Ising model with conserved magnetization on the human connectome: Implications on the relation structure-function in wakefulness and anesthesia

Stramaglia, Sebastiano; Pellicoro, M.; Angelini, L.; Amico, Enrico; Aerts, Hannelore; Cortés, Jesús M.; Laureys, Steven; Marinazzo, Daniele

doi:10.1063/1.4978999

Cited by 36 publications

(31 citation statements)

References 46 publications

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“…Atlhough this method is routinely employed in large-scale modeling studies, other methods are worth exploring. For example, similarity could be sought at the modular level, maximizing the cross-modularity between simulated and empirical functional connectivity (Diez et al, 2015; Stramaglia et al, 2017). These approaches have the additional advantage of reducing the dependency on the parcellation choice.…”

Section: Discussionmentioning

confidence: 99%

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Aerts

Schirner

Jeurissen

et al. 2018

eNeuro

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Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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Section: Discussionmentioning

confidence: 99%

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Aerts

Schirner

Jeurissen

et al. 2018

eNeuro

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“…We applied DTI preprocessing similar to previous work (Alonso- Montes et al, 2015;Amor et al, 2015;Diez, Bonifazi, et al, 2015;Diez et al, 2017;Kroos et al, 2017;Marinazzo et al, 2014;Rasero, Pellicoro, et al 2017;Rasero, Alonso-Montes, et al 2017;Stramaglia et al, 2017) using FSL (FMRIB Software Library v5.0) and the Diffusion Toolkit. First, an eddy current correction was applied to overcome the artifacts produced by variation in the direction of the gradient fields of the MR scanner, together with the artifacts produced by head motion.…”

Section: Imaging Preprocessing 231 | Diffusion Tensor Imagingmentioning

confidence: 99%

“…We applied resting fMRI preprocessing similar to previous work (Alonso-Montes et al, 2015;Amor et al, 2015;Diez, Erramuzpe, et al, 2015;Diez, Bonifazi, et al, 2015;Diez et al, 2017;Mäki-Marttunen, Diez, Cortes, Chialvo, & Villarreal, 2013;Marinazzo et al, 2014;Rasero, Pellicoro, et al, 2017;Stramaglia et al, 2016Stramaglia et al, , 2017Stramaglia, Angelini, Cortes, & Marinazzo, 2015) using FSL and AFNI (http://afni.nimh.nih.gov/afni/). First, slice-time correction was applied to the fMRI data set.…”

Section: Functional Mrimentioning

confidence: 99%

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Bonifazi

Erramuzpe

Diez

et al. 2018

Human Brain Mapping

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Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age—an age estimator resulting from a multi‐scale methodology applied to the structure–function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto‐striato‐thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural–functional connectivity patterns correlating to other biomarkers than ChA.

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“…Although this method is routinely employed in large-scale modeling studies, other methods that maximize the large-scale organization of both connectivity matrices might be more sensible and yield more robust results. For example, similarity could be sought at the modular level, maximizing the cross-modularity between simulated and empirical functional connectivity (Diez et al, 2015;Stramaglia et al, 2017).…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Modeling brain dynamics after tumor resection using The Virtual Brain

Aerts

Schirner

Dhollander

et al. 2019

Preprint

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Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, computational modeling of brain activity using so-called brain network models has been introduced as a promising tool for this purpose. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery" analyses to evaluate the potential of brain network modeling in predicting brain dynamics after tumor resection.We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. In addition, we identify several robust associations between individually optimized model parameters, structural network topology and cognitive performance from pre-to post-operative assessment. Concerning the virtual neurosurgery analyses, we obtain promising results in some patients, whereas the predictive accuracy of the currently applied model is poor in others. These findings reveal interesting avenues for future research, as well as important limitations that warrant further investigation.

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Ising model with conserved magnetization on the human connectome: Implications on the relation structure-function in wakefulness and anesthesia

Cited by 36 publications

References 46 publications

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain

Structure–function multi‐scale connectomics reveals a major role of the fronto‐striato‐thalamic circuit in brain aging

Modeling brain dynamics after tumor resection using The Virtual Brain

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