Frontiers in network physiology最新文献

{"title":"Review: seizure-related consolidation and the network theory of epilepsy.","authors":"Mark R Bower","doi":"10.3389/fnetp.2024.1430934","DOIUrl":"10.3389/fnetp.2024.1430934","url":null,"abstract":"<p><p>Epilepsy is a complex, multifaceted disease that affects patients in several ways in addition to seizures, including psychological, social, and quality of life issues, but epilepsy is also known to interact with sleep. Seizures often occur at the boundary between sleep and wake, patients with epilepsy often experience disrupted sleep, and the rate of inter-ictal epileptiform discharges increases during non-REM sleep. The Network Theory of Epilepsy did not address a role for sleep, but recent emphasis on the interaction between epilepsy and sleep suggests that post-seizure sleep may also be involved in the process by which seizures arise and become more severe with time (\"epileptogenesis\") by co-opting processes related to the formation of long-term memories. While it is generally acknowledged that recurrent seizures arise from the aberrant function of neural circuits, it is possible that the progression of epilepsy is aided by normal, physiological function of neural circuits during sleep that are driven by pathological signals. Studies recording multiple, single neurons prior to spontaneous seizures have shown that neural assemblies activated prior to the start of seizures were reactivated during post-seizure sleep, similar to the reactivation of behavioral neural assemblies, which is thought to be involved in the formation of long-term memories, a process known as Memory Consolidation. The reactivation of seizure-related neural assemblies during sleep was thus described as being a component of Seizure-Related Consolidation (SRC). These results further suggest that SRC may viewed as a network-related aspect of epilepsy, even in those seizures that have anatomically restricted neuroanatomical origins. As suggested by the Network Theory of Epilepsy as a means of interfering with ictogenesis, therapies that interfered with SRC may provide some anti-epileptogenic therapeutic benefit, even if the interference targeted structures that were not involved originally in the seizure. Here, we show how the Network Theory of Epilepsy can be expanded to include neural plasticity mechanisms associated with learning by providing an overview of Memory Consolidation, the mechanisms thought to underlie MC, their relation to Seizure-Related Consolidation, and suggesting novel, anti-epileptogenic therapies targeting interference with network activation in epilepsy following seizures during post-seizure sleep.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11374659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity.","authors":"Enrico Caprioglio, Luc Berthouze","doi":"10.3389/fnetp.2024.1436046","DOIUrl":"10.3389/fnetp.2024.1436046","url":null,"abstract":"<p><p>Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation with empirical functional connectivity dynamics. Here, we propose and show that the brain's hierarchically modular mesoscale structure alone can give rise to robust metastable dynamics and (metastable) chimera states in the presence of phase frustration. We construct unweighted 3-layer hierarchical networks of identical Kuramoto-Sakaguchi oscillators, parameterized by the average degree of the network and a structural parameter determining the ratio of connections between and within blocks in the upper two layers. Together, these parameters affect the characteristic timescales of the system. Away from the critical synchronization point, we detect the emergence of metastable states in the lowest hierarchical layer coexisting with chimera and metastable states in the upper layers. Using the Laplacian renormalization group flow approach, we uncover two distinct pathways towards achieving the metastable regimes detected in these distinct layers. In the upper layers, we show how the symmetry-breaking states depend on the slow eigenmodes of the system. In the lowest layer instead, metastable dynamics can be achieved as the separation of timescales between layers reaches a critical threshold. Our results show an explicit relationship between metastability, chimera states, and the eigenmodes of the system, bridging the gap between harmonic based studies of empirical data and oscillatory models.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11372895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual stimulation of the interictal EEG network localizes the EZ as a measure of cortical excitability.","authors":"Sophia R Zhai, Sridevi V Sarma, Kristin Gunnarsdottir, Nathan E Crone, Adam G Rouse, Jennifer J Cheng, Michael J Kinsman, Patrick Landazuri, Utku Uysal, Carol M Ulloa, Nathaniel Cameron, Sara Inati, Kareem A Zaghloul, Varina L Boerwinkle, Sarah Wyckoff, Niravkumar Barot, Jorge A González-Martínez, Joon Y Kang, Rachel June Smith","doi":"10.3389/fnetp.2024.1425625","DOIUrl":"10.3389/fnetp.2024.1425625","url":null,"abstract":"<p><p><b>Introduction:</b> For patients with drug-resistant epilepsy, successful localization and surgical treatment of the epileptogenic zone (EZ) can bring seizure freedom. However, surgical success rates vary widely because there are currently no clinically validated biomarkers of the EZ. Highly epileptogenic regions often display increased levels of cortical excitability, which can be probed using single-pulse electrical stimulation (SPES), where brief pulses of electrical current are delivered to brain tissue. It has been shown that high-amplitude responses to SPES can localize EZ regions, indicating a decreased threshold of excitability. However, performing extensive SPES in the epilepsy monitoring unit (EMU) is time-consuming. Thus, we built patient-specific <i>in silico</i> dynamical network models from interictal intracranial EEG (iEEG) to test whether virtual stimulation could reveal information about the underlying network to identify highly excitable brain regions similar to physical stimulation of the brain. <b>Methods:</b> We performed virtual stimulation in 69 patients that were evaluated at five centers and assessed for clinical outcome 1 year post surgery. We further investigated differences in observed SPES iEEG responses of 14 patients stratified by surgical outcome. <b>Results:</b> Clinically-labeled EZ cortical regions exhibited higher excitability from virtual stimulation than non-EZ regions with most significant differences in successful patients and little difference in failure patients. These trends were also observed in responses to extensive SPES performed in the EMU. Finally, when excitability was used to predict whether a channel is in the EZ or not, the classifier achieved an accuracy of 91%. <b>Discussion:</b> This study demonstrates how excitability determined via virtual stimulation can capture valuable information about the EZ from interictal intracranial EEG.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How synaptic function controls critical transitions in spiking neuron networks: insight from a Kuramoto model reduction","authors":"L. Smirnov, V. O. Munyayev, M. Bolotov, Grigory V. Osipov, I. Belykh","doi":"10.3389/fnetp.2024.1423023","DOIUrl":"https://doi.org/10.3389/fnetp.2024.1423023","url":null,"abstract":"The dynamics of synaptic interactions within spiking neuron networks play a fundamental role in shaping emergent collective behavior. This paper studies a finite-size network of quadratic integrate-and-fire neurons interconnected via a general synaptic function that accounts for synaptic dynamics and time delays. Through asymptotic analysis, we transform this integrate-and-fire network into the Kuramoto-Sakaguchi model, whose parameters are explicitly expressed via synaptic function characteristics. This reduction yields analytical conditions on synaptic activation rates and time delays determining whether the synaptic coupling is attractive or repulsive. Our analysis reveals alternating stability regions for synchronous and partially synchronous firing, dependent on slow synaptic activation and time delay. We also demonstrate that the reduced microscopic model predicts the emergence of synchronization, weakly stable cyclops states, and non-stationary regimes remarkably well in the original integrate-and-fire network and its theta neuron counterpart. Our reduction approach promises to open the door to rigorous analysis of rhythmogenesis in networks with synaptic adaptation and plasticity.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The contribution of granger causality analysis to our understanding of cardiovascular homeostasis: from cardiovascular and respiratory interactions to central autonomic network control","authors":"Vincent Pichot, Christophe Corbier, F. Chouchou","doi":"10.3389/fnetp.2024.1315316","DOIUrl":"https://doi.org/10.3389/fnetp.2024.1315316","url":null,"abstract":"Homeostatic regulation plays a fundamental role in maintenance of multicellular life. At different scales and in different biological systems, this principle allows a better understanding of biological organization. Consequently, a growing interest in studying cause-effect relations between physiological systems has emerged, such as in the fields of cardiovascular and cardiorespiratory regulations. For this, mathematical approaches such as Granger causality (GC) were applied to the field of cardiovascular physiology in the last 20 years, overcoming the limitations of previous approaches and offering new perspectives in understanding cardiac, vascular and respiratory homeostatic interactions. In clinical practice, continuous recording of clinical data of hospitalized patients or by telemetry has opened new applicability for these approaches with potential early diagnostic and prognostic information. In this review, we describe a theoretical background of approaches based on linear GC in time and frequency domains applied to detect couplings between time series of RR intervals, blood pressure and respiration. Interestingly, these tools help in understanding the contribution of homeostatic negative feedback and the anticipatory feedforward mechanisms in homeostatic cardiovascular and cardiorespiratory controls. We also describe experimental and clinical results based on these mathematical tools, consolidating previous experimental and clinical evidence on the coupling in cardiovascular and cardiorespiratory studies. Finally, we propose perspectives allowing to complete the understanding of these interactions between cardiovascular and cardiorespiratory systems, as well as the interplay between brain and cardiac, and vascular and respiratory systems, offering a high integrative view of cardiovascular and cardiorespiratory homeostatic regulation.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case report: Bridging limbic network epilepsy with psychiatric, memory, and sleep comorbidities: case illustrations of reversible psychosis symptoms during continuous, high-frequency ANT-DBS","authors":"Lydia P. Wheeler, Samuel Worrell, I. Balzekas, Jordan Bilderbeek, Dora Hermes, Paul E. Croarkin, Steven Messina, Jamie J Van Gompel, Kai J. Miller, V. Kremen, Gregory A Worrell","doi":"10.3389/fnetp.2024.1426743","DOIUrl":"https://doi.org/10.3389/fnetp.2024.1426743","url":null,"abstract":"The network nature of focal epilepsy is exemplified by mesial temporal lobe epilepsy (mTLE), characterized by focal seizures originating from the mesial temporal neocortex, amygdala, and hippocampus. The mTLE network hypothesis is evident in seizure semiology and interictal comorbidities, both reflecting limbic network dysfunction. The network generating seizures also supports essential physiological functions, including memory, emotion, mood, and sleep. Pathology in the mTLE network often manifests as interictal behavioral disturbances and seizures. The limbic circuit is a vital network, and here we review one of the most common focal epilepsies and its comorbidities. We describe two people with drug resistant mTLE implanted with an investigational device enabling continuous hippocampal local field potential sensing and anterior nucleus of thalamus deep brain stimulation (ANT-DBS) who experienced reversible psychosis during continuous high-frequency stimulation. The mechanism(s) of psychosis remain poorly understood and here we speculate that the anti-epileptic effect of high frequency ANT-DBS may provide insights into the physiology of primary disorders associated with psychosis.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven and equation-free methods for neurological disorders: analysis and control of the striatum network.","authors":"Konstantinos Spiliotis, Rüdiger Köhling, Wolfram Just, Jens Starke","doi":"10.3389/fnetp.2024.1399347","DOIUrl":"10.3389/fnetp.2024.1399347","url":null,"abstract":"<p><p>The striatum as part of the basal ganglia is central to both motor, and cognitive functions. Here, we propose a large-scale biophysical network for this part of the brain, using modified Hodgkin-Huxley dynamics to model neurons, and a connectivity informed by a detailed human atlas. The model shows different spatio-temporal activity patterns corresponding to lower (presumably normal) and increased cortico-striatal activation (as found in, e.g., obsessive-compulsive disorder), depending on the intensity of the cortical inputs. By applying equation-free methods, we are able to perform a macroscopic network analysis directly from microscale simulations. We identify the mean synaptic activity as the macroscopic variable of the system, which shows similarity with local field potentials. The equation-free approach results in a numerical bifurcation and stability analysis of the macroscopic dynamics of the striatal network. The different macroscopic states can be assigned to normal/healthy and pathological conditions, as known from neurological disorders. Finally, guided by the equation-free bifurcation analysis, we propose a therapeutic close loop control scheme for the striatal network.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11335688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic approaches targeting seizure networks.","authors":"Jenna Langbein, Ujwal Boddeti, Matthew Kreinbrink, Ziam Khan, Ihika Rampalli, Muzna Bachani, Alexander Ksendzovsky","doi":"10.3389/fnetp.2024.1441983","DOIUrl":"10.3389/fnetp.2024.1441983","url":null,"abstract":"<p><p>Epilepsy is one of the most common neurological disorders, affecting over 65 million people worldwide. Despite medical management with anti-seizure medications (ASMs), many patients fail to achieve seizure freedom, with over one-third of patients having drug-resistant epilepsy (DRE). Even with surgical management through resective surgery and/or neuromodulatory interventions, over 50 <math><mi>%</mi></math> of patients continue to experience refractory seizures within a year of surgery. Over the past 2 decades, studies have increasingly suggested that treatment failure is likely driven by untreated components of a pathological seizure network, a shift in the classical understanding of epilepsy as a focal disorder. However, this shift in thinking has yet to translate to improved treatments and seizure outcomes in patients. Here, we present a narrative review discussing the process of surgical epilepsy management. We explore current surgical interventions and hypothesized mechanisms behind treatment failure, highlighting evidence of pathologic seizure networks. Finally, we conclude by discussing how the network theory may inform surgical management, guiding the identification and targeting of more appropriate surgical regions. Ultimately, we believe that adapting current surgical practices and neuromodulatory interventions towards targeting seizure networks offers new therapeutic strategies that may improve seizure outcomes in patients suffering from DRE.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11335476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The network is more important than the node: stereo-EEG evidence of neurocognitive networks in epilepsy","authors":"Nicholas W. G. Murray, Anthony C. Kneebone, Petra L. Graham, Chong H. Wong, Greg Savage, Lisa Gillinder, Michael W. K. Fong","doi":"10.3389/fnetp.2024.1424004","DOIUrl":"https://doi.org/10.3389/fnetp.2024.1424004","url":null,"abstract":"Neuropsychological assessment forms an integral part of the presurgical evaluation for patients with medically refractory focal epilepsy. Our understanding of cognitive impairment in epilepsy is based on seminal lesional studies that have demonstrated important structure-function relationships within the brain. However, a growing body of literature demonstrating heterogeneity in the cognitive profiles of patients with focal epilepsy (e.g., temporal lobe epilepsy; TLE) has led researchers to speculate that cognition may be impacted by regions outside the seizure onset zone, such as those involved in the interictal or “irritative” network.Neuropsychological data from 48 patients who underwent stereoelectroencephalography (SEEG) monitoring between 2012 and 2023 were reviewed. Patients were categorized based on the site of seizure onset, as well as their irritative network, to determine the impact of wider network activity on cognition. Neuropsychological data were compared with normative standards (i.e., z = 0), and between groups.There were very few distinguishing cognitive features between patients when categorized based purely on the seizure onset zone (i.e., frontal lobe vs. temporal lobe epilepsy). In contrast, patients with localized irritative networks (i.e., frontal or temporal interictal epileptiform discharges [IEDs]) demonstrated more circumscribed profiles of impairment compared with those demonstrating wider irritative networks (i.e., frontotemporal IEDs). Furthermore, the directionality of propagation within the irritative network was found to influence the manifestations of cognitive impairment.The findings suggest that neuropsychological assessment is sensitive to network activity beyond the site of seizure onset. As such, an overly focal interpretation may not accurately reflect the distribution of the underlying pathology. This has important implications for presurgical work-up in epilepsy, as well as subsequent surgical outcomes.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate modelling of heartbeat events for improved J-peak detection in BCG using deep learning","authors":"Christoph Schranz, Christina Halmich, Sebastian Mayr, Dominik P. J. Heib","doi":"10.3389/fnetp.2024.1425871","DOIUrl":"https://doi.org/10.3389/fnetp.2024.1425871","url":null,"abstract":"Sleep, or the lack thereof, has far-reaching consequences on many aspects of human physiology, cognitive performance, and emotional wellbeing. To ensure undisturbed sleep monitoring, unobtrusive measurements such as ballistocardiogram (BCG) are essential for sustained, real-world data acquisition. Current analysis of BCG data during sleep remains challenging, mainly due to low signal-to-noise ratio, physical movements, as well as high inter- and intra-individual variability. To overcome these challenges, this work proposes a novel approach to improve J-peak extraction from BCG measurements using a supervised deep learning setup. The proposed method consists of the modeling of the discrete reference heartbeat events with a symmetric and continuous kernel-function, referred to as surrogate signal. Deep learning models approximate this surrogate signal from which the target heartbeats are detected. The proposed method with various surrogate signals is compared and evaluated with state-of-the-art methods from both signal processing and machine learning approaches. The BCG dataset was collected over 17 nights using inertial measurement units (IMUs) embedded in a mattress, together with an ECG for reference heartbeats, for a total of 134 h. Moreover, we apply for the first time an evaluation metric specialized for the comparison of event-based time series to assess the quality of heartbeat detection. The results show that the proposed approach demonstrates superior accuracy in heartbeat estimation compared to existing approaches, with an MAE (mean absolute error) of 1.1 s in 64-s windows and 1.38 s in 8-s windows. Furthermore, it is shown that our novel approach outperforms current methods in detecting the location of heartbeats across various evaluation metrics. To the best of our knowledge, this is the first approach to encode temporal events using kernels and the first systematic comparison of various event encodings for event detection using a regression-based sequence-to-sequence model.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141820791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}