1
|
Li J, Yao C, Li Y, Liu X, Zhao Z, Shang Y, Yang J, Yao Z, Sheng Y, Hu B. Effects of second language acquisition on brain functional networks at different developmental stages. Brain Imaging Behav 2024; 18:808-818. [PMID: 38492128 DOI: 10.1007/s11682-024-00865-y] [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] [Accepted: 02/11/2024] [Indexed: 03/18/2024]
Abstract
Previous studies have shown that language acquisition influences both the structure and function of the brain. However, whether the acquisition of a second language at different periods of life alters functional network organization in different ways remains unclear. Here, functional magnetic resonance imaging data from 27 English-speaking monolingual controls and 52 Spanish-English bilingual individuals, including 22 early bilinguals who began learning a second language before the age of ten and 30 late bilinguals who started learning a second language at age fourteen or later, were collected from the OpenNeuro database. Topological metrics of resting-state functional networks, including small-world attributes, network efficiency, and rich- and diverse-club regions, that characterize functional integration and segregation of the networks were computed via a graph theoretical approach. The results showed obvious increases in network efficiency in early bilinguals and late bilinguals relative to the monolingual controls; for example, the global efficiency of late bilinguals and early bilinguals was improved relative to that of monolingual controls, and the local efficiency of early bilinguals occupied an intermediate position between that of late bilinguals and monolingual controls. Obvious increases in rich-club and diverse-club functional connectivity were observed in the bilinguals relative to the monolingual controls. Three network metrics were positively correlated with Spanish proficiency test scores. These findings demonstrated that early and late acquisition of a second language had different impacts on the functional networks of the brain.
Collapse
Affiliation(s)
- Jiajia Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Chaofan Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Yingying Shang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Jing Yang
- Lanzhou University Second Hospital, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China.
| | - Yucen Sheng
- School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China.
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University &, Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China.
| |
Collapse
|
2
|
Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
Collapse
Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| |
Collapse
|
3
|
Evangelista GG, Egger P, Brügger J, Beanato E, Koch PJ, Ceroni M, Fleury L, Cadic-Melchior A, Meyer NH, Rodríguez DDL, Girard G, Léger B, Turlan JL, Mühl A, Vuadens P, Adolphsen J, Jagella CE, Constantin C, Alvarez V, San Millán D, Bonvin C, Morishita T, Wessel MJ, Van De Ville D, Hummel FC. Differential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits. Stroke 2023; 54:955-963. [PMID: 36846963 PMCID: PMC10662579 DOI: 10.1161/strokeaha.122.040001] [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: 05/18/2022] [Revised: 12/01/2022] [Accepted: 12/15/2022] [Indexed: 03/01/2023]
Abstract
BACKGROUND Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretical methods may open new avenues of understanding. METHODS Fifty subacute stroke patients (7±3days poststroke) underwent diffusion-weighted magnetic resonance imaging and a battery of clinical tests of motor and cognitive functions. We defined indices of impairment in strength, dexterity, and attention. We also computed imaging-based probabilistic tractography and whole-brain connectomes. To efficiently integrate inputs from different sources, brain networks rely on a rich-club of a few hub nodes. Lesions harm efficiency, particularly when they target the rich-club. Overlaying individual lesion masks onto the tractograms enabled us to split the connectomes into their affected and unaffected parts and associate them to impairment. RESULTS We computed efficiency of the unaffected connectome and found it was more strongly correlated to impairment in strength, dexterity, and attention than efficiency of the total connectome. The magnitude of the correlation between efficiency and impairment followed the order attention>dexterity ≈ strength (strength: |r|=.03, P=0.02, dexterity: |r|=.30, P=0.05, attention: |r|=.55, P<0.001). Network weights associated with the rich-club were more strongly correlated to efficiency than non-rich-club weights. CONCLUSIONS Attentional impairment is more sensitive to disruption of coordinated networks between brain regions than motor impairment, which is sensitive to disruption of localized networks. Providing more accurate reflections of actually functioning parts of the network enables the incorporation of information about the impact of brain lesions on connectomics contributing to a better understanding of underlying stroke mechanisms.
Collapse
Affiliation(s)
- Giorgia G. Evangelista
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Philip Egger
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Julia Brügger
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Elena Beanato
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Philipp J. Koch
- Department of Neurology, University of Lübeck, Germany (P.J.K.)
- Center of Brain, Behavior and Metabolism, University of Lübeck, Germany (P.J.K.)
| | - Martino Ceroni
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Lisa Fleury
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Andéol Cadic-Melchior
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Nathalie H. Meyer
- Laboratory of Cognitive Neuroscience, CNP and BMI, EPFL, Switzerland (N.H.M., D.d.L.R.)
| | - Diego de León Rodríguez
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
- Laboratory of Cognitive Neuroscience, CNP and BMI, EPFL, Switzerland (N.H.M., D.d.L.R.)
- Department of Neurology, Hôpital du Valais, Switzerland (C.C., V.A., D.d.L.R., D.S.M., C.B.)
| | - Gabriel Girard
- Signal Processing Laboratory (LTS5), School of Engineering, EPFL, Switzerland (G.G.)
- Center for Biomedical Imaging (CIBM), Switzerland (G.G.)
- Department of Radiology, CHUV, Switzerland (G.G.)
| | - Bertrand Léger
- Clinique Romande de Réadaptation, Switzerland (B.L., A.M., P.V., J.-L.T.)
| | - Jean-Luc Turlan
- Clinique Romande de Réadaptation, Switzerland (B.L., A.M., P.V., J.-L.T.)
| | - Andreas Mühl
- Clinique Romande de Réadaptation, Switzerland (B.L., A.M., P.V., J.-L.T.)
| | - Philippe Vuadens
- Clinique Romande de Réadaptation, Switzerland (B.L., A.M., P.V., J.-L.T.)
| | | | | | - Christophe Constantin
- Department of Neurology, Hôpital du Valais, Switzerland (C.C., V.A., D.d.L.R., D.S.M., C.B.)
| | - Vincent Alvarez
- Department of Neurology, Hôpital du Valais, Switzerland (C.C., V.A., D.d.L.R., D.S.M., C.B.)
| | - Diego San Millán
- Department of Neurology, Hôpital du Valais, Switzerland (C.C., V.A., D.d.L.R., D.S.M., C.B.)
| | - Christophe Bonvin
- Department of Neurology, Hôpital du Valais, Switzerland (C.C., V.A., D.d.L.R., D.S.M., C.B.)
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
| | - Maximilian J. Wessel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
- Department of Neurology, University Hospital Würzburg, Germany (M.J.W.)
| | - Dimitri Van De Ville
- Medical Image Processing Laboratory, Institute of Bioengineering, EPFL, Switzerland (D.V.D.V.)
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Switzerland (D.V.D.V.)
| | - Friedhelm C. Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., D.d.L.R., T.M., M.J.W., F.C.H.)
- Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Clinique Romande de Réadaptation, Switzerland (G.G.E., P.E., J.B., E.B., M.C., L.F., A.C.-M., T.M., D.d.L.R., M.J.W., F.C.H.)
- Clinical Neuroscience, Geneva University Hospital (HUG), Switzerland (F.C.H.)
| |
Collapse
|
4
|
Fleury L, Koch PJ, Wessel MJ, Bonvin C, San Millan D, Constantin C, Vuadens P, Adolphsen J, Cadic Melchior A, Brügger J, Beanato E, Ceroni M, Menoud P, De Leon Rodriguez D, Zufferey V, Meyer NH, Egger P, Harquel S, Popa T, Raffin E, Girard G, Thiran JP, Vaney C, Alvarez V, Turlan JL, Mühl A, Léger B, Morishita T, Micera S, Blanke O, Van De Ville D, Hummel FC. Toward individualized medicine in stroke—The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke. Front Neurol 2022; 13:939640. [PMID: 36226086 PMCID: PMC9549862 DOI: 10.3389/fneur.2022.939640] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Despite recent improvements, complete motor recovery occurs in <15% of stroke patients. To improve the therapeutic outcomes, there is a strong need to tailor treatments to each individual patient. However, there is a lack of knowledge concerning the precise neuronal mechanisms underlying the degree and course of motor recovery and its individual differences, especially in the view of brain network properties despite the fact that it became more and more clear that stroke is a network disorder. The TiMeS project is a longitudinal exploratory study aiming at characterizing stroke phenotypes of a large, representative stroke cohort through an extensive, multi-modal and multi-domain evaluation. The ultimate goal of the study is to identify prognostic biomarkers allowing to predict the individual degree and course of motor recovery and its underlying neuronal mechanisms paving the way for novel interventions and treatment stratification for the individual patients. A total of up to 100 patients will be assessed at 4 timepoints over the first year after the stroke: during the first (T1) and third (T2) week, then three (T3) and twelve (T4) months after stroke onset. To assess underlying mechanisms of recovery with a focus on network analyses and brain connectivity, we will apply synergistic state-of-the-art systems neuroscience methods including functional, diffusion, and structural magnetic resonance imaging (MRI), and electrophysiological evaluation based on transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) and electromyography (EMG). In addition, an extensive, multi-domain neuropsychological evaluation will be performed at each timepoint, covering all sensorimotor and cognitive domains. This project will significantly add to the understanding of underlying mechanisms of motor recovery with a strong focus on the interactions between the motor and other cognitive domains and multimodal network analyses. The population-based, multi-dimensional dataset will serve as a basis to develop biomarkers to predict outcome and promote personalized stratification toward individually tailored treatment concepts using neuro-technologies, thus paving the way toward personalized precision medicine approaches in stroke rehabilitation.
Collapse
Affiliation(s)
- Lisa Fleury
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Philipp J. Koch
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Maximilian J. Wessel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | | | | | | | | | | | - Andéol Cadic Melchior
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Julia Brügger
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Elena Beanato
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Martino Ceroni
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Pauline Menoud
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Diego De Leon Rodriguez
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Valérie Zufferey
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Nathalie H. Meyer
- Laboratory of Cognitive Neuroscience, INX and BMI, EPFL, Campus Biotech, Geneva, Switzerland
| | - Philip Egger
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Sylvain Harquel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Traian Popa
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Estelle Raffin
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Gabriel Girard
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), EPFL, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), EPFL, Lausanne, Switzerland
| | | | | | | | - Andreas Mühl
- Clinique Romande de Réadaptation, Sion, Switzerland
| | | | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
| | - Silvestro Micera
- The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
- Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, EPFL, Lausanne, Switzerland
| | - Olaf Blanke
- Laboratory of Cognitive Neuroscience, INX and BMI, EPFL, Campus Biotech, Geneva, Switzerland
- Department of Clinical Neurosciences, University of Geneva (UNIGE), Geneva, Switzerland
| | - Dimitri Van De Ville
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Processing Lab, Center for Neuroprosthetics, Institute of Bioengineering, EPFL, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Friedhelm C. Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), EPFL, Campus Biotech, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, INX and BMI, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland
- Clinical Neuroscience, Geneva University Hospital, Geneva, Switzerland
- *Correspondence: Friedhelm C. Hummel
| |
Collapse
|
5
|
Shou G, Yuan H, Cha YH, Sweeney JA, Ding L. Age-related changes of whole-brain dynamics in spontaneous neuronal coactivations. Sci Rep 2022; 12:12140. [PMID: 35840643 PMCID: PMC9287374 DOI: 10.1038/s41598-022-16125-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/05/2022] [Indexed: 01/04/2023] Open
Abstract
Human brains experience whole-brain anatomic and functional changes throughout the lifespan. Age-related whole-brain network changes have been studied with functional magnetic resonance imaging (fMRI) to determine their low-frequency spatial and temporal characteristics. However, little is known about age-related changes in whole-brain fast dynamics at the scale of neuronal events. The present study investigated age-related whole-brain dynamics in resting-state electroencephalography (EEG) signals from 73 healthy participants from 6 to 65 years old via characterizing transient neuronal coactivations at a resolution of tens of milliseconds. These uncovered transient patterns suggest fluctuating brain states at different energy levels of global activations. Our results indicate that with increasing age, shorter lifetimes and more occurrences were observed in the brain states that show the global high activations and more consecutive visits to the global highest-activation brain state. There were also reduced transitional steps during consecutive visits to the global lowest-activation brain state. These age-related effects suggest reduced stability and increased fluctuations when visiting high-energy brain states and with a bias toward staying low-energy brain states. These age-related whole-brain dynamics changes are further supported by changes observed in classic alpha and beta power, suggesting its promising applications in examining the effect of normal healthy brain aging, brain development, and brain disease.
Collapse
Affiliation(s)
- Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA
| | - Han Yuan
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA.,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA
| | - Yoon-Hee Cha
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Lei Ding
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, USA. .,Institute for Biomedical Engineering, Science, and Technology, University of Oklahoma, Norman, USA. .,University of Oklahoma, 173 Felgar St., Gallogly Hall, Room 101, Norman, OK, 73019, USA.
| |
Collapse
|
6
|
Baldi S, Michielse S, Vriend C, van den Heuvel MP, van den Heuvel OA, Schruers KRJ, Goossens L. Abnormal white-matter rich-club organization in obsessive-compulsive disorder. Hum Brain Mapp 2022; 43:4699-4709. [PMID: 35735129 PMCID: PMC9491289 DOI: 10.1002/hbm.25984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/24/2022] [Accepted: 06/03/2022] [Indexed: 11/09/2022] Open
Abstract
Rich‐club organization is key to efficient global neuronal signaling and integration of information. Alterations interfere with higher‐order cognitive processes, and are common to several psychiatric and neurological conditions. A few studies examining the structural connectome in obsessive–compulsive disorder (OCD) suggest lower efficiency of information transfer across the brain. However, it remains unclear whether this is due to alterations in rich‐club organization. In the current study, the structural connectome of 28 unmedicated OCD patients, 8 of their unaffected siblings and 28 healthy controls was reconstructed by means of diffusion‐weighted imaging and probabilistic tractography. Topological and weighted measures of rich‐club organization and connectivity were computed, alongside global and nodal measures of network integration and segregation. The relationship between clinical scores and network properties was explored. Compared to healthy controls, OCD patients displayed significantly lower topological and weighted rich‐club organization, allocating a smaller fraction of all connection weights to the rich‐club core. Global clustering coefficient, local efficiency, and clustering of nonrich club nodes were significantly higher in OCD patients. Significant three‐group differences emerged, with siblings displaying highest and lowest values in different measures. No significant correlation with any clinical score was found. Our results suggest weaker structural connectivity between rich‐club nodes in OCD patients, possibly resulting in lower network integration in favor of higher network segregation. We highlight the need of looking at network‐based alterations in brain organization and function when investigating the neurobiological basis of this disorder, and stimulate further research into potential familial protective factors against the development of OCD.
Collapse
Affiliation(s)
- Samantha Baldi
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Chris Vriend
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Odile A van den Heuvel
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Koen R J Schruers
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Liesbet Goossens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
7
|
Riedel L, van den Heuvel MP, Markett S. Trajectory of rich club properties in structural brain networks. Hum Brain Mapp 2022; 43:4239-4253. [PMID: 35620874 PMCID: PMC9435005 DOI: 10.1002/hbm.25950] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 11/06/2022] Open
Abstract
Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8066 participants aged 5-80 years. Across all brain regions, structural connectivity strength followed an "inverted-U"-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a "first come, last served" model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.
Collapse
Affiliation(s)
- Levin Riedel
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, Berlin, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| |
Collapse
|
8
|
Xue X, Wu JJ, Huo BB, Xing XX, Ma J, Li YL, Wei D, Duan YJ, Shan CL, Zheng MX, Hua XY, Xu JG. Age-Related Changes in Topological Properties of Individual Brain Metabolic Networks in Rats. Front Aging Neurosci 2022; 14:895934. [PMID: 35645769 PMCID: PMC9136077 DOI: 10.3389/fnagi.2022.895934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague-Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (E loc ) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (E nod ) of left posterior dorsal hippocampus, and E nod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging.
Collapse
Affiliation(s)
- Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Jie Duan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| |
Collapse
|
9
|
Rao B, Cheng H, Xu H, Peng Y. Random Network and Non-rich-club Organization Tendency in Children With Non-syndromic Cleft Lip and Palate After Articulation Rehabilitation: A Diffusion Study. Front Neurol 2022; 13:790607. [PMID: 35185761 PMCID: PMC8847279 DOI: 10.3389/fneur.2022.790607] [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: 10/07/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022] Open
Abstract
Objective The neuroimaging pattern in brain networks after articulation rehabilitation can be detected using graph theory and multivariate pattern analysis (MVPA). In this study, we hypothesized that the characteristics of the topology pattern of brain structural network in articulation-rehabilitated children with non-syndromic cleft lip and palate (NSCLP) were similar to that in healthy comparisons. Methods A total of 28 children with NSCLP and 28 controls with typical development were scanned for diffusion tensor imaging on a 3T MRI scanner. Structural networks were constructed, and their topological properties were obtained. Besides, the Chinese language clear degree scale (CLCDS) scores were used for correlation analysis with topological features in patients with NSCLP. Results The NSCLP group showed a similar rich-club connection pattern, but decreased small-world index, normalized rich-club coefficient, and increased connectivity strength of connections compared to controls. The univariate and multivariate patterns of the structural network in articulation-rehabilitated children were primarily in the feeder and local connections, covering sensorimotor, visual, frontoparietal, default mode, salience, and language networks, and orbitofrontal cortex. In addition, the connections that were significantly correlated with the CLCDS scores, as well as the weighted regions for classification, were chiefly distributed in the dorsal and ventral stream associated with the language networks of the non-dominant hemisphere. Conclusion The average level rich-club connection pattern and the compensatory of the feeder and local connections mainly covering language networks may be related to the CLCDS in articulation-rehabilitated children with NSCLP. However, the patterns of small-world and rich-club structural organization in the articulation-rehabilitated children exhibited a random network and non-rich-club organization tendency. These findings enhanced the understanding of neuroimaging patterns in children with NSCLP after articulation rehabilitation.
Collapse
Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - Hua Cheng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China
- *Correspondence: Haibo Xu
| | - Yun Peng
- Department of Radiology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
- Yun Peng
| |
Collapse
|
10
|
Vuksanović V, Staff RT, Morson S, Ahearn T, Bracoud L, Murray AD, Bentham P, Kipps CM, Harrington CR, Wischik CM. Degeneration of basal and limbic networks is a core feature of behavioural variant frontotemporal dementia. Brain Commun 2021; 3:fcab241. [PMID: 34939031 PMCID: PMC8688778 DOI: 10.1093/braincomms/fcab241] [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: 12/07/2020] [Revised: 05/13/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
The behavioural variant of frontotemporal dementia is a clinical syndrome characterized by changes in behaviour, cognition and functional ability. Although atrophy in frontal and temporal regions would appear to be a defining feature, neuroimaging studies have identified volumetric differences distributed across large parts of the cortex, giving rise to a classification into distinct neuroanatomical subtypes. Here, we extended these neuroimaging studies to examine how distributed patterns of cortical atrophy map onto brain network hubs. We used baseline structural magnetic resonance imaging data collected from 213 behavioural variant of frontotemporal dementia patients meeting consensus diagnostic criteria and having definite evidence of frontal and/or temporal lobe atrophy from a global clinical trial conducted in 70 sites in Canada, United States of America, Australia, Asia and Europe. These were compared with data from 244 healthy elderly subjects from a well-characterized cohort study. We have used statistical methods of hierarchical agglomerative clustering of 68 regional cortical and subcortical volumes (34 in each hemisphere) to determine the reproducibility of previously described neuroanatomical subtypes in a global study. We have also attempted to link the structural findings to clinical features defined systematically using well-validated clinical scales (Addenbrooke’s Cognitive Examination Revised, the Mini-Mental Status Examination, the Frontotemporal Dementia Rating Scale and the Functional Assessment Questionnaire) and subscales derived from them. Whilst we can confirm that the subtypes are robust, they have limited value in explaining the clinical heterogeneity of the syndrome. We have found that a common pattern of degeneration affecting a small number of subcortical, limbic and frontal nodes within highly connected networks (most previously identified as rich club members or functional binding nodes) is shared by all the anatomical subtypes. Degeneration in these core regions is correlated with cognitive and functional impairment, but less so with behavioural impairment. These findings suggest that degeneration in highly connected basal, limbic and frontal networks is a core feature of the behavioural variant of frontotemporal dementia phenotype irrespective of neuroanatomical and clinical heterogeneity, and may underly the impairment of integration in cognition, function and behaviour responsible for the loss of insight that characterizes the syndrome.
Collapse
Affiliation(s)
- Vesna Vuksanović
- Swansea University Medical School, Health Data Research UK, Swansea University, Swansea SA2 8PP, UK.,School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK.,TauRx Therapeutics, Aberdeen AB24 5RP, UK
| | - Roger T Staff
- Medical Physics, NHS Grampian, Aberdeen AB25 2ZD, UK
| | - Suzannah Morson
- TauRx Therapeutics, Aberdeen AB24 5RP, UK.,School of Psychology, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Trevor Ahearn
- Medical Physics, NHS Grampian, Aberdeen AB25 2ZD, UK
| | | | - Alison D Murray
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | | | - Christopher M Kipps
- University Hospital Southampton and University of Southampton, Southampton SO16 6YD, UK
| | - Charles R Harrington
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK.,TauRx Therapeutics, Aberdeen AB24 5RP, UK
| | - Claude M Wischik
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen AB25 2ZD, UK.,TauRx Therapeutics, Aberdeen AB24 5RP, UK
| |
Collapse
|
11
|
McPherson BC, Pestilli F. A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects' age. Commun Biol 2021; 4:943. [PMID: 34354185 PMCID: PMC8342440 DOI: 10.1038/s42003-021-02451-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 06/15/2021] [Indexed: 02/07/2023] Open
Abstract
Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.
Collapse
Affiliation(s)
- Brent C McPherson
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA.
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
12
|
Li D, Cui X, Yan T, Liu B, Zhang H, Xiang J, Wang B. Abnormal Rich Club Organization in Hemispheric White Matter Networks of ADHD. J Atten Disord 2021; 25:1215-1229. [PMID: 31884863 DOI: 10.1177/1087054719892887] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Objective: Brain network studies have revealed abnormal topology asymmetry of white matter (WM) in ADHD. Recently, rich club organization was proposed to be a key feature of brain network topology. However, abnormalities in the rich club organization of hemispheric WM networks in ADHD remain unclear. Method: Forty ADHD patients and 51 normal controls participated in this study. Structural networks were reconstructed based on diffusion tensor imaging (DTI) and analyzed with graph theory. Results: The two groups exhibited different patterns of asymmetry in connectivity measures of rich club connections. ADHD patients showed more feeder connections than normal controls. Reduced rightward asymmetry was observed in connectivity measures of local connections involving several peripheral regions of the ADHD patients. In addition, abnormal regional asymmetry scores were associated with ADHD symptoms. Conclusion: The topological changes in rich club organization provide a novel insight into the alteration of WM connections in ADHD.
Collapse
Affiliation(s)
- Dandan Li
- Taiyuan University of Technology, China
| | | | - Ting Yan
- Shanxi Medical University, Taiyuan, China
| | - Bo Liu
- First Hospital of Shanxi Medical University, Taiyuan, China
| | - Hui Zhang
- First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jie Xiang
- Taiyuan University of Technology, China
| | - Bing Wang
- Taiyuan University of Technology, China
| |
Collapse
|
13
|
Escrichs A, Biarnes C, Garre-Olmo J, Fernández-Real JM, Ramos R, Pamplona R, Brugada R, Serena J, Ramió-Torrentà L, Coll-De-Tuero G, Gallart L, Barretina J, Vilanova JC, Mayneris-Perxachs J, Essig M, Figley CR, Pedraza S, Puig J, Deco G. Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club. Cereb Cortex 2021; 31:2466-2481. [PMID: 33350451 DOI: 10.1093/cercor/bhaa367] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/19/2022] Open
Abstract
Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.
Collapse
Affiliation(s)
- Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Carles Biarnes
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Josep Garre-Olmo
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Institut d'Assistència Sanitària, Salt (Girona), Spain
| | - José Manuel Fernández-Real
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Rafel Ramos
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,Primary Care Services, Catalan Institute of Health (ICS), Girona, Spain
| | - Reinald Pamplona
- Department of Experimental Medicine, Faculty of Medicine, University of Lleida-IRBLleida, Lleida, Spain
| | - Ramon Brugada
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Cardiovascular Genetics Center, IDIBGI, CIBER-CV, Girona, Spain
| | - Joaquin Serena
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Lluís Ramió-Torrentà
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Department of Neurology, Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Gabriel Coll-De-Tuero
- Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain.,Vascular Health Research Group of Girona (ISV-Girona), Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Girona, Spain.,CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Luís Gallart
- Biobanc, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Jordi Barretina
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Joan C Vilanova
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Jordi Mayneris-Perxachs
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Diabetes, Endocrinology and Nutrition, IDIBGI, Hospital Universitari de Girona Dr Josep Trueta, and CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Girona, Spain
| | - Marco Essig
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Chase R Figley
- Department of Radiology, University of Manitoba, Winnipeg, Canada
| | - Salvador Pedraza
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Josep Puig
- Department of Radiology (IDI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain.,Department of Medical Sciences, School of Medicine, University of Girona, Girona, Spain
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.,Institucio Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Catalonia, 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, Victoria, Australia
| |
Collapse
|
14
|
Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis. J Psychiatr Res 2020; 126:43-54. [PMID: 32416386 DOI: 10.1016/j.jpsychires.2020.04.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/21/2020] [Accepted: 04/27/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Late-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI. METHOD To address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests. RESULT The interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance. CONCLUSION The application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.
Collapse
|
15
|
Kim DJ, Min BK. Rich-club in the brain's macrostructure: Insights from graph theoretical analysis. Comput Struct Biotechnol J 2020; 18:1761-1773. [PMID: 32695269 PMCID: PMC7355726 DOI: 10.1016/j.csbj.2020.06.039] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023] Open
Abstract
The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.
Collapse
Key Words
- AD, Alzheimer’s disease
- ADHD, attention deficit hyperactivity disorder
- ASD, autism spectrum disorder
- BD, bipolar disorder
- Brain connectivity
- Brain network
- DTI, diffusion tensor imaging
- EEG, electroencephalography
- Graph theory
- MDD, major depressive disorder
- MEG, magnetoencephalography
- MRI, magnetic resonance imaging
- Neuroimaging
- Rich-club
- TBI, traumatic brain injury
Collapse
Affiliation(s)
- Dae-Jin Kim
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| |
Collapse
|
16
|
Mansur RB, Lee Y, McIntyre RS, Brietzke E. What is bipolar disorder? A disease model of dysregulated energy expenditure. Neurosci Biobehav Rev 2020; 113:529-545. [PMID: 32305381 DOI: 10.1016/j.neubiorev.2020.04.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2020] [Accepted: 04/05/2020] [Indexed: 12/24/2022]
Abstract
Advances in the understanding and management of bipolar disorder (BD) have been slow to emerge. Despite notable recent developments in neurosciences, our conceptualization of the nature of this mental disorder has not meaningfully progressed. One of the key reasons for this scenario is the continuing lack of a comprehensive disease model. Within the increasing complexity of modern research methods, there is a clear need for an overarching theoretical framework, in which findings are assimilated and predictions are generated. In this review and hypothesis article, we propose such a framework, one in which dysregulated energy expenditure is a primary, sufficient cause for BD. Our proposed model is centered on the disruption of the molecular and cellular network regulating energy production and expenditure, as well its potential secondary adaptations and compensatory mechanisms. We also focus on the putative longitudinal progression of this pathological process, considering its most likely periods for onset, such as critical periods that challenges energy homeostasis (e.g. neurodevelopment, social isolation), and the resulting short and long-term phenotypical manifestations.
Collapse
Affiliation(s)
- Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Yena Lee
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Elisa Brietzke
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Kingston General Hospital, Providence Care Hospital, Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| |
Collapse
|
17
|
Neuroanatomical Dysconnectivity Underlying Cognitive Deficits in Bipolar Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:152-162. [PMID: 31806486 DOI: 10.1016/j.bpsc.2019.09.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/06/2019] [Accepted: 09/07/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND Graph theory applied to brain networks is an emerging approach to understanding the brain's topological associations with human cognitive ability. Despite well-documented cognitive impairments in bipolar disorder (BD) and recent reports of altered anatomical network organization, the association between connectivity and cognitive impairments in BD remains unclear. METHODS We examined the role of anatomical network connectivity derived from T1- and diffusion-weighted magnetic resonance imaging in impaired cognitive performance in individuals with BD (n = 32) compared with healthy control individuals (n = 38). Fractional anisotropy- and number of streamlines-weighted anatomical brain networks were generated by mapping constrained spherical deconvolution-reconstructed white matter among 86 cortical/subcortical bilateral brain regions delineated in the individual's own coordinate space. Intelligence and executive function were investigated as distributed functions using measures of global, rich-club, and interhemispheric connectivity, while memory and social cognition were examined in relation to subnetwork connectivity. RESULTS Lower executive functioning related to higher global clustering coefficient in participants with BD, and lower IQ performance may present with a differential relationship between global and interhemispheric efficiency in individuals with BD relative to control individuals. Spatial recognition memory accuracy and response times were similar between diagnostic groups and associated with basal ganglia and thalamus interconnectivity and connectivity within extended anatomical subnetworks in all participants. No anatomical subnetworks related to episodic memory, short-term memory, or social cognition generally or differently in BD. CONCLUSIONS Results demonstrate selective influence of subnetwork patterns of connectivity in underlying cognitive performance generally and abnormal global topology underlying discrete cognitive impairments in BD.
Collapse
|
18
|
Longitudinal changes in rich club organization and cognition in cerebral small vessel disease. NEUROIMAGE-CLINICAL 2019; 24:102048. [PMID: 31706220 PMCID: PMC6978216 DOI: 10.1016/j.nicl.2019.102048] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 09/11/2019] [Accepted: 10/21/2019] [Indexed: 01/06/2023]
Abstract
Cerebral small vessel disease (SVD) is considered the most important vascular contributor to the development of cognitive impairment and dementia. There is increasing awareness that SVD exerts its clinical effects by disrupting white matter connections, predominantly disrupting connections between rich club nodes, a set of highly connected and interconnected regions. Here we examined the progression of disturbances in rich club organization in older adults with SVD and their associations with conventional SVD markers and cognitive decline. We additionally investigated associations of baseline network measures with dementia. In 270 participants of the RUN DMC study, we performed diffusion tensor imaging (DTI) and cognitive assessments longitudinally. Rich club organization was examined in structural networks derived from DTI followed by deterministic tractography. Global efficiency (p<0.05) and strength of rich club connections (p<0.001) declined during follow-up. Decline in strength of peripheral connections was associated with a decline in overall cognition (β=0.164; p<0.01), psychomotor speed (β=0.151; p<0.05) and executive function (β=0.117; p<0.05). Baseline network measures were reduced in participants with dementia, and the association between WMH and dementia was causally mediated by global efficiency (p = =0.037) and peripheral connection strength (p = =0.040). SVD-related disturbances in rich club organization progressed over time, predominantly in participants with severe SVD. In this study, we found no specific role of rich club connectivity disruption in causing cognitive decline or dementia. The effect of WMH on dementia was mediated by global network efficiency and the strength of peripheral connections, suggesting an important role for network disruption in causing cognitive decline and dementia in older adults with SVD.
Collapse
|
19
|
Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat Hum Behav 2019; 3:988-998. [DOI: 10.1038/s41562-019-0659-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 06/17/2019] [Indexed: 12/13/2022]
|
20
|
Abnormal synchronization of functional and structural networks in schizophrenia. Brain Imaging Behav 2019; 14:2232-2241. [PMID: 31376115 DOI: 10.1007/s11682-019-00175-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Synchronization is believed to play an important role in information processing of the brain. Mounting evidence supports the hypothesis that schizophrenia is related to impaired neural synchrony. However, most previous studies characterize brain synchronization from the perspective of temporal coordination of distributed neural activity, rather than network properties. Our aim was to investigate the network synchronization alterations in schizophrenia using publically available data. Resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were performed in 96 schizophrenia patients and 120 healthy controls. The whole-brain functional and structural networks were constructed and analyzed using graph theoretical approaches. Inter-group differences in network synchronization were investigated. Both the binary and weighted functional networks of schizophrenia patients exhibited decreased synchronizability (increased eigenratio) than those of healthy controls. With respect to the structural binary networks, schizophrenia patients showed a trend towards excessive synchronizability (decreased eigenratio). In addition, the excessive synchronizability of the structural binary networks was associated with more severe negative symptoms in schizophrenia patients. Our findings provide novel biological evidence that schizophrenia involves a disruption of neural synchrony from the perspective of network properties.
Collapse
|
21
|
|
22
|
Liang X, Hsu LM, Lu H, Sumiyoshi A, He Y, Yang Y. The Rich-Club Organization in Rat Functional Brain Network to Balance Between Communication Cost and Efficiency. Cereb Cortex 2019; 28:924-935. [PMID: 28108494 DOI: 10.1093/cercor/bhw416] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 12/23/2016] [Indexed: 01/22/2023] Open
Abstract
Network analyses of structural connectivity in the brain have highlighted a set of highly connected hubs that are densely interconnected, forming a "rich-club" substrate in diverse species. Here, we demonstrate the existence of rich-club organization in functional brain networks of rats. Densely interconnected rich-club regions are found to be distributed in multiple brain modules, with the majority located within the putative default mode network. Rich-club members exhibit high wiring cost (as measured by connection distance) and high metabolic running cost (as surrogated by cerebral blood flow), which may have evolved to achieve high network communications to support efficient brain functions. Furthermore, by adopting a forepaw electrical stimulation paradigm, we find that the rich-club organization of the rat functional network remains almost the same as in the resting state, whereas path motif analysis reveals significant differences, suggesting the rat brain reorganizes its topological routes by increasing locally oriented shortcuts but reducing rich-club member-involved paths to conserve metabolic running cost during unimodal stimulation. Together, our results suggest that the neuronal system is organized and dynamically operated in an economic way to balance between cost minimization and topological/functional efficiency.
Collapse
Affiliation(s)
- Xia Liang
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA.,Research Center of Basic Space Science, Harbin Institute of Technology, Harbin 150001, China
| | - Li-Ming Hsu
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 11221, Taiwan
| | - Hanbing Lu
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA
| | - Akira Sumiyoshi
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA.,Institute of Development, Aging and Cancer, Tohoku University, Sendai 9808575, Japan
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, USA
| |
Collapse
|
23
|
Homan P, Argyelan M, DeRosse P, Szeszko PR, Gallego JA, Hanna L, Robinson DG, Kane JM, Lencz T, Malhotra AK. Structural similarity networks predict clinical outcome in early-phase psychosis. Neuropsychopharmacology 2019; 44:915-922. [PMID: 30679724 PMCID: PMC6461949 DOI: 10.1038/s41386-019-0322-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 12/17/2018] [Accepted: 01/16/2019] [Indexed: 02/06/2023]
Abstract
Despite recent advances, there is still a major need for prediction of treatment success in schizophrenia, a condition long considered a disorder of dysconnectivity in the brain. Graph theory provides a means to characterize the connectivity in both healthy and abnormal brains. We calculated structural similarity networks in each participant and hypothesized that the "hubness", i.e., the number of edges connecting a node to the rest of the network, would be associated with clinical outcome. This prospective controlled study took place at an academic research center and included 82 early-phase psychosis patients (23 females; mean age [SD] = 21.6 [5.5] years) and 58 healthy controls. Medications were administered in a double-blind randomized manner, and patients were scanned at baseline prior to treatment with second-generation antipsychotics. Symptoms were assessed with the Brief Psychiatric Rating Scale at baseline and over the course of 12 weeks. Nodal degree of structural similarity networks was computed for each subject and entered as a predictor of individual treatment response into a partial least squares (PLS) regression. The model fit was significant in a permutation test with 1000 permutations (P = 0.006), and the first two PLS regression components explained 29% (95% CI: 27; 30) of the variance in treatment response after cross-validation. Nodes loading strongly on the first PLS component were primarily located in the orbito- and prefrontal cortex, whereas nodes loading strongly on the second PLS component were primarily located in the superior temporal, precentral, and middle cingulate cortex. These data suggest a link between brain network morphology and clinical outcome in early-phase psychosis.
Collapse
Affiliation(s)
- Philipp Homan
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA. .,Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA. .,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, USA.
| | - Miklos Argyelan
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Pamela DeRosse
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Philip R. Szeszko
- 0000 0004 0420 1184grid.274295.fJames J. Peters Veterans Affairs Medical Center, Bronx, NY USA
| | - Juan A. Gallego
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Lauren Hanna
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Delbert G. Robinson
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - John M. Kane
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Todd Lencz
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| | - Anil K. Malhotra
- 0000 0000 9566 0634grid.250903.dCenter for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY USA ,0000 0001 2168 3646grid.416477.7Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY USA ,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY USA
| |
Collapse
|
24
|
Zorlu N, Çapraz N, Oztekin E, Bagci B, Di Biase MA, Zalesky A, Gelal F, Bora E, Durmaz E, Beşiroğlu L, Sarıçiçek A. Rich club and reward network connectivity as endophenotypes for alcohol dependence: a diffusion tensor imaging study. Addict Biol 2019; 24:265-274. [PMID: 29280246 DOI: 10.1111/adb.12599] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 11/23/2017] [Accepted: 12/10/2017] [Indexed: 12/13/2022]
Abstract
We aimed to examine the whole-brain white matter connectivity and local topology of reward system nodes in patients with alcohol use disorder (AUD) and unaffected siblings, relative to healthy comparison individuals. Diffusion-weighted magnetic resonance imaging scans were acquired from 18 patients with AUD, 15 unaffected siblings of AUD patients and 15 healthy controls. Structural networks were examined using network-based statistic and connectomic analysis. Connectomic analysis showed a significant ordered difference in normalized rich club organization (AUD < Siblings < Controls). We also found rank ordered differences (Control > Sibling > AUD) for both nodal clustering coefficient and nodal local efficiency in reward system nodes, particularly left caudate, right putamen and left hippocampus. Network-based statistic analyses showed that AUD group had significantly weaker connectivity than controls in the right hemisphere, mostly in the edges connecting putamen and hippocampus with other brain regions. Our results suggest that reward system network abnormalities, especially in subcortical structures, and impairments in rich-club organization might be related to the familial predisposition for AUD.
Collapse
Affiliation(s)
- Nabi Zorlu
- Department of Psychiatry; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| | - Necip Çapraz
- Department of Psychiatry; Medipol University Health Research and Administration Center, Sefakoy Hospital; Istanbul Turkey
| | - Esra Oztekin
- Department of Psychiatry; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| | - Başak Bagci
- Department of Psychiatry; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| | - Maria A. Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry; The University of Melbourne and Melbourne Health; Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry; The University of Melbourne and Melbourne Health; Australia
| | - Fazıl Gelal
- Department of Radiodiagnostics; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| | - Emre Bora
- Melbourne Neuropsychiatry Centre, Department of Psychiatry; The University of Melbourne and Melbourne Health; Australia
- Department of Psychiatry; Dokuz Eylül University Medical School; Turkey
| | - Ercan Durmaz
- Department of Psychiatry; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| | - Lütfullah Beşiroğlu
- Department of Psychiatry; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| | - Aybala Sarıçiçek
- Department of Psychiatry; Katip Celebi University, Ataturk Training and Research Hospital; Turkey
| |
Collapse
|
25
|
Wang B, Zhan Q, Yan T, Imtiaz S, Xiang J, Niu Y, Liu M, Wang G, Cao R, Li D. Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks. Cereb Cortex 2019; 29:4889-4901. [PMID: 30810159 DOI: 10.1093/cercor/bhz027] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 01/23/2019] [Accepted: 02/03/2019] [Indexed: 02/07/2023] Open
Abstract
AbstractStructural and functional differences in brain hemispheric asymmetry have been well documented between female and male adults. However, potential differences in the connectivity patterns of the rich-club organization of hemispheric structural networks in females and males remain to be determined. In this study, diffusion tensor imaging was used to construct hemispheric structural networks in healthy subjects, and graph theoretical analysis approaches were applied to quantify hemisphere and gender differences in rich-club organization. The results showed that rich-club organization was consistently observed in both hemispheres of female and male adults. Moreover, a reduced level of connectivity was found in the left hemisphere. Notably, rightward asymmetries were mainly observed in feeder and local connections among one hub region and peripheral regions, many of which are implicated in visual processing and spatial attention functions. Additionally, significant gender differences were revealed in the rich-club, feeder, and local connections in rich-club organization. These gender-related hub and peripheral regions are involved in emotional, sensory, and cognitive control functions. The topological changes in rich-club organization provide novel insight into the hemisphere and gender effects on white matter connections and underlie a potential network mechanism of hemisphere- and gender-based differences in visual processing, spatial attention and cognitive control.
Collapse
Affiliation(s)
- Bin Wang
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
- Department of Pathology and Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Qionghui Zhan
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Ting Yan
- Department of Pathology and Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Sumaira Imtiaz
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Jie Xiang
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Yan Niu
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Miaomiao Liu
- Graduate School of Natural Science and Technology, Okayama University, 1-1-1Tsushimanaka, Kita-ku, Okayama, Japan
| | - Gongshu Wang
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Rui Cao
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| | - Dandan Li
- Department of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, China
| |
Collapse
|
26
|
Griffa A, Van den Heuvel MP. Rich-club neurocircuitry: function, evolution, and vulnerability. DIALOGUES IN CLINICAL NEUROSCIENCE 2018. [PMID: 30250389 PMCID: PMC6136122 DOI: 10.31887/dcns.2018.20.2/agriffa] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.
Collapse
Affiliation(s)
- Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
| | - Martijn P Van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
| |
Collapse
|
27
|
Lin SJ, Baumeister TR, Garg S, McKeown MJ. Cognitive Profiles and Hub Vulnerability in Parkinson's Disease. Front Neurol 2018; 9:482. [PMID: 29973913 PMCID: PMC6019441 DOI: 10.3389/fneur.2018.00482] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 06/04/2018] [Indexed: 12/12/2022] Open
Abstract
The clinicopathological correlations between aspects of cognition, disease severity and imaging in Parkinson's Disease (PD) have been unclear. We studied cognitive profiles, demographics, and functional connectivity patterns derived from resting-state fMRI data (rsFC) in 31 PD subjects from the Parkinson's Progression Markers Initiative (PPMI) database. We also examined rsFC from 19 healthy subjects (HS) from the Pacific Parkinson's Research Centre. Graph theoretical measures were used to summarize the rsFC patterns. Canonical correlation analysis (CCA) was used to relate separate cognitive profiles in PD that were associated with disease severity and demographic measures as well as rsFC network measures. The CCA model relating cognition to demographics suggested female gender and education supported cognitive function in PD, age and depression scores were anti-correlated with overall cognition, and UPDRS had little influence on cognition. Alone, rsFC global network measures did not significantly differ between PD and controls, yet some nodal network measures, such as network segregation, were distinguishable between PD and HS in cortical “hub” regions. The CCA model relating cognition to rsFC global network values, which was not related to the other CCA model relating cognition to demographic information, suggested modularity, rich club coefficient, and transitivity was also broadly related to cognition in PD. Our results suggest that education, aging, comorbidity, and gender impact cognition more than overall disease severity in PD. Cortical “hub” regions are vulnerable in PD, and impairments of processing speed, attention, scanning abilities, and executive skills are related to enhanced functional segregation seen in PD.
Collapse
Affiliation(s)
- Sue-Jin Lin
- Graduate Program in Neuroscience, University of British Columbia, Vancouver, BC, Canada.,Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Tobias R Baumeister
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada.,School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Saurabh Garg
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Martin J McKeown
- Graduate Program in Neuroscience, University of British Columbia, Vancouver, BC, Canada.,Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada.,Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
28
|
Yan T, Wang W, Yang L, Chen K, Chen R, Han Y. Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease. Theranostics 2018; 8:3237-3255. [PMID: 29930726 PMCID: PMC6010989 DOI: 10.7150/thno.23772] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 04/08/2018] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) has a preclinical phase that can last for decades prior to clinical dementia onset. Subjective cognitive decline (SCD) is regarded as the last preclinical AD stage prior to the development of amnestic mild cognitive decline (aMCI) and AD dementia (d-AD). The analysis of brain structural networks based on diffusion tensor imaging (DTI) has identified the so-called 'rich club', a set of cortical regions highly connected to each other, with other regions referred to as peripheral. It has been reported that rich club architecture is affected by regional atrophy and connectivity, which are reduced in patients with aMCI and d-AD. Methods: We recruited 62 normal controls, 47 SCD patients, 60 aMCI patients and 55 d-AD patients and collected DTI data to analyze rich-club organization. Results: We demonstrated that rich club organization was disrupted, with reduced structural connectivity among rich club nodes, in aMCI and d-AD patients but remained stable in SCD patients. In addition, SCD, aMCI and d-AD patients showed similar patterns of disrupted peripheral regions and reduced connectivity involving these regions, suggesting that peripheral regions might contribute to cognitive decline and that disruptions here could be regarded as an early marker of SCD. This organization could provide the fundamental structural architecture for complex cognitive functions and explain the low prevalence of cognitive problems in SCD patients. Conclusions: These findings reveal a disrupted pattern of the AD connectome that starts in peripheral regions and then hierarchically propagates to rich club regions, when patients show clinical symptoms. This pattern provides evidence that disruptions in rich club organization are a key factor in the progression of AD that can dynamically reflect the progression of AD, thus representing a potential biomarker for early diagnosis.
Collapse
Affiliation(s)
- Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China
| | - Wenhui Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China
| | - Liu Yang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET center, Phoenix, AZ, USA
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, USA
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- Beijing Institute of Geriatrics, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
| |
Collapse
|
29
|
Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM. Brain Topogr 2018; 31:577-590. [PMID: 29663098 DOI: 10.1007/s10548-018-0642-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 03/09/2018] [Indexed: 10/17/2022]
Abstract
Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.
Collapse
|
30
|
Verhelst H, Vander Linden C, De Pauw T, Vingerhoets G, Caeyenberghs K. Impaired rich club and increased local connectivity in children with traumatic brain injury: Local support for the rich? Hum Brain Mapp 2018. [PMID: 29528158 DOI: 10.1002/hbm.24041] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Recent evidence has shown the presence of a "rich club" in the brain, which constitutes a core network of highly interconnected and spatially distributed brain regions, important for high-order cognitive processes. This study aimed to map the rich club organization in 17 young patients with moderate to severe TBI (15.71 ± 1.75 years) in the chronic stage of recovery and 17 age- and gender-matched controls. Probabilistic tractography was performed on diffusion weighted imaging data to construct the edges of the structural connectomes using number of streamlines as edge weight. In addition, the whole-brain network was divided into a rich club network, a local network and a feeder network connecting the latter two. Functional outcome was measured with a parent questionnaire for executive functioning. Our results revealed a significantly decreased rich club organization (p values < .05) and impaired executive functioning (p < .001) in young patients with TBI compared with controls. Specifically, we observed reduced density values in all three subnetworks (p values < .005) and a reduced mean strength in the rich club network (p = .013) together with an increased mean strength in the local network (p = .002) in patients with TBI. This study provides new insights into the nature of TBI-induced brain network alterations and supports the hypothesis that the local subnetwork tries to compensate for the biologically costly subnetwork of rich club nodes after TBI.
Collapse
Affiliation(s)
- Helena Verhelst
- Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, University of Ghent, Ghent, Belgium
| | - Catharine Vander Linden
- Child Rehabilitation Center, Department of Physical Medicine and Rehabilitation, Ghent University Hospital, Ghent, Belgium
| | - Toon De Pauw
- Department of Electronics and ICT, Faculty of Industrial Sciences and Technology, Odisee University College, Belgium
| | - Guy Vingerhoets
- Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, University of Ghent, Ghent, Belgium
| | - Karen Caeyenberghs
- School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| |
Collapse
|
31
|
Hampel H, Toschi N, Babiloni C, Baldacci F, Black KL, Bokde AL, Bun RS, Cacciola F, Cavedo E, Chiesa PA, Colliot O, Coman CM, Dubois B, Duggento A, Durrleman S, Ferretti MT, George N, Genthon R, Habert MO, Herholz K, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lorenceau J, Neri C, Nisticò R, Nyasse-Messene F, Ritchie C, Rossi S, Santarnecchi E, Sporns O, Verdooner SR, Vergallo A, Villain N, Younesi E, Garaci F, Lista S. Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. J Alzheimers Dis 2018; 64:S47-S105. [PMID: 29562524 PMCID: PMC6008221 DOI: 10.3233/jad-179932] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.
Collapse
Affiliation(s)
- Harald Hampel
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Department of Radiology, “Athinoula A. Martinos” Center for Biomedical Imaging, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza”, Rome, Italy
- Institute for Research and Medical Care, IRCCS “San Raffaele Pisana”, Rome, Italy
| | - Filippo Baldacci
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Keith L. Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
| | - René S. Bun
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Francesco Cacciola
- Unit of Neurosurgery, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Enrica Cavedo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- IRCCS “San Giovanni di Dio-Fatebenefratelli”, Brescia, Italy
| | - Patrizia A. Chiesa
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Olivier Colliot
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France
| | - Cristina-Maria Coman
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Bruno Dubois
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Stanley Durrleman
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France
| | - Maria-Teresa Ferretti
- IREM, Institute for Regenerative Medicine, University of Zurich, Zürich, Switzerland
- ZNZ Neuroscience Center Zurich, Zürich, Switzerland
| | - Nathalie George
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle Épinière, ICM, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
| | - Remy Genthon
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Marie-Odile Habert
- Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Karl Herholz
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Foudil Lamari
- AP-HP, UF Biochimie des Maladies Neuro-métaboliques, Service de Biochimie Métabolique, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | | | - Stéphane Lehéricy
- Centre de NeuroImagerie de Recherche - CENIR, Institut du Cerveau et de la Moelle Épinière - ICM, F-75013, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Paris, France
| | - Jean Lorenceau
- Institut de la Vision, INSERM, Sorbonne Universités, UPMC Univ Paris 06, UMR_S968, CNRS UMR7210, Paris, France
| | - Christian Neri
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, CNRS UMR 8256, Institut de Biologie Paris-Seine (IBPS), Place Jussieu, F-75005, Paris, France
| | - Robert Nisticò
- Department of Biology, University of Rome “Tor Vergata” & Pharmacology of Synaptic Disease Lab, European Brain Research Institute (E.B.R.I.), Rome, Italy
| | - Francis Nyasse-Messene
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Simone Rossi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Department of Medicine, Surgery and Neurosciences, Section of Human Physiology University of Siena, Siena, Italy
| | - Emiliano Santarnecchi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
| | | | - Andrea Vergallo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicolas Villain
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | | | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Casa di Cura “San Raffaele Cassino”, Cassino, Italy
| | - Simone Lista
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| |
Collapse
|
32
|
Abnormal topological organization in white matter structural networks in survivors of acute lymphoblastic leukaemia with chemotherapy treatment. Oncotarget 2017; 8:60568-60575. [PMID: 28947994 PMCID: PMC5601162 DOI: 10.18632/oncotarget.19104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 06/16/2017] [Indexed: 11/25/2022] Open
Abstract
Previous diffusion tensor imaging (DTI) studies have detected white matter (WM) integrity abnormalities in some specific fibre bundles in acute lymphoblastic leukaemia (ALL) patients with chemotherapy. However, little is known about the changes in the topological organization of the WM structural network in ALL patients with chemotherapy. In the present study, we acquired DTI datasets from 28 ALL patients (mean age: 40.71 ± 8.58 years, years since diagnosis: 7–38) with chemotherapy and 20 matched healthy controls (mean age: 42.95 ± 6.39 years) and performed WM network analysis using a deterministic fibre-tracking approach. Graph theoretical analysis was used to compare the topological parameters of the WM networks between the two groups. Both ALL patients with chemotherapy and healthy controls had small-worldness in their WM networks. ALL patients showed significantly reduced global network efficiency, as indicated by the abnormally decreased clustering coefficient Cp and the normalized clustering coefficient γ and increased shortest path length Lp compared with healthy controls. Moreover, hubs were located more in parietal regions of healthy controls and in temporal regions in the ALL patients. We revealed the abnormal topological organization of the WM networks of ALL patients with chemotherapy, which may improve our understanding of the neural mechanism of chemotherapy in ALL from a WM topological organization level.
Collapse
|
33
|
Markett S, de Reus MA, Reuter M, Montag C, Weber B, Schoene-Bake JC, van den Heuvel MP. Serotonin and the Brain's Rich Club-Association Between Molecular Genetic Variation on the TPH2 Gene and the Structural Connectome. Cereb Cortex 2017; 27:2166-2174. [PMID: 26975194 DOI: 10.1093/cercor/bhw059] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The rich club comprises a densely mutually connected set of hub regions in the brain, thought to serve as a processing and integration core. We assessed the impact of normal variation of the tryptophane hydroxylase 2 gene's promotor region (TPH2 rs4570625) on structural connectivity of the rich club pathways by means of a candidate gene association design. Tryptophane hydroxylase 2 (TPH2) is a rate-limiting enzyme in the biosynthesis of serotonin and is known to inhibit, in addition to its role as a trans-synaptic messenger, axonal and dendritic growth. The TPH2 T-variant has been associated with reduced mRNA expression and reduced serotonin levels, which may particularly influence the development of macroscale anatomical connectivity. Here, we show larger mean connectivity in the rich club in carriers of the T-variant, suggesting potential effects of upregulation of neural connectivity growth in this central core system. In addition, by edge-removal statistics, we show that the TPH2-associated higher levels of rich club connectivity are of importance for the functioning of the total structural network. The observed association is speculated to result from an effect of serotonin levels on brain development, potentially leading to stronger structural connectivity in heavily interconnected hubs.
Collapse
Affiliation(s)
| | - Marcel A de Reus
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands
| | - Martin Reuter
- Department of Psychology.,Center for Economics and Neuroscience
| | | | - Bernd Weber
- Center for Economics and Neuroscience.,Department of Epileptology, University of Bonn, Germany.,Neuroimaging Section, Life and Brain Center, Bonn, Germany
| | - Jan-Christoph Schoene-Bake
- Department of Epileptology, University of Bonn, Germany.,Neuroimaging Section, Life and Brain Center, Bonn, Germany
| | - Martijn P van den Heuvel
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands
| |
Collapse
|
34
|
Tuladhar AM, Lawrence A, Norris DG, Barrick TR, Markus HS, de Leeuw F. Disruption of rich club organisation in cerebral small vessel disease. Hum Brain Mapp 2016; 38:1751-1766. [PMID: 27935154 PMCID: PMC6866838 DOI: 10.1002/hbm.23479] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 11/13/2016] [Accepted: 11/16/2016] [Indexed: 11/07/2022] Open
Abstract
Cerebral small vessel disease (SVD) is an important cause of vascular cognitive impairment. Recent studies have demonstrated that structural connectivity of brain networks in SVD is disrupted. However, little is known about the extent and location of the reduced connectivity in SVD. Here they investigate the rich club organisation-a set of highly connected and interconnected regions-and investigate whether there is preferential rich club disruption in SVD. Diffusion tensor imaging (DTI) and cognitive assessment were performed in a discovery sample of SVD patients (n = 115) and healthy control subjects (n = 50). Results were replicated in an independent dataset (49 SVD with confluent WMH cases and 108 SVD controls) with SVD patients having a similar SVD phenotype to that of the discovery cases. Rich club organisation was examined in structural networks derived from DTI followed by deterministic tractography. Structural networks in SVD patients were less dense with lower network strength and efficiency. Reduced connectivity was found in SVD, which was preferentially located in the connectivity between the rich club nodes rather than in the feeder and peripheral connections, a finding confirmed in both datasets. In discovery dataset, lower rich club connectivity was associated with lower scores on psychomotor speed (β = 0.29, P < 0.001) and executive functions (β = 0.20, P = 0.009). These results suggest that SVD is characterized by abnormal connectivity between rich club hubs in SVD and provide evidence that abnormal rich club organisation might contribute to the development of cognitive impairment in SVD. Hum Brain Mapp 38:1751-1766, 2017. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Anil M. Tuladhar
- Department of NeurologyRadboud University Medical Center, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
- Centre for Cognitive NeuroimagingRadboud University, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
| | - Andrew Lawrence
- Department of Clinical Neurosciences, Neurology UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - David. G. Norris
- Centre for Cognitive NeuroimagingRadboud University, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg‐EssenArendahls Wiese 199, Tor 3EssenD‐45141Germany
- MIRA Institute for Biomedical Technology and Technical Medicine, University of TwenteEnschedeThe Netherlands
| | - Thomas R. Barrick
- St. George's University of London, Neuroscience Research Centre, Cardiovascular and Cell Sciences Research InstituteLondonUnited Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, Neurology UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - Frank‐Erik de Leeuw
- Department of NeurologyRadboud University Medical Center, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
| |
Collapse
|
35
|
Llufriu S, Martinez-Heras E, Solana E, Sola-Valls N, Sepulveda M, Blanco Y, Martinez-Lapiscina EH, Andorra M, Villoslada P, Prats-Galino A, Saiz A. Structural networks involved in attention and executive functions in multiple sclerosis. NEUROIMAGE-CLINICAL 2016; 13:288-296. [PMID: 28050344 PMCID: PMC5192049 DOI: 10.1016/j.nicl.2016.11.026] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 11/22/2016] [Accepted: 11/26/2016] [Indexed: 12/18/2022]
Abstract
Attention and executive deficits are disabling symptoms in multiple sclerosis (MS) that have been related to disconnection mechanisms. We aimed to investigate changes in structural connectivity in MS and their association with attention and executive performance applying an improved framework that combines high order probabilistic tractography and anatomical exclusion criteria postprocessing. We compared graph theory metrics of structural networks and fractional anisotropy (FA) of white matter (WM) connections or edges between 72 MS subjects and 38 healthy volunteers (HV) and assessed their correlation with cognition. Patients displayed decreased network transitivity, global efficiency and increased path length compared with HV (p < 0.05, corrected). Also, nodal strength was decreased in 26 of 84 gray matter regions. The distribution of nodes with stronger connections or hubs of the network was similar among groups except for the right pallidum and left insula, which became hubs in patients. MS subjects presented reduced edge FA widespread in the network, while FA was increased in 24 connections (p < 0.05, corrected). Decreased integrity of frontoparietal networks, deep gray nuclei and insula correlated with worse attention and executive performance (r between 0.38 and 0.55, p < 0.05, corrected). Contrarily, higher strength in the right transverse temporal cortex and increased FA of several connections (mainly from cingulate, frontal and occipital cortices) were associated with worse functioning (r between − 0.40 and − 0.47, p < 0.05 corrected). In conclusion, structural brain connectivity is disturbed in MS due to widespread impairment of WM connections and gray matter structures. The increased edge connectivity suggests the presence of reorganization mechanisms at the structural level. Importantly, attention and executive performance relates to frontoparietal networks, deep gray nuclei and insula. These results support the relevance of network integrity to maintain optimal cognitive skills. High order tractography and anatomical exclusion criteria improve connectivity analyses. Structural connectivity is less efficient in multiple sclerosis. Attentional and executive functions relate to integrity of strategic networks. Increased connectivity suggests structural reorganization mechanisms.
Collapse
Affiliation(s)
- Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Nuria Sola-Valls
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Maria Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Magi Andorra
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Pablo Villoslada
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Alberto Prats-Galino
- Laboratory of Surgical NeuroAnatomy (LSNA), Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| |
Collapse
|
36
|
van den Heuvel MP, Scholtens LH, Turk E, Mantini D, Vanduffel W, Feldman Barrett L. Multimodal analysis of cortical chemoarchitecture and macroscale fMRI resting-state functional connectivity. Hum Brain Mapp 2016; 37:3103-13. [PMID: 27207489 PMCID: PMC5111767 DOI: 10.1002/hbm.23229] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 04/13/2016] [Accepted: 04/15/2016] [Indexed: 12/14/2022] Open
Abstract
The cerebral cortex is well known to display a large variation in excitatory and inhibitory chemoarchitecture, but the effect of this variation on global scale functional neural communication and synchronization patterns remains less well understood. Here, we provide evidence of the chemoarchitecture of cortical regions to be associated with large-scale region-to-region resting-state functional connectivity. We assessed the excitatory versus inhibitory chemoarchitecture of cortical areas as an ExIn ratio between receptor density mappings of excitatory (AMPA, M1 ) and inhibitory (GABAA , M2 ) receptors, computed on the basis of data collated from pioneering studies of autoradiography mappings as present in literature of the human (2 datasets) and macaque (1 dataset) cortex. Cortical variation in ExIn ratio significantly correlated with total level of functional connectivity as derived from resting-state functional connectivity recordings of cortical areas across all three datasets (human I: P = 0.0004; human II: P = 0.0008; macaque: P = 0.0007), suggesting cortical areas with an overall more excitatory character to show higher levels of intrinsic functional connectivity during resting-state. Our findings are indicative of the microscale chemoarchitecture of cortical regions to be related to resting-state fMRI connectivity patterns at the global system's level of connectome organization. Hum Brain Mapp 37:3103-3113, 2016. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Martijn P van den Heuvel
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands
| | - Lianne H Scholtens
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands
| | - Elise Turk
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, The Netherlands
| | - Dante Mantini
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Wim Vanduffel
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
- Department of Radiology, Harvard Medical School, Boston, Massachusetts
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, Massachusetts
- Psychiatric Neuroimaging Program, Department of Psychiatry, and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| |
Collapse
|
37
|
Comparative Connectomics. Trends Cogn Sci 2016; 20:345-361. [PMID: 27026480 DOI: 10.1016/j.tics.2016.03.001] [Citation(s) in RCA: 196] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Revised: 02/23/2016] [Accepted: 03/01/2016] [Indexed: 12/30/2022]
Abstract
We introduce comparative connectomics, the quantitative study of cross-species commonalities and variations in brain network topology that aims to discover general principles of network architecture of nervous systems and the identification of species-specific features of brain connectivity. By comparing connectomes derived from simple to more advanced species, we identify two conserved themes of wiring: the tendency to organize network topology into communities that serve specialized functionality and the general drive to enable high topological integration by means of investment of neural resources in short communication paths, hubs, and rich clubs. Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.
Collapse
|
38
|
Zhu J, Wang C, Liu F, Qin W, Li J, Zhuo C. Alterations of Functional and Structural Networks in Schizophrenia Patients with Auditory Verbal Hallucinations. Front Hum Neurosci 2016; 10:114. [PMID: 27014042 PMCID: PMC4791368 DOI: 10.3389/fnhum.2016.00114] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/29/2016] [Indexed: 12/22/2022] Open
Abstract
Background: There have been many attempts at explaining the underlying neuropathological mechanisms of auditory verbal hallucinations (AVH) in schizophrenia on the basis of regional brain changes, with the most consistent findings being that AVH are associated with functional and structural impairments in auditory and speech-related regions. However, the human brain is a complex network and the global topological alterations specific to AVH in schizophrenia remain unclear. Methods: Thirty-five schizophrenia patients with AVH, 41 patients without AVH, and 50 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). The whole-brain functional and structural networks were constructed and analyzed using graph theoretical approaches. Inter-group differences in global network metrics (including small-world properties and network efficiency) were investigated. Results: We found that three groups had a typical small-world topology in both functional and structural networks. More importantly, schizophrenia patients with and without AVH exhibited common disruptions of functional networks, characterized by decreased clustering coefficient, global efficiency and local efficiency, and increased characteristic path length; structural networks of only schizophrenia patients with AVH showed increased characteristic path length compared with those of healthy controls. Conclusion: Our findings suggest that less “small-worldization” and lower network efficiency of functional networks may be an independent trait characteristic of schizophrenia, and regularization of structural networks may be the underlying pathological process engaged in schizophrenic AVH symptom expression.
Collapse
Affiliation(s)
- Jiajia Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital Tianjin, China
| | - Chunli Wang
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Anding Hospital, Tianjin Mental Health Center Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital Tianjin, China
| | - Jie Li
- Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Anding Hospital, Tianjin Mental Health Center Tianjin, China
| | - Chuanjun Zhuo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General HospitalTianjin, China; Department of Psychiatry Functional Neuroimaging Laboratory, Tianjin Anding Hospital, Tianjin Mental Health CenterTianjin, China; Tianjin Anning HospitalTianjin, China
| |
Collapse
|
39
|
Alagapan S, Franca E, Pan L, Leondopulos S, Wheeler BC, DeMarse TB. Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies. Front Bioeng Biotechnol 2016; 4:15. [PMID: 26973833 PMCID: PMC4770194 DOI: 10.3389/fbioe.2016.00015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Accepted: 02/04/2016] [Indexed: 11/13/2022] Open
Abstract
In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.
Collapse
Affiliation(s)
- Sankaraleengam Alagapan
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA
| | - Eric Franca
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA
| | - Liangbin Pan
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA
| | - Stathis Leondopulos
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA
| | - Bruce C Wheeler
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Department of Biomedical Engineering, University of California San Diego, San Diego, CA, USA
| | - Thomas B DeMarse
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA; Department of Pediatric Neurology, University of Florida, Gainesville, FL, USA
| |
Collapse
|
40
|
Collin G, Turk E, van den Heuvel MP. Connectomics in Schizophrenia: From Early Pioneers to Recent Brain Network Findings. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:199-208. [PMID: 29560880 DOI: 10.1016/j.bpsc.2016.01.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/15/2016] [Accepted: 01/19/2016] [Indexed: 12/15/2022]
Abstract
Schizophrenia has been conceptualized as a brain network disorder. The historical roots of connectomics in schizophrenia go back to the late 19th century, when influential scholars such as Theodor Meynert, Carl Wernicke, Emil Kraepelin, and Eugen Bleuler worked on a theoretical understanding of the multifaceted syndrome that is currently referred to as schizophrenia. Their work contributed to the understanding that symptoms such as psychosis and cognitive disorganization might stem from abnormal integration or dissociation due to disruptions in the brain's association fibers. As methods to test this hypothesis were long lacking, the claims of these early pioneers remained unsupported by empirical evidence for almost a century. In this review, we revisit and pay tribute to the old masters and, discussing recent findings from the developing field of disease connectomics, we examine how their pioneering hypotheses hold up in light of current evidence.
Collapse
Affiliation(s)
- Guusje Collin
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands.
| | - Elise Turk
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Martijn P van den Heuvel
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| |
Collapse
|
41
|
Nigam S, Shimono M, Ito S, Yeh FC, Timme N, Myroshnychenko M, Lapish CC, Tosi Z, Hottowy P, Smith WC, Masmanidis SC, Litke AM, Sporns O, Beggs JM. Rich-Club Organization in Effective Connectivity among Cortical Neurons. J Neurosci 2016; 36:670-84. [PMID: 26791200 PMCID: PMC4719009 DOI: 10.1523/jneurosci.2177-15.2016] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2015] [Revised: 11/08/2015] [Accepted: 11/12/2015] [Indexed: 11/21/2022] Open
Abstract
The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.
Collapse
Affiliation(s)
| | | | - Shinya Ito
- Santa Cruz Institute for Particle Physics, University of California at Santa Cruz, Santa Cruz, California 95064
| | - Fang-Chin Yeh
- Duke-NUS Graduate Medical School Singapore, Department of Neuroscience and Behavioural Disorders, Singapore 169857
| | | | | | - Christopher C Lapish
- School of Science Institute for Mathematical Modeling and Computational Sciences, Indiana University-Purdue University, Indianapolis, Indianapolis, Indiana 46202
| | - Zachary Tosi
- School of Informatics and Computing, College of Arts and Sciences, and
| | - Pawel Hottowy
- Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Krakow, Poland, and
| | | | - Sotiris C Masmanidis
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095
| | - Alan M Litke
- Duke-NUS Graduate Medical School Singapore, Department of Neuroscience and Behavioural Disorders, Singapore 169857
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47401
| | | |
Collapse
|
42
|
Karolis VR, Froudist-Walsh S, Brittain PJ, Kroll J, Ball G, Edwards AD, Dell'Acqua F, Williams SC, Murray RM, Nosarti C. Reinforcement of the Brain's Rich-Club Architecture Following Early Neurodevelopmental Disruption Caused by Very Preterm Birth. Cereb Cortex 2016; 26:1322-35. [PMID: 26742566 PMCID: PMC4737614 DOI: 10.1093/cercor/bhv305] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The second half of pregnancy is a crucial period for the development of structural brain connectivity, and an abrupt interruption of the typical processes of development during this phase caused by the very preterm birth (<33 weeks of gestation) is likely to result in long-lasting consequences. We used structural and diffusion imaging data to reconstruct the brain structural connectome in very preterm-born adults. We assessed its rich-club organization and modularity as 2 characteristics reflecting the capacity to support global and local information exchange, respectively. Our results suggest that the establishment of global connectivity patterns is prioritized over peripheral connectivity following early neurodevelopmental disruption. The very preterm brain exhibited a stronger rich-club architecture than the control brain, despite possessing a relative paucity of white matter resources. Using a simulated lesion approach, we also investigated whether putative structural reorganization takes place in the very preterm brain in order to compensate for its anatomical constraints. We found that connections between the basal ganglia and (pre-) motor regions, as well as connections between subcortical regions, assumed an altered role in the structural connectivity of the very preterm brain, and that such alterations had functional implications for information flow, rule learning, and verbal IQ.
Collapse
Affiliation(s)
- Vyacheslav R Karolis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
| | - Sean Froudist-Walsh
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
| | - Philip J Brittain
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
| | - Jasmin Kroll
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
| | - Gareth Ball
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering
| | - A David Edwards
- Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering
| | - Flavio Dell'Acqua
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Steven C Williams
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience
| | - Chiara Nosarti
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience Centre for the Developing Brain, Division of Imaging Sciences & Biomedical Engineering
| |
Collapse
|
43
|
McColgan P, Seunarine KK, Razi A, Cole JH, Gregory S, Durr A, Roos RAC, Stout JC, Landwehrmeyer B, Scahill RI, Clark CA, Rees G, Tabrizi SJ. Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington's disease. Brain 2015; 138:3327-44. [PMID: 26384928 PMCID: PMC4620513 DOI: 10.1093/brain/awv259] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 07/21/2015] [Accepted: 07/22/2015] [Indexed: 01/05/2023] Open
Abstract
Huntington's disease can be predicted many years before symptom onset, and thus makes an ideal model for studying the earliest mechanisms of neurodegeneration. Diffuse patterns of structural connectivity loss occur in the basal ganglia and cortex early in the disease. However, the organizational principles that underlie these changes are unclear. By understanding such principles we can gain insight into the link between the cellular pathology caused by mutant huntingtin and its downstream effect at the macroscopic level. The 'rich club' is a pattern of organization established in healthy human brains, where specific hub 'rich club' brain regions are more highly connected to each other than other brain regions. We hypothesized that selective loss of rich club connectivity might represent an organizing principle underlying the distributed pattern of structural connectivity loss seen in Huntington's disease. To test this hypothesis we performed diffusion tractography and graph theoretical analysis in a pseudo-longitudinal study of 50 premanifest and 38 manifest Huntington's disease participants compared with 47 healthy controls. Consistent with our hypothesis we found that structural connectivity loss selectively affected rich club brain regions in premanifest and manifest Huntington's disease participants compared with controls. We found progressive network changes across controls, premanifest Huntington's disease and manifest Huntington's disease characterized by increased network segregation in the premanifest stage and loss of network integration in manifest disease. These regional and whole brain network differences were highly correlated with cognitive and motor deficits suggesting they have pathophysiological relevance. We also observed greater reductions in the connectivity of brain regions that have higher network traffic and lower clustering of neighbouring regions. This provides a potential mechanism that results in a characteristic pattern of structural connectivity loss targeting highly connected brain regions with high network traffic and low clustering of neighbouring regions. Our findings highlight the role of the rich club as a substrate for the structural connectivity loss seen in Huntington's disease and have broader implications for understanding the connection between molecular and systems level pathology in neurodegenerative disease.
Collapse
Affiliation(s)
- Peter McColgan
- 1 Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Kiran K. Seunarine
- 2 Developmental Imaging and Biophysics Section, UCL Institute of Child Health, London, WC1N 1EH, UK
| | - Adeel Razi
- 3 Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK
- 4 Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - James H. Cole
- 5 Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, W12 0HS, UK
| | - Sarah Gregory
- 3 Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Alexandra Durr
- 6 APHP Department of Genetics, Groupe Hospitalier Pitié-Salpêtrière, and Institut du Cerveau et de la Moelle, INSERM U1127, CNRS UMR7225, Sorbonne Universités – UPMC Université Paris VI UMR_S1127, Paris, France
| | - Raymund A. C. Roos
- 7 Department of Neurology, Leiden University Medical Centre, 2300RC Leiden, The Netherlands
| | - Julie C. Stout
- 8 School of Psychological Sciences, Monash University, VIC, Australia
| | - Bernhard Landwehrmeyer
- 9 Department of Neurology, University of Ulm, Oberer Eselsberg 45-1, D-89081, Ulm, Germany
| | - Rachael I. Scahill
- 1 Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Chris A. Clark
- 2 Developmental Imaging and Biophysics Section, UCL Institute of Child Health, London, WC1N 1EH, UK
| | - Geraint Rees
- 3 Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, WC1N 3BG, UK
| | - Sarah J. Tabrizi
- 1 Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1N 3BG, UK
- 10 National Hospital for Neurology and Neurosurgery, Queen Square, London, WC1N 3BG, UK
| |
Collapse
|
44
|
Collin G, van den Heuvel MP, Abramovic L, Vreeker A, de Reus MA, van Haren NEM, Boks MPM, Ophoff RA, Kahn RS. Brain network analysis reveals affected connectome structure in bipolar I disorder. Hum Brain Mapp 2015; 37:122-34. [PMID: 26454006 DOI: 10.1002/hbm.23017] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Revised: 09/18/2015] [Accepted: 09/24/2015] [Indexed: 12/14/2022] Open
Abstract
The notion that healthy brain function emerges from coordinated neural activity constrained by the brain's network of anatomical connections--i.e., the connectome--suggests that alterations in the connectome's wiring pattern may underlie brain disorders. Corroborating this hypothesis, studies in schizophrenia are indicative of altered connectome architecture including reduced communication efficiency, disruptions of central brain hubs, and affected "rich club" organization. Whether similar deficits are present in bipolar disorder is currently unknown. This study examines structural connectome topology in 216 bipolar I disorder patients as compared to 144 healthy controls, focusing in particular on central regions (i.e., brain hubs) and connections (i.e., rich club connections, interhemispheric connections) of the brain's network. We find that bipolar I disorder patients exhibit reduced global efficiency (-4.4%, P =0.002) and that this deficit relates (r = 0.56, P < 0.001) to reduced connectivity strength of interhemispheric connections (-13.0%, P = 0.001). Bipolar disorder patients were found not to show predominant alterations in the strength of brain hub connections in general, or of connections spanning brain hubs (i.e., "rich club" connections) in particular (all P > 0.1). These findings highlight a role for aberrant brain network architecture in bipolar I disorder with reduced global efficiency in association with disruptions in interhemispheric connectivity, while the central "rich club" system appears not to be particularly affected.
Collapse
Affiliation(s)
- Guusje Collin
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn P van den Heuvel
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lucija Abramovic
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Annabel Vreeker
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marcel A de Reus
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Neeltje E M van Haren
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marco P M Boks
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Roel A Ophoff
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.,Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, California.,Department of Human Genetics, University of California Los Angeles, Los Angeles, California
| | - René S Kahn
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|