège, Belgium
- *Correspondence: Andrea Piarulli,
| | - Lorenza Melosini
- Pneumology Branch, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Danilo Menicucci
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
| | - Angelo Gemignani
- Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, Italy
- Clinical Psychology Branch, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
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Scharwächter L, Schmitt FJ, Pallast N, Fink GR, Aswendt M. Network analysis of neuroimaging in mice. Neuroimage 2022; 253:119110. [PMID: 35311664 DOI: 10.1016/j.neuroimage.2022.119110] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/01/2022] [Accepted: 03/15/2022] [Indexed: 10/18/2022] Open
Abstract
Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.
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Affiliation(s)
- Leon Scharwächter
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Felix J Schmitt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; University of Cologne, Institute of Zoology, Dept. of Computational Systems Neuroscience, Cologne, Germany
| | - Niklas Pallast
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany
| | - Markus Aswendt
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Dept. of Neurology, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Germany.
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50
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Frieske J, Pareto D, García-Vidal A, Cuypers K, Meesen RL, Alonso J, Arévalo MJ, Galán I, Renom M, Vidal-Jordana Á, Auger C, Montalban X, Rovira À, Sastre-Garriga J. Can cognitive training reignite compensatory mechanisms in advanced multiple sclerosis patients? An explorative morphological network approach. Neuroscience 2022; 495:86-96. [DOI: 10.1016/j.neuroscience.2022.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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