Background: Electroconvulsive therapy is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging. This study aims to compare rs-fMRI of depressive disorder patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.Method: Resting-state functional magnetic resonance imaging data were (...) collected from 119 patients with depression or depressive disorder, and 61 healthy participants, with an age mean of 52.25 years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode and cognitive control network were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.Results: The main findings include depressive disorder patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network and default mode network was relatively higher than other states, Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes. This means that those DEP patients who spent less time in this state showed more HDRS change, and The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2.Conclusion: Our finding suggests that dynamic functional network connectivity features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients. (shrink)

Precision targeting of specific white matter bundles that traverse the subcallosal cingulate has been linked to efficacy of deep brain stimulation for treatment resistant depression. Methods to confirm optimal target engagement in this heterogenous region are now critical to establish an objective treatment protocol. As yet unexamined are the time-frequency features of the SCC evoked potential, including spectral power and phase-clustering. We examined these spectral features—evoked power and phase clustering—in a sample of TRD patients with implanted SCC stimulators. Electroencephalogram was (...) recorded during wakeful rest. Location of electrical stimulation in the SCC target region was the experimental manipulation. EEG was analyzed at the surface level with an average reference for a cluster of frontal sensors and at a time window identified by prior study. Morlet wavelets generated indices of evoked power and inter-trial phase clustering. Enhanced phase clustering at theta frequency was observed in every subject and was significantly correlated with SCC-EP magnitude, but only during left SCC stimulation. Stimulation to dorsal SCC evinced stronger phase clustering than ventral SCC. There was a weak correlation between phase clustering and white matter density. An increase in evoked delta power was also coincident with SCC-EP, but was less consistent across participants. DBS evoked time-frequency features index mm-scale changes to the location of stimulation in the SCC target region and correlate with structural characteristics implicated in treatment optimization. Results also imply a shared generative mechanism between evoked potentials evinced by electrical stimulation and evoked potentials evinced by auditory/visual stimuli and behavioral tasks. Understanding how current injection impacts downstream cortical activity is essential to building new technologies that adapt treatment parameters to individual differences in neurophysiology. (shrink)