Carlos Coronel-Oliveros, Vicente Medel, Grace A Whitaker, Aland Astudillo, David Gallagher, Lucía Zepeda-Rivero, Pavel Prado, W. El-Deredy, P. Orio, Alejandro Weinstein
High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from sixteen healthy volunteers during acute high-altitude hypoxia (at 4000 masl) and at sea-level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration in using graph theory tools. High altitude led to slower brain oscillations, i.e., increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high-altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.
{"title":"Elevating Understanding: Linking High-Altitude Hypoxia to Brain Aging\u0000 Through EEG Functional Connectivity and Spectral Analyses","authors":"Carlos Coronel-Oliveros, Vicente Medel, Grace A Whitaker, Aland Astudillo, David Gallagher, Lucía Zepeda-Rivero, Pavel Prado, W. El-Deredy, P. Orio, Alejandro Weinstein","doi":"10.1162/netn_a_00352","DOIUrl":"https://doi.org/10.1162/netn_a_00352","url":null,"abstract":"High-altitude hypoxia triggers brain function changes reminiscent of those in healthy aging and Alzheimer's disease, compromising cognition and executive functions. Our study sought to validate high-altitude hypoxia as a model for assessing brain activity disruptions akin to aging. We collected EEG data from sixteen healthy volunteers during acute high-altitude hypoxia (at 4000 masl) and at sea-level, focusing on relative changes in power and aperiodic slope of the EEG spectrum due to hypoxia. Additionally, we examined functional connectivity using wPLI, and functional segregation and integration in using graph theory tools. High altitude led to slower brain oscillations, i.e., increased δ and reduced α power, and flattened the 1/f aperiodic slope, indicating higher electrophysiological noise, akin to healthy aging. Notably, functional integration strengthened in the θ band, exhibiting unique topographical patterns at the subnetwork level, including increased frontocentral and reduced occipitoparietal integration. Moreover, we discovered significant correlations between subjects' age, 1/f slope, θ band integration, and observed robust effects of hypoxia after adjusting for age. Our findings shed light on how reduced oxygen levels at high-altitudes influence brain activity patterns resembling those in neurodegenerative disorders and aging, making high-altitude hypoxia a promising model for comprehending the brain in health and disease.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"74 5","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600143","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}
Ramakrishnan Bhuvanasundaram, Samantha Washburn, Joanna Krzyspiak, K. Khodakhah
The zona incerta (ZI) is a subthalamic structure that has been implicated in locomotion, fear, and anxiety. Recently interest has grown in its therapeutic efficacy in deep brain stimulation (DBS) in movement disorders. This efficacy might be due to the ZI’s functional projections to the other brain regions. Notwithstanding some evidence of anatomical connections between the ZI and the inferior olive (IO) and the pontine nuclei (PN), how the ZI modulates the neuronal activity in these regions remains to be determined. We first tested this by monitoring responses of single neurons in the PN and IO to optogenetic activation of channelrhodopsin-expressing ZI axons in wild-type mice, using an in vivo awake preparation. Stimulation of short, single pulses and trains of stimuli at 20 Hz elicited rapid responses in the majority of recorded cells in the PN and IO. Furthermore, the excitatory response of PN neurons scaled with the strength of ZI activation. Next, we used in vitro electrophysiology to study synaptic transmission at ZI-IO synapses. Optogenetic activation of ZI axons evoked a strong excitatory post-synaptic response in IO neurons, which remained robust with repeated stimulation at 20 Hz. Overall, our results demonstrate a functional connection within ZI-PN and ZI-IO pathways.
突触区（ZI）是眼下丘结构，与运动、恐惧和焦虑有关。最近，人们对其在运动障碍的深部脑刺激（DBS）治疗中的疗效越来越感兴趣。这种疗效可能得益于 ZI 对其他脑区的功能性投射。尽管有证据表明 ZI 与下橄榄核（IO）和桥脑核（PN）之间存在解剖学联系，但 ZI 如何调节这些区域的神经元活动仍有待确定。我们首先利用体内清醒制备方法，在野生型小鼠体内监测 PN 和 IO 中的单个神经元对光电激活表达通道荧光素的 ZI 轴突的反应，从而检验了这一问题。20赫兹的单个短脉冲刺激和一连串刺激在PN和IO的大多数记录细胞中引起了快速反应。此外，PN神经元的兴奋反应与ZI激活的强度成比例。接下来，我们利用体外电生理学研究了 ZI-IO 突触的突触传递。ZI轴突的光遗传激活在IO神经元中诱发了强烈的兴奋性突触后反应，这种反应在20赫兹的重复刺激下仍然保持稳定。总之，我们的研究结果证明了 ZI-PN 和 ZI-IO 通路之间的功能联系。
{"title":"Zona incerta modulation of the inferior olive and the pontine nuclei","authors":"Ramakrishnan Bhuvanasundaram, Samantha Washburn, Joanna Krzyspiak, K. Khodakhah","doi":"10.1162/netn_a_00350","DOIUrl":"https://doi.org/10.1162/netn_a_00350","url":null,"abstract":"The zona incerta (ZI) is a subthalamic structure that has been implicated in locomotion, fear, and anxiety. Recently interest has grown in its therapeutic efficacy in deep brain stimulation (DBS) in movement disorders. This efficacy might be due to the ZI’s functional projections to the other brain regions. Notwithstanding some evidence of anatomical connections between the ZI and the inferior olive (IO) and the pontine nuclei (PN), how the ZI modulates the neuronal activity in these regions remains to be determined. We first tested this by monitoring responses of single neurons in the PN and IO to optogenetic activation of channelrhodopsin-expressing ZI axons in wild-type mice, using an in vivo awake preparation. Stimulation of short, single pulses and trains of stimuli at 20 Hz elicited rapid responses in the majority of recorded cells in the PN and IO. Furthermore, the excitatory response of PN neurons scaled with the strength of ZI activation. Next, we used in vitro electrophysiology to study synaptic transmission at ZI-IO synapses. Optogenetic activation of ZI axons evoked a strong excitatory post-synaptic response in IO neurons, which remained robust with repeated stimulation at 20 Hz. Overall, our results demonstrate a functional connection within ZI-PN and ZI-IO pathways.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"1 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139266668","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}
Abstract Time-varying functional connectivity (FC) methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behavior, estimates must be robust and reproducible. Here, we propose two solutions using the hidden Markov model (HMM) as a descriptive model of time-varying FC. The first, best ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical-clustered HMM, generates stable cluster state time series by applying hierarchical clustering to the state time series obtained from multiple runs. Experimental results on fMRI and magnetoencephalography data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical-clustered HMM is preferred when the inference variability is high, while the best ranked HMM performs better otherwise.
{"title":"Towards stability of dynamic FC estimates in neuroimaging and electrophysiology: solutions and limits","authors":"Sonsoles Alonso, Diego Vidaurre","doi":"10.1162/netn_a_00331","DOIUrl":"https://doi.org/10.1162/netn_a_00331","url":null,"abstract":"Abstract Time-varying functional connectivity (FC) methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behavior, estimates must be robust and reproducible. Here, we propose two solutions using the hidden Markov model (HMM) as a descriptive model of time-varying FC. The first, best ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical-clustered HMM, generates stable cluster state time series by applying hierarchical clustering to the state time series obtained from multiple runs. Experimental results on fMRI and magnetoencephalography data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical-clustered HMM is preferred when the inference variability is high, while the best ranked HMM performs better otherwise.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135584117","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}
Abstract Listening to music is an enjoyable behaviour that engages multiple networks of brain regions. As such, the act of music listening may offer a way to interrogate network activity, and to examine the reconfigurations of brain networks that have been observed in healthy aging. The present study is an exploratory examination of brain network dynamics during music listening in healthy older and younger adults. Network measures were extracted and analyzed together with behavioural data using a combination of hidden Markov modelling and partial least squares. We found age- and preference-related differences in fMRI data collected during music listening in healthy younger and older adults. Both age groups showed higher occupancy (the proportion of time a network was active) in a temporal-mesolimbic network while listening to self-selected music. Activity in this network was strongly positively correlated with liking and familiarity ratings in younger adults, but less so in older adults. Additionally, older adults showed a higher degree of correlation between liking and familiarity ratings consistent with past behavioural work on age-related dedifferentiation. We conclude that, while older adults do show network and behaviour patterns consistent with dedifferentiation, activity in the temporal-mesolimbic network is relatively robust to dedifferentiation. These findings may help explain how music listening remains meaningful and rewarding in old age.
{"title":"Age-related variability in network engagement during music listening","authors":"S. Faber, A. G. Belden, P. Loui, A. R. McIntosh","doi":"10.1162/netn_a_00333","DOIUrl":"https://doi.org/10.1162/netn_a_00333","url":null,"abstract":"Abstract Listening to music is an enjoyable behaviour that engages multiple networks of brain regions. As such, the act of music listening may offer a way to interrogate network activity, and to examine the reconfigurations of brain networks that have been observed in healthy aging. The present study is an exploratory examination of brain network dynamics during music listening in healthy older and younger adults. Network measures were extracted and analyzed together with behavioural data using a combination of hidden Markov modelling and partial least squares. We found age- and preference-related differences in fMRI data collected during music listening in healthy younger and older adults. Both age groups showed higher occupancy (the proportion of time a network was active) in a temporal-mesolimbic network while listening to self-selected music. Activity in this network was strongly positively correlated with liking and familiarity ratings in younger adults, but less so in older adults. Additionally, older adults showed a higher degree of correlation between liking and familiarity ratings consistent with past behavioural work on age-related dedifferentiation. We conclude that, while older adults do show network and behaviour patterns consistent with dedifferentiation, activity in the temporal-mesolimbic network is relatively robust to dedifferentiation. These findings may help explain how music listening remains meaningful and rewarding in old age.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"38 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135584120","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}
Abstract A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multi-scale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.
{"title":"Linking fast and slow: the case for generative models","authors":"Johan Medrano, Karl Friston, Peter Zeidman","doi":"10.1162/netn_a_00343","DOIUrl":"https://doi.org/10.1162/netn_a_00343","url":null,"abstract":"Abstract A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multi-scale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"142 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270946","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}
Francis Normand, Mehul Gajwani, Daniel C. Côté, Antoine Allard
Abstract Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last fifteen years. Mounting evidence support the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the Network-based statistic-simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established Network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early-psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS.
{"title":"NBS-SNI, an extension of the Network-based statistic: Abnormal functional connections between important structural actors","authors":"Francis Normand, Mehul Gajwani, Daniel C. Côté, Antoine Allard","doi":"10.1162/netn_a_00344","DOIUrl":"https://doi.org/10.1162/netn_a_00344","url":null,"abstract":"Abstract Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last fifteen years. Mounting evidence support the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the Network-based statistic-simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established Network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early-psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"144 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271081","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}
Hanna M. Tolle, Juan Carlos Farah, Pablo Mallaroni, Natasha L. Mason, Johannes G. Ramaekers, Enrico Amico
Abstract The emerging neuroscientific frontier of brain fingerprinting has recently established that human functional connectomes (FCs) exhibit fingerprint-like idiosyncratic features, which map onto heterogeneously distributed behavioural traits. Here, we harness brain-fingerprinting tools to extract FC features that predict subjective drug experience induced by the psychedelic psilocybin. Specifically, in neuroimaging data of healthy volunteers under the acute influence of psilocybin or a placebo, we show that, post psilocybin administration, FCs become more idiosyncratic due to greater inter-subject dissimilarity. Moreover, whereas in placebo subjects idiosyncratic features are primarily found in the frontoparietal network, in psilocybin subjects they concentrate in the default-mode network (DMN). Crucially, isolating the latter revealed an FC pattern that predicts subjective psilocybin experience and is characterised by reduced within-DMN and DMN-limbic connectivity, as well as increased connectivity between the DMN and attentional systems. Overall, these results contribute to bridging the gap between psilocybin-mediated effects on brain and behaviour, while demonstrating the value of a brain-fingerprinting approach to pharmacological neuroimaging.
{"title":"The Unique Neural Signature of Your Trip: Functional Connectome Fingerprints of Subjective Psilocybin Experience","authors":"Hanna M. Tolle, Juan Carlos Farah, Pablo Mallaroni, Natasha L. Mason, Johannes G. Ramaekers, Enrico Amico","doi":"10.1162/netn_a_00349","DOIUrl":"https://doi.org/10.1162/netn_a_00349","url":null,"abstract":"Abstract The emerging neuroscientific frontier of brain fingerprinting has recently established that human functional connectomes (FCs) exhibit fingerprint-like idiosyncratic features, which map onto heterogeneously distributed behavioural traits. Here, we harness brain-fingerprinting tools to extract FC features that predict subjective drug experience induced by the psychedelic psilocybin. Specifically, in neuroimaging data of healthy volunteers under the acute influence of psilocybin or a placebo, we show that, post psilocybin administration, FCs become more idiosyncratic due to greater inter-subject dissimilarity. Moreover, whereas in placebo subjects idiosyncratic features are primarily found in the frontoparietal network, in psilocybin subjects they concentrate in the default-mode network (DMN). Crucially, isolating the latter revealed an FC pattern that predicts subjective psilocybin experience and is characterised by reduced within-DMN and DMN-limbic connectivity, as well as increased connectivity between the DMN and attentional systems. Overall, these results contribute to bridging the gap between psilocybin-mediated effects on brain and behaviour, while demonstrating the value of a brain-fingerprinting approach to pharmacological neuroimaging.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"144 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271080","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}
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.
{"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}
Noelia Martínez-Molina, Anira Escrichs, Yonatan Sanz-Perl, Aleksi J. Sihvonen, Teppo Särkämö, Morten L. Kringelbach, Gustavo Deco
Abstract It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using a rsfMRI dataset from a cohort of moderate-to-severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3-, 6- and 12-months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6-months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.
{"title":"The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury","authors":"Noelia Martínez-Molina, Anira Escrichs, Yonatan Sanz-Perl, Aleksi J. Sihvonen, Teppo Särkämö, Morten L. Kringelbach, Gustavo Deco","doi":"10.1162/netn_a_00346","DOIUrl":"https://doi.org/10.1162/netn_a_00346","url":null,"abstract":"Abstract It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using a rsfMRI dataset from a cohort of moderate-to-severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3-, 6- and 12-months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6-months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"142 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270944","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}
Zachary T. Goodman, Jason S. Nomi, Salome Kornfeld, Taylor Bolt, Roger A. Saumure, Celia Romero, Sierra A. Bainter, Lucina Q. Uddin
Abstract Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching – skills falling under the broad domain of executive functions which fluctuate over the lifespan. Neural variability can be quantified via the blood-oxygenated level-dependent (BOLD) signal in resting-state functional magnetic resonance imaging (fMRI). Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the lifespan maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the lifespan. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the lifespan. Findings suggest brain signal variability may help to explain to age-related differences in executive functions across the lifespan.
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