The missing role of gray matter in studying brain controllability

Jamalabadi, Hamidreza; Zuberer, Agnieszka; Kumar, Vinod; Li, Meng; Alizadeh, Sarah; Moradi, Ali Amani; Gaser, Christian; Esterman, Michael; Walter, Martin

doi:10.1101/2020.04.07.030015

Cited by 3 publications

(2 citation statements)

References 53 publications

(46 reference statements)

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“…Second, our estimation of controllability is based upon DTI tractography which itself is limited in its ability to accurately quantify the structural connectome (for an introduction, see [46]). Currently, several novel approaches to controllability quantification are being explored including estimation from gray matter [47] and resting-state functional dynamics [42].…”

Section: Discussionmentioning

confidence: 99%

Towards a Network Control Theory of Electroconvulsive Therapy Response

Hahn¹,

Jamalabadi²,

Nozari³

et al. 2021

Preprint

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Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of predicting individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI) -an ECT seizure quality index -and whole-brain modal and average controllability, NCT metrics based on white matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N=50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.

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Section: Discussionmentioning

confidence: 99%

Towards a Network Control Theory of Electroconvulsive Therapy Response

Hahn¹,

Jamalabadi²,

Nozari³

et al. 2021

Preprint

Self Cite

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show abstract

“…Second, our estimation of controllability is based upon Diffusion Tensor Imaging (DTI) tractography which in itself is limited in its ability to accurately quantify the structural connectome (for an introduction, see [58]). Currently, several novel approaches to controllability quantification are being explored including estimation from gray matter [59] and resting-state functional dynamics [56]. Empirically comparing and theoretically reconciling results from these methods will be crucial for robust parameter estimation in Network Control Theory studies of the brain.…”

Section: Discussionmentioning

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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