Journal: Translational psychiatry

Article Title: Altered brain dynamic in major depressive disorder: state and trait features.

doi: 10.1038/s41398-023-02540-0

Figure Lengend Snippet: Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden Markov Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.

Article Snippet: We used the Hidden Markov Model - Multivariate Autoregressive (HMM-MAR) toolbox implemented in MATLAB (https:// github.com/OHBA-analysis/HMM-MAR) to perform variational Bayes inversion of HMM with 500 cycles to define states by multivariate Gaussian distribution [49], the finite number of states should be set as a prior in the model.

Techniques: Imaging

Journal: Translational psychiatry

Article Title: Altered brain dynamic in major depressive disorder: state and trait features.

doi: 10.1038/s41398-023-02540-0

Figure Lengend Snippet: Fig. 2 Group comparisons of the temporal features. Applying the hidden Markov model (HMM) resulted in six spatial states, with the brain map of averaged functional activity represented for each state (blue to red is indicating the negative to positive averaged functional activity, range −0.15 to 0.15). This figure contains the finding of fractional occupancy and averaged lifetime of state #1, #4 and #6 and the findings related to states #2, #3 and #5 can be found in Supplementary Fig. 4. The range of −0.15 and 0.15 for the averaged functional activity represents the level of functional activity observed during a particular state in the current dataset. In general, the magnitude and direction of the values can indicate the degree and type of neural activity occurring during a particular state. The positive values may indicate increased neural activity, while negative values may indicate decreased activity. Functional activity is averaged blood-oxygen-level-dependent (BOLD) time-series at that state for each region. The violin plots represent the group comparisons (HC vs. all MDD-diagnosed patients and HC vs. asymptomatic or symptomatic patients) of the temporal features (fractional occupancy and averaged lifetime). The value on the top of each comparison is an uncorrected p-value and the p-values that are significant also after the Bonferroni correction are indicated by red color and asterisks.

Article Snippet: We used the Hidden Markov Model - Multivariate Autoregressive (HMM-MAR) toolbox implemented in MATLAB (https:// github.com/OHBA-analysis/HMM-MAR) to perform variational Bayes inversion of HMM with 500 cycles to define states by multivariate Gaussian distribution [49], the finite number of states should be set as a prior in the model.

Techniques: Functional Assay, Activity Assay, Comparison