Network Neuroscience最新文献

{"title":"Spectral Dynamic Causal Modelling: A Didactic Introduction and its Relationship with Functional Connectivity","authors":"Leonardo Novelli, Karl Friston, Adeel Razi","doi":"10.1162/netn_a_00348","DOIUrl":"https://doi.org/10.1162/netn_a_00348","url":null,"abstract":"Abstract We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modelling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements—at all time lags—including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictability of cortico-cortical connections in the mammalian brain","authors":"Ferenc Molnár, Szabolcs Horvát, Ana R. Ribeiro Gomes, Jorge Martinez Armas, Botond Molnár, Mária Ercsey-Ravasz, Kenneth Knoblauch, Henry Kennedy, Zoltan Toroczkai","doi":"10.1162/netn_a_00345","DOIUrl":"https://doi.org/10.1162/netn_a_00345","url":null,"abstract":"Abstract Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a non-human primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an Area Under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85–90% accuracy (mouse) and 70–80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighting the Structural Connectome: Exploring its Impact on Network Properties and Predicting Cognitive Performance in the Human Brain","authors":"Hila Gast, Yaniv Assaf","doi":"10.1162/netn_a_00342","DOIUrl":"https://doi.org/10.1162/netn_a_00342","url":null,"abstract":"Abstract Brain function does not emerge from isolated activity, but rather from the interactions and exchanges between neural elements which form a network known as the connectome. The human connectome consists of structural and functional aspects. The structural connectome (SC) represents the anatomical connections and the functional connectome represents the resulting dynamics which emerge from this arrangement of structures. As there are different ways of weighting these connections, it is important to consider how such different approaches impact study conclusions. Here, we propose that different weighted connectomes result in varied network properties and while neither superior the other, selection might affect interpretation and conclusions in different study cases. We present three different weighting models, namely, Number of Streamlines (NOS), Fractional Anisotropy (FA), and Axon-Diameter Distribution (ADD), to demonstrate these differences. The later, is extracted using recently published AxSI method, and is first compared to commonly used weighting methods. Moreover, we explore the functional relevance of each weighted SC, using the HCP database. By analyzing intelligencerelated data, we develop a predictive model for cognitive performance based on graph properties and the NIH toolbox. Results demonstrate that the ADD SC, combined with a functional subnetwork model, outperforms other models in estimating cognitive performance.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"144 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circuits in the Motor Cortex Explain Oscillatory Responses to Transcranial Magnetic Stimulation","authors":"Lysea Haggie, Thor Besier, Angus McMorland","doi":"10.1162/netn_a_00341","DOIUrl":"https://doi.org/10.1162/netn_a_00341","url":null,"abstract":"Abstract Transcranial Magnetic Stimulation (TMS) is a popular method used to investigate brain function. Stimulation over the motor cortex evokes muscle contractions known as motor evoked potentials (MEPs) and also high frequency volleys of electrical activity measured in the cervical spinal cord. The physiological mechanisms of these experimentally derived responses remain unclear, but it is thought that the connections between circuits of excitatory and inhibitory neurons play a vital role. Using a spiking neural network model of the motor cortex, we explained the generation of waves of activity, so called ‘I-waves’, following cortical stimulation. The model reproduces a number of experimentally known responses including direction of TMS, increased inhibition and changes in strength. Using populations of thousands of neurons in a model of cortical circuitry we showed that the cortex generated transient oscillatory responses without any tuning, and that neuron parameters such as refractory period and delays influenced the pattern and timing of those oscillations. By comparing our network with simpler, previously proposed circuits, we explored the contributions of specific connections and found that recurrent inhibitory connections are vital in producing later waves which significantly impact the production of motor evoked potentials in downstream muscles (Thickbroom, 2011). This model builds on previous work to increase our understanding of how complex circuitry of the cortex is involved in the generation of I-waves.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135325885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hub overload and failure as a final common pathway in neurological brain network disorders","authors":"Cornelis Jan Stam","doi":"10.1162/netn_a_00339","DOIUrl":"https://doi.org/10.1162/netn_a_00339","url":null,"abstract":"Abstract Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer’s disease hyperactive hub nodes are directly associated with amyloid beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer’s disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controversies and progress on standardization of large-scale brain network nomenclature.","authors":"Lucina Q Uddin, Richard F Betzel, Jessica R Cohen, Jessica S Damoiseaux, Felipe De Brigard, Simon B Eickhoff, Alex Fornito, Caterina Gratton, Evan M Gordon, Angela R Laird, Linda Larson-Prior, A Randal McIntosh, Lisa D Nickerson, Luiz Pessoa, Ana Luísa Pinho, Russell A Poldrack, Adeel Razi, Sepideh Sadaghiani, James M Shine, Anastasia Yendiki, B T Thomas Yeo, R Nathan Spreng","doi":"10.1162/netn_a_00323","DOIUrl":"10.1162/netn_a_00323","url":null,"abstract":"<p><p>Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"864-905"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41148136","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":"Resolving inter-regional communication capacity in the human connectome.","authors":"Filip Milisav, Vincent Bazinet, Yasser Iturria-Medina, Bratislav Misic","doi":"10.1162/netn_a_00318","DOIUrl":"10.1162/netn_a_00318","url":null,"abstract":"<p><p>Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"1051-1079"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41133793","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":"High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle.","authors":"Sarah Greenwell,&nbsp;Joshua Faskowitz,&nbsp;Laura Pritschet,&nbsp;Tyler Santander,&nbsp;Emily G Jacobs,&nbsp;Richard F Betzel","doi":"10.1162/netn_a_00307","DOIUrl":"https://doi.org/10.1162/netn_a_00307","url":null,"abstract":"<p><p>Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and brain connectivity. However, how hormonal fluctuations impact fast changes in brain network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations between the activity of pairs of brain regions at a framewise timescale. In previous studies we showed that time points corresponding to high-amplitude co-fluctuations disproportionately contributed to the time-averaged functional connectivity pattern and that these co-fluctuation patterns could be clustered into a low-dimensional set of recurring \"states.\" Here, we assessed the relationship between these network states and quotidian variation in hormone concentrations. Specifically, we were interested in whether the frequency with which network states occurred was related to hormone concentration. We addressed this question using a dense-sampling dataset (<i>N</i> = 1 brain). In this dataset, a single individual was sampled over the course of two endocrine states: a natural menstrual cycle and while the subject underwent selective progesterone suppression via oral hormonal contraceptives. During each cycle, the subject underwent 30 daily resting-state fMRI scans and blood draws. Our analysis of the imaging data revealed two repeating network states. We found that the frequency with which state 1 occurred in scan sessions was significantly correlated with follicle-stimulating and luteinizing hormone concentrations. We also constructed representative networks for each scan session using only \"event frames\"-those time points when an event was determined to have occurred. We found that the weights of specific subsets of functional connections were robustly correlated with fluctuations in the concentration of not only luteinizing and follicle-stimulating hormones, but also progesterone and estradiol.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"1181-1205"},"PeriodicalIF":4.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473261/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41143741","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":"Predicting longitudinal brain atrophy in Parkinson's disease using a Susceptible-Infected-Removed agent-based model.","authors":"Alaa Abdelgawad,&nbsp;Shady Rahayel,&nbsp;Ying-Qiu Zheng,&nbsp;Christina Tremblay,&nbsp;Andrew Vo,&nbsp;Bratislav Misic,&nbsp;Alain Dagher","doi":"10.1162/netn_a_00296","DOIUrl":"https://doi.org/10.1162/netn_a_00296","url":null,"abstract":"<p><p>Parkinson's disease is a progressive neurodegenerative disorder characterized by accumulation of abnormal isoforms of alpha-synuclein. Alpha-synuclein is proposed to act as a prion in Parkinson's disease: In its misfolded pathologic state, it favors the misfolding of normal alpha-synuclein molecules, spreads trans-neuronally, and causes neuronal damage as it accumulates. This theory remains controversial. We have previously developed a Susceptible-Infected-Removed (SIR) computational model that simulates the templating, propagation, and toxicity of alpha-synuclein molecules in the brain. In this study, we test this model with longitudinal MRI collected over 4 years from the Parkinson's Progression Markers Initiative (1,068 T1 MRI scans, 790 Parkinson's disease scans, and 278 matched control scans). We find that brain deformation progresses in subcortical and cortical regions. The SIR model recapitulates the spatiotemporal distribution of brain atrophy observed in Parkinson's disease. We show that connectome topology and geometry significantly contribute to model fit. We also show that the spatial expression of two genes implicated in alpha-synuclein synthesis and clearance, <i>SNCA</i> and <i>GBA</i>, also influences the atrophy pattern. We conclude that the progression of atrophy in Parkinson's disease is consistent with the prion-like hypothesis and that the SIR model is a promising tool to investigate multifactorial neurodegenerative diseases over time.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"906-925"},"PeriodicalIF":4.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152482","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 arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network.","authors":"Gustavo Deco,&nbsp;Yonatan Sanz Perl,&nbsp;Laura de la Fuente,&nbsp;Jacobo D Sitt,&nbsp;B T Thomas Yeo,&nbsp;Enzo Tagliazucchi,&nbsp;Morten L Kringelbach","doi":"10.1162/netn_a_00300","DOIUrl":"10.1162/netn_a_00300","url":null,"abstract":"<p><p>A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"966-998"},"PeriodicalIF":4.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473271/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41172896","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}