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Markett S, Boeken OJ, Wudarczyk OA. Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences. Eur J Neurosci 2024. [PMID: 38779858 DOI: 10.1111/ejn.16384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.
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Affiliation(s)
| | - Ole J Boeken
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin, Berlin, Germany
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Coronel‐Oliveros C, Gómez RG, Ranasinghe K, Sainz‐Ballesteros A, Legaz A, Fittipaldi S, Cruzat J, Herzog R, Yener G, Parra M, Aguillon D, Lopera F, Santamaria‐Garcia H, Moguilner S, Medel V, Orio P, Whelan R, Tagliazucchi E, Prado P, Ibañez A. Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling. Alzheimers Dement 2024; 20:3228-3250. [PMID: 38501336 PMCID: PMC11095480 DOI: 10.1002/alz.13788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results. METHODS We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls). RESULTS Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings. DISCUSSION The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.
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Affiliation(s)
- Carlos Coronel‐Oliveros
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)Universidad de ValparaísoValparaísoChile
| | - Raúl Gónzalez Gómez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Center for Social and Cognitive NeuroscienceSchool of Psychology, Universidad Adolfo IbáñezSantiagoChile
| | - Kamalini Ranasinghe
- Memory and Aging CenterDepartment of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | | | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
| | - Gorsev Yener
- Izmir University of Economics, Faculty of Medicine, Fevzi Çakmak, Balçova/İzmirSakaryaTurkey
- Dokuz Eylül University, Brain Dynamics Multidisciplinary Research Center, KonakAlsancakTurkey
| | - Mario Parra
- School of Psychological Sciences and HealthUniversity of StrathclydeGlasgowScotland
| | - David Aguillon
- Neuroscience Research Group, University of AntioquiaBogotáColombia
| | - Francisco Lopera
- Neuroscience Research Group, University of AntioquiaBogotáColombia
| | - Hernando Santamaria‐Garcia
- Pontificia Universidad Javeriana, PhD Program of NeuroscienceBogotáColombia
- Hospital Universitario San Ignacio, Center for Memory and Cognition IntellectusBogotáColombia
| | - Sebastián Moguilner
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Brain and Mind Centre, The University of SydneySydneyNew South WalesAustralia
- Department of NeuroscienceUniversidad de Chile, IndependenciaSantiagoChile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)Universidad de ValparaísoValparaísoChile
- Instituto de NeurocienciaFacultad de Ciencias, Universidad de Valparaíso, Playa AnchaValparaísoChile
| | - Robert Whelan
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Buenos Aires Physics Institute and Physics DepartmentUniversity of Buenos Aires, Intendente Güiraldes 2160 – Ciudad UniversitariaBuenos AiresArgentina
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la RehabilitaciónUniversidad San Sebastián, Región MetropolitanaSantiagoChile
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo Ibáñez, PeñalolénSantiagoChile
- Global Brain Health Institute (GBHI)University of California San Francisco (UCSFA)San FranciscoCaliforniaUSA
- Trinity College DublinDublinIreland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Provincia de Buenos AiresVictoriaArgentina
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
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Brzyski D, Hu X, Goni J, Ances B, Randolph TW, Harezlak J. Matrix-Variate Regression for Sparse, Low-Rank Estimation of Brain Connectivities Associated With a Clinical Outcome. IEEE Trans Biomed Eng 2024; 71:1378-1390. [PMID: 37995175 PMCID: PMC11127715 DOI: 10.1109/tbme.2023.3336241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
OBJECTIVE We address the problem of finding brain connectivities that are associated with a clinical outcome or phenotype. METHODS The proposed framework regresses a (scalar) clinical outcome on matrix-variate predictors which arise in the form of brain connectivity matrices. For example, in a large cohort of subjects we estimate those regions of functional connectivities that are associated with neurocognitive scores. We approach this high-dimensional yet highly structured estimation problem by formulating a regularized estimation process that results in a low-rank coefficient matrix having a sparse set of nonzero entries which represent regions of biologically relevant connectivities. In contrast to the recent literature on estimating a sparse, low-rank matrix from a single noisy observation, our scalar-on-matrix regression framework produces a data-driven extraction of structures that are associated with a clinical response. The method, called Sparsity Inducing Nuclear-Norm Estimator (SpINNEr), simultaneously constrains the regression coefficient matrix in two ways: a nuclear norm penalty encourages low-rank structure while an l1 norm encourages entry-wise sparsity. RESULTS Our simulations show that SpINNEr outperforms other methods in estimation accuracy when the response-related entries (representing the brain's functional connectivity) are arranged in well-connected communities. SpINNEr is applied to investigate associations between HIV-related outcomes and functional connectivity in the human brain. CONCLUSION AND SIGNIFICANCE Overall, this work demonstrates the potential of SpINNEr to recover sparse and low-rank estimates under scalar-on-matrix regression framework.
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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 2024:10.1007/s11682-024-00865-y. [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] [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.
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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.
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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.
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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
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Papo D, Buldú JM. Does the brain behave like a (complex) network? I. Dynamics. Phys Life Rev 2024; 48:47-98. [PMID: 38145591 DOI: 10.1016/j.plrev.2023.12.006] [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: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/27/2023]
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.
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Affiliation(s)
- D Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy; Center for Translational Neurophysiology, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy.
| | - J M Buldú
- Complex Systems Group & G.I.S.C., Universidad Rey Juan Carlos, Madrid, Spain
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7
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Zhou Y, Jing J, Zhang Z, Pan Y, Cai X, Zhu W, Li Z, Liu C, Liu H, Meng X, Cheng J, Wang Y, Li H, Wang S, Niu H, Wen W, Sachdev PS, Wei T, Liu T, Wang Y. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study. Hum Brain Mapp 2024; 45:e26598. [PMID: 38339955 PMCID: PMC10839741 DOI: 10.1002/hbm.26598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.
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Affiliation(s)
- Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jing Jing
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xueli Cai
- Department of Neurology, Lishui HospitalZhejiang University School of MedicineLishuiZhejiangChina
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Xia Meng
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Hao Li
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of MedicineLishuiZhejiangChina
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Wei Wen
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Tiemin Wei
- Department of Cardiology, Lishui HospitalZhejiang University School of MedicineZhejiangChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Research Unit of Artificial Intelligence in Cerebrovascular DiseaseChinese Academy of Medical Sciences, 2019RU018BeijingChina
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Li YL, Wu JJ, Li WK, Gao X, Wei D, Xue X, Hua XY, Zheng MX, Xu JG. Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors. Cereb Cortex 2024; 34:bhad439. [PMID: 37991271 DOI: 10.1093/cercor/bhad439] [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: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.
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Affiliation(s)
- Yu-Lin Li
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Wei-Kai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200233, China
| | - Dong Wei
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xin Xue
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jian-Guang Xu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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9
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Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JSW, Schiavi S, Jin H, Tavakol S, Vos de Wael R, Rodriguez-Cruces R, Pike GB, Bernhardt BC, Daducci A, Misic B, Tardif CL. The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Netw Neurosci 2023; 7:1363-1388. [PMID: 38144691 PMCID: PMC10697181 DOI: 10.1162/netn_a_00330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/19/2023] [Indexed: 12/26/2023] Open
Abstract
A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.
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Affiliation(s)
- Mark C. Nelson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jessica Royer
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Wen Da Lu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ilana R. Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jennifer S. W. Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Hyerang Jin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Shahin Tavakol
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Raul Rodriguez-Cruces
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - G. Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Boris C. Bernhardt
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | | | - Bratislav Misic
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Christine L. Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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10
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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11
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Chinen K, Kawabata A, Tanaka H, Komura Y. Inaccessible time to visual awareness during attentional blinks in macaques and humans. iScience 2023; 26:108208. [PMID: 38223787 PMCID: PMC10784117 DOI: 10.1016/j.isci.2023.108208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/26/2023] [Accepted: 10/11/2023] [Indexed: 01/16/2024] Open
Abstract
Even when we attend to successive visual events, we often cannot notice an event occurring during a certain temporal window. Such an inaccessible time for visual awareness is known as "attentional blink" (AB). Whether AB is a phenomenon unique to humans or exists also in other animals is unclear. Using a dual-task paradigm shared between macaques and humans, we here demonstrate a nonhuman primate model of AB. Although macaques also showed behavioral signatures of AB, their AB effect lasted longer than that of humans. To map the relation between macaque and human ABs, we introduced a time warping analysis. The analysis revealed a formal structure behind the interspecies difference of AB; the temporal window of macaque AB was scaled from that of human AB. The present study opens the door to combining the approaches of neuroscience, psychophysics, and theoretical models to further identify a scale-invariant biological substrate of visual awareness.
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Affiliation(s)
- Koji Chinen
- Graduate School of Human and Environmental Studies, Kyoto University, Yoshida-Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Akira Kawabata
- Graduate School of Human and Environmental Studies, Kyoto University, Yoshida-Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Hitoshi Tanaka
- Graduate School of Human and Environmental Studies, Kyoto University, Yoshida-Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan
| | - Yutaka Komura
- Graduate School of Human and Environmental Studies, Kyoto University, Yoshida-Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan
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12
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Kim SJ, Bae YJ, Park YH, Jang H, Kim JP, Seo SW, Seong JK, Kim GH. Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease. Front Aging Neurosci 2023; 15:1209027. [PMID: 37771522 PMCID: PMC10525353 DOI: 10.3389/fnagi.2023.1209027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023] Open
Abstract
Background and objectives Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD. Methods In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis. Results We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia. Conclusion Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.
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Affiliation(s)
- Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Youn Jung Bae
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, Republic of Korea
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Geon Ha Kim
- Department of Neurology, Ewha Womans University College of Medicine, Seoul, Republic of Korea
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13
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Panigrahy A, Schmithorst V, Ceschin R, Lee V, Beluk N, Wallace J, Wheaton O, Chenevert T, Qiu D, Lee JN, Nencka A, Gagoski B, Berman JI, Yuan W, Macgowan C, Coatsworth J, Fleysher L, Cannistraci C, Sleeper LA, Hoskoppal A, Silversides C, Radhakrishnan R, Markham L, Rhodes JF, Dugan LM, Brown N, Ermis P, Fuller S, Cotts TB, Rodriguez FH, Lindsay I, Beers S, Aizenstein H, Bellinger DC, Newburger JW, Umfleet LG, Cohen S, Zaidi A, Gurvitz M. Design and Harmonization Approach for the Multi-Institutional Neurocognitive Discovery Study (MINDS) of Adult Congenital Heart Disease (ACHD) Neuroimaging Ancillary Study: A Technical Note. J Cardiovasc Dev Dis 2023; 10:381. [PMID: 37754810 PMCID: PMC10532244 DOI: 10.3390/jcdd10090381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
Dramatic advances in the management of congenital heart disease (CHD) have improved survival to adulthood from less than 10% in the 1960s to over 90% in the current era, such that adult CHD (ACHD) patients now outnumber their pediatric counterparts. ACHD patients demonstrate domain-specific neurocognitive deficits associated with reduced quality of life that include deficits in educational attainment and social interaction. Our hypothesis is that ACHD patients exhibit vascular brain injury and structural/physiological brain alterations that are predictive of specific neurocognitive deficits modified by behavioral and environmental enrichment proxies of cognitive reserve (e.g., level of education and lifestyle/social habits). This technical note describes an ancillary study to the National Heart, Lung, and Blood Institute (NHLBI)-funded Pediatric Heart Network (PHN) "Multi-Institutional Neurocognitive Discovery Study (MINDS) in Adult Congenital Heart Disease (ACHD)". Leveraging clinical, neuropsychological, and biospecimen data from the parent study, our study will provide structural-physiological correlates of neurocognitive outcomes, representing the first multi-center neuroimaging initiative to be performed in ACHD patients. Limitations of the study include recruitment challenges inherent to an ancillary study, implantable cardiac devices, and harmonization of neuroimaging biomarkers. Results from this research will help shape the care of ACHD patients and further our understanding of the interplay between brain injury and cognitive reserve.
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Affiliation(s)
- Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
- Department of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, 45th Str., Penn Ave., Pittsburgh, PA 15201, USA
| | - Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Vince Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Nancy Beluk
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Olivia Wheaton
- HealthCore Inc., 480 Pleasant Str., Watertown, MA 02472, USA;
| | - Thomas Chenevert
- Department of Radiology, Michigan Medicine University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA;
- Congenital Heart Center, C. S. Mott Children’s Hospital, 1540 E Hospital Dr., Ann Arbor, MI 48109, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory School of Medicine, 1364 Clifton Rd., Atlanta, GA 30322, USA;
| | - James N Lee
- Department of Radiology, The University of Utah, 50 2030 E, Salt Lake City, UT 84112, USA;
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave., Milwaukee, WI 53226, USA;
| | - Borjan Gagoski
- Department of Radiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA;
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA;
| | - Weihong Yuan
- Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA;
- Department of Radiology, University of Cincinnati College of Medicine, 3230 Eden Ave., Cincinnati, OH 45267, USA
| | - Christopher Macgowan
- Department of Medical Biophysics, University of Toronto, 101 College Str. Suite 15-701, Toronto, ON M5G 1L7, Canada;
- The Hospital for Sick Children Division of Translational Medicine, 555 University Ave., Toronto, ON M5G 1X8, Canada
| | - James Coatsworth
- Department of Radiology, Medical University of South Carolina, 171 Ashley Ave., Room 372, Charleston, SC 29425, USA;
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Christopher Cannistraci
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
| | - Arvind Hoskoppal
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Candice Silversides
- Department of Cardiology, University of Toronto, C. David Naylor Building, 6 Queen’s Park Crescent West, Third Floor, Toronto, ON M5S 3H2, Canada;
| | - Rupa Radhakrishnan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 University Blvd., Indianapolis, IN 46202, USA;
| | - Larry Markham
- Department of Cardiology, University of Indiana School of Medicine, 545 Barnhill Dr., Indianapolis, IN 46202, USA;
| | - John F. Rhodes
- Department of Cardiology, Medical University of South Carolina, 96 Jonathan Lucas Str. Ste. 601, MSC 617, Charleston, SC 29425, USA;
| | - Lauryn M. Dugan
- Department of Cardiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA; (L.M.D.); (N.B.)
| | - Nicole Brown
- Department of Cardiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA; (L.M.D.); (N.B.)
| | - Peter Ermis
- Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA; (P.E.); (S.F.)
| | - Stephanie Fuller
- Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA; (P.E.); (S.F.)
| | - Timothy Brett Cotts
- Departments of Internal Medicine and Pediatrics, Michigan Medicine University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA;
| | - Fred Henry Rodriguez
- Department of Cardiology, Emory School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322, USA;
| | - Ian Lindsay
- Department of Cardiology, The University of Utah, 95 S 2000 E, Salt Lake City, UT 84112, USA;
| | - Sue Beers
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara Str., Pittsburgh, PA 15213, USA; (S.B.); (H.A.)
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara Str., Pittsburgh, PA 15213, USA; (S.B.); (H.A.)
| | - David C. Bellinger
- Cardiac Neurodevelopmental Program, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA;
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
| | - Laura Glass Umfleet
- Department of Neuropsychology, Medical College of Wisconsin, 9200 W Wisconsin Ave., Milwaukee, WI 53226, USA;
| | - Scott Cohen
- Heart and Vascular Center, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Ali Zaidi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Michelle Gurvitz
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
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14
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de Sousa AA, Beaudet A, Calvey T, Bardo A, Benoit J, Charvet CJ, Dehay C, Gómez-Robles A, Gunz P, Heuer K, van den Heuvel MP, Hurst S, Lauters P, Reed D, Salagnon M, Sherwood CC, Ströckens F, Tawane M, Todorov OS, Toro R, Wei Y. From fossils to mind. Commun Biol 2023; 6:636. [PMID: 37311857 PMCID: PMC10262152 DOI: 10.1038/s42003-023-04803-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 04/04/2023] [Indexed: 06/15/2023] Open
Abstract
Fossil endocasts record features of brains from the past: size, shape, vasculature, and gyrification. These data, alongside experimental and comparative evidence, are needed to resolve questions about brain energetics, cognitive specializations, and developmental plasticity. Through the application of interdisciplinary techniques to the fossil record, paleoneurology has been leading major innovations. Neuroimaging is shedding light on fossil brain organization and behaviors. Inferences about the development and physiology of the brains of extinct species can be experimentally investigated through brain organoids and transgenic models based on ancient DNA. Phylogenetic comparative methods integrate data across species and associate genotypes to phenotypes, and brains to behaviors. Meanwhile, fossil and archeological discoveries continuously contribute new knowledge. Through cooperation, the scientific community can accelerate knowledge acquisition. Sharing digitized museum collections improves the availability of rare fossils and artifacts. Comparative neuroanatomical data are available through online databases, along with tools for their measurement and analysis. In the context of these advances, the paleoneurological record provides ample opportunity for future research. Biomedical and ecological sciences can benefit from paleoneurology's approach to understanding the mind as well as its novel research pipelines that establish connections between neuroanatomy, genes and behavior.
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Affiliation(s)
| | - Amélie Beaudet
- Laboratoire de Paléontologie, Évolution, Paléoécosystèmes et Paléoprimatologie (PALEVOPRIM), UMR 7262 CNRS & Université de Poitiers, Poitiers, France.
- University of Cambridge, Cambridge, UK.
| | - Tanya Calvey
- Division of Clinical Anatomy and Biological Anthropology, University of Cape Town, Cape Town, South Africa.
| | - Ameline Bardo
- UMR 7194, CNRS-MNHN, Département Homme et Environnement, Musée de l'Homme, Paris, France
- Skeletal Biology Research Centre, School of Anthropology and Conservation, University of Kent, Canterbury, UK
| | - Julien Benoit
- Evolutionary Studies Institute, University of the Witwatersrand, Johannesburg, South Africa
| | - Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Colette Dehay
- University of Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, F-69500, Bron, France
| | | | - Philipp Gunz
- Department of Human Origins, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, D-04103, Leipzig, Germany
| | - Katja Heuer
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | | | - Shawn Hurst
- University of Indianapolis, Indianapolis, IN, USA
| | - Pascaline Lauters
- Institut royal des Sciences naturelles, Direction Opérationnelle Terre et Histoire de la Vie, Brussels, Belgium
| | - Denné Reed
- Department of Anthropology, University of Texas at Austin, Austin, TX, USA
| | - Mathilde Salagnon
- CNRS, CEA, IMN, GIN, UMR 5293, Université Bordeaux, Bordeaux, France
- PACEA UMR 5199, CNRS, Université Bordeaux, Pessac, France
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, Washington, DC, USA
| | - Felix Ströckens
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Mirriam Tawane
- Ditsong National Museum of Natural History, Pretoria, South Africa
| | - Orlin S Todorov
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique, F-75015, Paris, France
| | - Yongbin Wei
- Beijing University of Posts and Telecommunications, Beijing, China
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15
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Vo A, Nguyen N, Fujita K, Schindlbeck KA, Rommal A, Bressman SB, Niethammer M, Eidelberg D. Disordered network structure and function in dystonia: pathological connectivity vs. adaptive responses. Cereb Cortex 2023; 33:6943-6958. [PMID: 36749014 PMCID: PMC10233302 DOI: 10.1093/cercor/bhad012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/21/2022] [Accepted: 01/10/2023] [Indexed: 02/08/2023] Open
Abstract
Primary dystonia is thought to emerge through abnormal functional relationships between basal ganglia and cerebellar motor circuits. These interactions may differ across disease subtypes and provide a novel biomarker for diagnosis and treatment. Using a network mapping algorithm based on resting-state functional MRI (rs-fMRI), a method that is readily implemented on conventional MRI scanners, we identified similar disease topographies in hereditary dystonia associated with the DYT1 or DYT6 mutations and in sporadic patients lacking these mutations. Both networks were characterized by contributions from the basal ganglia, cerebellum, thalamus, sensorimotor areas, as well as cortical association regions. Expression levels for the two networks were elevated in hereditary and sporadic dystonia, and in non-manifesting carriers of dystonia mutations. Nonetheless, the distribution of abnormal functional connections differed across groups, as did metrics of network organization and efficiency in key modules. Despite these differences, network expression correlated with dystonia motor ratings, significantly improving the accuracy of predictions based on thalamocortical tract integrity obtained with diffusion tensor MRI (DTI). Thus, in addition to providing unique information regarding the anatomy of abnormal brain circuits, rs-fMRI functional networks may provide a widely accessible method to help in the objective evaluation of new treatments for this disorder.
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Affiliation(s)
- An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Koji Fujita
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Andrea Rommal
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, New York, NY 10003, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
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16
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Schnellbächer GJ, Rajkumar R, Veselinović T, Ramkiran S, Hagen J, Shah NJ, Neuner I. Structural alterations of the insula in depression patients - A 7-Tesla-MRI study. Neuroimage Clin 2022; 36:103249. [PMID: 36451355 PMCID: PMC9668670 DOI: 10.1016/j.nicl.2022.103249] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 09/26/2022] [Accepted: 10/23/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION The insular cortex is part of a network of highly connected cerebral "rich club" - regions and has been implicated in the pathophysiology of various psychiatric and neurological disorders, of which major depressive disease is one of the most prevalent. "Rich club" vulnerability can be a contributing factor in disease development. High-resolution structural subfield analysis of insular volume in combination with cortical thickness measurements and psychological testing might elucidate the way in which the insula is changed in depression. MATERIAL AND METHODS High-resolution structural images of the brain were acquired using a 7T-MRI scanner. The mean grey matter volume and cortical thickness within the insular subfields were analysed using voxel-based morphometry (VBM) and surface analysis techniques respectively. Insular subfields were defined according to the Brainnetome Atlas for VBM - and the Destrieux-Atlas for cortical thickness - analysis. Thirty-three patients with confirmed major depressive disease, as well as thirty-one healthy controls matched for age and gender, were measured. The severity of depression in MDD patients was measured via a BDI-II score and objective clinical assessment (AMDP). Intergroup statistical analysis was performed using ANCOVA. An intragroup multivariate regression analysis of patient psychological test results was calculated. Corrections for multiple comparisons was performed using FDR. RESULTS Significant differences between groups were observed in the left granular dorsal insula according to VBM-analysis. AMDP-scores positively correlated with cortical thickness in the right superior segment of the circular insular sulcus. CONCLUSIONS The combination of differences in grey matter volume between healthy controls and patients with a positive correlation of cortical thickness with disease severity underscores the insula's role in the pathogeneses of MDD. The connectivity hub insular cortex seems vulnerable to disruption in context of affective disease.
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Affiliation(s)
- Gereon J. Schnellbächer
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - Ravichandran Rajkumar
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Shukti Ramkiran
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Jana Hagen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany,Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 11, INM-11, Forschungszentrum Jülich, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany,Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany,JARA-BRAIN, 52074 Aachen, Germany,Corresponding author.
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17
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Targeting disrupted rich-club network organization with neuroplasticity-based computerized cognitive remediation in major depressive disorder patients. Psychiatry Res 2022; 316:114742. [PMID: 35917652 DOI: 10.1016/j.psychres.2022.114742] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022]
Abstract
Disrupted rich-club organization has been extensively studied in major depressive disorder (MDD) patients. Although data indicate that neuroplasticity-based computerized cognitive remediation (nCCR) can accelerate clinical responses in MDD patients, the mechanisms underlying its antidepressant efficacy are unknown. In this study, all MDD patients underwent two (baseline and week 4) neuropsychological assessments and DTI imaging. Additionally, 17 MDD patients did nCCR for 30 hours spread across 4 weeks. Rich-club organization was calculated with a graph-theoretical approach, and SC-FC coupling was explored. After 4 weeks of treatment, the number of rich-club connections, global efficiency, and SC-FC coupling strength increased significantly and were negatively associated with TMT-B scores. The effects of nCCR on disrupted rich-club organization may partly underlie its efficacy in improving the executive function of patients with MDD. Effects of nCCR on disrupted rich-club organization may partly underlie its efficacy in improving the executive function of patients with MDD.
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18
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Schmithorst VJ, Adams PS, Badaly D, Lee VK, Wallace J, Beluk N, Votava-Smith JK, Weinberg JG, Beers SR, Detterich J, Wood JC, Lo CW, Panigrahy A. Impaired Neurovascular Function Underlies Poor Neurocognitive Outcomes and Is Associated with Nitric Oxide Bioavailability in Congenital Heart Disease. Metabolites 2022; 12:metabo12090882. [PMID: 36144286 PMCID: PMC9504090 DOI: 10.3390/metabo12090882] [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: 08/13/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 12/03/2022] Open
Abstract
We use a non-invasive MRI proxy of neurovascular function (pnvf) to assess the ability of the vasculature to supply baseline metabolic demand, to compare pediatric and young adult congenital heart disease (CHD) patients to normal referents and relate the proxy to neurocognitive outcomes and nitric oxide bioavailability. In a prospective single-center study, resting-state blood-oxygen-level-dependent (BOLD) and arterial spin labeling (ASL) MRI scans were successfully obtained from 24 CHD patients (age = 15.4 ± 4.06 years) and 63 normal referents (age = 14.1 ± 3.49) years. Pnvf was computed on a voxelwise basis as the negative of the ratio of functional connectivity strength (FCS) estimated from the resting-state BOLD acquisition to regional cerebral blood flow (rCBF) as estimated from the ASL acquisition. Pnvf was used to predict end-tidal CO2 (PETCO2) levels and compared to those estimated from the BOLD data. Nitric oxide availability was obtained via nasal measurements (nNO). Pnvf was compared on a voxelwise basis between CHD patients and normal referents and correlated with nitric oxide availability and neurocognitive outcomes as assessed via the NIH Toolbox. Pnvf was shown as highly predictive of PETCO2 using theoretical modeling. Pnvf was found to be significantly reduced in CHD patients in default mode network (DMN, comprising the ventromedial prefrontal cortex and posterior cingulate/precuneus), salience network (SN, comprising the insula and dorsal anterior cingulate), and central executive network (CEN, comprising posterior parietal and dorsolateral prefrontal cortex) regions with similar findings noted in single cardiac ventricle patients. Positive correlations of Pnvf in these brain regions, as well as the hippocampus, were found with neurocognitive outcomes. Similarly, positive correlations between Pnvf and nitric oxide availability were found in frontal DMN and CEN regions, with particularly strong correlations in subcortical regions (putamen). Reduced Pnvf in CHD patients was found to be mediated by nNO. Mediation analyses further supported that reduced Pnvf in these regions underlies worse neurocognitive outcome in CHD patients and is associated with nitric oxide bioavailability. Impaired neuro-vascular function, which may be non-invasively estimated via combined arterial-spin label and BOLD MR imaging, is a nitric oxide bioavailability dependent factor implicated in adverse neurocognitive outcomes in pediatric and young adult CHD.
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Affiliation(s)
| | - Phillip S. Adams
- Department of Pediatric Anesthesiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | - Daryaneh Badaly
- Learning and Development Center, Child Mind Institute, New York, NY 10022, USA
| | - Vincent K. Lee
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Julia Wallace
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | - Nancy Beluk
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | | | | | - Sue R. Beers
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jon Detterich
- Heart Institute, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - John C. Wood
- Heart Institute, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - Cecilia W. Lo
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ashok Panigrahy
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Correspondence: ; Tel.: +1-412-692-5510; Fax: +1-412-692-6929
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19
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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20
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Li F, Liu Y, Lu L, Shang S, Chen H, Haidari NA, Wang P, Yin X, Chen YC. Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment. Quant Imaging Med Surg 2022; 12:3932-3946. [PMID: 35782237 PMCID: PMC9246720 DOI: 10.21037/qims-21-915] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/30/2022] [Indexed: 06/12/2024]
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) is typically characterized by temporally limited cognitive impairment and regarded as a brain connectome disorder. Recent findings have suggested that a higher level of organization named the "rich-club" may play a central role in enabling the integration of information and efficient communication across different systems of the brain. However, the alterations in rich-club organization and hub topology in mTBI and its relationship with cognitive impairment after mTBI have been scarcely elucidated. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 88 patients with mTBI and 85 matched healthy controls (HCs). Large-scale functional brain networks were established for each participant. Rich-club organizations and network properties were assessed and analyzed between groups. Finally, we analyzed the correlations between the cognitive performance and changes in rich-club organization and network properties. RESULTS Both mTBI and HCs groups showed significant rich-club organization. Meanwhile, the rich-club organization was aberrant, with enhanced functional connectivity (FC) among rich-club nodes and peripheral regions in acute mTBI. In addition, significant differences in partial global and local network topological property measures were found between mTBI patients and HCs (P<0.01). In patients with mTBI, changes in rich-club organization and network properties were found to be related to early cognitive impairment after mTBI (P<0.05). CONCLUSIONS Our findings suggest that such patterns of disruption and reorganization will provide the basic functional architecture for cognitive function, which may subsequently be used as an earlier biomarker for cognitive impairment after mTBI.
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Affiliation(s)
| | | | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Song’an Shang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Nasir Ahmad Haidari
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Peng Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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21
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Yang Z, Cieri F, Kinney JW, Cummings JL, Cordes D, Caldwell JZK. Brain functional topology differs by sex in cognitively normal older adults. Cereb Cortex Commun 2022; 3:tgac023. [PMID: 35795479 PMCID: PMC9252274 DOI: 10.1093/texcom/tgac023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Introduction Late onset Alzheimer's disease (AD) is the most common form of dementia, in which almost 70% of patients are women. Hypothesis We hypothesized that women show worse global FC metrics compared to men, and further hypothesized a sex-specific positive correlation between FC metrics and cognitive scores in women. Methods We studied cognitively healthy individuals from the Alzheimer's Disease Neuroimaging Initiative cohort, with resting-state functional Magnetic Resonance Imaging. Metrics derived from graph theoretical analysis and functional connectomics were used to assess the global/regional sex differences in terms of functional integration and segregation, considering the amyloid status and the contributions of APOE E4. Linear mixed effect models with covariates (education, handedness, presence of apolipoprotein [APOE] E4 and intra-subject effect) were utilized to evaluate sex differences. The associations of verbal learning and memory abilities with topological network properties were assessed. Result Women had a significantly lower magnitude of the global and regional functional network metrics compared to men. Exploratory association analysis showed that higher global clustering coefficient was associated with lower percent forgetting in women and worse cognitive scores in men. Conclusion Women overall show lower magnitude on measures of resting state functional network topology and connectivity. This factor can play a role in their different vulnerability to AD. Significance statement Two thirds of AD patients are women but the reasons for these sex difference are not well understood. When this late onset form dementia arises is too late to understand the potential causes of this sex disparities. Studies on cognitively healthy elderly population are a fundamental approach to explore in depth this different vulnerability to the most common form of dementia, currently affecting 6.2 million Americans aged 65 and older are, which means that >1 in 9 people (11.3%) 65 and older are affected by AD. Approaches such as resting-state functional network topology and connectivity may play a key role in understanding and elucidate sex-dependent differences relevant to late-onset dementia syndromes.
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Affiliation(s)
| | - Filippo Cieri
- Corresponding author: Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, United States.
| | - Jefferson W Kinney
- Department of Brain Health, University of Nevada, Mail Stop: 4022; 4505 S. Maryland Pkwy. Room 1172, Las Vegas, NV 89154, United States,Chambers-Grundy Center for Transformative Neuroscience, University of Nevada, Box 454022, 4505 S. Maryland Pkwy, Las Vegas, NV 89154-4022, United States
| | - Jeffrey L Cummings
- Department of Brain Health, University of Nevada, Mail Stop: 4022; 4505 S. Maryland Pkwy. Room 1172, Las Vegas, NV 89154, United States,Chambers-Grundy Center for Transformative Neuroscience, University of Nevada, Box 454022, 4505 S. Maryland Pkwy, Las Vegas, NV 89154-4022, United States
| | - Dietmar Cordes
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV 89106, United States,Department of Brain Health, University of Nevada, Mail Stop: 4022; 4505 S. Maryland Pkwy. Room 1172, Las Vegas, NV 89154, United States,Department of Psychology and Neuroscience, University of Colorado, 3100 Marine St., Boulder, CO 80309, United States
| | - Jessica Z K Caldwell
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, 888 W Bonneville Ave, Las Vegas, NV 89106, United States
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22
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Sun L, Liang X, Duan D, Liu J, Chen Y, Wang X, Liao X, Xia M, Zhao T, He Y. Structural insight into the individual variability architecture of the functional brain connectome. Neuroimage 2022; 259:119387. [PMID: 35752416 DOI: 10.1016/j.neuroimage.2022.119387] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/11/2022] [Accepted: 06/13/2022] [Indexed: 11/15/2022] Open
Abstract
Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.
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Affiliation(s)
- Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xinyuan Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuhan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing, 102206, China.
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23
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Griffa A, Legdeur N, Badissi M, van den Heuvel MP, Stam CJ, Visser PJ, Hillebrand A. Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study. Front Aging Neurosci 2021; 13:746373. [PMID: 34899269 PMCID: PMC8656941 DOI: 10.3389/fnagi.2021.746373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022] Open
Abstract
The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.
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Affiliation(s)
- Alessandra Griffa
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Nienke Legdeur
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Maryam Badissi
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neuroscience and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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24
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Sun Y, Wang X, Tian S, Chen Z, Wang H, Xue L, Yan R, Yao Z, Lu Q. An Investigation into the Association Between Dopamine Receptor D1 Multilocus Genetic Variation, Multiparametric Magnetic Resonance Imaging, and Antidepressant Treatment. J Magn Reson Imaging 2021; 56:282-290. [PMID: 34870351 DOI: 10.1002/jmri.28017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Combining genetic variants with neuroimaging phenotypes may facilitate understanding of the biological mechanisms for the etiology and pharmacology of antidepressant treatment of major depressive disorder (MDD). PURPOSE To explore the latent pathway of dopamine gene-hierarchical brain network-antidepressant treatment. STUDY TYPE Retrospective. POPULATION One hundred and sixty-eight MDD inpatients divided into responders (N = 98) or nonresponders (N = 70) based on the treatment outcome of antidepressant. FIELD STRENGTH/SEQUENCE Diffusion tensors imaging and resting-state functional magnetic resonance imaging at 3.0T using echo-planar sequence. ASSESSMENT Four genetic variations of the dopamine receptor D1 (DRD1) were genotyped. Strengths of rich-club, feeder, and local connections were calculated based on the rich-club organizations of structural and functional brain networks at baseline and following 4 weeks of selective serotonin reuptake inhibitor (SSRI) therapy. STATISTICAL TESTS Logistic and linear regressions were used to analyze the impact of DRD1 multilocus genetic profile score on the treatment response of SSRI, and their associations with strengths of rich-club, feeder, and local connections. Mediation models were developed to explore the mediation role of rich-club organizations on the relationship between DRD1 and SSRI therapy response. A P value <0.05 was considered to be statistically significant. RESULTS Multiple genetic variations of DRD1 were significantly related to the strengths of feeder connections both in structural and functional networks, and to the treatment response of SSRI. Furthermore, the strength of the structural feeder connection significantly modulated the effect of DRD1 variants on SSRI treatment outcome. DATA CONCLUSION DRD1 displayed close connections both with SSRI treatment outcome and rich-club organizations of structural and functional data. Moreover, structural feeder connection played a mediating role in the relationship between DRD1 and antidepressant therapy. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Yurong Sun
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Shui Tian
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhilu Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huan Wang
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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25
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Pini L, Salvalaggio A, De Filippo De Grazia M, Zorzi M, Thiebaut de Schotten M, Corbetta M. A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction. Brain Commun 2021; 3:fcab259. [PMID: 34859213 PMCID: PMC8633453 DOI: 10.1093/braincomms/fcab259] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/23/2021] [Accepted: 09/07/2021] [Indexed: 11/12/2022] Open
Abstract
Lesion network mapping estimates functional network abnormalities caused by a focal brain lesion. The method requires embedding the volume of the lesion into a normative functional connectome and using the average functional magnetic resonance imaging signal from that volume to compute the temporal correlation with all other brain locations. Lesion network mapping yields a map of potentially functionally disconnected regions. Although promising, this approach does not predict behavioural deficits well. We modified lesion network mapping by using the first principal component of the functional magnetic resonance imaging signal computed from the voxels within the lesioned area for temporal correlation. We measured potential improvements in connectivity strength, anatomical specificity of the lesioned network and behavioural prediction in a large cohort of first-time stroke patients at 2-weeks post-injury (n = 123). This principal component functional disconnection approach localized mainly cortical voxels of high signal-to-noise; and it yielded networks with higher anatomical specificity, and stronger behavioural correlation than the standard method. However, when examined with a rigorous leave-one-out machine learning approach, principal component functional disconnection approach did not perform better than the standard lesion network mapping in predicting neurological deficits. In summary, even though our novel method improves the specificity of disconnected networks and correlates with behavioural deficits post-stroke, it does not improve clinical prediction. Further work is needed to capture the complex adjustment of functional networks produced by focal damage in relation to behaviour.
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Affiliation(s)
- Lorenzo Pini
- Padova Neuroscience Center (PNC), University of Padova, Padova, 35100 Italy
| | - Alessandro Salvalaggio
- Padova Neuroscience Center (PNC), University of Padova, Padova, 35100 Italy.,Clinica Neurologica, Department of Neuroscience, University of Padova, Padova, 35100 Italy
| | | | - Marco Zorzi
- IRCCS San Camillo Hospital, Venice, 30126 Italy.,Department of General Psychology, University of Padova, Padova, 35100 Italy
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, 75006 France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, 33076 France
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, Padova, 35100 Italy.,Clinica Neurologica, Department of Neuroscience, University of Padova, Padova, 35100 Italy.,Venetian Institute of Molecular Medicine, VIMM, Padova, 35100 Italy
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26
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Oldham S, Ball G, Fornito A. Early and late development of hub connectivity in the human brain. Curr Opin Psychol 2021; 44:321-329. [PMID: 34896927 DOI: 10.1016/j.copsyc.2021.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/14/2021] [Accepted: 10/28/2021] [Indexed: 12/28/2022]
Abstract
Human brain networks undergo pronounced changes during development. The emergence of highly connected hub regions that can support integrated brain function is central to this maturational process, with these areas undergoing a particularly protracted period of development that extends into adulthood. The location of cortical network hubs emerges early but connections to and from hubs continue to strengthen throughout childhood and adolescence. Patterns of functional coupling in cortical association hubs are immature and incomplete at birth, but gradually strengthen during development. Early establishment of hub connectivity may provide a stable substrate that is refined by changes in tissue organization and microstructure, resulting in the emergence of complex functional dynamics by adulthood.
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Affiliation(s)
- Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia; Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia.
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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27
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Paredes O, López JB, Covantes-Osuna C, Ocegueda-Hernández V, Romo-Vázquez R, Morales JA. A Transcriptome Community-and-Module Approach of the Human Mesoconnectome. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1031. [PMID: 34441171 PMCID: PMC8393183 DOI: 10.3390/e23081031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.
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Affiliation(s)
| | | | | | | | - Rebeca Romo-Vázquez
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
| | - J. Alejandro Morales
- Computer Sciences Department, Exact Sciences and Engineering University Centre, Universidad de Guadalajara, Guadalajara 44430, Mexico; (O.P.); (J.B.L.); (C.C.-O.); (V.O.-H.)
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28
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Spatiospectral brain networks reflective of improvisational experience. Neuroimage 2021; 242:118458. [PMID: 34363958 DOI: 10.1016/j.neuroimage.2021.118458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 07/18/2021] [Accepted: 08/04/2021] [Indexed: 11/20/2022] Open
Abstract
Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.
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29
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Argyropoulou MI, Xydis VG, Drougia A, Giantsouli AS, Giapros V, Astrakas LG. Structural and functional brain connectivity in moderate-late preterm infants with low-grade intraventricular hemorrhage. Neuroradiology 2021; 64:197-204. [PMID: 34342681 DOI: 10.1007/s00234-021-02770-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/11/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE Brain functional connectivity (FC) changes and microstructural abnormalities are reported in infants born moderate and late preterm (MLPT). We evaluated the effect of low-grade (grades I, II) intraventricular hemorrhage (IVH) in MLPT babies on brain structural connectivity (SC) and FC. METHODS Babies born MLPT between January 2014 and May 2017 underwent brain ultrasound (US) at 72 h and 7 days after birth, and MRI at around term equivalent. The MRI protocol comprised T1- and T2-weighted sequences, diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). SC and FC were assessed using graph analysis. RESULTS Of 350 MLPT neonates, 15 showed low-grade IVH on US at 72 h, for which brain MRI was available in 10. These 10 infants, with mean gestational age (GA) 34.0 ± 0.8 weeks, comprised the study group, and 10 MLPT infants of mean GA 33.9 ± 1.1 weeks, with no abnormalities on brain US and MRI, were control subjects. All study subjects presented modularity, small world topology, and rich club organization for both SC and FC. The patients with low-grade IVH had lower FC rich club coefficient and lower SC betweenness centrality in the left frontoparietal operculum, and lower SC rich club coefficient in the right superior orbitofrontal cortex than the control subjects. CONCLUSIONS Topological and functional properties of mature brain connectivity are present in MLPT infants. IVH in these infants was associated with structural and functional abnormalities in the left frontoparietal operculum and right orbitofrontal cortex, regions related to language and cognition.
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Affiliation(s)
- Maria I Argyropoulou
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece.
| | - Vasileios G Xydis
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece
| | - Aikaterini Drougia
- Neonatal Intensive Care Unit, Child Health Department, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | - Anastasia S Giantsouli
- Department of Radiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece
| | - Vasileios Giapros
- Neonatal Intensive Care Unit, Child Health Department, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Faculty of Medicine, University of Ioannina, Ioannina, Greece
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30
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Coronel-Oliveros C, Castro S, Cofré R, Orio P. Structural Features of the Human Connectome That Facilitate the Switching of Brain Dynamics via Noradrenergic Neuromodulation. Front Comput Neurosci 2021; 15:687075. [PMID: 34335217 PMCID: PMC8316621 DOI: 10.3389/fncom.2021.687075] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/11/2021] [Indexed: 11/27/2022] Open
Abstract
The structural connectivity of human brain allows the coexistence of segregated and integrated states of activity. Neuromodulatory systems facilitate the transition between these functional states and recent computational studies have shown how an interplay between the noradrenergic and cholinergic systems define these transitions. However, there is still much to be known about the interaction between the structural connectivity and the effect of neuromodulation, and to what extent the connectome facilitates dynamic transitions. In this work, we use a whole brain model, based on the Jasen and Rit equations plus a human structural connectivity matrix, to find out which structural features of the human connectome network define the optimal neuromodulatory effects. We simulated the effect of the noradrenergic system as changes in filter gain, and studied its effects related to the global-, local-, and meso-scale features of the connectome. At the global-scale, we found that the ability of the network of transiting through a variety of dynamical states is disrupted by randomization of the connection weights. By simulating neuromodulation of partial subsets of nodes, we found that transitions between integrated and segregated states are more easily achieved when targeting nodes with greater connection strengths-local feature-or belonging to the rich club-meso-scale feature. Overall, our findings clarify how the network spatial features, at different levels, interact with neuromodulation to facilitate the switching between segregated and integrated brain states and to sustain a richer brain dynamics.
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Affiliation(s)
- Carlos Coronel-Oliveros
- Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias, Mención Biofísica y Biología Computacional, Universidad de Valparaíso, Valparaíso, Chile
| | - Samy Castro
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), Faculté de Psychologie, Université de Strasbourg, Strasbourg, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Université de Strasbourg, Strasbourg, France
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
| | - Patricio Orio
- Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Facultad de Ciencias, Instituto de Neurociencias, Universidad de Valparaíso, Valparaíso, Chile
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31
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Varga B, Soós B, Jákli B, Bálint E, Somogyvári Z, Négyessy L. Network Path Convergence Shapes Low-Level Processing in the Visual Cortex. Front Syst Neurosci 2021; 15:645709. [PMID: 34108867 PMCID: PMC8181740 DOI: 10.3389/fnsys.2021.645709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/21/2021] [Indexed: 11/13/2022] Open
Abstract
Hierarchical counterstream via feedforward and feedback interactions is a major organizing principle of the cerebral cortex. The counterstream, as a topological feature of the network of cortical areas, is captured by the convergence and divergence of paths through directed links. So defined, the convergence degree (CD) reveals the reciprocal nature of forward and backward connections, and also hierarchically relevant integrative properties of areas through their inward and outward connections. We asked if topology shapes large-scale cortical functioning by studying the role of CD in network resilience and Granger causal coupling in a model of hierarchical network dynamics. Our results indicate that topological synchronizability is highly vulnerable to attacking edges based on CD, while global network efficiency depends mostly on edge betweenness, a measure of the connectedness of a link. Furthermore, similar to anatomical hierarchy determined by the laminar distribution of connections, CD highly correlated with causal coupling in feedforward gamma, and feedback alpha-beta band synchronizations in a well-studied subnetwork, including low-level visual cortical areas. In contrast, causal coupling did not correlate with edge betweenness. Considering the entire network, the CD-based hierarchy correlated well with both the anatomical and functional hierarchy for low-level areas that are far apart in the hierarchy. Conversely, in a large part of the anatomical network where hierarchical distances are small between the areas, the correlations were not significant. These findings suggest that CD-based and functional hierarchies are interrelated in low-level processing in the visual cortex. Our results are consistent with the idea that the interplay of multiple hierarchical features forms the basis of flexible functional cortical interactions.
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Affiliation(s)
- Bálint Varga
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Bettina Soós
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands
| | - Balázs Jákli
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Eszter Bálint
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
| | - Zoltán Somogyvári
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - László Négyessy
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
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32
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Wystrach A. Movements, embodiment and the emergence of decisions. Insights from insect navigation. Biochem Biophys Res Commun 2021; 564:70-77. [PMID: 34023071 DOI: 10.1016/j.bbrc.2021.04.114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 04/06/2021] [Accepted: 04/27/2021] [Indexed: 02/07/2023]
Abstract
We readily infer that animals make decisions, but what this implies is usually not clearly defined. The notion of 'decision-making' ultimately stems from human introspection, and is thus loaded with anthropomorphic assumptions. Notably, the decision is made internally, is based on information, and precedes the goal directed behaviour. Also, making a decision implies that 'something' did it, thus hints at the presence of a cognitive mind, whose existence is independent of the decision itself. This view may convey some truth, but here I take the opposite stance. Using examples from research in insect navigation, this essay highlights how apparent decisions can emerge without a brain, how actions can precede information or how sophisticated goal directed behaviours can be implemented without neural decisions. This perspective requires us to shake off the idea that behaviour is a consequence of the brain; and embrace the concept that movements arise from - as much as participate in - distributed interactions between various computational centres - including the body - that reverberate in closed-loop with the environment. From this perspective we may start to picture how a cognitive mind can be the consequence, rather than the cause, of such neural and body movements.
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Affiliation(s)
- Antoine Wystrach
- Research Centre on Animal Cognition, Centre for Integrative Biology, CNRS, University of Toulouse, 118 route deNarbonne, F-31062, Toulouse, France.
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33
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Roy D, Uddin LQ. Atypical core-periphery brain dynamics in autism. Netw Neurosci 2021; 5:295-321. [PMID: 34189366 PMCID: PMC8233106 DOI: 10.1162/netn_a_00181] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 12/31/2020] [Indexed: 11/06/2022] Open
Abstract
The intrinsic function of the human brain is dynamic, giving rise to numerous behavioral subtypes that fluctuate distinctively at multiple timescales. One of the key dynamical processes that takes place in the brain is the interaction between core-periphery brain regions, which undergoes constant fluctuations associated with developmental time frames. Core-periphery dynamical changes associated with macroscale brain network dynamics span multiple timescales and may lead to atypical behavior and clinical symptoms. For example, recent evidence suggests that brain regions with shorter intrinsic timescales are located at the periphery of brain networks (e.g., sensorimotor hand, face areas) and are implicated in perception and movement. On the contrary, brain regions with longer timescales are core hub regions. These hubs are important for regulating interactions between the brain and the body during self-related cognition and emotion. In this review, we summarize a large body of converging evidence derived from time-resolved fMRI studies in autism to characterize atypical core-periphery brain dynamics and how they relate to core and contextual sensory and cognitive profiles.
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Affiliation(s)
- Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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34
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Friedrich P, Forkel SJ, Amiez C, Balsters JH, Coulon O, Fan L, Goulas A, Hadj-Bouziane F, Hecht EE, Heuer K, Jiang T, Latzman RD, Liu X, Loh KK, Patil KR, Lopez-Persem A, Procyk E, Sallet J, Toro R, Vickery S, Weis S, Wilson CRE, Xu T, Zerbi V, Eickoff SB, Margulies DS, Mars RB, Thiebaut de Schotten M. Imaging evolution of the primate brain: the next frontier? Neuroimage 2021; 228:117685. [PMID: 33359344 PMCID: PMC7116589 DOI: 10.1016/j.neuroimage.2020.117685] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 11/22/2022] Open
Abstract
Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.
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Affiliation(s)
- Patrick Friedrich
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Céline Amiez
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Joshua H Balsters
- Department of Psychology, Royal Holloway University of London, United Kingdom
| | - Olivier Coulon
- Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Fadila Hadj-Bouziane
- Lyon Neuroscience Research Center, ImpAct Team, INSERM U1028, CNRS UMR5292, Université Lyon 1, Bron, France
| | - Erin E Hecht
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, United States
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), Université de Paris, Inserm, Paris 75004, France; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; The Queensland Brain Institute, University of Queensland, Brisbane QLD 4072, Australia
| | - Robert D Latzman
- Department of Psychology, Georgia State University, Atlanta, United States
| | - Xiaojin Liu
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Kep Kee Loh
- Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Alizée Lopez-Persem
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Emmanuel Procyk
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Jerome Sallet
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), Université de Paris, Inserm, Paris 75004, France; Neuroscience department, Institut Pasteur, UMR 3571, CNRS, Université de Paris, Paris 75015, France
| | - Sam Vickery
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Susanne Weis
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Charles R E Wilson
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Ting Xu
- Child Mind Institute, New York, United States
| | - Valerio Zerbi
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Simon B Eickoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Daniel S Margulies
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, 75006, Paris, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
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35
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Heiney K, Huse Ramstad O, Fiskum V, Christiansen N, Sandvig A, Nichele S, Sandvig I. Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation. Front Comput Neurosci 2021; 15:611183. [PMID: 33643017 PMCID: PMC7902700 DOI: 10.3389/fncom.2021.611183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/18/2021] [Indexed: 01/03/2023] Open
Abstract
It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.
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Affiliation(s)
- Kristine Heiney
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Vegard Fiskum
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nicholas Christiansen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Holistic Systems, Simula Metropolitan, Oslo, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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36
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Klauser P, Cropley VL, Baumann PS, Lv J, Steullet P, Dwir D, Alemán-Gómez Y, Bach Cuadra M, Cuenod M, Do KQ, Conus P, Pantelis C, Fornito A, Van Rheenen TE, Zalesky A. White Matter Alterations Between Brain Network Hubs Underlie Processing Speed Impairment in Patients With Schizophrenia. SCHIZOPHRENIA BULLETIN OPEN 2021; 2:sgab033. [PMID: 34901867 PMCID: PMC8650074 DOI: 10.1093/schizbullopen/sgab033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Processing speed (PS) impairment is one of the most severe and common cognitive deficits in schizophrenia. Previous studies have reported correlations between PS and white matter diffusion properties, including fractional anisotropy (FA), in several fiber bundles in schizophrenia, suggesting that white matter alterations could underpin decreased PS. In schizophrenia, white matter alterations are most prevalent within inter-hub connections of the rich club. However, the spatial and topological characteristics of this association between PS and FA have not been investigated in patients. In this context, we tested whether structural connections comprising the rich club network would underlie PS impairment in 298 patients with schizophrenia or schizoaffective disorder and 190 healthy controls from the Australian Schizophrenia Research Bank. PS, measured using the digit symbol coding task, was largely (Cohen’s d = 1.33) and significantly (P < .001) reduced in the patient group when compared with healthy controls. Significant associations between PS and FA were widespread in the patient group, involving all cerebral lobes. FA was not associated with other cognitive measures of phonological fluency and verbal working memory in patients, suggesting specificity to PS. A topological analysis revealed that despite being spatially widespread, associations between PS and FA were over-represented among connections forming the rich club network. These findings highlight the need to consider brain network topology when investigating high-order cognitive functions that may be spatially distributed among several brain regions. They also reinforce the evidence that brain hubs and their interconnections may be particularly vulnerable parts of the brain in schizophrenia.
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Affiliation(s)
- Paul Klauser
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Philipp S Baumann
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Jinglei Lv
- School of Biomedical Engineering and Brain and Mind Center, University of Sydney, Sydney, New South Whales,Australia
| | - Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Center for Biomedical Imaging, University of Lausanne, Lausanne, Switzerland
| | - Michel Cuenod
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Centre for Mental Health, School of Health Sciences, Faculty of Health, Arts and Design, Swinburne University, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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37
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Dynamic whole-brain metabolic connectivity during vestibular compensation in the rat. Neuroimage 2020; 226:117588. [PMID: 33249212 DOI: 10.1016/j.neuroimage.2020.117588] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/05/2020] [Accepted: 11/19/2020] [Indexed: 12/16/2022] Open
Abstract
Unilateral damage to the inner ear results in an acute vestibular syndrome, which is compensated within days to weeks due to adaptive cerebral plasticity. This process, called central vestibular compensation (VC), involves a wide range of functional and structural mechanisms at the cellular and network level. The short-term dynamics of whole-brain functional network recruitment and recalibration during VC has not been depicted in vivo. The purpose of this study was to investigate the interplay of separate and distinct brain regions and in vivo networks in the course of VC by sequential [18F]-FDG-PET-based statistical and graph theoretical analysis with the aim of revealing the metabolic connectome before and 1, 3, 7, and 15 days post unilateral labyrinthectomy (UL) in the rat. Temporal changes in metabolic brain connectivity were determined by Pearson's correlation (|r| > 0.5, p < 0.001) of regional cerebral glucose metabolism (rCGM) in 57 segmented brain regions. Metabolic connectivity analysis was compared to univariate voxel-wise statistical analysis of rCGM over time and to behavioral scores of static and dynamic sensorimotor recovery. Univariate statistical analysis revealed an ipsilesional relative rCGM decrease (compared to baseline) and a contralesional rCGM increase in vestibular and limbic networks and an increase in bilateral cerebellar and sensorimotor networks. Quantitative analysis of the metabolic connections showed a maximal increase from baseline to day 3 post UL (interhemispheric: 2-fold, ipsilesional: 3-fold, contralesional: 12-fold) and a gradual decline until day 15 post UL, which paralleled the dynamics of vestibular symptoms. In graph theoretical analysis, an increase in connectivity occurred especially within brain regions associated with brainstem-cerebellar and thalamocortical vestibular networks and cortical sensorimotor networks. At the symptom peak (day 3 post UL), brain networks were found to be organized in large ensembles of distinct and highly connected hubs of brain regions, which separated again with progressing VC. Thus, we found rapid changes in network organization at the subcortical and cortical level and in both hemispheres, which may indicate an initial functional substitution of vestibular loss and subsequent recalibration and reorganization of sensorimotor networks during VC.
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Park BY, Vos de Wael R, Paquola C, Larivière S, Benkarim O, Royer J, Tavakol S, Cruces RR, Li Q, Valk SL, Margulies DS, Mišić B, Bzdok D, Smallwood J, Bernhardt BC. Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. Neuroimage 2020; 224:117429. [PMID: 33038538 DOI: 10.1016/j.neuroimage.2020.117429] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/13/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
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Affiliation(s)
- Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Qiongling Li
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Bratislav Mišić
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, New York, United Kingdom
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
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39
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Park BY, Byeon K, Lee MJ, Chung CS, Kim SH, Morys F, Bernhardt B, Dagher A, Park H. Whole-brain functional connectivity correlates of obesity phenotypes. Hum Brain Mapp 2020; 41:4912-4924. [PMID: 32804441 PMCID: PMC7643372 DOI: 10.1002/hbm.25167] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/09/2020] [Accepted: 08/01/2020] [Indexed: 12/11/2022] Open
Abstract
Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole‐brain functional connectivity remains poorly understood. Here, we identified a neuroimaging‐based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large‐scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole‐brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole‐brain functional connectivity and have important implications for the obesity neuroimaging community.
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Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Kyoungseob Byeon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Mi Ji Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Chin-Sang Chung
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Se-Hong Kim
- Department of Family Medicine, St. Vincent's Hospital, Catholic University College of Medicine, Suwon, South Korea
| | - Filip Morys
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea
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40
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A Look Ahead. Concussion 2020. [DOI: 10.1016/b978-0-323-65384-8.00015-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023] Open
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41
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Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. Sci Rep 2019; 9:18833. [PMID: 31827105 PMCID: PMC6906376 DOI: 10.1038/s41598-019-54950-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
The diffuse nature of mild traumatic brain injury (mTBI) impacts brain white-matter pathways with potentially long-term consequences, even after initial symptoms have resolved. To understand post-mTBI recovery in adolescents, longitudinal studies are needed to determine the interplay between highly individualised recovery trajectories and ongoing development. To capture the distributed nature of mTBI and recovery, we employ connectomes to probe the brain’s structural organisation. We present a diffusion MRI study on adolescent mTBI subjects scanned one day, two weeks and one year after injury with controls. Longitudinal global network changes over time suggests an altered and more ‘diffuse’ network topology post-injury (specifically lower transitivity and global efficiency). Stratifying the connectome by its back-bone, known as the ‘rich-club’, these network changes were driven by the ‘peripheral’ local subnetwork by way of increased network density, fractional anisotropy and decreased diffusivities. This increased structural integrity of the local subnetwork may be to compensate for an injured network, or it may be robust to mTBI and is exhibiting a normal developmental trend. The rich-club also revealed lower diffusivities over time with controls, potentially indicative of longer-term structural ramifications. Our results show evolving, diffuse alterations in adolescent mTBI connectomes beginning acutely and continuing to one year.
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42
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Human Self-Domestication and the Extended Evolutionary Synthesis of Addiction: How Humans Evolved a Unique Vulnerability. Neuroscience 2019; 419:100-107. [PMID: 31654715 DOI: 10.1016/j.neuroscience.2019.09.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/10/2019] [Accepted: 09/11/2019] [Indexed: 01/19/2023]
Abstract
Humans are more vulnerable to addiction in comparison to all other mammals, including nonhuman primates, yet there is a lack of research addressing this. This paper reviews the field of comparative addiction neuroscience, highlighting the significant inter-species variation in the mesocortical dopaminergic and other neuromodulatory systems involved in addiction. Artificial selection gives rise to significant changes in neuroanatomy, neurophysiology and behaviour as shown in certain rodent strains and other domesticated animals. These changes occur over a few generations, relatively short periods of time in evolutionary terms, and demonstrate how dynamic these neuromodulatory systems are in response to the environment. During the course of human evolution, traits crucial to our survival, expansion and domination (traits such as the ability to innovate, adapt to different environments and thrive in a civilization) have been positively selected for, yet also predispose humans to addiction. This is evident in our unique neurochemistry and receptor-drug activation potencies. Examples of these are provided as possible targets for precision medicine.
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43
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Guo X, Simas T, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok ANV, Bullmore ET, Baron-Cohen S, Chen H, Suckling J. Enhancement of indirect functional connections with shortest path length in the adult autistic brain. Hum Brain Mapp 2019; 40:5354-5369. [PMID: 31464062 PMCID: PMC6864892 DOI: 10.1002/hbm.24777] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/23/2019] [Accepted: 08/18/2019] [Indexed: 12/30/2022] Open
Abstract
Autism is a neurodevelopmental condition characterized by atypical brain functional organization. Here we investigated the intrinsic indirect (semi‐metric) connectivity of the functional connectome associated with autism. Resting‐state functional magnetic resonance imaging scans were acquired from 65 neurotypical adults (33 males/32 females) and 61 autistic adults (30 males/31 females). From functional connectivity networks, semi‐metric percentages (SMPs) were calculated to assess the proportion of indirect shortest functional pathways at global, hemisphere, network, and node levels. Group comparisons were then conducted to ascertain differences between autism and neurotypical control groups. Finally, the strength and length of edges were examined to explore the patterns of semi‐metric connections associated with autism. Compared with neurotypical controls, autistic adults displayed significantly higher SMP at all spatial scales, similar to prior observations in adolescents. Differences were primarily in weaker, longer‐distance edges in the majority between networks. However, no significant diagnosis‐by‐sex interaction effects were observed on global SMP. These findings suggest increased indirect functional connectivity in the autistic brain is persistent from adolescence to adulthood and is indicative of reduced functional network integration.
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Affiliation(s)
- Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - Tiago Simas
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Meng-Chuan Lai
- Centre for Addiction and Mental Health and the Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Canada.,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Italian Institute of Technology, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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44
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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]
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45
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Curto C, Morrison K. Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience. Curr Opin Neurobiol 2019; 58:11-20. [PMID: 31319287 DOI: 10.1016/j.conb.2019.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/22/2019] [Indexed: 11/29/2022]
Abstract
We review recent work relating network connectivity to the dynamics of neural activity. While concepts stemming from network science provide a valuable starting point, the interpretation of graph-theoretic structures and measures can be highly dependent on the dynamics associated to the network. Properties that are quite meaningful for linear dynamics, such as random walk and network flow models, may be of limited relevance in the neuroscience setting. Theoretical and computational neuroscience are playing a vital role in understanding the relationship between network connectivity and the nonlinear dynamics associated to neural networks.
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Affiliation(s)
- Carina Curto
- The Pennsylvania State University, PA 16802, United States.
| | - Katherine Morrison
- School of Mathematical Sciences, University of Northern Colorado, Greeley, CO 80639, USA
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46
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Ardesch DJ, Scholtens LH, van den Heuvel MP. The human connectome from an evolutionary perspective. PROGRESS IN BRAIN RESEARCH 2019; 250:129-151. [PMID: 31703899 DOI: 10.1016/bs.pbr.2019.05.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The connectome describes the comprehensive set of neuronal connections of a species' central nervous system. Identifying the network characteristics of the human macroscale connectome and comparing these features with connectomes of other species provides insight into the evolution of human brain connectivity and its role in brain function. Several network properties of the human connectome are conserved across species, with emerging evidence also indicating potential human-specific adaptations of connectome topology. This review describes the human macroscale structural and functional connectome, focusing on common themes of brain wiring in the animal kingdom and network adaptations that may underlie human brain function. Evidence is drawn from comparative studies across a wide range of animal species, and from research comparing human brain wiring with that of non-human primates. Approaching the human connectome from a comparative perspective paves the way for network-level insights into the evolution of human brain structure and function.
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Affiliation(s)
- Dirk Jan Ardesch
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands; Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
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47
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Hofman MA. On the nature and evolution of the human mind. PROGRESS IN BRAIN RESEARCH 2019; 250:251-283. [DOI: 10.1016/bs.pbr.2019.03.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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