Network Neuroscience最新文献

{"title":"Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy.","authors":"Mikhael Azilinon, Huifang E Wang, Julia Makhalova, Wafaa Zaaraoui, Jean-Philippe Ranjeva, Fabrice Bartolomei, Maxime Guye, Viktor Jirsa","doi":"10.1162/netn_a_00371","DOIUrl":"10.1162/netn_a_00371","url":null,"abstract":"<p><p>Patients presenting with drug-resistant epilepsy are eligible for surgery aiming to remove the regions involved in the production of seizure activities, the so-called epileptogenic zone network (EZN). Thus the accurate estimation of the EZN is crucial. Data-driven, personalized virtual brain models derived from patient-specific anatomical and functional data are used in Virtual Epileptic Patient (VEP) to estimate the EZN via optimization methods from Bayesian inference. The Bayesian inference approach used in previous VEP integrates priors, based on the features of stereotactic-electroencephalography (SEEG) seizures' recordings. Here, we propose new priors, based on quantitative ²³Na-MRI. The ²³Na-MRI data were acquired at 7T and provided several features characterizing the sodium signal decay. The hypothesis is that the sodium features are biomarkers of neuronal excitability related to the EZN and will add additional information to VEP estimation. In this paper, we first proposed the mapping from ²³Na-MRI features to predict the EZN via a machine learning approach. Then, we exploited these predictions as priors in the VEP pipeline. The statistical results demonstrated that compared with the results from current VEP, the result from VEP based on ²³Na-MRI prior has better balanced accuracy, and the similar weighted harmonic mean of the precision and recall.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"673-696"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11340996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system.","authors":"Raúl Luna, Jia Li, Roman Bauer, Cees van Leeuwen","doi":"10.1162/netn_a_00370","DOIUrl":"10.1162/netn_a_00370","url":null,"abstract":"<p><p>Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich-club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neighbors of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in the lateral geniculate nuclei. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"653-672"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11340993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of deep brain stimulation on cortico-subcortical networks in Parkinson's disease patients with freezing of gait: Exhaustive exploration of a basic model.","authors":"Mariia Popova, Arnaud Messé, Alessandro Gulberti, Christian Gerloff, Monika Pötter-Nerger, Claus C Hilgetag","doi":"10.1162/netn_a_00376","DOIUrl":"10.1162/netn_a_00376","url":null,"abstract":"<p><p>Current treatments of Parkinson's disease (PD) have limited efficacy in alleviating freezing of gait (FoG). In this context, concomitant deep brain stimulation (DBS) of the subthalamic nucleus (STN) and the substantia nigra pars reticulata (SNr) has been suggested as a potential therapeutic approach. However, the mechanisms underlying this approach are unknown. While the current rationale relies on network-based hypotheses of intensified disinhibition of brainstem locomotor areas to facilitate the release of gait motor programs, it is still unclear how simultaneous high-frequency DBS in two interconnected basal ganglia nuclei affects large-scale cortico-subcortical network activity. Here, we use a basic model of neural excitation, the susceptible-excited-refractory (SER) model, to compare effects of different stimulation modes of the network underlying FoG based on the mouse brain connectivity atlas. We develop a network-based computational framework to compare subcortical DBS targets through exhaustive analysis of the brain attractor dynamics in the healthy, PD, and DBS states. We show that combined STN+SNr DBS outperforms STN DBS in terms of the normalization of spike propagation flow in the FoG network. The framework aims to move toward a mechanistic understanding of the network effects of DBS and may be applicable to further perturbation-based therapies of brain disorders.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 3","pages":"926-945"},"PeriodicalIF":3.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142362337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individualized epidemic spreading models predict epilepsy surgery outcomes: A pseudo-prospective study.","authors":"Ana P Millán, Elisabeth C W van Straaten, Cornelis J Stam, Ida A Nissen, Sander Idema, Piet Van Mieghem, Arjan Hillebrand","doi":"10.1162/netn_a_00361","DOIUrl":"10.1162/netn_a_00361","url":null,"abstract":"<p><p>Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients, but up to 50% of patients continue to have seizures one year after the resection. In order to aid presurgical planning and predict postsurgical outcome on a patient-by-patient basis, we developed a framework of individualized computational models that combines epidemic spreading with patient-specific connectivity and epileptogeneity maps: the Epidemic Spreading Seizure and Epilepsy Surgery framework (ESSES). ESSES parameters were fitted in a retrospective study (<i>N</i> = 15) to reproduce invasive electroencephalography (iEEG)-recorded seizures. ESSES reproduced the iEEG-recorded seizures, and significantly better so for patients with good (seizure-free, SF) than bad (nonseizure-free, NSF) outcome. We illustrate here the clinical applicability of ESSES with a <i>pseudo-prospective study</i> (<i>N</i> = 34) with a blind setting (to the resection strategy and surgical outcome) that emulated presurgical conditions. By setting the model parameters in the retrospective study, ESSES could be applied also to patients without iEEG data. ESSES could predict the chances of good outcome after <i>any</i> resection by finding patient-specific model-based optimal resection strategies, which we found to be smaller for SF than NSF patients, suggesting an intrinsic difference in the network organization or presurgical evaluation results of NSF patients. The actual surgical plan overlapped more with the model-based optimal resection, and had a larger effect in decreasing modeled seizure propagation, for SF patients than for NSF patients. Overall, ESSES could correctly predict 75% of NSF and 80.8% of SF cases pseudo-prospectively. Our results show that individualised computational models may inform surgical planning by suggesting alternative resections and providing information on the likelihood of a good outcome after a proposed resection. This is the first time that such a model is validated with a fully independent cohort and without the need for iEEG recordings.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"437-465"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11142635/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum: Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?","authors":"Elliz P Scheijbeler, Anne M van Nifterick, Cornelis J Stam, Arjan Hillebrand, Alida A Gouw, Willem de Haan","doi":"10.1162/netn_x_00380","DOIUrl":"https://doi.org/10.1162/netn_x_00380","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1162/netn_a_00224.].</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"i"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11205260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inducing a meditative state by artificial perturbations: A mechanistic understanding of brain dynamics underlying meditation.","authors":"Paulina Clara Dagnino, Javier A Galadí, Estela Càmara, Gustavo Deco, Anira Escrichs","doi":"10.1162/netn_a_00366","DOIUrl":"10.1162/netn_a_00366","url":null,"abstract":"<p><p>Contemplative neuroscience has increasingly explored meditation using neuroimaging. However, the brain mechanisms underlying meditation remain elusive. Here, we implemented a mechanistic framework to explore the spatiotemporal dynamics of expert meditators during meditation and rest, and controls during rest. We first applied a model-free approach by defining a probabilistic metastable substate (PMS) space for each condition, consisting of different probabilities of occurrence from a repertoire of dynamic patterns. Moreover, we implemented a model-based approach by adjusting the PMS of each condition to a whole-brain model, which enabled us to explore <i>in silico</i> perturbations to transition from resting-state to meditation and vice versa. Consequently, we assessed the sensitivity of different brain areas regarding their perturbability and their mechanistic local-global effects. Overall, our work reveals distinct whole-brain dynamics in meditation compared to rest, and how transitions can be induced with localized artificial perturbations. It motivates future work regarding meditation as a practice in health and as a potential therapy for brain disorders.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"517-540"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of packetization on communication dynamics in brain networks.","authors":"Makoto Fukushima, Kenji Leibnitz","doi":"10.1162/netn_a_00360","DOIUrl":"10.1162/netn_a_00360","url":null,"abstract":"<p><p>Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"418-436"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11142457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141478366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy.","authors":"Jonathan Wirsich, Giannina Rita Iannotti, Ben Ridley, Elhum A Shamshiri, Laurent Sheybani, Frédéric Grouiller, Fabrice Bartolomei, Margitta Seeck, François Lazeyras, Jean-Philippe Ranjeva, Maxime Guye, Serge Vulliemoz","doi":"10.1162/netn_a_00362","DOIUrl":"10.1162/netn_a_00362","url":null,"abstract":"<p><p>Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"466-485"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11142634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.","authors":"Giampiero Bardella, Valentina Giuffrida, Franco Giarrocco, Emiliano Brunamonti, Pierpaolo Pani, Stefano Ferraina","doi":"10.1162/netn_a_00365","DOIUrl":"10.1162/netn_a_00365","url":null,"abstract":"<p><p>Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"597-622"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measures of the coupling between fluctuating brain network organization and heartbeat dynamics.","authors":"Diego Candia-Rivera, Mario Chavez, Fabrizio De Vico Fallani","doi":"10.1162/netn_a_00369","DOIUrl":"10.1162/netn_a_00369","url":null,"abstract":"<p><p>In recent years, there has been an increasing interest in studying brain-heart interactions. Methodological advancements have been proposed to investigate how the brain and the heart communicate, leading to new insights into some neural functions. However, most frameworks look at the interaction of only one brain region with heartbeat dynamics, overlooking that the brain has functional networks that change dynamically in response to internal and external demands. We propose a new framework for assessing the functional interplay between cortical networks and cardiac dynamics from noninvasive electrophysiological recordings. We focused on fluctuating network metrics obtained from connectivity matrices of EEG data. Specifically, we quantified the coupling between cardiac sympathetic-vagal activity and brain network metrics of clustering, efficiency, assortativity, and modularity. We validate our proposal using open-source datasets: one that involves emotion elicitation in healthy individuals, and another with resting-state data from patients with Parkinson's disease. Our results suggest that the connection between cortical network segregation and cardiac dynamics may offer valuable insights into the affective state of healthy participants, and alterations in the network physiology of Parkinson's disease. By considering multiple network properties, this framework may offer a more comprehensive understanding of brain-heart interactions. Our findings hold promise in the development of biomarkers for diagnostic and cognitive/motor function evaluation.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 2","pages":"557-575"},"PeriodicalIF":3.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}