1
|
Desrosiers J, Caron-Desrochers L, René A, Gaudet I, Pincivy A, Paquette N, Gallagher A. Functional connectivity development in the prenatal and neonatal stages measured by functional magnetic resonance imaging: A systematic review. Neurosci Biobehav Rev 2024; 163:105778. [PMID: 38936564 DOI: 10.1016/j.neubiorev.2024.105778] [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: 12/07/2023] [Revised: 04/28/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
The prenatal and neonatal periods are two of the most important developmental stages of the human brain. It is therefore crucial to understand normal brain development and how early connections are established during these periods, in order to advance the state of knowledge on altered brain development and eventually identify early brain markers of neurodevelopmental disorders and diseases. In this systematic review (Prospero ID: CRD42024511365), we compiled resting state functional magnetic resonance imaging (fMRI) studies in healthy fetuses and neonates, in order to outline the main characteristics of typical development of the functional brain connectivity during the prenatal and neonatal periods. A systematic search of five databases identified a total of 12 573 articles. Of those, 28 articles met pre-established selection criteria based determined by the authors after surveying and compiling the major limitations reported within the literature. Inclusion criteria were: (1) resting state studies; (2) presentation of original results; (3) use of fMRI with minimum one Tesla; (4) a population ranging from 20 weeks of GA to term birth (around 37-42 weeks of PMA); (5) singleton pregnancy with normal development (absence of any complications known to alter brain development). Exclusion criteria were: (1) preterm studies; (2) post-mortem studies; (3) clinical or pathological studies; (4) twin studies; (5) papers with a sole focus on methodology (i.e. focused on tool and analysis development); (6) volumetric studies; (7) activation map studies; (8) cortical analysis studies; (9) conference papers. A risk of bias assessment was also done to evaluate each article's methodological rigor. 1877 participants were included across all the reviewed articles. Results consistently revealed a developmental gradient of increasing functional brain connectivity from posterior to anterior regions and from proximal-to-distal regions. A decrease in local small-world organization shortly after birth was also observed; small-world characteristics were present in fetuses and newborns, but appeared weaker in the latter group. Also, the posterior-to-anterior gradient could be associated with earlier development of the sensorimotor networks in the posterior regions while more complex higher-order networks (e.g. attention-related) mature later in the anterior regions. The main limitations of this systematic review stem from the inherent limitations of functional imaging in fetuses, mainly: unevenly distributed populations and limited sample sizes; fetal movements in the womb and other imaging obstacles; and a large voxel resolution when imaging a small brain. Another limitation specific to this review is the relatively small number of included articles compared to very a large search result, which may have led to relevant articles having been overlooked.
Collapse
Affiliation(s)
- Jérémi Desrosiers
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; School of Psychoeducation, University of Montreal, QC, Canada
| | - Laura Caron-Desrochers
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Andréanne René
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Isabelle Gaudet
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Health Sciences, Université du Québec à Chicoutimi, QC, Canada
| | - Alix Pincivy
- Sainte-Justine University Health Center and Research Center Libraries, Montreal, QC, Canada
| | - Natacha Paquette
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada
| | - Anne Gallagher
- Neurodevelopmental Optical Imaging Laboratory (LIONLAB), Sainte-Justine University Hospital Research Center, Montreal, QC, Canada; Department of Psychology, University of Montreal, QC, Canada.
| |
Collapse
|
2
|
Zhou Y, Jing J, Zhang Z, Pan Y, Cai X, Zhu W, Li Z, Liu C, Liu H, Meng X, Cheng J, Wang Y, Li H, Wang S, Niu H, Wen W, Sachdev PS, Wei T, Liu T, Wang Y. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study. Hum Brain Mapp 2024; 45:e26598. [PMID: 38339955 PMCID: PMC10839741 DOI: 10.1002/hbm.26598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.
Collapse
Affiliation(s)
- Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jing Jing
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xueli Cai
- Department of Neurology, Lishui HospitalZhejiang University School of MedicineLishuiZhejiangChina
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Xia Meng
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Hao Li
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of MedicineLishuiZhejiangChina
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Wei Wen
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Tiemin Wei
- Department of Cardiology, Lishui HospitalZhejiang University School of MedicineZhejiangChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Research Unit of Artificial Intelligence in Cerebrovascular DiseaseChinese Academy of Medical Sciences, 2019RU018BeijingChina
| |
Collapse
|
3
|
Li MY, Zhang YT, Zhou WX. Temporal rich club phenomenon and its formation mechanisms. Phys Rev E 2024; 109:014126. [PMID: 38366487 DOI: 10.1103/physreve.109.014126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 01/03/2024] [Indexed: 02/18/2024]
Abstract
The temporal rich club (TRC) phenomenon is widespread in real systems, forming a tight and continuous collection of the prominent nodes that control the system. However, there is still a lack of sufficient understanding of the mechanisms of TRC formation. Here we use the international N-nutrient trade network as an example of an in-depth identification, analysis, and modeling of its TRC phenomenon. The system exhibits a statistically significant TRC phenomenon, with eight economies forming the cornerstone club. Our analysis reveals that node degree is the most influential factor in TRC formation compared to other variables. The mathematical evolution models we constructed propose that the TRC in the N-nutrient trade network arises from the coexistence of degree-homophily and path-dependence mechanisms. By comprehending these mechanisms, we introduce a different perspective on TRC formation. Although our analysis is limited to the international trade system, the methodology can be extended to analyze the mechanisms underlying TRC emergence in other systems.
Collapse
Affiliation(s)
- Mu-Yao Li
- School of Business, East China University of Science and Technology, Shanghai 200237, China
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
| | - Yin-Ting Zhang
- School of Business, East China University of Science and Technology, Shanghai 200237, China
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
| | - Wei-Xing Zhou
- School of Business, East China University of Science and Technology, Shanghai 200237, China
- Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China
- School of Mathematics, East China University of Science and Technology, Shanghai 200237, China
| |
Collapse
|
4
|
Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JSW, Schiavi S, Jin H, Tavakol S, Vos de Wael R, Rodriguez-Cruces R, Pike GB, Bernhardt BC, Daducci A, Misic B, Tardif CL. The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Netw Neurosci 2023; 7:1363-1388. [PMID: 38144691 PMCID: PMC10697181 DOI: 10.1162/netn_a_00330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/19/2023] [Indexed: 12/26/2023] Open
Abstract
A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.
Collapse
Affiliation(s)
- Mark C. Nelson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jessica Royer
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Wen Da Lu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ilana R. Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jennifer S. W. Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Hyerang Jin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Shahin Tavakol
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Raul Rodriguez-Cruces
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - G. Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Boris C. Bernhardt
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | | | - Bratislav Misic
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Christine L. Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| |
Collapse
|
5
|
Eussen MJ, Jansen JF, Voncken TP, Debeij-Van Hall MH, Hendriksen JG, Vermeulen RJ, Klinkenberg S, Backes WH, Drenthen GS. Exploring the core network of the structural covariance network in childhood absence epilepsy. Heliyon 2023; 9:e22657. [PMID: 38107302 PMCID: PMC10724663 DOI: 10.1016/j.heliyon.2023.e22657] [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: 02/09/2023] [Revised: 10/04/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023] Open
Abstract
Childhood absence epilepsy (CAE) is a generalized pediatric epilepsy, which is generally considered to be a benign condition since most children become seizure-free before reaching adulthood. However, cognitive deficits and changes of brain morphological have been previously reported in CAE. These morphological changes, even if they might be very subtle, are not independent due to the underlying network structure and can be captured by the structural covariance network (SCN). In this study, SCNs were used to quantify the structural brain network for children with CAE as well as controls. Seventeen children with CAE (6-12y) and fifteen controls (6-12y) were included. To estimate the SCN, T1-weighted images were acquired and parcellated into 68 cortical regions. Graph measures characterizing the core network architecture, i.e. the assortativity and rich-club coefficient, were calculated for all individuals. Multivariable linear regression models, including age and sex as covariates, were used to assess differences between children with CAE and controls. Additionally, potential relations between the core network and cognitive performance was investigated. A lower assortativity (i.e. less efficiently organized core network organization) was found for children with CAE compared to controls. Moreover, better cognitive performance was found to relate to stronger assortative mixing pattern (i.e. more efficient core network structure). Rich-club coefficients did not differ between groups, nor relate to cognitions. The core network organization of the SCN in children with CAE tend to be less efficient organized compared to controls, and relates to cognitive performance, and therefore this study provides novel insights into the SCN organization in relation to CAE and cognition.
Collapse
Affiliation(s)
- Merel J.A. Eussen
- Department of Biomedical Technology, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jacobus F.A. Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Twan P.C. Voncken
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, the Netherlands
| | | | - Jos G.M. Hendriksen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
- Epilepsy Center Kempenhaeghe, Heeze, the Netherlands
| | - R. Jeroen Vermeulen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Sylvia Klinkenberg
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H. Backes
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Gerhard S. Drenthen
- Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| |
Collapse
|
6
|
Long Z, Chen D, Lei X. Enhanced rich club connectivity in mild or moderate depression after nonpharmacological treatment: A preliminary study. Brain Behav 2023; 13:e3198. [PMID: 37680015 PMCID: PMC10570500 DOI: 10.1002/brb3.3198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/14/2023] [Accepted: 07/21/2023] [Indexed: 09/09/2023] Open
Abstract
INTRODUCTION It has been suggested that the rich club organization in major depressive disorder (MDD) was altered. However, it remained unclear whether the rich club organization could be served as a biomarker that predicted the improvement of clinical symptoms in MDD. METHODS The current study included 29 mild or moderate patients with MDD, who were grouped into a treatment group (receiving cognitive behavioral therapy or real-time fMRI feedback treatment) and a no-treatment group. Resting-state MRI scans were obtained for all participants. Graph theory was employed to investigate the treatment-related changes in network properties and rich club organization. RESULTS We found that patients in the treatment group had decreased depressive symptom scores and enhanced rich club connectivity following the nonpharmacological treatment. Moreover, the changes in rich club connectivity were significantly correlated with the changes in depressive symptom scores. In addition, the nonpharmacological treatment on patients with MDD increased functional connectivity mainly among the salience network, default mode network, frontoparietal network, and subcortical network. Patients in the no-treatment group did not show significant changes in depressive symptom scores and rich club organization. CONCLUSIONS Those results suggested that the remission of depressive symptoms after nonpharmacological treatment in MDD patients was associated with the increased efficiency of global information processing.
Collapse
Affiliation(s)
- Zhiliang Long
- Sleep and NeuroImaging CenterFaculty of PsychologySouthwest UniversityChongqingP. R. China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of EducationChongqingP. R. China
| | - Danni Chen
- Sleep and NeuroImaging CenterFaculty of PsychologySouthwest UniversityChongqingP. R. China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of EducationChongqingP. R. China
| | - Xu Lei
- Sleep and NeuroImaging CenterFaculty of PsychologySouthwest UniversityChongqingP. R. China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of EducationChongqingP. R. China
| |
Collapse
|
7
|
Tilsley P, Strohmeyer IA, Heinrich I, Rosenthal F, Patra S, Schulz KH, Rosenkranz SC, Ramien C, Pöttgen J, Heesen C, Has AC, Gold SM, Stellmann JP. Physical fitness moderates the association between brain network impairment and both motor function and cognition in progressive multiple sclerosis. J Neurol 2023; 270:4876-4888. [PMID: 37341806 DOI: 10.1007/s00415-023-11806-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 06/22/2023]
Abstract
BACKGROUND Neurodegeneration leads to continuous accumulation of disability in progressive Multiple Sclerosis (MS). Exercise is considered to counteract disease progression, but little is known on the interaction between fitness, brain networks and disability in MS. OBJECTIVE The aim of this study to explore functional and structural brain connectivity and the interaction between fitness and disability based on motor and cognitive functional outcomes in a secondary analysis of a randomised, 3-month, waiting group controlled arm ergometry intervention in progressive MS. METHODS We modelled individual structural and functional brain networks based on magnetic resonance imaging (MRI). We used linear mixed effect models to compare changes in brain networks between the groups and explore the association between fitness, brain connectivity and functional outcomes in the entire cohort. RESULTS We recruited 34 persons with advanced progressive MS (pwMS, mean age 53 years, females 71%, mean disease duration 17 years and an average walking restriction of < 100 m without aid). Functional connectivity increased in highly connected brain regions of the exercise group (p = 0.017), but no structural changes (p = 0.817) were observed. Motor and cognitive task performance correlated positively with nodal structural connectivity but not nodal functional connectivity. We also found that the correlation between fitness and functional outcomes was stronger with lower connectivity. CONCLUSIONS Functional reorganisation seems to be an early indicator of exercise effects on brain networks. Fitness moderates the relationship between network disruption and both motor and cognitive outcomes, with growing importance in more disrupted brain networks. These findings underline the need and opportunities associated with exercise in advanced MS.
Collapse
Affiliation(s)
- Penelope Tilsley
- CEMEREM, APHM La Timone, 264 Rue Saint-Pierre, 13385, Marseille, France
- CNRS, CRMBM, UMR 7339, Aix-Marseille Univ, Marseille, France
| | - Isanbert Arun Strohmeyer
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Inga Heinrich
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Neurologische Klinik, Klinikum Aschaffenburg-Alzenau, Aschaffenburg, Germany
| | - Friederike Rosenthal
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Patra
- Universitäres Kompetenzzentrum für Sport- und Bewegungsmedizin (Athleticum) und Institut und Poliklinik für Medizinische Psychologie, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Heinz Schulz
- Universitäres Kompetenzzentrum für Sport- und Bewegungsmedizin (Athleticum) und Institut und Poliklinik für Medizinische Psychologie, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sina C Rosenkranz
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Caren Ramien
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jana Pöttgen
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Heesen
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Arzu Ceylan Has
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan M Gold
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
- Division of Psychosomatic Medicine, Medical Department, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Jan-Patrick Stellmann
- CEMEREM, APHM La Timone, 264 Rue Saint-Pierre, 13385, Marseille, France.
- CNRS, CRMBM, UMR 7339, Aix-Marseille Univ, Marseille, France.
- Institute of Neuroimmunology and Multiple Sclerosis (INIMS), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| |
Collapse
|
8
|
Emery BA, Hu X, Khanzada S, Kempermann G, Amin H. High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity. Biosens Bioelectron 2023; 237:115471. [PMID: 37379793 DOI: 10.1016/j.bios.2023.115471] [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: 01/05/2023] [Revised: 05/17/2023] [Accepted: 06/10/2023] [Indexed: 06/30/2023]
Abstract
Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.
Collapse
Affiliation(s)
- Brett Addison Emery
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Xin Hu
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Shahrukh Khanzada
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany
| | - Gerd Kempermann
- Research Group "Adult Neurogenesis", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany; Center for Regenerative Therapies TU Dresden (CRTD), Fetscherstraße 105, 01307, Dresden, Germany
| | - Hayder Amin
- Research Group "Biohybrid Neuroelectronics", German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, 01307, Dresden, Germany; TU Dresden, Faculty of Medicine Carl Gustav Carus, Bergstraße 53, 01069, Dresden, Germany.
| |
Collapse
|
9
|
Kim SJ, Bae YJ, Park YH, Jang H, Kim JP, Seo SW, Seong JK, Kim GH. Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease. Front Aging Neurosci 2023; 15:1209027. [PMID: 37771522 PMCID: PMC10525353 DOI: 10.3389/fnagi.2023.1209027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023] Open
Abstract
Background and objectives Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD. Methods In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis. Results We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia. Conclusion Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.
Collapse
Affiliation(s)
- Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Youn Jung Bae
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Geon Ha Kim
- Department of Neurology, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
10
|
Nazari R, Salehi M. Early development of the functional brain network in newborns. Brain Struct Funct 2023; 228:1725-1739. [PMID: 37493690 DOI: 10.1007/s00429-023-02681-4] [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: 12/24/2022] [Accepted: 07/06/2023] [Indexed: 07/27/2023]
Abstract
During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.
Collapse
Affiliation(s)
- Reza Nazari
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | - Mostafa Salehi
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
- School of Computer Science, Institute for Research in Fundamental Science (IPM), Tehran, P.O.Box 19395-5746, Iran.
| |
Collapse
|
11
|
Zdorovtsova N, Jones J, Akarca D, Benhamou E, The Calm Team, Astle DE. Exploring neural heterogeneity in inattention and hyperactivity. Cortex 2023; 164:90-111. [PMID: 37207412 DOI: 10.1016/j.cortex.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/21/2023] [Accepted: 04/04/2023] [Indexed: 05/21/2023]
Abstract
Inattention and hyperactivity are cardinal symptoms of Attention Deficit Hyperactivity Disorder (ADHD). These characteristics have also been observed across a range of other neurodevelopmental conditions, such as autism and dyspraxia, suggesting that they might best be studied across diagnostic categories. Here, we evaluated the associations between inattention and hyperactivity behaviours and features of the structural brain network (connectome) in a large transdiagnostic sample of children (Centre for Attention, Learning, and Memory; n = 383). In our sample, we found that a single latent factor explains 77.6% of variance in scores across multiple questionnaires measuring inattention and hyperactivity. Partial Least-Squares (PLS) regression revealed that variability in this latent factor could not be explained by a linear component representing nodewise properties of connectomes. We then investigated the type and extent of neural heterogeneity in a subset of our sample with clinically-elevated levels of inattention and hyperactivity. Multidimensional scaling combined with k-means clustering revealed two neural subtypes in children with elevated levels of inattention and hyperactivity (n = 232), differentiated primarily by nodal communicability-a measure which demarcates the extent to which neural signals propagate through specific brain regions. These different clusters had similar behavioural profiles, which included high levels of inattention and hyperactivity. However, one of the clusters scored higher on multiple cognitive assessment measures of executive function. We conclude that inattention and hyperactivity are so common in children with neurodevelopmental difficulties because they emerge through multiple different trajectories of brain development. In our own data, we can identify two of these possible trajectories, which are reflected by measures of structural brain network topology and cognition.
Collapse
Affiliation(s)
- Natalia Zdorovtsova
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Jonathan Jones
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Elia Benhamou
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - The Calm Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; Department of Psychiatry, University of Cambridge, Cambridge, UK
| |
Collapse
|
12
|
Saccaro LF, Gaviria J, Ville DVD, Piguet C. Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder. Brain Behav 2023; 13:e3010. [PMID: 37062926 PMCID: PMC10275545 DOI: 10.1002/brb3.3010] [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: 01/06/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/18/2023] Open
Abstract
OBJECTIVES Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent switching between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may play a key role in the neural processing of BD symptoms. However, its dynamic functional connectivity (dFC) remains unclear. Therefore, the present study explores hippocampal dFC in relation to BD symptoms. METHODS We assessed hippocampus-based dFC coactivation patterns (CAPs) on resting-state fMRI data of 25 euthymic BD patients and 25 age- and sex-matched healthy controls (HC). RESULTS Bilateral hippocampal dFC with somatomotor networks (SMN) was reduced in BD, compared to HC, while at the same time dFC between the left hippocampus and midcingulo-insular salience system (SN) was higher in BD. Correlational analysis between CAPs and clinical scores revealed that dFC between the bilateral hippocampus and the default-like network (DMN) correlated with depression scores in BD. Furthermore, pathological hyperconnectivity between the default mode network (DMN) and SMN and the frontoparietal network (FPN) was modulated by the same depression scores in BD. CONCLUSIONS Overall, we observed alterations of large-scale functional brain networks associated with decreased flexibility in cognitive control, salience detection, and emotion processing in BD. Additionally, the present study provides new insights on the neural architecture underlying a self-centered perspective on the environment in BD patients. dFC markers may improve detection, treatment, and follow-up of BD patients and of disabling residual depressive symptoms in particular.
Collapse
Affiliation(s)
- Luigi F Saccaro
- Faculty of Medicine, Psychiatry DepartmentUniversity of GenevaGenevaSwitzerland
- Psychiatry DivisionGeneva University HospitalGenevaSwitzerland
| | - Julian Gaviria
- Faculty of Medicine, Psychiatry DepartmentUniversity of GenevaGenevaSwitzerland
- Department of Basic NeurosciencesUniversity of GenevaGenevaSwitzerland
- Swiss Center for Affective SciencesCampus BiotechGenevaSwitzerland
| | - Dimitri Van De Ville
- Swiss Center for Affective SciencesCampus BiotechGenevaSwitzerland
- Faculty of Medicine, Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- Neuro‐X Institute, School of EngineeringEcole Polytechnique Fédérale de Lausanne (EPFL)GenevaSwitzerland
| | - Camille Piguet
- Faculty of Medicine, Psychiatry DepartmentUniversity of GenevaGenevaSwitzerland
- Child and Adolescence Psychiatry DivisionGeneva University HospitalGenevaSwitzerland
| |
Collapse
|
13
|
Vo A, Nguyen N, Fujita K, Schindlbeck KA, Rommal A, Bressman SB, Niethammer M, Eidelberg D. Disordered network structure and function in dystonia: pathological connectivity vs. adaptive responses. Cereb Cortex 2023; 33:6943-6958. [PMID: 36749014 PMCID: PMC10233302 DOI: 10.1093/cercor/bhad012] [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: 08/18/2022] [Revised: 12/21/2022] [Accepted: 01/10/2023] [Indexed: 02/08/2023] Open
Abstract
Primary dystonia is thought to emerge through abnormal functional relationships between basal ganglia and cerebellar motor circuits. These interactions may differ across disease subtypes and provide a novel biomarker for diagnosis and treatment. Using a network mapping algorithm based on resting-state functional MRI (rs-fMRI), a method that is readily implemented on conventional MRI scanners, we identified similar disease topographies in hereditary dystonia associated with the DYT1 or DYT6 mutations and in sporadic patients lacking these mutations. Both networks were characterized by contributions from the basal ganglia, cerebellum, thalamus, sensorimotor areas, as well as cortical association regions. Expression levels for the two networks were elevated in hereditary and sporadic dystonia, and in non-manifesting carriers of dystonia mutations. Nonetheless, the distribution of abnormal functional connections differed across groups, as did metrics of network organization and efficiency in key modules. Despite these differences, network expression correlated with dystonia motor ratings, significantly improving the accuracy of predictions based on thalamocortical tract integrity obtained with diffusion tensor MRI (DTI). Thus, in addition to providing unique information regarding the anatomy of abnormal brain circuits, rs-fMRI functional networks may provide a widely accessible method to help in the objective evaluation of new treatments for this disorder.
Collapse
Affiliation(s)
- An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Koji Fujita
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Andrea Rommal
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, New York, NY 10003, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| |
Collapse
|
14
|
Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
Collapse
Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| |
Collapse
|
15
|
Westlin C, Theriault JE, Katsumi Y, Nieto-Castanon A, Kucyi A, Ruf SF, Brown SM, Pavel M, Erdogmus D, Brooks DH, Quigley KS, Whitfield-Gabrieli S, Barrett LF. Improving the study of brain-behavior relationships by revisiting basic assumptions. Trends Cogn Sci 2023; 27:246-257. [PMID: 36739181 PMCID: PMC10012342 DOI: 10.1016/j.tics.2022.12.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 02/05/2023]
Abstract
Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.
Collapse
Affiliation(s)
| | - Jordan E Theriault
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yuta Katsumi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alfonso Nieto-Castanon
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aaron Kucyi
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Sebastian F Ruf
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
| | - Sarah M Brown
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA
| | - Misha Pavel
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA; Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Karen S Quigley
- Department of Psychology, Northeastern University, Boston, MA, USA
| | | | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
16
|
Tanglay O, Dadario NB, Chong EHN, Tang SJ, Young IM, Sughrue ME. Graph Theory Measures and Their Application to Neurosurgical Eloquence. Cancers (Basel) 2023; 15:556. [PMID: 36672504 PMCID: PMC9857081 DOI: 10.3390/cancers15020556] [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: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.
Collapse
Affiliation(s)
- Onur Tanglay
- UNSW School of Clinical Medicine, Faulty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St, New Brunswick, NJ 08901, USA
| | - Elizabeth H. N. Chong
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Si Jie Tang
- School of Medicine, University of California Davis, Sacramento, CA 95817, USA
| | - Isabella M. Young
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| |
Collapse
|
17
|
Wang M, Xu B, Hou X, Shi Q, Zhao H, Gui Q, Wu G, Dong X, Xu Q, Shen M, Cheng Q, Feng H. Altered brain networks and connections in chronic heart failure patients complicated with cognitive impairment. Front Aging Neurosci 2023; 15:1153496. [PMID: 37122379 PMCID: PMC10140296 DOI: 10.3389/fnagi.2023.1153496] [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: 01/29/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023] Open
Abstract
Objective Accumulating evidence shows that cognitive impairment (CI) in chronic heart failure (CHF) patients is related to brain network dysfunction. This study investigated brain network structure and rich-club organization in chronic heart failure patients with cognitive impairment based on graph analysis of diffusion tensor imaging data. Methods The brain structure networks of 30 CHF patients without CI and 30 CHF patients with CI were constructed. Using graph theory analysis and rich-club analysis, changes in global and local characteristics of the subjects' brain network and rich-club organization were quantitatively calculated, and the correlation with cognitive function was analyzed. Results Compared to the CHF patients in the group without CI group, the CHF patients in the group with CI group had lower global efficiency, local efficiency, clustering coefficient, the small-world attribute, and increased shortest path length. The CHF patients with CI group showed lower nodal degree centrality in the fusiform gyrus on the right (FFG.R) and nodal efficiency in the orbital superior frontal gyrus on the left (ORB sup. L), the orbital inferior frontal gyrus on the left (ORB inf. L), and the posterior cingulate gyrus on the right (PCG.R) compared with CHF patients without CI group. The CHF patients with CI group showed a smaller fiber number of edges in specific regions. In CHF patients with CI, global efficiency, local efficiency and the connected edge of the orbital superior frontal gyrus on the right (ORB sup. R) to the orbital middle frontal gyrus on the right (ORB mid. R) were positively correlated with Visuospatial/Executive function. The connected edge of the orbital superior frontal gyrus on the right to the orbital inferior frontal gyrus on the right (ORB inf. R) is positively correlated to attention/calculation. Compared with the CHF patients without CI group, the connection strength of feeder connection and local connection in CHF patients with CI group was significantly reduced, although the strength of rich-club connection in CHF patients complicated with CI group was decreased compared with the control, there was no statistical difference. In addition, the rich-club connection strength was related to the orientation (direction force) of the Montreal cognitive assessment (MoCA) scale, and the feeder and local connection strength was related to Visuospatial/Executive function of MoCA scale in the CHF patients with CI. Conclusion Chronic heart failure patients with CI exhibited lower global and local brain network properties, reduced white matter fiber connectivity, as well as a decreased strength in local and feeder connections in key brain regions. The disrupted brain network characteristics and connectivity was associated with cognitive impairment in CHF patients. Our findings suggest that impaired brain network properties and decreased connectivity, a feature of progressive disruption of brain networks, predict the development of cognitive impairment in patients with chronic heart failure.
Collapse
|
18
|
EEG emotion recognition based on PLV-rich-club dynamic brain function network. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04366-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
19
|
Kim SE, Kim HS, Kwak Y, Ahn MH, Choi KM, Min BK. Neurodynamic correlates for the cross-frequency coupled transcranial alternating current stimulation during working memory performance. Front Neurosci 2022; 16:1013691. [PMID: 36263365 PMCID: PMC9574066 DOI: 10.3389/fnins.2022.1013691] [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: 08/07/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Transcranial current stimulation is a neuromodulation technique used to modulate brain oscillations and, in turn, to enhance human cognitive function in a non-invasive manner. This study investigated whether cross-frequency coupled transcranial alternating current stimulation (CFC-tACS) improved working memory performance. Participants in both the tACS-treated and sham groups were instructed to perform a modified Sternberg task, where a combination of letters and digits was presented. Theta-phase/high-gamma-amplitude CFC-tACS was administered over electrode F3 and its four surrounding return electrodes (Fp1, Fz, F7, and C3) for 20 min. To identify neurophysiological correlates for the tACS-mediated enhancement of working memory performance, we analyzed EEG alpha and theta power, cross-frequency coupling, functional connectivity, and nodal efficiency during the retention period of the working memory task. We observed significantly reduced reaction times in the tACS-treated group, with suppressed treatment-mediated differences in frontal alpha power and unidirectional Fz-delta-phase to Oz-high-gamma-amplitude modulation during the second half of the retention period when network analyses revealed tACS-mediated fronto-occipital dissociative neurodynamics between alpha suppression and delta/theta enhancement. These findings indicate that tACS modulated top-down control and functional connectivity across the fronto-occipital regions, resulting in improved working memory performance. Our observations are indicative of the feasibility of enhancing cognitive performance by the CFC-formed tACS.
Collapse
Affiliation(s)
- Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, South Korea
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, South Korea
| | - Youngchul Kwak
- Department of Electronics Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Min-Hee Ahn
- Laboratory of Brain and Cognitive Science for Convergence Medicine, College of Medicine, Hallym University, Anyang, South Korea
| | - Kyung Mook Choi
- Institute for Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Byoung-Kyong Min
- Institute for Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Interdisciplinary Program in Brain and Cognitive Sciences, Korea University, Seoul, South Korea
- *Correspondence: Byoung-Kyong Min,
| |
Collapse
|
20
|
Targeting disrupted rich-club network organization with neuroplasticity-based computerized cognitive remediation in major depressive disorder patients. Psychiatry Res 2022; 316:114742. [PMID: 35917652 DOI: 10.1016/j.psychres.2022.114742] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022]
Abstract
Disrupted rich-club organization has been extensively studied in major depressive disorder (MDD) patients. Although data indicate that neuroplasticity-based computerized cognitive remediation (nCCR) can accelerate clinical responses in MDD patients, the mechanisms underlying its antidepressant efficacy are unknown. In this study, all MDD patients underwent two (baseline and week 4) neuropsychological assessments and DTI imaging. Additionally, 17 MDD patients did nCCR for 30 hours spread across 4 weeks. Rich-club organization was calculated with a graph-theoretical approach, and SC-FC coupling was explored. After 4 weeks of treatment, the number of rich-club connections, global efficiency, and SC-FC coupling strength increased significantly and were negatively associated with TMT-B scores. The effects of nCCR on disrupted rich-club organization may partly underlie its efficacy in improving the executive function of patients with MDD. Effects of nCCR on disrupted rich-club organization may partly underlie its efficacy in improving the executive function of patients with MDD.
Collapse
|
21
|
Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
Collapse
Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
22
|
Cui W, Wang S, Chen B, Fan G. White matter structural network alterations in congenital bilateral profound sensorineural hearing loss children: A graph theory analysis. Hear Res 2022; 422:108521. [PMID: 35660126 DOI: 10.1016/j.heares.2022.108521] [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: 07/14/2021] [Revised: 03/22/2022] [Accepted: 05/14/2022] [Indexed: 11/25/2022]
Abstract
Functional magnetic resonance imaging (fMRI) studies have revealed a functional reorganization in patients with sensorineural hearing loss (SNHL). The structural basement of functional changes has also been investigated recently. Graph theory analysis brings a new understanding of the structural connectome and topological features in central neural system diseases. However, little is known about the structural network connectome changes in SNHL patients, especially in children. We explored the differences in topologic organization, rich-club organization, and structural connection between children with congenital bilateral profound SNHL and normal hearing under the age of three using graph theory analysis and probabilistic tractography. Compared with the normal-hearing (NH) group, the SNHL group showed no difference in global and nodal topological parameters. Increased structural connection strength were found in the right cortico-striatal-thalamus-cortical circuity. Decreased cross-hemisphere connections were found between the right precuneus and the left auditory cortex as well as the left subcortical regions. Rich-club organization analysis found increased local connection in the SNHL group. These results revealed structural organizations after hearing deprivation in congenital bilateral profound SNHL children.
Collapse
Affiliation(s)
- Wenzhuo Cui
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, LN, China
| | - Shanshan Wang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, LN, China
| | - Boyu Chen
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, LN, China
| | - Guoguang Fan
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, LN, China.
| |
Collapse
|
23
|
Zhang P, Wan X, Ai K, Zheng W, Liu G, Wang J, Huang W, Fan F, Yao Z, Zhang J. Rich-club reorganization and related network disruptions are associated with the symptoms and severity in classic trigeminal neuralgia patients. Neuroimage Clin 2022; 36:103160. [PMID: 36037660 PMCID: PMC9434131 DOI: 10.1016/j.nicl.2022.103160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 07/20/2022] [Accepted: 08/18/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alterations in white matter microstructure and functional activity have been demonstrated to be involved in the central nervous system mechanism of classic trigeminal neuralgia (CTN). However, the rich-club organization and related topological alterations in the CTN brain networks remain unclear. METHODS We simultaneously collected diffusion-tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI) data from 29 patients with CTN (9 males, mean age = 54.59 years) and 34 matched healthy controls (HCs) (12 males, mean age = 54.97 years) to construct structural networks (SNs) and functional networks (FNs). Rich-club organization was determined separately based on each group's SN and different kinds of connections. For both network types, we calculated the basic connectivity properties (network density and strength) and topological properties (global/local/nodal efficiency and small worldness). Moreover, SN-FN coupling was obtained. The relationships between all those properties and clinical measures were evaluated. RESULTS Compared to their FN, the SN of CTN patients was disrupted more severely, including its topological properties (reduced network efficiency and small-worldness), and a decrease in network density and strength was observed. Patients showed reorganization of the rich-club architecture, wherein the nodes with decreased nodal efficiency in the SN were mainly non-hub regions, and the local connections were closely related to altered global efficiency and whole brain coupling. While the cortical-subcortical connections of feeder were found to be strengthened in the SN of patients, the coupling between networks increased in all types of connections. Finally, disease severity (duration, pain intensity, and affective alterations) was negatively correlated with coupling (rich-club, feeder, and whole brain) and network strength (the rich-club of the SN and local connections of the FN). A positive correlation was only found between pain intensity and the coupling of local connections. CONCLUSIONS The SN of patients with CTN may be more vulnerable. Accompanied by the reorganization of the rich-club, the less efficient network communication and the impaired functional dynamics were largely attributable to the dysfunction of non-hub regions. As compensation, the pain transmission pathway of feeder connections involving in pain processing and emotional regulation may strengthen. The local and feeder sub-networks may serve as potential biomarkers for diagnosis or prognosis.
Collapse
Affiliation(s)
- Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730000, China,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China
| | - Xinyue Wan
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Kai Ai
- Philips, Healthcare, Xi’an 710000, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Guangyao Liu
- Second Clinical School, Lanzhou University, Lanzhou 730000, China,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou 730000, China,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China
| | - Wenjing Huang
- Second Clinical School, Lanzhou University, Lanzhou 730000, China,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China
| | - Fengxian Fan
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China,Corresponding authors at: Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730000, China (Z. Yao). Department of Magnetic Resonance, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China (J. Zhang).
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730000, China,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China,Corresponding authors at: Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730000, China (Z. Yao). Department of Magnetic Resonance, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China (J. Zhang).
| |
Collapse
|
24
|
Melillo R, Leisman G, Machado C, Machado-Ferrer Y, Chinchilla-Acosta M, Kamgang S, Melillo T, Carmeli E. Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders. Front Neurol 2022; 13:922322. [PMID: 35873782 PMCID: PMC9301367 DOI: 10.3389/fneur.2022.922322] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
We provide evidence to support the contention that many aspects of Autistic Spectrum Disorder (ASD) are related to interregional brain functional disconnectivity associated with maturational delays in the development of brain networks. We think a delay in brain maturation in some networks may result in an increase in cortical maturation and development in other networks, leading to a developmental asynchrony and an unevenness of functional skills and symptoms. The paper supports the close relationship between retained primitive reflexes and cognitive and motor function in general and in ASD in particular provided to indicate that the inhibition of RPRs can effect positive change in ASD.
Collapse
Affiliation(s)
- Robert Melillo
- Movement and Cognition Laboratory, Department of Physical Therapy, University of Haifa, Haifa, Israel
| | - Gerry Leisman
- Movement and Cognition Laboratory, Department of Physical Therapy, University of Haifa, Haifa, Israel
- Department of Neurology, University of the Medical Sciences of Havana, Havana, Cuba
| | - Calixto Machado
- Department of Clinical Neurophysiology, Institute for Neurology and Neurosurgery, Havana, Cuba
| | - Yanin Machado-Ferrer
- Department of Clinical Neurophysiology, Institute for Neurology and Neurosurgery, Havana, Cuba
| | | | - Shanine Kamgang
- Department of Neuroscience, Carleton University, Ottawa, ON, Canada
| | - Ty Melillo
- Northeast College of the Health Sciences, Seneca Falls, New York, NY, United States
| | - Eli Carmeli
- Movement and Cognition Laboratory, Department of Physical Therapy, University of Haifa, Haifa, Israel
| |
Collapse
|
25
|
A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis. SUSTAINABILITY 2022. [DOI: 10.3390/su14137722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.
Collapse
|
26
|
A Structural Characterisation of the Mitogen-Activated Protein Kinase Network in Cancer. Symmetry (Basel) 2022. [DOI: 10.3390/sym14051009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Gene regulatory networks represent collections of regulators that interact with each other and with other molecules to govern gene expression. Biological signalling networks model how signals are transmitted and how activities are coordinated in the cell. The study of the structure of such networks in complex diseases such as cancer can provide insights into how they function, and consequently, suggest suitable treatment approaches. Here, we explored such topological characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network derived from published studies in cancer. We employed well-established techniques to conduct network analyses, and collected information on gene function as obtained from large-scale public databases. This allowed us to map topological and functional relationships, and build hypotheses on this network’s functional consequences. In particular, we find that the topology of this MAPK network is highly non-random, modular and robust. Moreover, analysis of the network’s structure indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical manner across communities of the network. Finally, our results indicate that the organisation of this network renders it problematic to use treatment approaches that focus on a single target. Our analysis suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network functions. Overall, we propose that complex network analyses combined with pharmacological insights will help inform on future treatment strategies, exploiting structural vulnerabilities of signalling and regulatory networks in cancer.
Collapse
|
27
|
Mayneris-Perxachs J, Castells-Nobau A, Arnoriaga-Rodríguez M, Martin M, de la Vega-Correa L, Zapata C, Burokas A, Blasco G, Coll C, Escrichs A, Biarnés C, Moreno-Navarrete JM, Puig J, Garre-Olmo J, Ramos R, Pedraza S, Brugada R, Vilanova JC, Serena J, Gich J, Ramió-Torrentà L, Pérez-Brocal V, Moya A, Pamplona R, Sol J, Jové M, Ricart W, Portero-Otin M, Deco G, Maldonado R, Fernández-Real JM. Microbiota alterations in proline metabolism impact depression. Cell Metab 2022; 34:681-701.e10. [PMID: 35508109 DOI: 10.1016/j.cmet.2022.04.001] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 01/31/2022] [Accepted: 04/04/2022] [Indexed: 02/08/2023]
Abstract
The microbiota-gut-brain axis has emerged as a novel target in depression, a disorder with low treatment efficacy. However, the field is dominated by underpowered studies focusing on major depression not addressing microbiome functionality, compositional nature, or confounding factors. We applied a multi-omics approach combining pre-clinical models with three human cohorts including patients with mild depression. Microbial functions and metabolites converging onto glutamate/GABA metabolism, particularly proline, were linked to depression. High proline consumption was the dietary factor with the strongest impact on depression. Whole-brain dynamics revealed rich club network disruptions associated with depression and circulating proline. Proline supplementation in mice exacerbated depression along with microbial translocation. Human microbiota transplantation induced an emotionally impaired phenotype in mice and alterations in GABA-, proline-, and extracellular matrix-related prefrontal cortex genes. RNAi-mediated knockdown of proline and GABA transporters in Drosophila and mono-association with L. plantarum, a high GABA producer, conferred protection against depression-like states. Targeting the microbiome and dietary proline may open new windows for efficient depression treatment.
Collapse
Affiliation(s)
- Jordi Mayneris-Perxachs
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain.
| | - Anna Castells-Nobau
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain
| | - María Arnoriaga-Rodríguez
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain; Department of Medical Sciences, School of Medicine, Girona, Spain
| | - Miquel Martin
- Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Lisset de la Vega-Correa
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain
| | - Cristina Zapata
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain
| | - Aurelijus Burokas
- Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain; Institute of Biochemistry, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Gerard Blasco
- Institute of Diagnostic Imaging (IDI)-Research Unit (IDIR), Parc Sanitari Pere Virgili, Barcelona, Spain; Medical Imaging, IDIBGI, Girona, Spain
| | - Clàudia Coll
- Girona Neuroimmunology and Multiple Sclerosis Unit, Department of Neurology, Dr. Josep Trueta Hospital, Girona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Carles Biarnés
- Institute of Diagnostic Imaging (IDI)-Research Unit (IDIR), Parc Sanitari Pere Virgili, Barcelona, Spain; Medical Imaging, IDIBGI, Girona, Spain; Department of Radiology (IDI), Dr. Josep Trueta Hospital, Girona, Spain
| | - José María Moreno-Navarrete
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain; Department of Medical Sciences, School of Medicine, Girona, Spain
| | - Josep Puig
- Department of Medical Sciences, School of Medicine, Girona, Spain; Institute of Diagnostic Imaging (IDI)-Research Unit (IDIR), Parc Sanitari Pere Virgili, Barcelona, Spain; Medical Imaging, IDIBGI, Girona, Spain; Department of Radiology (IDI), Dr. Josep Trueta Hospital, Girona, Spain
| | - Josep Garre-Olmo
- Research Group on Aging, Disability, and Health, Girona Biomedical Research Institute (IdibGi), Girona, Spain; Serra-Hunter Fellow, Department of Nursing, University of Girona, Girona, Spain; Institut d'Assistència Sanitària, Girona, Spain
| | - Rafel Ramos
- Department of Medical Sciences, School of Medicine, Girona, Spain; Vascular Health Research Group of Girona (ISV-Girona), Jordi Gol Institute for Primary Care Research (Institut Universitari Recerca Atenció Primària Jordi Gol i Gorina-IDIAPJGol), Girona, Spain; IDIBGI, Dr. Josep Trueta Hospital, Girona, Spain
| | - Salvador Pedraza
- Department of Medical Sciences, School of Medicine, Girona, Spain; Medical Imaging, IDIBGI, Girona, Spain; Department of Radiology (IDI), Dr. Josep Trueta Hospital, Girona, Spain
| | - Ramón Brugada
- IDIBGI, Dr. Josep Trueta Hospital, Girona, Spain; Biomedical Research Networking Center for Cardiovascular Diseases (CIBER), Madrid, Spain
| | - Joan Carles Vilanova
- Department of Radiology (IDI), Dr. Josep Trueta Hospital, Girona, Spain; IDIBGI, Dr. Josep Trueta Hospital, Girona, Spain
| | - Joaquín Serena
- IDIBGI, Dr. Josep Trueta Hospital, Girona, Spain; Girona Neurodegeneration and Neuroinflammation Group, IDIBGI, Girona, Spain
| | - Jordi Gich
- Department of Medical Sciences, School of Medicine, Girona, Spain; Girona Neurodegeneration and Neuroinflammation Group, IDIBGI, Girona, Spain
| | - Lluís Ramió-Torrentà
- Department of Medical Sciences, School of Medicine, Girona, Spain; Girona Neuroimmunology and Multiple Sclerosis Unit, Department of Neurology, Dr. Josep Trueta Hospital, Girona, Spain; Girona Neurodegeneration and Neuroinflammation Group, IDIBGI, Girona, Spain
| | - Vicente Pérez-Brocal
- Area of Genomics and Health, Foundation for the Promotion of Health and Biomedical Research of València Region (FISABIO-Public Health), València, Spain; Biomedical Research Networking Center for Epidemiology and Public Health (CIBEResp), Madrid, Spain
| | - Andrés Moya
- Area of Genomics and Health, Foundation for the Promotion of Health and Biomedical Research of València Region (FISABIO-Public Health), València, Spain; Biomedical Research Networking Center for Epidemiology and Public Health (CIBEResp), Madrid, Spain; Institute for Integrative Systems Biology (I2Sysbio), University of València and Spanish Research Council (CSIC), València, Spain
| | - Reinald Pamplona
- Metabolic Physiopathology Research Group, Experimental Medicine Department, Lleida University-Lleida Biochemical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Joaquim Sol
- Metabolic Physiopathology Research Group, Experimental Medicine Department, Lleida University-Lleida Biochemical Research Institute (UdL-IRBLleida), Lleida, Spain; Institut Català de la Salut, Atenció Primària, Lleida, Spain; Research Support Unit, Fundació Institut Universitari recerca l'Atenció Primària Salut Jordi Gol i Gorina (IDIAPJGol), Lleida, Spain
| | - Mariona Jové
- Metabolic Physiopathology Research Group, Experimental Medicine Department, Lleida University-Lleida Biochemical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Wifredo Ricart
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain; Department of Medical Sciences, School of Medicine, Girona, Spain
| | - Manuel Portero-Otin
- Metabolic Physiopathology Research Group, Experimental Medicine Department, Lleida University-Lleida Biochemical Research Institute (UdL-IRBLleida), Lleida, Spain
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institucio Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences, Leipzig, Germany; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Rafael Maldonado
- Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain; Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.
| | - José Manuel Fernández-Real
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta Hospital, Girona, Spain; Girona Biomedical Research Institute (IDIBGI), Girona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Girona, Spain; Department of Medical Sciences, School of Medicine, Girona, Spain.
| |
Collapse
|
28
|
Namiranian R, Rahimi Malakshan S, Abrishami Moghaddam H, Khadem A, Jafari R. Normal development of the brain: a survey of joint structural-functional brain studies. Rev Neurosci 2022; 33:745-765. [PMID: 35304982 DOI: 10.1515/revneuro-2022-0017] [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: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 11/15/2022]
Abstract
Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain's maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain's performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state and cognitive experiments. However, nonsignificant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were partially inconsistent with previous nonjoint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.
Collapse
Affiliation(s)
- Roxana Namiranian
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran
| | - Sahar Rahimi Malakshan
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran
| | - Hamid Abrishami Moghaddam
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran.,Inserm UMR 1105, Université de Picardie Jules Verne, 80054 Amiens, France
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran
| | - Reza Jafari
- Department of Electrical and Computer Engineering, Thompson Engineering Building, University of Western Ontario, London, ON N6A 5B9, Canada
| |
Collapse
|
29
|
Miao G, Rao B, Wang S, Fang P, Chen Z, Chen L, Zhang X, Zheng J, Xu H, Liao W. Decreased Functional Connectivities of Low-Degree Level Rich Club Organization and Caudate in Post-stroke Cognitive Impairment Based on Resting-State fMRI and Radiomics Features. Front Neurosci 2022; 15:796530. [PMID: 35250435 PMCID: PMC8890030 DOI: 10.3389/fnins.2021.796530] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/31/2021] [Indexed: 11/13/2022] Open
Abstract
BackgroundStroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).MethodsWe collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).ResultsNormalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1–8 in iPSCI, k = 2–8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕnorm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.ConclusionThis study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.
Collapse
Affiliation(s)
- Guofu Miao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sirui Wang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Pinyan Fang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Radiology, TEDA International Cardiovascular Hospital, Tianjin, China
| | - Zhuo Chen
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Linglong Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xin Zhang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jun Zheng
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu,
| | - Weijing Liao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
- Weijing Liao,
| |
Collapse
|
30
|
Zeng H, Chen S, Fink GR, Weidner R. Information Exchange between Cortical Areas: The Visual System as a Model. Neuroscientist 2022; 29:370-384. [PMID: 35057664 DOI: 10.1177/10738584211069061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As nearly all brain functions, perception, motion, and higher-order cognitive functions require coordinated neural information processing within distributed cortical networks. Over the past decades, new theories and techniques emerged that advanced our understanding of how information is transferred between cortical areas. This review surveys critical aspects of interareal information exchange. We begin by examining the brain’s structural connectivity, which provides the basic framework for interareal communication. We then illustrate information exchange between cortical areas using the visual system as an example. Next, well-studied and newly proposed theories that may underlie principles of neural communication are reviewed, highlighting recent work that offers new perspectives on interareal information exchange. We finally discuss open questions in the study of the neural mechanisms underlying interareal information exchange.
Collapse
Affiliation(s)
- Hang Zeng
- Center for Educational Science and Technology, Beijing Normal University, Zhuhai, China
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany
| | - Siyi Chen
- Ludwig-Maximilians-Universität München, München, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany
- Department of Neurology, University Hospital Cologne, Cologne University, Cologne, Germany
| | - Ralph Weidner
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany
| |
Collapse
|
31
|
Paredes O, López JB, Covantes-Osuna C, Ocegueda-Hernández V, Romo-Vázquez R, Morales JA. A Transcriptome Community-and-Module Approach of the Human Mesoconnectome. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1031. [PMID: 34441171 PMCID: PMC8393183 DOI: 10.3390/e23081031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.
Collapse
Affiliation(s)
| | | | | | | | - Rebeca Romo-Vázquez
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
| | - J. Alejandro Morales
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
| |
Collapse
|
32
|
Brain Structural Connectivity Differences in Patients with Normal Cognition and Cognitive Impairment. Brain Sci 2021; 11:brainsci11070943. [PMID: 34356177 PMCID: PMC8305196 DOI: 10.3390/brainsci11070943] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/05/2021] [Accepted: 07/15/2021] [Indexed: 11/17/2022] Open
Abstract
Advances in magnetic resonance imaging, particularly diffusion imaging, have allowed researchers to analyze brain connectivity. Identification of structural connectivity differences between patients with normal cognition, cognitive impairment, and dementia could lead to new biomarker discoveries that could improve dementia diagnostics. In our study, we analyzed 22 patients (11 control group patients, 11 dementia group patients) that underwent 3T MRI diffusion tensor imaging (DTI) scans and the Montreal Cognitive Assessment (MoCA) test. We reconstructed DTI images and used the Desikan-Killiany-Tourville cortical parcellation atlas. The connectivity matrix was calculated, and graph theoretical analysis was conducted using DSI Studio. We found statistically significant differences between groups in the graph density, network characteristic path length, small-worldness, global efficiency, and rich club organization. We did not find statistically significant differences between groups in the average clustering coefficient and the assortativity coefficient. These statistically significant graph theory measures could potentially be used as quantitative biomarkers in cognitive impairment and dementia diagnostics.
Collapse
|
33
|
Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks. Diagnostics (Basel) 2021; 11:diagnostics11060970. [PMID: 34072192 PMCID: PMC8227047 DOI: 10.3390/diagnostics11060970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/07/2021] [Accepted: 05/26/2021] [Indexed: 12/15/2022] Open
Abstract
The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.
Collapse
|
34
|
The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations. ENTROPY 2021; 23:e23030319. [PMID: 33800360 PMCID: PMC7999889 DOI: 10.3390/e23030319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/22/2021] [Accepted: 03/01/2021] [Indexed: 01/02/2023]
Abstract
One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (ε0) and the interaction enthalpy (ε1). Two different initial topographies (“scale-free-like” and “extreme rich club-like”) were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before ε1 approaches the known divergence as ε1→0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where ε0<3 and ε1<0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended “spider legs” connecting previously unconnected “islands,” and as well as evolution of “peninsulas” in what were previously solid masses.
Collapse
|
35
|
Zhang J, Wang L, Ding H, Fan K, Tian Q, Liang M, Sun Z, Shi D, Qin W. Abnormal large-scale structural rich club organization in Leber's hereditary optic neuropathy. NEUROIMAGE-CLINICAL 2021; 30:102619. [PMID: 33752075 PMCID: PMC8010853 DOI: 10.1016/j.nicl.2021.102619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 02/23/2021] [Accepted: 03/02/2021] [Indexed: 12/24/2022]
Abstract
LHON patients suffered large-scale structural network disruption. Non-rich club connections may be more vulnerable in the LHON. Both primary and secondary connectivity damage may coexist in the LHON.
Objective The purpose of this study was to investigate whether the large-scale structural rich club organization was abnormal in patients with Leber's hereditary optic neuropathy (LHON) using diffusion tensor imaging (DTI), and the associations among disrupted brain structural connectivity, disease duration, and neuro-ophthalmological impairment. Methods Nineteen acute, 34 chronic LHON patients, and 36 healthy controls (HC) underwent DTI and neuro-ophthalmological measurements. The brain structural network and rich club organization were constructed based on deterministic fiber tracking at the individual level. Then intergroup differences among the acute, chronic LHON patients and healthy controls (HC) in three types of structural connections, including rich club, feeder, and local ones, were compared. Network-based Statistics (NBS) was also used to test the intergroup connectivity differences for each fiber. Several linear and nonlinear curve fit models were applied to explore the associations among large-scale brain structural connectivity, disease duration, and neuro-ophthalmological metrics. Results Compared to the HC, both the acute and chronic LHON patients had consistently significantly lower fractional anisotropy (FA) and higher radial diffusion (RD) for feeder connections (p < 0.05, FDR correction). Acute LHON patients had significantly lower FA and higher RD for local connections (p < 0.05, FDR correction). There was no significant difference in large-scale brain structural connectivity between acute and chronic LHON (p > 0.05, FDR correction). NBS also identified reduced FA of three feeder connections and five local ones linking visual, auditory, and basal ganglia areas in LHON patients (p < 0.05, FDR correction). No structural connections showed linear or nonlinear association with either disease duration or neuro-ophthalmological indicators (p > 0.05, FDR correction). A significant negative correlation was shown between the retinal nerve fiber layer (RNFL) thickness and disease duration (p < 0.05, FDR correction). Conclusions Abnormal rich club organization of the structural network was identified in both the acute and chronic LHON. Furthermore, our findings suggest the coexistence of both primary and secondary connectivity damage in the LHON.
Collapse
Affiliation(s)
- Jiahui Zhang
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Ling Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Hao Ding
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Ke Fan
- Henan Eye Institute, Henan Eye Hospital, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Qin Tian
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - Meng Liang
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China; School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Zhihua Sun
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| | - Dapeng Shi
- Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou 450003, China.
| | - Wen Qin
- Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China.
| |
Collapse
|
36
|
Heiney K, Huse Ramstad O, Fiskum V, Christiansen N, Sandvig A, Nichele S, Sandvig I. Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation. Front Comput Neurosci 2021; 15:611183. [PMID: 33643017 PMCID: PMC7902700 DOI: 10.3389/fncom.2021.611183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/18/2021] [Indexed: 01/03/2023] Open
Abstract
It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.
Collapse
Affiliation(s)
- Kristine Heiney
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Vegard Fiskum
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nicholas Christiansen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Holistic Systems, Simula Metropolitan, Oslo, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| |
Collapse
|