University of Groningen
The neuroanatomical organization of intrinsic brain activity measured by fMRI activity in the
human visual cortex
Gravel Araneda, Nicolas Gaspar
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Publication date:
2018
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Gravel Araneda, N. G. (2018). The neuroanatomical organization of intrinsic brain activity measured by
fMRI activity in the human visual cortex. Rijksuniversiteit Groningen.
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The National Commission for Science and Technology of Chile, the Research School of Behavioural and Cognitive Neurosciences of the University of Groningen, the Laboratory of Experimental Ophthalmology of the University Medical Center Groningen, the Graduate School of Medical Sciences of the University of Groningen and the Professor Mulder Stichting.
ISBN digital version: 978-94-034-0592-6 ISBN printed version: 978-94-034-0593-3 Publisher: University of Groningen Cover art: Nicolás Gravel,
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van
de rector magnificus prof. dr. E. Sterken en volgens het besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 18 april 2018 om 12:45 uur
door
geboren op 6 mei 1985 te Santiago, Chile
Prof. dr. F. W. Cornelissen Prof. dr. N. M. Jansonius
Dr. R. Renken
Prof. dr. B. van de Berg Prof. dr. C. F. Beckmann Prof. dr. J. B.T.M. Roerdink
1.1 Outline . . . 2
1.2 Background . . . 3
1.2.1 Visual pathways . . . 3
1.2.2 Retinotopic organization of the visual cortex . . . 3
1.2.3 Hierarchical organization of the visual cortex . . . 4
1.2.4 Neurovascular coupling . . . 4
1.2.5 Functional magnetic resonance imaging . . . 5
1.2.6 Retinotopic mapping using population receptive field (pRF) and connective field (CF) modeling . . . 5
1.2.7 Resting state . . . 6
1.3 References . . . 7
2.1 Introduction . . . 13
2.2 Materials and Methods . . . 15
2.2.1 Subjects . . . 15 2.2.2 Stimulus . . . 15 2.2.3 Resting state . . . 15 2.2.4 Data acquisition . . . 15 2.2.5 Preprocessing . . . 16 2.2.6 Analysis . . . 16 2.3 Results . . . 19
2.3.1 Deriving connective field models based on resting state fMRI data . . . 19
2.3.2 Spatial aspects of resting state connective field map es-timation . . . 20
2.4 Discussion . . . 25 i
ii
2.4.1 Connective field models can be estimated based on
rest-ing state data . . . 25
2.4.2 Agreement between resting state and visual field map-ping based connective field parameters . . . 25
2.4.3 Spatial changes: possible mechanisms . . . 26
2.4.4 Limitations and future directions . . . 26
2.4.5 Concluding remarks . . . 27 2.5 Supplementary Material . . . 27 2.6 References . . . 27 3.1 Introduction . . . 33 3.2 Methods . . . 35 3.2.1 Data . . . 35 3.2.2 Analysis . . . 36
3.2.3 Reproducibility of the synchronization clusters . . . . 38
3.2.4 Visuotopic organization of phase synchronization clusters 39 3.2.5 Spatial extent of phase synchronization . . . 39
3.2.6 Intra and inter-hemispheric cluster connectivity . . . 40
3.3 Results . . . 41
3.3.1 Synchronization clusters derived from resting state and visual field mapping are similar . . . 41
3.3.2 Visuotopic organization of the synchronization clusters 41 3.3.3 Comparison of the spatial extent of phase synchroniz-ation between RS and VFM . . . 42
3.3.4 Homotopic anatomical connectivity of cluster synchron-ization . . . 44
3.4 Discussion . . . 46
3.4.1 Phase-synchronization-based parcellation of RS-fMRI signals reveals topographically organized clusters in early visual cortex . . . 47
3.4.2 Similar location and shape, but different spatial extent of phase synchronization clusters for resting state and visual field mapping . . . 47
3.4.3 Spatial extent of phase synchrony resting: possible mech-anisms . . . 49
3.4.4 Limitations and future directions . . . 50
3.5 Conclusion . . . 51
3.6 Supplementary Material . . . 51
3.7 References . . . 52
4.1 Introduction . . . 59
4.2 Materials and Methods . . . 60
4.2.1 Participants . . . 60
iii 4.2.3 Resting state . . . 61 4.2.4 Data acquisition . . . 61 4.2.5 Preprocessing . . . 62 4.2.6 Analysis . . . 62 4.3 Results . . . 64
4.3.1 Deriving connective field estimates from VFM and RS 3T fMRI activity . . . 64
4.3.2 Synchronization clusters derived from VFM and RS 3T fMRI activity . . . 66 4.3.3 Limitations . . . 67 4.4 Conclusion . . . 69 4.5 References . . . 69 5.1 Introduction . . . 73 5.2 Methods . . . 75 5.2.1 Data . . . 75
5.2.2 Selection of regions of interest . . . 76
5.2.3 Effective connectivity model for BOLD propagation . 77 5.3 Results . . . 81
5.3.1 Propagation of BOLD activity across early visual cor-tex measured with a noise-diffusion network model of EC . . . 81
5.3.2 Common underlying structures in EC . . . 82
5.3.3 Differences in EC and Σ between RS and VFM . . . 85
5.4 Discussion . . . 88
5.4.1 Recurrent connectivity and its role in visual processing 88 5.4.2 Cortical excitability: possible mechanisms . . . 90
5.4.3 Relation of the BOLD autocovariance decay constant to behavioral condition . . . 90
5.4.4 Limitations and interpretability of the model . . . 91
5.5 Concluding remarks . . . 94
5.6 Supplementary Material . . . 95
5.7 References . . . 95
6.1 Summary of findings . . . 103
6.1.1 Visuotopic maps from resting state . . . 103
6.1.2 Similar synchronization clusters in resting state and visual field mapping . . . 104
6.1.3 It is feasible to obtain connective field maps and syn-chronization clusters from 3T fMRI activity . . . 104
6.1.4 Propagation of BOLD activity reveals task-dependent changes in effective connectivity . . . 105
6.2 Discussion . . . 105
6.2.1 Evolution, development and homeostasis . . . 105
6.2.2 Evoked versus intrinsic activity . . . 106
iv
6.3 Future research and applications . . . 107 6.4 Conclusions . . . 108 6.5 References . . . 109
2.1 Distributions of explained variance . . . 20
2.2 Overall modeling performance characteristics . . . 21
2.3 Visualization of connective field maps . . . 22
2.4 Position scatter for V1-referred connective fields . . . 23
2.5 V1-referred connective field size during visual field mapping and resting state scans grouped over participants . . . 24
2.6 Relation between eccentricity and V1-referred connective field size in visual areas V2 and V3 grouped over participants . . . 24
3.1 Synchronization clusters obtained from VFM and RS . . . 42
3.2 Spatial aspects of the synchronization clusters . . . 43
3.3 Phase synchronization as a function of cortical distance in areas V1, V2 and V3 . . . 44
3.4 Phase synchronization as a function of visuotopic distance . . . . 45
3.5 Voxels in clusters with similar visual field position selectivity have higher PLV across visual field maps and hemispheres . . . 46
4.1 Visualization of pRF and connective field maps obtained from VFM and RS 3T f-MRI data . . . 65
4.2 Connective field size as a function of pRF eccentricity in V2 and V3 67 4.3 Synchronization clusters obtained from VFM and RS fMRI data 68 4.4 Cluster membership probability as a function of visual field dis-tance in RS and VFM . . . 68
5.1 Apparent propagation of BOLD activity during RS depicted in the flattened cortical surface reconstruction . . . 82
5.2 Modeling the propagation of BOLD activity across visual field maps V1, V2 and V3 . . . 83
5.3 Common underlying structure of EC across visual cortical areas V1, V2 and V3 . . . 84
5.4 Common structure in EC for RS and VFM illustrated in visual field space . . . 86
5.5 Differences in EC and Σ between RS and VFM suggest a re-configuration of feedforward and feedback interactions . . . 87
4.1 Correlations between visual field maps derived using population receptive field and connective field modeling . . . 64 4.2 Correlations between CF maps derived from VFM and RS data . 66 5.1 Goodness of fit between modeled and empirical spatiotemporal
co-variances . . . 85
Blood Oxygen Level Dependent Connective Field
Direct Current
Dynamic Causal Modeling Discrete Cosine Transform Effective Connectivity Functional Connectivity
Functional Magnetic Resonance Imaging Full Width at Half Maximum Lateral Geniculate Nucleus Lyapunov Optimization
Multi-Variate Auto-Regressive Normalized Mutual Information Phase Locking Values
Phase Synchronization Population Receptive Field
Retinal Ganglion Cells Region Of Interest Resting State
Synchronization Cluster Signal to Noise Ratio
Longitudinal relaxation time of functional scan Transverse relaxation time of anatomical scan
Time to Echo Time to Repetition Variance Explained
Visual Field Mapping
Human visual cortical areas 1,2 and 3 3/7 Tesla (units of magnetic flux density)
