Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat

Díaz-Parra, Antonio; Osborn, Zachary G.; Canals, Santiago; Moratal, David; Sporns, Olaf

doi:10.1016/j.neuroimage.2017.07.046

Cited by 41 publications

(39 citation statements)

References 102 publications

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“…Mouse Connectivity Atlas to study structure-function relations at the group level using voltage-sensitive dyes (35) and FC (6,22,36). In addition, a recent work in rats (37) used TVB to simulate FC based on SC and found strong correlation at the group level. A similar finding has been reported in humans (38).…”

Section: Discussionmentioning

confidence: 99%

Individual structural features constrain the mouse functional connectome

Melozzi

Bergmann

Harris

et al. 2019

Preprint

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Whole brain dynamics intuitively depends upon the internal wiring of the brain; butto which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure-function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine. SIGNIFICANCE STATEMENTThe structural connectome is a key determinant of brain function and dysfunction. The connectome-based model approach aims to understand the functional organization of the brain by modeling the brain as a dynamical system and then studying how the functional architecture rises from the underlying structural skeleton. Here, taking advantage of mice studies, we systematically investigated the informative content of different structural features in explaining the emergence of the functional ones. We demonstrate that individual variations define a specific structural fingerprint with a direct impact upon the functional organization of individual brains stressing the importance of using individualized models to understand brain function. We show how limitations of connectome reconstruction with the diffusion-MRI method restrict our comprehension of the structural-functional relation. PP Tracerfiltered = 0.461 ± 0.005 PP Tracer = 0.488 ± 0.005 P = 0.002 PP Individual−det = 0.415 ± 0.005 P = 2e -10 PP Tracersym = 0.418 ± 0.004 PP Tracer = 0.488 ± 0.005 P = 2e -20 P = 1.0 PP Tracersym = 0.418 ± 0.004

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Section: Discussionmentioning

confidence: 99%

Individual structural features constrain the mouse functional connectome

Melozzi

Bergmann

Harris

et al. 2019

Preprint

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“…However, we did not find a correlation between functional connectivity and meso-scale neuronal tracer-based structural connectivity strength. One previous study investigated this relationship at the meso-scale in rats and reported a positive structure-function correlation (r=0.48) (Díaz-Parra et al 2017). However, this study did not include essential interhemispheric connections.…”

Section: Distinct Global Structure-function Relationships Across Diffmentioning

confidence: 99%

Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

Straathof

Sinke

Roelofs

et al. 2019

Preprint

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An improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with highresolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macroand meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior. The brain is a complex organ that can be regarded as a structural and functional network of interacting regions at the micro-, meso-and macroscopic level. At the macro-scale, whole-brain functional networks can be non-invasively mapped with resting-state functional MRI (resting-state fMRI). In resting-state fMRI data the inter-regional temporal correlations of spontaneous lowfrequency blood oxygenation level-dependent (BOLD) fluctuations reflect functional connectivity (Biswal et al. 1995; Fox and Raichle 2007). Based on clusters of functionally connected regions, various resting-state networks have been identified, such as the default mode network (Greicius et al. 2003). These networks have been related to behavioral functioning in health and disease, and abnormalities partially explain pathophysiological processes and disease progression (van den Heuvel and Hulshoff Pol 2010; Zhang and Raichle 2010; Raichle 2015). The exact nature of functional connectivity is nonetheless not yet fully established. Since functional connectivity measured with resting-state fMRI relies on synchronous BOLD signals, understanding functional connectivity starts with understanding the origin of BOLD signals. The BOLD signal captures hemodynamic changes, such as blood flow, in response to neural activity.Although it is clear that BOLD signals reflect aspects of neural responses, it is still unclear which processes a...

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“…The simulated data was fitted to determine clinical epilepsy propagation zones. TVB was also used to simulate rat brains (35) and macaque brains (36). With an evolutionary optimization algorithm and the Generic 2D oscillator the simulated FC was tuned to fit its empirical counterpart.…”

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confidence: 99%

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

Triebkorn

Zimmermann

Stefanovski

et al. 2020

Preprint

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Using The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters:conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain. Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.Brain network models allow us to investigate how realistic neural behaviour arises as a function of variations in specific neural parameters. Large-sc...

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Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat

Cited by 41 publications

References 102 publications

Individual structural features constrain the mouse functional connectome

Individual structural features constrain the mouse functional connectome

Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

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