Automatic Control and Feedback Loops in Biology and Medicine

Śmieja, Jarosław

doi:10.1007/978-3-030-13273-6_1

Cited by 2 publications

(1 citation statement)

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“…In short, any intervention-from medication to psychotherapy-can be conceptualized as an attempt to control the large-scale, dynamic network state transitions in the brain [1,12,13]. Control Theory as the study and practice of controlling dynamical systems is ubiquitous in medicine and biology [14], framing any intervention-from the optimization of cancer chemotherapy [15,16] and the design of artificial organs [17] to real-time drug administration and non-pharmaceutical pandemic defense strategies [18]-as a control problem.…”

Section: Introductionmentioning

confidence: 99%

Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder

et al. 2023

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Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain’s large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability—i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.

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