In their timely Review, Váša and Mišić provide an insightful review of the range of null models available for hypothesis testing in network neuroscience (Váša, F. & Mišić, B. Null models in network neuroscience. Nat. Rev. Neurosci. 23, 493–504 (2022))1. A central part of their Review is dedicated to generative models, understood as models for the processes that lead (brain) networks to exhibit the organization that they do. Here, we wish to draw attention to a distinct but complementary kind of generative model that can be used to embody and then test null hypotheses in network neuroscience: namely, dynamical models of mechanisms. In these models, brain network structure (and, increasingly, additional aspects such as regional heterogeneity) gives rise to the ebb and flow of brain activity and functional connectivity that unfold over time2,3,4 (Fig. 1).
In a notable recent example5, the topology of the structural connectome underlying a Kuramoto model was systematically varied, showing that a modular but not a random topology produces ‘events’ in the simulated time-resolved functional connectivity, analogous to those observed in empirical functional MRI signals. From the perspective of null models, this result showed that such events are attributable neither to motion and physiological artefacts nor solely to cognitive operations, as the model incorporates neither. Rather, the topology of the structural network is sufficient to determine whether such events will be observed in the functional dynamics. This work exemplifies how dynamical models can be used to arbitrate between plausible causal mechanisms for a phenomenon of interest, by explicitly building such mechanisms into the model and assessing whether the phenomenon in question emerges.
Another notable study enriched connectome-based mean-field models with PET-derived maps for the 5-HT2A receptor, recapitulating blood oxygen level-dependent dynamics under the effects of the serotonergic psychedelic lysergic acid diethylamide (LSD)6. Null models enriched with alternative 5-HT receptors, or with uniform or scrambled 5-HT2A maps, allowed the authors to embody and then reject alternative hypotheses whereby the spatial distribution of the 5-HT2A receptor does not contribute to the dynamics of LSD. A subsequent transcriptomics-enriched model evaluated non-5-HT receptors and the role of spatial autocorrelation7, highlighting the synergy between different facets of null hypothesis testing in network neuroscience. Increasingly, dynamical models are being enriched with other aspects of neurobiology, such as T1w:T2w ratio8, excitatory:inhibitory ratio9, the principal component of gene expression9, or feedback versus feedforward connectivity10 (to name just a few in a fast-growing literature), representing additional avenues with which to embody and test hypotheses about annotated brain networks.
Overall, generative models of brain dynamics represent a powerful recent addition to the hypothesis-testing toolset of network neuroscientists. They provide an avenue to embody and assess mechanistic hypotheses, not only of how the organization of structural brain networks came to be, but also of how in turn it interacts with regional dynamics to shape the temporal unfolding of brain activity. We hope that explicit recognition of this flexible framework for testing mechanistic hypotheses will facilitate the emerging research on the interrelationships of brain network structure, dynamics and neuromodulation, bringing additional aspects of neurobiology into consideration for the study of both structural and functional brain networks.
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A.I.L. was supported by a Gates Cambridge Scholarship (OPP 1144), the E. G. Fearnsides Travel Fund and a Travel Grant from the Boehringer Ingelheim Fonds. J.C. is funded by the Portuguese Foundation for Science and Technology grants UIDB/50026/2020, UIDP/50026/2020 and CEECIND/03325/2017, Portugal. R.C. and A.D. are supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). G.D. is supported by HBP SGA3 Human Brain Project Specific Grant Agreement 3 (945539), funded by the EU H2020 FET Flagship program; the Spanish national research project (PID2019-105772GB-I00 MCIU AEI), funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI); SGR Research Support Group (reference 2017 SGR 1545), funded by the Catalan Agency for Management of University and Research Grants (AGAUR); Neurotwin Digital twins for model-driven non-invasive electrical brain stimulation (grant agreement 101017716), funded by the EU H2020 FET Proactive program; euSNN (grant agreement 860563), funded by the EU H2020 MSCA-ITN Innovative Training Networks; The Emerging Human Brain Cluster (CECH) (001-P-001682) within the framework of the European Research Development Fund Operational Program of Catalonia 2014–2020; Brain-Connects: Brain Connectivity during Stroke Recovery and Rehabilitation (201725.33), funded by the Fundacio La Marato TV3; and Corticity, FLAG-ERA JTC 2017 (reference PCI2018-092891), funded by the MCIU, AEI. M.L.K. is supported by the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117), and the Centre for Eudaimonia and Human Flourishing, funded by the Pettit Foundation and Carlsberg Foundation.
The authors declare no competing interests.
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Luppi, A.I., Cabral, J., Cofre, R. et al. Dynamical models to evaluate structure–function relationships in network neuroscience. Nat Rev Neurosci 23, 767–768 (2022). https://doi.org/10.1038/s41583-022-00646-w
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