Precision Functional Mapping of the Frontoparietal Network at the Level of Cortical Layers
Poster No:
1404
Submission Type:
Abstract Submission
Authors:
Maria-Aradia Wilms1,2, Denis Chaimow1,3, Kenny Seidel4,5, Matilde Vaghi6, Nikolaus Weiskopf7,8,9, Romy Lorenz10,3
Institutions:
1Max Planck Institute for Biological Cybernetics, Tübingen, Baden-Württemberg, 2Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Brandenburg, Germany, 3Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Sachsen, Germany, 4Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Sachsen, 5University of Leipzig, Leipzig, Sachsen, Germany, 6School of Psychological Sciences, Birkbeck, University of London, London, Greater London, 7Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony, 8Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Sachsen, Germany, 9Wellcome Centre for Human Neuroimaging, UCL, London, London, United Kingdom, 10Max Planck Institute for Biological Cybernetics, Tübingen, Germany
First Author:
Maria-Aradia Wilms
Max Planck Institute for Biological Cybernetics|Berlin School of Mind and Brain, Humboldt-Universität zu Berlin
Tübingen, Baden-Württemberg|Berlin, Brandenburg, Germany
Max Planck Institute for Biological Cybernetics|Berlin School of Mind and Brain, Humboldt-Universität zu Berlin
Tübingen, Baden-Württemberg|Berlin, Brandenburg, Germany
Co-Author(s):
Denis Chaimow
Max Planck Institute for Biological Cybernetics|Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences
Tübingen, Baden-Württemberg|Leipzig, Sachsen, Germany
Max Planck Institute for Biological Cybernetics|Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences
Tübingen, Baden-Württemberg|Leipzig, Sachsen, Germany
Kenny Seidel
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences|University of Leipzig
Leipzig, Sachsen|Leipzig, Sachsen, Germany
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences|University of Leipzig
Leipzig, Sachsen|Leipzig, Sachsen, Germany
Matilde Vaghi
School of Psychological Sciences, Birkbeck, University of London
London, Greater London
School of Psychological Sciences, Birkbeck, University of London
London, Greater London
Nikolaus Weiskopf
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences|Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University|Wellcome Centre for Human Neuroimaging, UCL
Leipzig, Saxony|Leipzig, Sachsen, Germany|London, London, United Kingdom
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences|Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University|Wellcome Centre for Human Neuroimaging, UCL
Leipzig, Saxony|Leipzig, Sachsen, Germany|London, London, United Kingdom
Romy Lorenz
Max Planck Institute for Biological Cybernetics|Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences
Tübingen, Germany|Leipzig, Sachsen, Germany
Max Planck Institute for Biological Cybernetics|Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences
Tübingen, Germany|Leipzig, Sachsen, Germany
Introduction:
The frontoparietal network (FPN) plays a central role in cognitive control and executive functioning [1]. Interestingly, even among healthy individuals, the FPN displays the greatest degree of inter-subject variability in its functional connectivity (FC) [2] and network topology [3]. Thus, standard resting-state approaches, which rely on short time series from large cohorts to produce group-averaged connectivity maps, obscure the idiosyncratic features of FPN organization [4], making them unsuitable for studying the precise functioning of the FPN. Moreover, to achieve mechanistic insights into the directed communication pathways within and between large-scale networks, resting-state FC should ideally be studied at the resolution of cortical layers [5]. Here, we extend previous multi-dense sampling efforts, acquiring and analyzing a unique dataset consisting of 5h of resting-state data per subject using ultrahigh-resolution fMRI at 7T. The aim is to resolve cortical layers to study directed FC within the individually mapped FPN and between the FPN and other large-scale brain networks.
Methods:
The data consists of 5 subjects, each undergoing 5 sessions each consisting of 3 x 10 min resting-state runs, using both full-brain (TR=1.04s, voxel resolution: 1.6mm3, 2.5h/subject) and high-resolution slab GE-EPI fMRI (TR=2s, voxel resolution: 0.8mm3, 2.5h/subject) at a 7T MR scanner. All functional data underwent motion, gradient, and susceptibility distortion correction and were registered to an anatomical MP2RAGE image while combining all transformations into a single interpolation. Further preprocessing steps of the fMRI data were performed following [3]. Cortical surfaces were reconstructed from MP2RAGE data using a Freesurfer-based pipeline [6]. A surface-based registration to fs_LR space was computed [7], and surface coordinates were transformed into functional space. From these transformed gray matter surfaces, voxel-wise cortical depths were computed to delineate three equivolume cortical layers (i.e., deep, middle, superficial). Reliability of Pearson's FC estimates was assessed using a split-half approach using network parcels from [8]. Using the full-brain data, we first estimated individual resting-state FC maps on the vertex level using the Infomap community detection algorithm [9]. Next, individual resting-state networks were projected into each subjects' high-resolution slab and time courses for each layer were extracted separately.
Results:
1. With full-brain 1.6mm³ data, ~25 min of cleaned (i.e. censored) data were required to achieve a good split-half reliability of 0.85 in all subjects, replicating [3] (Fig 1a).
2. Individual differences in FC dominated similarity in functional brain networks (Fig. 1c-e); this effect was more pronounced for control networks such as the FPN, replicating [2].
3. We found substantial individual differences in the FPN topology when using Infomap to derive individual networks (Fig. 2, top row). Importantly, individual FPN network estimation is reliable across split-half comparisons (Fig. 2, bottom row).
4. Using the high-resolution layer-specific slab data, we show that with ~40 min clean data, high reliability of layer-specific FC estimates can be achieved in all subjects (Fig. 1b) (only 2 subjects are shown due to unsatisfactory registration in the others, which currently undergo manual intervention).
2. Individual differences in FC dominated similarity in functional brain networks (Fig. 1c-e); this effect was more pronounced for control networks such as the FPN, replicating [2].
3. We found substantial individual differences in the FPN topology when using Infomap to derive individual networks (Fig. 2, top row). Importantly, individual FPN network estimation is reliable across split-half comparisons (Fig. 2, bottom row).
4. Using the high-resolution layer-specific slab data, we show that with ~40 min clean data, high reliability of layer-specific FC estimates can be achieved in all subjects (Fig. 1b) (only 2 subjects are shown due to unsatisfactory registration in the others, which currently undergo manual intervention).
Conclusions:
These results corroborate the necessity of large amounts of within-subject data for reliable estimates of resting-state networks, which is even more pronounced when moving to the investigation of directed FC at the level of cortical layers. Moreover, these results highlight the high inter-subject variability in the FPN's FC architecture and network topology. Future steps in this project will be the optimization of registration and the investigation of layer-specific FC analyses between resting-state networks in all subjects.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Cortical Layers
FUNCTIONAL MRI
HIGH FIELD MR
Other - Dense Sampling; Individual Differences; Frontoparietal Network
1|2Indicates the priority used for review
Abstract Information
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