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Duong-Tran D, Nguyen N, Mu S, Chen J, Bao J, Xu F, Garai S, Cadena-Pico J, Kaplan AD, Chen T, Zhao Y, Shen L, Goñi J. A principled framework to assess the information-theoretic fitness of brain functional sub-circuits. ARXIV 2024:arXiv:2406.18531v2. [PMID: 38979488 PMCID: PMC11230349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects' functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods and provide insights for future research in individualized parcellations.
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Affiliation(s)
- Duy Duong-Tran
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Nghi Nguyen
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Frederick Xu
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sumita Garai
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jose Cadena-Pico
- Machine Learning Group, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Alan David Kaplan
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Tianlong Chen
- Department of Computer Science, The University of North Carolina at Chapel Hill
| | - Yize Zhao
- School of Public Health, Yale University, New Heaven, CT, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics (DBEI), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Stam CJ, de Haan W. Network Hyperexcitability in Early-Stage Alzheimer's Disease: Evaluation of Functional Connectivity Biomarkers in a Computational Disease Model. J Alzheimers Dis 2024; 99:1333-1348. [PMID: 38759000 PMCID: PMC11191539 DOI: 10.3233/jad-230825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2024] [Indexed: 05/19/2024]
Abstract
Background There is increasing evidence from animal and clinical studies that network hyperexcitability (NH) may be an important pathophysiological process and potential target for treatment in early Alzheimer's disease (AD). Measures of functional connectivity (FC) have been proposed as promising biomarkers for NH, but it is unknown which measure has the highest sensitivity for early-stage changes in the excitation/inhibition balance. Objective We aim to test the performance of different FC measures in detecting NH at the earliest stage using a computational approach. Methods We use a whole brain computational model of activity dependent degeneration to simulate progressive AD pathology and NH. We investigate if and at what stage four measures of FC (amplitude envelope correlation corrected [AECc], phase lag index [PLI], joint permutation entropy [JPE] and a new measure: phase lag time [PLT]) can detect early-stage AD pathophysiology. Results The activity dependent degeneration model replicates spectral changes in line with clinical data and demonstrates increasing NH. Compared to relative theta power as a gold standard the AECc and PLI are shown to be less sensitive in detecting early-stage NH and AD-related neurophysiological abnormalities, while the JPE and the PLT show more sensitivity with excellent test characteristics. Conclusions Novel FC measures, which are better in detecting rapid fluctuations in neural activity and connectivity, may be superior to well-known measures such as the AECc and PLI in detecting early phase neurophysiological abnormalities and in particular NH in AD. These markers could improve early diagnosis and treatment target identification.
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Affiliation(s)
- Cornelis Jan Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center (Amsterdam UMC), Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center (Amsterdam UMC), Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam University Medical Center (Amsterdam UMC), Amsterdam, The Netherlands
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Ladisich B, Rampp S, Trinka E, Weisz N, Schwartz C, Kraus T, Sherif C, Marhold F, Demarchi G. Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study. Ther Adv Neurol Disord 2023; 16:17562864231190298. [PMID: 37655227 PMCID: PMC10467269 DOI: 10.1177/17562864231190298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/07/2023] [Indexed: 09/02/2023] Open
Abstract
Background It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. Objectives We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy. Methods Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts. Results We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level. Conclusion Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.
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Affiliation(s)
- Barbara Ladisich
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Department of Neurosurgery, University Hospital St. Poelten, Dunant-Platz 1, St Polten 3100 Austria
- Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Stefan Rampp
- Department of Neurosurgery, Department of Neuroradiology, University Hospital Erlangen, Germany
- Department of Neurosurgery, University Hospital Halle (Saale), Germany
| | - Eugen Trinka
- Department of Neurology, Center for Cognitive Neuroscience Salzburg, Member of the European Reference Network, EpiCARE, Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Salzburg, Austria
| | - Nathan Weisz
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
| | - Christoph Schwartz
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Theo Kraus
- Institute of Pathology, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Camillo Sherif
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Gianpaolo Demarchi
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
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Kavčič A, Demšar J, Georgiev D, Meglič NP, Šalamon AS. EEG functional connectivity after perinatal stroke. Cereb Cortex 2023; 33:9927-9935. [PMID: 37415237 DOI: 10.1093/cercor/bhad255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
Impaired cognitive functioning after perinatal stroke has been associated with long-term functional brain network changes. We explored brain functional connectivity using a 64-channel resting-state electroencephalogram in 12 participants, aged 5-14 years with a history of unilateral perinatal arterial ischemic or haemorrhagic stroke. A control group of 16 neurologically healthy subjects was also included-each test subject was compared with multiple control subjects, matched by sex and age. Functional connectomes from the alpha frequency band were calculated for each subject and the differences in network graph metrics between the 2 groups were analyzed. Our results suggest that the functional brain networks of children with perinatal stroke show evidence of disruption even years after the insult and that the scale of changes appears to be influenced by the lesion volume. The networks remain more segregated and show a higher synchronization at both whole-brain and intrahemispheric level. Total interhemispheric strength was higher in children with perinatal stroke compared with healthy controls.
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Affiliation(s)
- Alja Kavčič
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Bohoričeva 20, 1000 Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Jure Demšar
- Faculty of Computer and Information Sciences, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
- Department of Psychology, Faculty of Arts, University of Ljubljana, Aškerčeva 2, 1000 Ljubljana, Slovenia
| | - Dejan Georgiev
- Faculty of Computer and Information Sciences, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia
- Department of Neurology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Nuška Pečarič Meglič
- Department of Neuroradiology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Aneta Soltirovska Šalamon
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Bohoričeva 20, 1000 Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
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van Nifterick AM, Gouw AA, van Kesteren RE, Scheltens P, Stam CJ, de Haan W. A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing. Alzheimers Res Ther 2022; 14:101. [PMID: 35879779 PMCID: PMC9310500 DOI: 10.1186/s13195-022-01041-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 07/02/2022] [Indexed: 01/30/2023]
Abstract
Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients. Methods To simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline. Results All but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD. Conclusions Using a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01041-4.
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6
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Tao Y, Tsapkini K, Rapp B. Inter-hemispheric synchronicity and symmetry: The functional connectivity consequences of stroke and neurodegenerative disease. NEUROIMAGE: CLINICAL 2022; 36:103263. [PMID: 36451366 PMCID: PMC9668669 DOI: 10.1016/j.nicl.2022.103263] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022] Open
Abstract
Stroke and neurodegenerative diseases differ along several dimensions, including their temporal trajectories -abrupt onset versus slow disease progression. Despite these differences, they can give rise to very similar cognitive impairments, such as specific forms of aphasia. What has been scarcely investigated, however, is the extent to which the underlying functional neuroplastic consequences are similar or different for these diseases. Here, for the first time, we directly compare changes in the brain's functional network connectivity, measured with resting-state fMRI, in stroke and progressive neurological disease. Specifically, we examined two groups of individuals with chronic post-stroke aphasia or non-fluent primary progressive aphasia, matched for their behavioral profiles and distribution of left-hemisphere damage. Using previous proposals regarding the neural functional connectivity (FC) phenotype of stroke as a starting point, we compared the two diseases in terms of homotopic FC, intra-hemispheric FC changes and also the symmetry of the FC patterns between the two hemispheres. We found, first, that progressive disease showed significantly higher levels of homotopic connectivity than neurotypical controls and, further, that stroke showed the reverse pattern. For both groups these effects were found to be behaviorally relevant. In addition, within the directly impacted left hemisphere, FC changes for the two diseases were significantly correlated. In contrast, in the right hemisphere, the FC changes differed markedly between the two groups, with the progressive disease group exhibiting rather symmetrical FC changes across the hemispheres whereas the post-stroke group showed asymmetrical FC changes across the hemispheres. These findings constitute novel evidence that the functional connectivity consequences of stroke and neurodegenerative disease can be very different despite similar behavioral outcomes and damage foci. Specifically, stroke may lead to greater independence of hemispheric responses, while neurodegenerative disease may produce more symmetrical changes across the hemispheres and more synchronized activity between the two hemispheres.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21218, USA,Corresponding author.
| | - Kyrana Tsapkini
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21217, USA
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA,Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
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7
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Millán AP, van Straaten ECW, Stam CJ, Nissen IA, Idema S, Baayen JC, Van Mieghem P, Hillebrand A. Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings. Sci Rep 2022; 12:4086. [PMID: 35260657 PMCID: PMC8904850 DOI: 10.1038/s41598-022-07730-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 02/24/2022] [Indexed: 11/08/2022] Open
Abstract
Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.
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Affiliation(s)
- Ana P Millán
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander Idema
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Shu P, Zhu H, Jin W, Zhou J, Tong S, Sun J. The Resilience and Vulnerability of Human Brain Networks Across the Lifespan. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1756-1765. [PMID: 34410925 DOI: 10.1109/tnsre.2021.3105991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Resilience, the ability for a system to maintain its basic functionality when suffering from lesions, is a critical property for human brain, especially in the brain aging process. This study adopted a novel metric of network resilience, the Resilience Index (RI), to assess human brain resilience with three different lifespan datasets. Based on the structural brain networks constructed from diffusion tensor imaging (DTI), we observed an inverted-U relationship between RI and age, that is, RI increased during development and early adulthood, reached a peak at about 35 years old, and then decreased during aging, which suggested that brain resilience could be quantified by RI. Furthermore, we studied brain network vulnerability by the decreases in RI when virtual lesions occurred to nodes (i.e., brain regions) or edges (i.e., structural brain connectivity). We found that the strong edges were markedly vulnerable, and the homotopic edges were the most prominent representatives of vulnerable edges. In other words, an arbitrary attack on homotopic edges would have a high probability to degrade brain network resilience. These findings suggest the change of human brain resilience across the lifespan and provide a new perspective for exploring human brain vulnerability.
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Tao Y, Rapp B. Investigating the network consequences of focal brain lesions through comparisons of real and simulated lesions. Sci Rep 2021; 11:2213. [PMID: 33500494 PMCID: PMC7838400 DOI: 10.1038/s41598-021-81107-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
Given the increased interest in the functional human connectome, a number of computer simulation studies have sought to develop a better quantitative understanding of the effects of focal lesions on the brain’s functional network organization. However, there has been little work evaluating the predictions of this simulation work vis a vis real lesioned connectomes. One of the few relevant studies reported findings from real chronic focal lesions that only partially confirmed simulation predictions. We hypothesize that these discrepancies arose because although the effects of focal lesions likely consist of two components: short-term node subtraction and long-term network re-organization, previous simulation studies have primarily modeled only the short-term consequences of the subtraction of lesioned nodes and their connections. To evaluate this hypothesis, we compared network properties (modularity, participation coefficient, within-module degree) between real functional connectomes obtained from chronic stroke participants and “pseudo-lesioned” functional connectomes generated by subtracting the same sets of lesioned nodes/connections from healthy control connectomes. We found that, as we hypothesized, the network properties of real-lesioned connectomes in chronic stroke differed from those of the pseudo-lesioned connectomes which instantiated only the short-term consequences of node subtraction. Reflecting the long-term consequences of focal lesions, we found re-organization of the neurotopography of global and local hubs in the real but not the pseudo-lesioned connectomes. We conclude that the long-term network re-organization that occurs in response to focal lesions involves changes in functional connectivity within the remaining intact neural tissue that go well beyond the short-term consequences of node subtraction.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA.
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA
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10
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Gross B, Sanhedrai H, Shekhtman L, Havlin S. Interconnections between networks acting like an external field in a first-order percolation transition. Phys Rev E 2020; 101:022316. [PMID: 32168699 DOI: 10.1103/physreve.101.022316] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 02/05/2020] [Indexed: 11/07/2022]
Abstract
Many interdependent, real-world infrastructures involve interconnections between different communities or cities. Here we show how the effects of such interconnections can be described as an external field for interdependent networks experiencing a first-order percolation transition. We find that the critical exponents γ and δ, related to the external field, can also be defined for first-order transitions but that they have different values than those found for second-order transitions. Surprisingly, we find that both sets of different exponents (for first and second order) can even be found within a single model of interdependent networks, depending on the dependency coupling strength. Nevertheless, in both cases both sets satisfy Widom's identity, δ-1=γ/β, which further supports the validity of their definitions. Furthermore, we find that both Erdős-Rényi and scale-free networks have the same values of the exponents in the first-order regime, implying that these models are in the same universality class. In addition, we find that in k-core percolation the values of the critical exponents related to the field are the same as for interdependent networks, suggesting that these systems also belong to the same universality class.
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Affiliation(s)
- Bnaya Gross
- Department of Physics, Bar Ilan University, Ramat Gan, Israel
| | | | - Louis Shekhtman
- Network Science Institute, Northeastern University, Boston 02115, USA
| | - Shlomo Havlin
- Department of Physics, Bar Ilan University, Ramat Gan, Israel.,Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama 226-8503, Japan
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11
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Demšar J, Forsyth R. Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity. Neural Comput 2019; 32:424-446. [PMID: 31835005 DOI: 10.1162/neco_a_01257] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Neural mass models offer a way of studying the development and behavior of large-scale brain networks through computer simulations. Such simulations are currently mainly research tools, but as they improve, they could soon play a role in understanding, predicting, and optimizing patient treatments, particularly in relation to effects and outcomes of brain injury. To bring us closer to this goal, we took an existing state-of-the-art neural mass model capable of simulating connection growth through simulated plasticity processes. We identified and addressed some of the model's limitations by implementing biologically plausible mechanisms. The main limitation of the original model was its instability, which we addressed by incorporating a representation of the mechanism of synaptic scaling and examining the effects of optimizing parameters in the model. We show that the updated model retains all the merits of the original model, while being more stable and capable of generating networks that are in several aspects similar to those found in real brains.
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Affiliation(s)
- Jure Demšar
- Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia, and MBLab, Department of Psychology, Faculty of Arts, University of Ljubljana, Slovenia
| | - Rob Forsyth
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 4LP, U.K.
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Douw L, van Dellen E, Gouw AA, Griffa A, de Haan W, van den Heuvel M, Hillebrand A, Van Mieghem P, Nissen IA, Otte WM, Reijmer YD, Schoonheim MM, Senden M, van Straaten ECW, Tijms BM, Tewarie P, Stam CJ. The road ahead in clinical network neuroscience. Netw Neurosci 2019; 3:969-993. [PMID: 31637334 PMCID: PMC6777944 DOI: 10.1162/netn_a_00103] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Alida A. Gouw
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alessandra Griffa
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn van den Heuvel
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Ida A. Nissen
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem M. Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Elisabeth C. W. van Straaten
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Casanova MF, Casanova EL. The modular organization of the cerebral cortex: Evolutionary significance and possible links to neurodevelopmental conditions. J Comp Neurol 2019; 527:1720-1730. [PMID: 30303529 PMCID: PMC6784310 DOI: 10.1002/cne.24554] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/22/2018] [Accepted: 09/21/2018] [Indexed: 12/11/2022]
Abstract
The recognition of discernible anatomical regularities that appear to self-organize during development makes apparent the modular organization of the cerebral cortex. The metabolic cost engendered in sustaining interneuronal communications has emphasized the viability of short connections among neighboring neurons. This pattern of connectivity establishes a microcircuit which is repeated in parallel throughout the cerebral cortex. This canonical circuit is contained within the smallest module of information processing of the cerebral cortex; one which Vernon Mountcastle called the minicolumn. Plasticity within the brain is accounted, in part, by the presence of weak linkages that allow minicolumns to process information from a variety of sources and to quickly adapt to environmental exigencies without a need for genetic change. Recent research suggests that interlaminar correlated firing between minicolumns during the decision phase of target selection provides for the emergence of some executive functions. Bottlenecks of information processing within this modular minicolumnar organization may account for a variety of mental disorders observed in neurodevelopmental conditions.
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Affiliation(s)
- Manuel F Casanova
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, South Carolina
| | - Emily L Casanova
- Department of Pediatrics, Greenville Health System, Greenville, South Carolina
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Damicelli F, Hilgetag CC, Hütt MT, Messé A. Topological reinforcement as a principle of modularity emergence in brain networks. Netw Neurosci 2019; 3:589-605. [PMID: 31157311 PMCID: PMC6542620 DOI: 10.1162/netn_a_00085] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/21/2019] [Indexed: 12/02/2022] Open
Abstract
Modularity is a ubiquitous topological feature of structural brain networks at various scales. Although a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap between nodes, that is, iteratively allowing connections between non-neighbor nodes with high neighborhood similarity. This rule reliably evolves synthetic random networks toward a modular architecture. Such final modular structure reflects initial "proto-modules," thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable dynamics, where the plasticity rule acts based on the functional connectivity (co-activations) between nodes. Results produced by the activity-based model are consistent with the ones from the purely topological rule in terms of the final network configuration and modules composition. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism contributing to modularity emergence in brain networks.
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Affiliation(s)
- Fabrizio Damicelli
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany
| | - Arnaud Messé
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
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White matter network topology relates to cognitive flexibility and cumulative neurological risk in adult survivors of pediatric brain tumors. NEUROIMAGE-CLINICAL 2018; 20:485-497. [PMID: 30148064 PMCID: PMC6105768 DOI: 10.1016/j.nicl.2018.08.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/13/2018] [Accepted: 08/09/2018] [Indexed: 01/08/2023]
Abstract
Adult survivors of pediatric brain tumors exhibit deficits in executive functioning. Given that brain tumors and medical treatments for brain tumors result in disruptions to white matter, a network analysis was used to explore the topological properties of white matter networks. This study used diffusion tensor imaging and deterministic tractography in 38 adult survivors of pediatric brain tumors (mean age in years = 23.11 (SD = 4.96), 54% female, mean years post diagnosis = 14.09 (SD = 6.19)) and 38 healthy peers matched by age, gender, handedness, and socioeconomic status. Nodes were defined using the Automated Anatomical Labeling (AAL) parcellation scheme, and edges were defined as the mean fractional anisotropy of streamlines that connected each node pair. Global efficiency and average clustering coefficient were reduced in survivors compared to healthy peers with preferential impact to hub regions. Global efficiency mediated differences in cognitive flexibility between survivors and healthy peers, as well as the relationship between cumulative neurological risk and cognitive flexibility. These results suggest that adult survivors of pediatric brain tumors, on average one and a half decades post brain tumor diagnosis and treatment, exhibit altered white matter topology in the form of suboptimal integration and segregation of large scale networks, and that disrupted topology may underlie executive functioning impairments. Network based studies provided important topographic insights on network organization in long-term survivors of pediatric brain tumor. Long term brain tumor survivorship is associated with altered white matter networks. Hub regions were preferentially impacted in survivors. Network properties explain cognitive flexibility differences between survivors and peers.
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Dong G, Fan J, Shekhtman LM, Shai S, Du R, Tian L, Chen X, Stanley HE, Havlin S. Resilience of networks with community structure behaves as if under an external field. Proc Natl Acad Sci U S A 2018; 115:6911-6915. [PMID: 29925594 PMCID: PMC6142202 DOI: 10.1073/pnas.1801588115] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Although detecting and characterizing community structure is key in the study of networked systems, we still do not understand how community structure affects systemic resilience and stability. We use percolation theory to develop a framework for studying the resilience of networks with a community structure. We find both analytically and numerically that interlinks (the connections among communities) affect the percolation phase transition in a way similar to an external field in a ferromagnetic- paramagnetic spin system. We also study universality class by defining the analogous critical exponents δ and γ, and we find that their values in various models and in real-world coauthor networks follow the fundamental scaling relations found in physical phase transitions. The methodology and results presented here facilitate the study of network resilience and also provide a way to understand phase transitions under external fields.
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Affiliation(s)
- Gaogao Dong
- Institute of Applied System Analysis, Faculty of Science, Jiangsu University, Zhenjiang, 212013 Jiangsu, China
- Center for Polymer Studies, Boston University, Boston, MA 02215
- Department of Physics, Boston University, Boston, MA 02215
| | - Jingfang Fan
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
| | | | - Saray Shai
- Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT 06549
| | - Ruijin Du
- Institute of Applied System Analysis, Faculty of Science, Jiangsu University, Zhenjiang, 212013 Jiangsu, China
- Center for Polymer Studies, Boston University, Boston, MA 02215
- Department of Physics, Boston University, Boston, MA 02215
| | - Lixin Tian
- School of Mathematical Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Jiangsu 210023, P. R. China;
- Energy Development and Environmental Protection Strategy Research Center, Faculty of Science, Jiangsu University, Zhenjiang, 212013 Jiangsu, China
| | - Xiaosong Chen
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - H Eugene Stanley
- Center for Polymer Studies, Boston University, Boston, MA 02215;
- Department of Physics, Boston University, Boston, MA 02215
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8502, Japan
| | - Shlomo Havlin
- Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8502, Japan
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17
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Cheng JX, Zhang HY, Peng ZK, Xu Y, Tang H, Wu JT, Xu J. Divergent topological networks in Alzheimer's disease: a diffusion kurtosis imaging analysis. Transl Neurodegener 2018; 7:10. [PMID: 29719719 PMCID: PMC5921324 DOI: 10.1186/s40035-018-0115-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/10/2018] [Indexed: 02/06/2023] Open
Abstract
Background Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD). Methods Magnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches. Results We found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks. Conclusions Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.
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Affiliation(s)
- Jia-Xing Cheng
- Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Hong-Ying Zhang
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Zheng-Kun Peng
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Yao Xu
- Department of Neurology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Hui Tang
- Medical Experimental Center, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Jing-Tao Wu
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China
| | - Jun Xu
- 4Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, 100050 China.,5Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, School of Medicine, Yangzhou University, Yangzhou, 225001 Jiangsu China
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18
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de Haan W, van Straaten ECW, Gouw AA, Stam CJ. Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease. PLoS Comput Biol 2017; 13:e1005707. [PMID: 28938009 PMCID: PMC5627940 DOI: 10.1371/journal.pcbi.1005707] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 10/04/2017] [Accepted: 07/17/2017] [Indexed: 01/03/2023] Open
Abstract
Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.
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Affiliation(s)
- Willem de Haan
- Department of Clinical Neurophysiology and MEG, VUmc, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, VUmc, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands
| | | | - Alida A. Gouw
- Department of Clinical Neurophysiology and MEG, VUmc, Amsterdam, The Netherlands
- Alzheimer Center and Department of Neurology, VUmc, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG, VUmc, Amsterdam, The Netherlands
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19
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The Temporal Pattern of a Lesion Modulates the Functional Network Topology of Remote Brain Regions. Neural Plast 2017; 2017:3530723. [PMID: 28845308 PMCID: PMC5560088 DOI: 10.1155/2017/3530723] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 06/20/2017] [Indexed: 12/14/2022] Open
Abstract
Focal brain lesions can alter the morphology and function of remote brain areas. When the damage is inflicted more slowly, the functional compensation by and structural reshaping of these areas seem to be more effective. It remains unclear, however, whether the momentum of lesion development also modulates the functional network topology of the remote brain areas. In this study, we compared resting-state functional connectivity data of patients with a slowly growing low-grade glioma (LGG) with that of patients with a faster-growing high-grade glioma (HGG). Using graph theory, we examined whether the tumour growth velocity modulated the functional network topology of remote areas, more specifically of the hemisphere contralateral to the lesion. We observed that the contralesional network topology characteristics differed between patient groups. Based only on the connectivity of the hemisphere contralateral to the lesion, patients could be classified in the correct tumour-grade group with 70% accuracy. Additionally, LGG patients showed smaller contralesional intramodular connectivity, smaller contralesional ratio between intra- and intermodular connectivity, and larger contralesional intermodular connectivity than HGG patients. These results suggest that, in the hemisphere contralateral to the lesion, there is a lower capacity for local, specialized information processing coupled to a higher capacity for distributed information processing in LGG patients. These results underline the utility of a network perspective in evaluating effects of focal brain injury.
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Abstract
Although brain network analysis in neurodegenerative disease is still a fairly young discipline, expectations are high. The robust theoretical basis, the straightforward detection and explanation of otherwise intangible complex system phenomena, and the correlations of network features with pathology and cognitive status are qualities that show the potential power of this new instrument. We expect “connectomics” to eventually better explain and predict that essential but still poorly understood aspect of dementia: the relation between pathology and cognitive symptoms. But at this point, our newly acquired knowledge has not yet translated into practical methods or applications in the medical field, and most doctors regard brain connectivity analysis as a wonderful but exotic research niche that is too technical and abstract to benefit patients directly. This article aims to provide a personal perspective on how brain connectivity research may get closer to obtaining a clinical role. I will argue that network intervention modeling, which unites the strengths of network analysis and computational modeling, is a great candidate for this purpose, as it can offer an attractive test environment in which positive and negative influences on network integrity can be explored, with the ultimate aim to find effective countermeasures against neurodegenerative network damage. The virtual trial approach might become what both dementia and connectivity researchers have been waiting for: a versatile tool that turns our growing connectome knowledge into clinical predictions.
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Affiliation(s)
- Willem de Haan
- Department of Neurology, VU University Medical Center Amsterdam, Netherlands
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21
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Dynamic hub load predicts cognitive decline after resective neurosurgery. Sci Rep 2017; 7:42117. [PMID: 28169349 PMCID: PMC5294457 DOI: 10.1038/srep42117] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 01/04/2017] [Indexed: 01/31/2023] Open
Abstract
Resective neurosurgery carries the risk of postoperative cognitive deterioration. The concept of ‘hub (over)load’, caused by (over)use of the most important brain regions, has been theoretically postulated in relation to symptomatology and neurological disease course, but lacks experimental confirmation. We investigated functional hub load and postsurgical cognitive deterioration in patients undergoing lesion resection. Patients (n = 28) underwent resting-state magnetoencephalography and neuropsychological assessments preoperatively and 1-year after lesion resection. We calculated stationary hub load score (SHub) indicating to what extent brain regions linked different subsystems; high SHub indicates larger processing pressure on hub regions. Dynamic hub load score (DHub) assessed its variability over time; low values, particularly in combination with high SHub values, indicate increased load, because of consistently high usage of hub regions. Hypothetically, increased SHub and decreased DHub relate to hub overload and thus poorer/deteriorating cognition. Between time points, deteriorating verbal memory performance correlated with decreasing upper alpha DHub. Moreover, preoperatively low DHub values accurately predicted declining verbal memory performance. In summary, dynamic hub load relates to cognitive functioning in patients undergoing lesion resection: postoperative cognitive decline can be tracked and even predicted using dynamic hub load, suggesting it may be used as a prognostic marker for tailored treatment planning.
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Scariati E, Schaer M, Karahanoglu I, Schneider M, Richiardi J, Debbané M, Van De Ville D, Eliez S. Large-scale functional network reorganization in 22q11.2 deletion syndrome revealed by modularity analysis. Cortex 2016; 82:86-99. [PMID: 27371790 DOI: 10.1016/j.cortex.2016.06.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 02/16/2016] [Accepted: 06/03/2016] [Indexed: 12/11/2022]
Abstract
The 22q11.2 deletion syndrome (22q11DS) is associated with cognitive impairments and a 41% risk of developing schizophrenia. While several studies performed on patients with 22q11DS showed the presence of abnormal functional connectivity in this syndrome, how these alterations affect large-scale network organization is still unknown. Here we performed a network modularity analysis on whole-brain functional connectomes derived from the resting-state fMRI of 40 patients with 22q11DS and 41 healthy control participants, aged between 9 and 30 years old. We then split the sample at 18 years old to obtain two age subgroups and repeated the modularity analyses. We found alterations of modular communities affecting the visuo-spatial network and the anterior cingulate cortex (ACC) in both age groups. These results corroborate previous structural and functional studies in 22q11DS that showed early impairment of visuo-spatial processing regions. Furthermore, as ACC has been linked to the development of psychotic symptoms in 22q11DS, the early impairment of its functional connectivity provide further support that ACC alterations may provide potential biomarkers for an increased risk of schizophrenia. Finally, we found an abnormal modularity partition of the dorsolateral prefrontal cortex (DLPFC) only in adults with 22q11DS, suggesting the presence of an abnormal development of functional network communities during adolescence in 22q11DS.
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Affiliation(s)
- Elisa Scariati
- Office Médico-Pédagogique Research Unit, Department of Psychiatry, University of Geneva School of Medicine, Geneva 8, Switzerland.
| | - Marie Schaer
- Stanford Cognitive and Systems Neuroscience Laboratory, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Isik Karahanoglu
- Office Médico-Pédagogique Research Unit, Department of Psychiatry, University of Geneva School of Medicine, Geneva 8, Switzerland; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Medical Image Processing Lab, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Maude Schneider
- Office Médico-Pédagogique Research Unit, Department of Psychiatry, University of Geneva School of Medicine, Geneva 8, Switzerland
| | - Jonas Richiardi
- Laboratory for Neurology and imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - Martin Debbané
- Office Médico-Pédagogique Research Unit, Department of Psychiatry, University of Geneva School of Medicine, Geneva 8, Switzerland; Adolescence Clinical Psychology Research Unit, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Medical Image Processing Lab, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Stephan Eliez
- Office Médico-Pédagogique Research Unit, Department of Psychiatry, University of Geneva School of Medicine, Geneva 8, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland
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Babiloni C, Lizio R, Marzano N, Capotosto P, Soricelli A, Triggiani AI, Cordone S, Gesualdo L, Del Percio C. Brain neural synchronization and functional coupling in Alzheimer's disease as revealed by resting state EEG rhythms. Int J Psychophysiol 2016; 103:88-102. [PMID: 25660305 DOI: 10.1016/j.ijpsycho.2015.02.008] [Citation(s) in RCA: 202] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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From phenotype to genotype in complex brain networks. Sci Rep 2016; 6:19790. [PMID: 26795752 PMCID: PMC4726251 DOI: 10.1038/srep19790] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 11/30/2015] [Indexed: 01/29/2023] Open
Abstract
Generative models are a popular instrument for illuminating the relationships between the hidden variables driving the growth of a complex network and its final topological characteristics, a process known as the "genotype to phenotype problem". However, the definition of a complete methodology encompassing all stages of the analysis, and in particular the validation of the final model, is still an open problem. We here discuss a framework that allows to quantitatively optimise and validate each step of the model creation process. It is based on the execution of a classification task, and on estimating the additional precision provided by the modelled genotype. This encompasses the three main steps of the model creation, namely the selection of topological features, the optimisation of the parameters of the generative model, and the validation of the obtained results. We provide a minimum requirement for a generative model to be useful, prescribing the function mapping genotype to phenotype to be non-monotonic; and we further show how a previously published model does not fulfil such condition, casting doubts on its fitness for the study of neurological disorders. The generality of such framework guarantees its applicability beyond neuroscience, like the emergence of social or technological networks.
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25
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Stam CJ, van Straaten ECW, Van Dellen E, Tewarie P, Gong G, Hillebrand A, Meier J, Van Mieghem P. The relation between structural and functional connectivity patterns in complex brain networks. Int J Psychophysiol 2015; 103:149-60. [PMID: 25678023 DOI: 10.1016/j.ijpsycho.2015.02.011] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE An important problem in systems neuroscience is the relation between complex structural and functional brain networks. Here we use simulations of a simple dynamic process based upon the susceptible-infected-susceptible (SIS) model of infection dynamics on an empirical structural brain network to investigate the extent to which the functional interactions between any two brain areas depend upon (i) the presence of a direct structural connection; and (ii) the degree product of the two areas in the structural network. METHODS For the structural brain network, we used a 78×78 matrix representing known anatomical connections between brain regions at the level of the AAL atlas (Gong et al., 2009). On this structural network we simulated brain dynamics using a model derived from the study of epidemic processes on networks. Analogous to the SIS model, each vertex/brain region could be in one of two states (inactive/active) with two parameters β and δ determining the transition probabilities. First, the phase transition between the fully inactive and partially active state was investigated as a function of β and δ. Second, the statistical interdependencies between time series of node states were determined (close to and far away from the critical state) with two measures: (i) functional connectivity based upon the correlation coefficient of integrated activation time series; and (ii) effective connectivity based upon conditional co-activation at different time intervals. RESULTS We find a phase transition between an inactive and a partially active state for a critical ratio τ=β/δ of the transition rates in agreement with the theory of SIS models. Slightly above the critical threshold, node activity increases with degree, also in line with epidemic theory. The functional, but not the effective connectivity matrix closely resembled the underlying structural matrix. Both functional connectivity and, to a lesser extent, effective connectivity were higher for connected as compared to disconnected (i.e.: not directly connected) nodes. Effective connectivity scaled with the degree product. For functional connectivity, a weaker scaling relation was only observed for disconnected node pairs. For random networks with the same degree distribution as the original structural network, similar patterns were seen, but the scaling exponent was significantly decreased especially for effective connectivity. CONCLUSIONS Even with a very simple dynamical model it can be shown that functional relations between nodes of a realistic anatomical network display clear patterns if the system is studied near the critical transition. The detailed nature of these patterns depends on the properties of the functional or effective connectivity measure that is used. While the strength of functional interactions between any two nodes clearly depends upon the presence or absence of a direct connection, this study has shown that the degree product of the nodes also plays a large role in explaining interaction strength, especially for disconnected nodes and in combination with an effective connectivity measure. The influence of degree product on node interaction strength probably reflects the presence of large numbers of indirect connections.
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Affiliation(s)
- C J Stam
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands.
| | - E C W van Straaten
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands
| | - E Van Dellen
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, the Netherlands
| | - P Tewarie
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands
| | - G Gong
- National Key Laboratory of Cognitive Neuroscience and Learning, School of Brain and Cognitive Sciences, Beijing Normal University, Beijing, China
| | - A Hillebrand
- Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands
| | - J Meier
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands
| | - P Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, PO Box 5031, 2600 GA Delft, The Netherlands.
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Butz M, Steenbuck ID, van Ooyen A. Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke. Front Neuroanat 2014; 8:115. [PMID: 25360087 PMCID: PMC4199279 DOI: 10.3389/fnana.2014.00115] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Accepted: 09/24/2014] [Indexed: 01/12/2023] Open
Abstract
After brain lesions caused by tumors or stroke, or after lasting loss of input (deafferentation), inter- and intra-regional brain networks respond with complex changes in topology. Not only areas directly affected by the lesion but also regions remote from the lesion may alter their connectivity—a phenomenon known as diaschisis. Changes in network topology after brain lesions can lead to cognitive decline and increasing functional disability. However, the principles governing changes in network topology are poorly understood. Here, we investigated whether homeostatic structural plasticity can account for changes in network topology after deafferentation and brain lesions. Homeostatic structural plasticity postulates that neurons aim to maintain a desired level of electrical activity by deleting synapses when neuronal activity is too high and by providing new synaptic contacts when activity is too low. Using our Model of Structural Plasticity, we explored how local changes in connectivity induced by a focal loss of input affected global network topology. In accordance with experimental and clinical data, we found that after partial deafferentation, the network as a whole became more random, although it maintained its small-world topology, while deafferentated neurons increased their betweenness centrality as they rewired and returned to the homeostatic range of activity. Furthermore, deafferentated neurons increased their global but decreased their local efficiency and got longer tailed degree distributions, indicating the emergence of hub neurons. Together, our results suggest that homeostatic structural plasticity may be an important driving force for lesion-induced network reorganization and that the increase in betweenness centrality of deafferentated areas may hold as a biomarker for brain repair.
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Affiliation(s)
- Markus Butz
- Simulation Lab Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Forschungszentrum Jülich Jülich, Germany
| | - Ines D Steenbuck
- Student of the Medical Faculty, University of Freiburg Freiburg, Germany
| | - Arjen van Ooyen
- Department of Integrative Neurophysiology, VU University Amsterdam Amsterdam, Netherlands
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Synchronization and stochastic resonance of the small-world neural network based on the CPG. Cogn Neurodyn 2014; 8:217-26. [PMID: 24808930 DOI: 10.1007/s11571-013-9275-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 10/19/2013] [Accepted: 11/07/2013] [Indexed: 10/26/2022] Open
Abstract
According to biological knowledge, the central nervous system controls the central pattern generator (CPG) to drive the locomotion. The brain is a complex system consisting of different functions and different interconnections. The topological properties of the brain display features of small-world network. The synchronization and stochastic resonance have important roles in neural information transmission and processing. In order to study the synchronization and stochastic resonance of the brain based on the CPG, we establish the model which shows the relationship between the small-world neural network (SWNN) and the CPG. We analyze the synchronization of the SWNN when the amplitude and frequency of the CPG are changed and the effects on the CPG when the SWNN's parameters are changed. And we also study the stochastic resonance on the SWNN. The main findings include: (1) When the CPG is added into the SWNN, there exists parameters space of the CPG and the SWNN, which can make the synchronization of the SWNN optimum. (2) There exists an optimal noise level at which the resonance factor Q gets its peak value. And the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the noise intensity. The results could have important implications for biological processes which are about interaction between the neural network and the CPG.
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Epilepsy surgery outcome and functional network alterations in longitudinal MEG: A minimum spanning tree analysis. Neuroimage 2014; 86:354-63. [DOI: 10.1016/j.neuroimage.2013.10.010] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 09/26/2013] [Accepted: 10/04/2013] [Indexed: 11/21/2022] Open
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Engel AK, Gerloff C, Hilgetag CC, Nolte G. Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron 2014; 80:867-86. [PMID: 24267648 DOI: 10.1016/j.neuron.2013.09.038] [Citation(s) in RCA: 307] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2013] [Indexed: 01/10/2023]
Abstract
Intrinsic coupling constitutes a key feature of ongoing brain activity, which exhibits rich spatiotemporal patterning and contains information that influences cognitive processing. We discuss evidence for two distinct types of intrinsic coupling modes which seem to reflect the operation of different coupling mechanisms. One type arises from phase coupling of band-limited oscillatory signals, whereas the other results from coupled aperiodic fluctuations of signal envelopes. The two coupling modes differ in their dynamics, their origins, and their putative functions and with respect to their alteration in neuropsychiatric disorders. We propose that the concept of intrinsic coupling modes can provide a unifying framework for capturing the dynamics of intrinsically generated neuronal interactions at multiple spatial and temporal scales.
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Affiliation(s)
- Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
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Integrative biological analysis for neuropsychopharmacology. Neuropsychopharmacology 2014; 39:5-23. [PMID: 23800968 PMCID: PMC3857644 DOI: 10.1038/npp.2013.156] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Revised: 04/18/2013] [Accepted: 04/19/2013] [Indexed: 01/24/2023]
Abstract
Although advances in psychotherapy have been made in recent years, drug discovery for brain diseases such as schizophrenia and mood disorders has stagnated. The need for new biomarkers and validated therapeutic targets in the field of neuropsychopharmacology is widely unmet. The brain is the most complex part of human anatomy from the standpoint of number and types of cells, their interconnections, and circuitry. To better meet patient needs, improved methods to approach brain studies by understanding functional networks that interact with the genome are being developed. The integrated biological approaches--proteomics, transcriptomics, metabolomics, and glycomics--have a strong record in several areas of biomedicine, including neurochemistry and neuro-oncology. Published applications of an integrated approach to projects of neurological, psychiatric, and pharmacological natures are still few but show promise to provide deep biological knowledge derived from cells, animal models, and clinical materials. Future studies that yield insights based on integrated analyses promise to deliver new therapeutic targets and biomarkers for personalized medicine.
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Miller A, Jin DZ. Potentiation decay of synapses and length distributions of synfire chains self-organized in recurrent neural networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:062716. [PMID: 24483495 DOI: 10.1103/physreve.88.062716] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Indexed: 06/03/2023]
Abstract
Synfire chains are thought to underlie precisely timed sequences of spikes observed in various brain regions and across species. How they are formed is not understood. Here we analyze self-organization of synfire chains through the spike-timing dependent plasticity (STDP) of the synapses, axon remodeling, and potentiation decay of synaptic weights in networks of neurons driven by noisy external inputs and subject to dominant feedback inhibition. Potentiation decay is the gradual, activity-independent reduction of synaptic weights over time. We show that potentiation decay enables a dynamic and statistically stable network connectivity when neurons spike spontaneously. Periodic stimulation of a subset of neurons leads to formation of synfire chains through a random recruitment process, which terminates when the chain connects to itself and forms a loop. We demonstrate that chain length distributions depend on the potentiation decay. Fast potentiation decay leads to long chains with wide distributions, while slow potentiation decay leads to short chains with narrow distributions. We suggest that the potentiation decay, which corresponds to the decay of early long-term potentiation of synapses, is an important synaptic plasticity rule in regulating formation of neural circuity through STDP.
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Affiliation(s)
- Aaron Miller
- Department of Physics, Bridgewater College, Bridgewater, Virginia 22812, USA
| | - Dezhe Z Jin
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiol Aging 2013; 34:2023-36. [PMID: 23541878 DOI: 10.1016/j.neurobiolaging.2013.02.020] [Citation(s) in RCA: 277] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 02/19/2013] [Accepted: 02/25/2013] [Indexed: 11/22/2022]
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van Dellen E, Hillebrand A, Douw L, Heimans JJ, Reijneveld JC, Stam CJ. Local polymorphic delta activity in cortical lesions causes global decreases in functional connectivity. Neuroimage 2013; 83:524-32. [PMID: 23769919 DOI: 10.1016/j.neuroimage.2013.06.009] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2013] [Revised: 05/30/2013] [Accepted: 06/05/2013] [Indexed: 10/26/2022] Open
Abstract
Increasing evidence from neuroimaging and modeling studies suggests that local lesions can give rise to global network changes in the human brain. These changes are often attributed to the disconnection of the lesioned areas. However, damaged brain areas may still be active, although the activity is altered. Here, we hypothesize that empirically observed global decreases in functional connectivity in patients with brain lesions can be explained by specific alterations of local neural activity that are the result of damaged tissue. We simulated local polymorphic delta activity (PDA), which typically characterizes EEG/MEG recordings of patients with cerebral lesions, in a realistic model of human brain activity. 78 neural masses were coupled according to the human structural brain network. Lesions were created by altering the parameters of individual neural masses in order to create PDA (i.e. simulating acute focal brain damage); combining this PDA with weakening of structural connections (i.e. simulating brain tumors), and fully deleting structural connections (i.e. simulating a full resection). Not only structural disconnection but also PDA in itself caused a global decrease in functional connectivity, similar to the observed alterations in MEG recordings of patients with PDA due to brain lesions. Interestingly, connectivity between regions that were not lesioned directly also changed. The impact of PDA depended on the network characteristics of the lesioned region in the structural connectome. This study shows for the first time that locally disturbed neural activity, i.e. PDA, may explain altered functional connectivity between remote areas, even when structural connections are unaffected. We suggest that focal brain lesions and the corresponding altered neural activity should be considered in the framework of the full functionally interacting brain network, implying that the impact of lesions reaches far beyond focal damage.
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Affiliation(s)
- E van Dellen
- Department of Neurology, Cancer Center Amsterdam, VU University Medical Center, De Boelaan 1117, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands.
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Omidvarnia A, Fransson P, Metsäranta M, Vanhatalo S. Functional Bimodality in the Brain Networks of Preterm and Term Human Newborns. Cereb Cortex 2013; 24:2657-68. [DOI: 10.1093/cercor/bht120] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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Babiloni C, Infarinato F, Triggiani AI, Lizio R, Percio CD, Marzano N, Richardson JC. Resting state EEG rhythms as network disease markers for drug discovery in Alzheimer's disease. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/j.ddstr.2014.02.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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van Dellen E, Douw L, Hillebrand A, Ris-Hilgersom IHM, Schoonheim MM, Baayen JC, De Witt Hamer PC, Velis DN, Klein M, Heimans JJ, Stam CJ, Reijneveld JC. MEG network differences between low- and high-grade glioma related to epilepsy and cognition. PLoS One 2012; 7:e50122. [PMID: 23166829 PMCID: PMC3498183 DOI: 10.1371/journal.pone.0050122] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Accepted: 10/19/2012] [Indexed: 11/19/2022] Open
Abstract
Objective To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups. Methods We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients. Results LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4–8 Hz), similar to NGL patients. HGG patients’ networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients. Conclusion Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients’ networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.
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Affiliation(s)
- Edwin van Dellen
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
- * E-mail:
| | - Linda Douw
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Irene H. M. Ris-Hilgersom
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Menno M. Schoonheim
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Johannes C. Baayen
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | | | - Demetrios N. Velis
- Department of Clinical Neurophysiology and Epilepsy Monitoring Unit, Dutch Epilepsy Clinics Foundation, Heemstede, The Netherlands
| | - Martin Klein
- Department of Medical Psychology, VU University Medical Center, Amsterdam, The Netherlands
| | - Jan J. Heimans
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Jaap C. Reijneveld
- Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands
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de Haan W, Mott K, van Straaten ECW, Scheltens P, Stam CJ. Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. PLoS Comput Biol 2012; 8:e1002582. [PMID: 22915996 PMCID: PMC3420961 DOI: 10.1371/journal.pcbi.1002582] [Citation(s) in RCA: 268] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2011] [Accepted: 05/07/2012] [Indexed: 11/18/2022] Open
Abstract
Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.
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Affiliation(s)
- Willem de Haan
- Department of Clinical Neurophysiology and MEG, VU University Medical Center, Amsterdam, The Netherlands.
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Stam C, van Straaten E. The organization of physiological brain networks. Clin Neurophysiol 2012; 123:1067-87. [PMID: 22356937 DOI: 10.1016/j.clinph.2012.01.011] [Citation(s) in RCA: 346] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2011] [Revised: 01/12/2012] [Accepted: 01/15/2012] [Indexed: 01/08/2023]
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