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. 2022 Mar-Apr;15(2):454-456.
doi: 10.1016/j.brs.2022.02.010. Epub 2022 Feb 24.

Closed-loop control of bistable symptom states

Mengsen Zhang  1 , Justin Riddle  1 , Flavio Frohlich  2
Affiliations

Closed-loop control of bistable symptom states

Mengsen Zhang et al. Brain Stimul. 2022 Mar-Apr.
No abstract available

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Conflict of interest statement

Declaration of interests The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FF is the lead inventor of IP filed by UNC. FF is the founder, shareholder, and chief science officer of Pulvinar Neuro, which did not play any role in the writing of this article. FF has received honoraria from the following entities in the last twelve months: Sage Therapeutics, Academic Press, Insel Spital, and Strategic Innovation. MZ and JR have nothing to declare.

Figures

Fig. 1.
Fig. 1.. Closed-loop stimulation destabilizes the high symptom state.
(a) High and low symptom (Sx) states were classified during the baseline using symptom data and state labels provided in the GitHub repository for Scangos et al., 2021. The visual analog scale for anxiety (VAS–A) and depression (VAS-D) were used for classifying a low symptom (blue) and high symptom (red) state. Classification boundary (dashed line) was (VAS-A + VAS-D) > 45. (b) Dynamic bistability of the symptom states can be understood as an energy landscape where the size of the valleys (i.e., basins of attraction) reflect state stability and the peak between the valleys (i.e., separatrix) influences transition probability. The symptom dynamics during the Stage I 10-day baseline period (c) and the closed-loop deep brain stimulation (DBS) period (d) were modeled as Markov chains with two symptom states. With stimulation, the low symptom state became more likely (greater node size) and more stable (thicker self-loop) with decreased transition probability to the high symptom state. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2.
Fig. 2.. The nonlinear relation between gamma power and symptom severity in Stage I baseline.
This figure is created using the source data of Fig. 1 of Scangos et al., 2021 [1]. The relation between high gamma power (rank) in the right amygdala (lead 1) to (a) clinician-rated depression severity (HAMD-6) and (b) self-reported depression severity (VAS-D). While six features for gamma power were correlated to symptom severity (Fig. 1c of Scangos et al., 2021 [1]), the relationship was quantitatively the same for each feature. The right amygdala was selected as this site was used for closed-loop DBS. Spearman correlation coefficients (r) and the corresponding p-values are displayed. When gamma power was low (left to the dashed line), both VAS-D and HAMD increased with gamma power (r = 0.710, p = 0.021 for VAS-D, and r = 0.505, p = 0.137 for HAMD-6). However, as gamma increased past a critical value (dashed line), the symptom scores began to decrease with gamma power and eventually plateaued.
See this image and copyright information in PMC

References

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