Network controllability: Connecting theory with empirical observations

The human connectome describes a map of whole-brain structural connectivity [1]. At the macroscale, this network map comprises brain regions—nodes—whose local biological properties and interconnectivity— edges—determine their capacity to communicate with one another. Modeling this complex structure- function relationship remains an outstanding challenge in neuroscience. To study this relationship, we have developed and implemented an approach called Network Control Theory (NCT) [2]. NCT stipulates neural dynamics that evolve on the connectome as a function of (i) nodes’ intrinsic dynamics and (ii) their extrinsic connectivity, and models control inputs that guide those dynamics to transition between empirically measured brain states. In NCT, nodes' intrinsic dynamics are encoded by their internal decay rate, which governs their intrinsic neural timescales (INTs) and can be thought of as their propensity towards self-inhibition; higher self-inhibition yields faster dissipation of perturbations, which equates to faster INTs. Conventionally, internal decay rates are set uniformly across the connectome, yielding nodes that exhibit consistent self-inhibition and, thus, uniform INTs. However, this setup is at odds with our understanding of how neuronal time scales vary across the brain in tandem with diverse spatially patterned properties of neurobiology [3]. Thus, to improve the biological realism of NCT, we developed a data-driven approach to optimizing nodes' internal decay rates. Compared to previous work [4], our approach does not require a priori knowledge of neurobiology. This independence allows us to validate our optimized INTs against known neurobiological correlates, and to fit our model on a per-transition and a per-subject basis. We found that using optimized decay rates yields lower control energy associated with transitions between empirical brain states. Further, we found that optimized decay rates coupled to in vivo empirical measures of INTs as well as ex vivo measures of gene expression and cell-type densities that underpin structure- function coupling [5]. Finally, when applied to single-subject connectomes, our approach significantly improves the out-of-sample prediction of behavior in healthy young adults. In summary, we provide a novel extension to NCT to better model and validate the complex interaction between brain structure and function with subject-level specificity.
References
1. Sporns, O., Tononi, G. & Kötter, R. The Human Connectome: A Structural Description of the Human Brain. PLoS Comp Biol 1, e42 (2005).
2. Parkes, L. et al. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc (2024) doi:10.1038/s41596-024-01023-w.
3. Wolff, A. et al. Intrinsic neural timescales: temporal integration and segregation. Trends in Cognitive Sciences S1364661321002928 (2022) doi:10.1016/j.tics.2021.11.007.
4. Luppi, A. I. et al. Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies. Nat. Biomed. Eng (2024) doi:10.1038/s41551-024-01242- 2.
5. Zhang, X.-H. et al. The cell-type underpinnings of the human functional cortical connectome. Nat Neurosci (2024) doi:10.1038/s41593-024-01812-2. 

Transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC) is a frontier intervention for refractory depression. The antidepressant effects of TMS are thought to involve communication between the DLPFC and the subgenual cortex (SGC), a deep cortical structure that is not accessible to non-invasive stimulation. Interestingly, DLPFC and SGC are not connected by a direct white matter fibre. TMS propagation between these regions is therefore polysynaptic, i.e., mediated by intermediate regions interlinked via white matter connectivity. This limits the use of current tractography-based methods—which only consider direct, “one-hop”, fibres—to understand the anatomical basis of DLPFC-SGC communication. Here, we use advances in network neuroscience to model the polysynaptic propagation of TMS from the DLPFC to the SGC via the connectome. We apply models of network communication to quantify the structural capacity for signal transmission between stimulation sites in the DLPFC and the SGC. The models identify polysynaptic routes comprising sequences of two or more white matter tracts, and can therefore delineate multi-hop pathways between gray matter structures that are not connected by a direct fiber. We show that DLPFC-SGC communication via the connectome predicts symptom remission in two independent cohorts of depression patients. We delineate two putative pathways underpinning this process. A primary subcortical pathway mediated by the caudate and nucleus accumbens, and a secondary cortical pathway comprising superior frontal and anterior cingulate structures. Our work sheds light into the mechanisms of antidepressant TMS and paves the way for stimulation protocols based on polysynaptic fiber pathways. 

Computational psychiatry allows us to get closer to uncovering the neural bases of mental health disorders and, eventually, novel interventions to treat them. A particularly powerful tool has emerged in this realm - Network Control Theory (NCT) - which takes engineering principles and applies them to enable quantification of brain dynamics. Specifically, NCT allows mapping of the amount of control energy needed by individuals’ brains to transition between recurring states of activation, called transition energy (TE). Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure. Herein, we utilized NCT to quantify sex-specific differences in brain activity dynamics in youth with and without a family history of SUD, drawn from a large cohort of substance-naïve youth from the Adolescent Brain Cognitive Development Study (N=1894, 1018 female, aged 9-11). Our findings reveal that a family history of SUD is associated with alterations in the brain's dynamics wherein: i) independent of sex, certain regions' transition energies are higher in those with a family history of SUD and ii) there exist sex-specific differences in SUD family history groups at multiple levels of transition energy (global, network, and regional). Family history-by-sex effects reveal that energetic demand is increased in females with a family history of SUD and decreased in males with a family history of SUD, compared to their same-sex counterparts with no SUD family history. Specifically, we localize these effects to higher energetic demands of the default mode network in females with a family history of SUD and lower energetic demands of attention networks in males with a family history of SUD. These results suggest a family history of SUD may increase reward saliency in males (easier time “stepping on the gas") and decrease efficiency of top-down inhibitory control in females (harder time “stepping on the brakes”). This work could be used to inform personalized intervention strategies that may target differing cognitive mechanisms that predispose individuals to the development of SUD.

 

Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique that is a promising alternative to pharmacological treatments for psychiatric and neurological disorders [1,2]. While traditional functional analysis has played a crucial role in understanding TUS effects, further investigation is needed to reveal the underlying mechanisms [3-4]. Whole-brain models, meanwhile, are valuable for testing hypotheses and predicting outcomes in real experimental settings [5-6]. In this study, we developed a whole-brain model representing functional changes measured by fMRI to explain how TUS spread throughout the brain as stimulus intensity increases. We implemented two mechanisms: one based on distance and another on a broadcasting process, enabling us to examine plasticity-driven changes in specific brain regions at several stimulus intensities. Furthermore, we demonstrated the extent of high-order functional interactions, localizing the spatial effects of off-line TUS in humans at two target areas—the right thalamus and inferior frontal cortex—highlighting distinct patterns of functional reorganization [7].

References
[1] Legon, W., Sato, T., Opitz, A. et al. Transcranial focused ultrasound modulates the activity of primary somatosen- sory cortex in humans. Nat Neurosci 17, 322–329 (2014). https://doi.org/10.1038/nn.3620
[2] Polania, R., Nitsche, M.A. & Ruff, C.C. Studying and modifying brain function with non-invasive brain stimulation. Nat Neurosci 21, 174–187 (2018). https://doi.org/10.1038/s41593-017-0054-4
[3] Dell’Italia J, Sanguinetti JL, Monti MM, Bystritsky A, Reggente N. Current State of Potential Mechanisms Sup- porting Low Intensity Focused Ultrasound for Neuromodulation. Front Hum Neurosci. 2022 Apr 25;16:872639. https://doi.org/10.3389/fnhum.2022.872639
[4] Sarica C, Nankoo JF, Fomenko A, Grippe TC, Yamamoto K, Samuel N, Milano V, Vetkas A, Darmani G, Cizmeci MN, Lozano AM, Chen R. Human Studies of Transcranial Ultrasound neuromodulation: A systematic review of effectiveness and safety. Brain Stimul. 2022 May-Jun;15(3):737-746. https://doi.org/10.1016/j.brs.2022.05.002 [5] Deco, G., Tononi, G., Boly, M. et al. Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci 16, 430–439 (2015).https://doi.org/10.1038/nrn3963
[6] Lynn, C.W., Bassett, D.S. The physics of brain network structure, function and control. Nat Rev Phys 1, 318–332 (2019). https://doi.org/10.1038/s42254-019-0040-8
[7] In preparation Marilyn Gatica, Cyril Atkinson-Clement, Carlos Coronel-Oliveros, Mohammad Alkhawashki, Pedro A. M. Mediano, Enzo Tagliazucchi, Fernando E. Rosas, Marcus Kaiser, Giovanni Petri