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Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GeorgiaNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, China
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GeorgiaDepartment of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GeorgiaDepartment of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, GeorgiaDepartment of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GeorgiaDepartment of Psychiatry, Yale School of Medicine, Yale University, New Haven, Connecticut
Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy.
Methods
In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis–based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis.
Results
Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT.
Conclusions
These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
Depression is one of the leading causes of disability globally, affecting millions of lives and being the second contributor to the global burden of disease (
Global Burden of Disease Study 2013 Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013.
). Fortunately, depression is among the most treatable of mental disorders, with many forms of treatments successfully applied in clinical trials. Electroconvulsive therapy (ECT) under general anesthesia and with the use of adequate muscle relaxation is regarded as a potent treatment with rapid acting in severe mental illness (
). However, the relationship between brain changes and ECT response is still far from understood. Characterizing ECT responsive brain markers and unveiling the neurobiological underpinnings of ECT using less invasive approaches will help to improve the treatment of severe depression with a more benign side effect profile (
). Multiple lines of evidence have shown that ECT causes extensive memory impairments, including anterograde amnesia, retrograde amnesia, and memory complaints (
), making the persistence, severity, and precise characterization of such memory changes still a debated topic in this area. Although many treatment modifications have been introduced, including new electrode placements and waveforms (
), which have provided a better overall cognitive profile after ECT, those side effects, in particular the short-term memory changes, have not been eradicated.
The human brain continuously integrates information among multiple brain regions related to the neural basis of perception, cognition, and emotion (
). The changes in such integration (so-called functional connectivity [FC]) play a critical role in the generation of mood symptoms in patients with depression. Abnormal FC during both cognitive and emotional tasks has been found in patients with depression, which provides insights into how the dysfunctional brain relates to abnormal behavioral response patterns induced by depressive episodes (DEPs) (
Amygdala and ventromedial prefrontal cortex are inversely coupled during regulation of negative affect and predict the diurnal pattern of cortisol secretion among older adults.
Decreased functional coupling of the amygdala and supragenual cingulate is related to increased depression in unmedicated individuals with current major depressive disorder.
). Taken together, FC might serve as a key intersecting point that can help to link DEP, treatment outcomes, and treatment side effects. However, conventional ECT studies assumed constant FC during the resting state, and constant FC might unfortunately oversimplify the functional relationships between brain regions (
). The human brain is highly dynamic and incorporates the evolution of local activities and the reconfiguration of brain interactions, adapting to both internal and external stimuli (
). Challenging the static assumption of FC, recent studies have shown that functional magnetic resonance imaging FC continuously fluctuates in different temporal scales (
To date, no studies have examined dynamic FC (dFC) in the context of ECT response in patients with DEPs. In this work, we aimed to comprehensively investigate different dFC characteristics in DEP and explore the dFC changes responsive to ECT. We hypothesized that DEP might be associated with abnormal occurrences in dynamic brain states and abnormal whole-brain dynamism, such as dynamic FC fluidity and range. We also hypothesized that ECT would provoke alterations in the occurrence of brain states and global dynamism, which might be associated with changes in depressive symptoms and memories. We applied an analytic framework combining a novel independent component analysis–based pipeline, the sliding window cross-correlation, and two clustering analyses to a large sample dataset including 110 patients with DEPs and 60 healthy subjects. This framework is a powerful approach that can probe dFC characteristics from different perspectives (
Healthy control (HC) participants and patients with DEP (including diagnosis of unipolar or bipolar depression) were recruited from the University of New Mexico and the University of California, Los Angeles. Written informed consent was obtained from all participants under protocols approved by the Institutional Review Boards of University of New Mexico and University of California, Los Angeles. We selected data samples according to the following criteria: 1) subjects without neurodegenerative or neurologic disorders; 2) subjects without other psychiatric disorders, such as schizophrenia; 3) subjects without current alcohol or drug dependence, without pregnancy; 4) subjects with head motion ≤ 3° rotation and ≤ 3-mm translation; and 5) subjects with good functional data normalization (
Altered static and dynamic functional network connectivity in Alzheimer’s disease and subcortical ischemic vascular disease: Shared and specific brain connectivity abnormalities.
). These criteria yielded a total of 110 patients with DEP and 60 HC subjects. Patients were scanned within 7 days of the ECT start and of finishing the ECT series. HC subjects were scanned twice between 4- and 6-week intervals. Details of demographics, clinical assessments, and the ECT protocol are provided in Table 1 and the Supplement.
Table 1Demographics and Clinical Characteristics of Participants
Characteristics
Participants
t Statistic, p Value
DEP (n = 110)
HC (n = 60)
Age, Years, Mean ± SD
56.08 ± 15.76
48.57 ± 14.91
.0029
Gender, Female/Male, n
68/42
34/26
.5152
Handedness, Right/Left, n
103/7
56/4
.8725
Bipolar/Unipolar, n
6/104
NA
NA
Psychotic/Nonpsychotic, n
28/82
NA
NA
RUL/Mixed RUL-Bitemporal, n
75/35
NA
NA
Number of ECT, Mean ± SD
11.20 ± 3.21
NA
NA
Responder, n (%)
64 (58%)
NA
NA
Remitter, n (%)
42 (38%)
NA
NA
HDRS (Pre-ECT), Mean ± SD
25.69 ± 6.18
NA
NA
HDRS (Post-ECT), Mean ± SD
11.46 ± 8.76
NA
NA
Memory Recall (Pre-ECT), Mean ± SD
74.54 ± 63.34
NA
NA
Memory Recall (Post-ECT), Mean ± SD
68.16 ± 29.95
NA
NA
DEP, depressive episode; ECT, electroconvulsive therapy; HC, healthy control; HDRS, Hamilton Depression Rating Scale; NA, not applicable; RUL, right unilateral.
Before the dFC analysis, we performed typical functional magnetic resonance imaging preprocessing based on SPM12 within the MATLAB release 2016a (The MathWorks, Inc.) environment (detailed procedures are provided in the Supplement). Flowchart of the dFC analysis is displayed in Figure S1. The Neuromark pipeline (
) was first used to calculate highly corresponding component templates. The templates were used as the references to guide the back-reconstruction of independent components for each subject from the DEP dataset. Detailed information of the Neuromark is provided in the Supplement. A sliding window approach was used to estimate dFC. Then, subject exemplars were chosen as those windows with local maxima in FC variance, and k-means clustering was performed on the subject exemplars. Finally, the hard-clustering analysis and the fuzzy metastate analysis were conducted on all dFC estimates.
Dynamic FC Estimation and Clustering Analyses
We used a tapered window, created by convolving a rectangle (width = 80, repetition times = 36.8 s) with a Gaussian (σ = 3 repetition times) for segmenting the time courses. The window size within the range of 30 seconds to 1 minute can capture reliable resting-state FC dynamics (
). We estimated covariance from the regularized precision matrix, which is obtained via the graphical lasso method on the windowed data. The covariance matrices of windows were finally concatenated to form a C × C × W (C is the number of components and W is the number of windows) array for each subject that represents the dynamic changes in FC as a function of time.
A hard-clustering analysis based on the k-means clustering was first applied to the windowed dFC estimates. The fractional rate (FRA) and mean dwell time (MDT) (
Altered static and dynamic functional network connectivity in Alzheimer’s disease and subcortical ischemic vascular disease: Shared and specific brain connectivity abnormalities.
) were calculated based on the clustering vectors, evaluating how frequently and how long an individual stays in a given state. A fuzzy metastate analysis based on the k-means clustering was further performed to investigate the global characteristics of the whole-brain dFC (
). Four dynamism measures were defined by this metastate analysis, including 1) metastates number, 2) metastates switching times, 3) occupied metastates range, and 4) overall traveled distance. Details of the clustering analyses can be found in the Supplement.
We employed a general linear model to investigate whether patients with DEP and HC subjects have different FRA and MDT in each functional state and whether they have different dynamism measures within pre- and post-ECT sessions, respectively. Age, sex, and site were used as the covariates in the statistical analysis. A pairwise t test was then performed to investigate whether the dFC characteristics (FRA, MDT, and 4 dynamism measures) are responsive to ECT by comparing them between pre-ECT DEP and post-ECT DEP. We also performed a pairwise t test between 2 HC sessions to examine whether the dFC characteristics change between HC scans. To further investigate whether the changes in dynamic characteristics are associated with the changes in symptoms and cognitive performance after ECT, we calculated the difference in dynamic features and the difference in Hamilton Depression Rating Scale-17 items and memory recall scores between pre- and post-ECT session and used the general linear model to examine their potential associations. For comparison, we further calculated the Pearson correlation to measure the static FC between components and performed the same statistical analysis on the static FC estimates (results are provided in the Supplement).
Results
Brain Parcellation
A total of 53 pairs of components were identified by the Neuromark pipeline (Figures S2 and S3), arranging into 7 functional networks, including the subcortical (SC) (5 components), auditory (AUD) (2 components), sensorimotor (SM) (9 components), visual (VIS) (9 components), cognitive control (17 components), default mode (DM) (7 components), and cerebellar (4 components) networks.
Group Difference in Occurrences of dFC States
Five dFC states were identified by k-means clustering, and the results are displayed in Figure 1 (detailed methodologies for clustering brain states are provided in the Supplement). The upper panel shows the FC matrix representing the centroid of a cluster and reflecting a dynamic brain state stably present within this dataset. The middle panel shows the functional profile of each brain state that retains only strong connectivity (absolute FC > 0.2). The lower panel shows the histogram of connectivity strength of FC pairs for each brain state. We note that brain states presented many distinct FC patterns and showed different connectivity distributions validating our hypothesis. State 4 accounted for more than 40% of all windows and was the most sparsely connected brain state with weak interregional connectivity. FC patterns in the other four states were observed less frequently (occurrences are between 11% and 17%) but represented connectivity diverging substantially from each other. State 1 was the most interconnected brain state, with strongly positive connectivity within sensory networks and antagonism between SC and sensory networks. A large sensory module was present, composed of components dedicated to the AUD, SM, and VIS networks. States 2, 3, and 5 displayed different within-network connectivity and between-network antagonism. State 2 revealed strongly functional integration between posterior DM components and anterior DM components. In state 3, consistently positive FC was observed between SC and sensory networks. Components from SC and sensory networks also acted in synchrony with the hippocampus component that was assigned to the cognitive control network. Brain states can also be differentiated by the FC between sensory networks (i.e., FC between AUD and SM, between AUD and VIS, and between SM and VIS). In most dynamic states, the FC between sensory networks showed at least weak positive correlations. However, in state 5, components from different sensory networks showed negative correlations, indicating functional segregation of the sensory module.
Figure 1Recurring dynamic brain states in patients with depressive episodes and healthy control subjects. Five brain states are identified. (A) Functional network connectivity patterns of brain states. (B) Top 250 functional connectivity with absolute connectivity strength > 0.2 in each state, representing the strongest functional relationships between brain regions. (C) Histogram of functional connectivity strength between components. ACC, anterior cingulate cortex; AUD, auditory; CB, cerebellar; CC, cognitive control; DM, default mode; HiPP, hippocampus; IFG, inferior frontal gyrus; IOG, inferior occipital gyrus; IPL, inferior parietal lobule; L, left; MCC, middle cingulate cortex; MFG, medial frontal gyrus; MiFG, middle frontal gyrus; MOG, middle occipital gyrus; MTG, medial temporal gyrus; ParaCL, paracentral lobule; PCC, posterior cingulate cortex; PoCG, postcentral gyrus; R, right; SC, subcortical; SFG, superior frontal gyrus; SM, sensorimotor; SMA, supplementary motor area; SMPG, superior medial frontal gyrus; SPL, superior parietal lobule; STG, superior temporal gyrus; VS, visual.
The results of group comparisons are shown in Figure 2. Compared with HC subjects, pre-ECT patients with DEP had significantly lower FRA and shorter MDT in state 1 (p < .05, false discovery rate [FDR] corrected). After ECT treatment, patients with DEP showed similar abnormalities in state 1. Beside decreased FRA and MDT in state 1, post-ECT patients with DEP had increased FRA in state 3 compared with HC subjects (p < .05, FDR corrected).
Figure 2Group differences in fractional rate and mean dwell time of pre– and post–electroconvulsive therapy (ECT) sessions. (A) Group differences in fractional rate and mean dwell time of dynamic state 1. (B) Group differences in fractional rate and mean dwell time of dynamic state 3. Bars represent the mean of occurrences and error bars represent standard deviation of the mean. ∗∗p < .05, false discovery rate corrected; ∗p < .05, uncorrected. ACC, anterior cingulate cortex; AUD, auditory; CB, cerebellar; CC, cognitive control; DM, default mode; HC, healthy control; HiPP, hippocampus; IFG, inferior frontal gyrus; IOG, inferior occipital gyrus; IPL, inferior parietal lobule; L, left; MCC, middle cingulate cortex; MFG, medial frontal gyrus; MiFG, middle frontal gyrus; MOG, middle occipital gyrus; MTG, medial temporal gyrus; ParaCL, paracentral lobule; PCC, posterior cingulate cortex; PoCG, postcentral gyrus; R, right; SC, subcortical; SFG, superior frontal gyrus; SM, sensorimotor; SMA, supplementary motor area; SMFG, superior medial frontal gyrus; SPL, superior parietal lobule; STG, superior temporal gyrus; VS, visual.
Group Difference in Higher Dimensional Metastate Dynamism
According to the results in Figure 3, compared with HC subjects, patients with DEP passed through fewer distinct metastates and stayed in a smaller radius of the metastate space (p < .05, FDR corrected). Patients with DEP also traveled less frequently between occupied metastates and less overall distance (p < .05, FDR corrected). After ECT treatment, two dynamism measures recovered to normal without showing any difference between patients and HC subjects. Post-ECT patients with DEP still showed the lower metastate number and metastate space span, although the differences were smaller (p < .05, FDR corrected).
Figure 3Group differences in high-dimensional metastate measures of pre– and post–electroconvulsive therapy (ECT) sessions. (A) Group differences in number of metastate. (B) Group differences in changes of metastate. (C) Group differences in the span of metastate. (D) Group differences in the total distance of metastate. Bars represent the mean of occurrences and error bars represent standard deviation of the mean. ∗∗p < .05, false discovery rate corrected. HC, healthy control.
Two brain states were identified with longitudinal discriminating patterns between pre-ECT DEP and post-ECT DEP (Figure 4). The FRA and MDT of state 3 significantly increased in patients with DEP after ECT treatment (p < .05, FDR corrected). In contrast, the FRA and MDT of state 4 decreased in patients with DEP after ECT (p < .05). For comparison, we also examined the changes in occurrences of brain states between two HC sessions, and there is no significant change in FRA and MDT in HC subjects (p > .05). Figure 5 displays the results of the ECT responsive metastate dynamism. All metastate measures significantly discriminated pre- and post-ECT DEP (p < .05, FDR corrected), with similar increasing trends after ECT treatment. Still, there is no difference identified in metastate measures between two HC sessions.
Figure 4Electroconvulsive therapy (ECT) responsive occurrences of dynamic brain states. (A) The fractional rate and mean dwell time of dynamic state 3 increase after ECT treatment. (B) The frational rate and mean dwell time of dynamic state 4 decrease after ECT treatment, Bars represent the mean of occurrences and error bars represent standard deviation of the mean. ∗∗p < .05, false discovery rate corrected; ∗p < .05, uncorrected. ACC, anterior cingulate cortex; AUD, auditory; CB, cerebellar; CC, cognitive-control; DM, default mode; HiPP, hippocampus; IFG, inferior frontal gyrus; IOG, inferior occipital gyrus; IPL, inferior parietal lobule; L, left; MCC, middle cingulate cortex; MFG, medial frontal gyrus; MiFG, middle frontal gyrus; MOG, middle occipital gyrus; MTG, medial temporal gyrus; ParaCL, paracentral lobule; PCC, posterior cingulate cortex; PoCG, postcentral gyrus; R, right; SC, subcortical; SFG, superior frontal gyrus; SMA, supplementary motor area; SM, sensorimotor; SMFG, superior medial frontal gyrus; SPL, superior parietal lobule; STG, superior temporal gyrus; VS, visual.
Figure 5Electroconvulsive therapy (ECT) responsive high-dimensional dynamism measures. (A) Number of metastate increases after ECT treatment. (B) Change of metastate increases after ECT treatment. (C) Span of metastate increases after ECT treatment. (D) Total distance of metastate increases after ECT treatment. Bars represent the mean of occurrences and error bars represent standard deviation of the mean. ∗∗p < .05, false discovery rate corrected.
Association With Depressive Symptom Scores and Memory Recall
Whether the captured dynamic characteristics of brain FC can index the changes in depressive symptoms and memory function during ECT was further investigated. We did not observe significant correlations between changes in antidepressant response/memory performance and those dynamic characteristics across all subjects with DEP. However, we found interesting associations when we divided the subjects into ECT responder/nonresponder and memory decline/rise subgroups.
For ECT responders, reduced Hamilton Depression Rating Scale was positively correlated with changes in the MDT of state 1 (r = 0.4001; p = .0012, FDR corrected) (Figure 6). This positive correlation was still significant if we restricted the analysis to ECT remitters (r = 0.3902, p = .0117). We also found that the memory decline (percent recall of the Hopkins Verbal Learning Test-Revised and Repeatable Battery for the Assessment of Neuropsychological Status decreased after ECT) was positively correlated with decreases in FRA and MDT of state 4 (FRA: r = 0.4540, p = .0013, FDR corrected; MDT: r = 0.3280, p = .0244) (Figure 7). In contrast, memory decline was negatively correlated with increases in the metastate number and metastate space span (metastate number: r = −0.3182, p = .0293; metastate space: r = −0.3036, p = .0380). In contrast, memory rise (percent recall of the Hopkins Verbal Learning Test-Revised and BRANS increased after ECT) was positively correlated with increases in the metastate changes and total distance (metastate change: r = 0.3170, p = .0338; metastate total distance r = 0.3132, p = .0362).
Figure 6Correlations between mean dwell time (MDT) of a dynamic brain state and Hamilton Depression Rating Scale (HDRS) reduction. (A) The reduced HDRS is positively correlated with the changes in the MDT of state 1 for electroconvulsive therapy (ECT) responders. (B) The reduced HDRS is positively correlated with the changes in the MDT of state 1 for ECT remitters.
Figure 7Correlations between dynamic characteristics and memory recall performance. (A) The memory decline is positively correlated with the decreases in the fractional rate of state 4. (B) The memory decline is positively correlated with the decreases in the mean dwell time of state 4. (C) The memory decline is negatively correlated with the increases in the metastate number. (D) The memory decline is negatively correlated with the increases in the metastate space span. (E) The memory rise is positively correlated with the increases in the metastate changes. (F) The memory rise is positively correlated with the increases in the total distance.
In this work, we investigated dynamic FC in a resting-state dataset with pre- and post-ECT sessions and found that patients with DEPs have significantly different occurrences in the dynamic states and a limited dynamic range and less dynamic fluidity compared with HC subjects. More interestingly, the above-captured dynamic characteristics changed significantly following ECT treatment, and the changes were associated with alterations in memory recall and depressive symptoms after ECT.
Abnormalities in Dynamic State Occurrences and Global Dynamism of Whole-Brain dFC
Increasing evidence is provided for supporting that the dynamic information embedded in the brain can be well-captured by the sliding window approach even using a small number of time points (
). The recurring FC state, a short period during which FC topography remains quasi-stable in a conceptual analogy to electroencephalography microstate, is a widely accepted representation of dFC (
). In this study, by combining the sliding window dFC and k-means clustering, we identified 5 brain states that recur in both patients and control subjects. The identified brain states 1, 2, and 3 have synchronous patterns between regions within the sensory networks but with significant transitions of the synchronization between subcortical and sensory networks. A previous study has observed frequently bidirectional transitions between striatal and somatosensory networks in rats (
). Our results extend this finding by showing that the subcortical network also has significant transitions with cortical networks in the human brain and provide further evidence indicating the presence of both bottom-up and top-down processing involving high-order cortical and low-order subcortical regions at rest (
). State 4, the most frequently occurring brain state, has the sparsely connected FC patterns that resemble static FC. Such a brain state with weak and diffused FC has been widely captured in previous studies, typically accounting for the largest percentage of time (
Patients with DEPs tended to spend consistently less time in state 1, a brain state characterized by the most strong FC (both positive and negative) between brain regions, in both pre- and post-ECT sessions. This result is in line with previous findings of hypoconnectivity in depressive disorder (
) and further provides a potential explanation of reduced connectivity between frontoparietal control systems and networks involved in internal or external attention. Fewer occurrences in the brain state with strong prefrontal-subcortical connections might influence the information-transferred efficiency in the prefrontal-subcortical pathways, which further results in more efforts spent by patients to reappraise negative stimuli characterized by accentuated activation in subcortical regions (
We also found that patients have smaller metastate dynamism than HC subjects, indicating a reduced functional brain fluidity and limited dynamic range in DEPs. These metastate results are consistent with our dFC state results described above, because it is believed that less time across well-defined dFC states with strong connectivity profiles can result in decreased diversity in visited metastates (
). The identified abnormal global dynamism is also supported by previous findings that pairwise brain FC showed higher temporal stability in patients with major depressive disorder (
). The stable pairwise FC might reflect self-focused thinking, because the systems involved in this FC are believed to support autobiographical memory or prospection (
). Our results extend previous findings by showing that not only the pairwise FC between certain brain systems but also the global functional brain network exhibit less variability in patients with depression.
ECT Responsive State Occurrences and Global Dynamism
We observed that the occurrences of dFC states are responsive to ECT treatment. On one hand, the occurrence of state 3 increased after ECT treatment. State 3 has consistently negative connectivity between the thalamus and DM network components. The thalamus is one of the most important brain regions that have been subjected to intense scrutiny in DEPs. Increased thalamic metabolism (
) to the DM network in DEPs have been widely observed in previous studies, suggesting that activity in the thalamus is excessively coupled with the activity in the DM network in subjects with depression. More occurrences in dFC state 3 with negative thalamus-DM connectivity will decrease the FC between the thalamus and DM network, which can be a potential mechanism of ECT for normalizing the hyperactivity and hyperconnectivity in DEPs. State 3 also shows positive connectivity between the hippocampus and SC/sensory networks. The disrupted function and structure in the hippocampus have become a particular area of interest in DEPs over decades (
). In light of these findings, we are encouraged by the findings of increased occurrence in state 3 after ECT. We speculate that the increased hippocampal volumes induced by ECT allow for the brain to have more capacity to stay in a brain state with a large amount of communication between the hippocampus and cortical regions, which further results in enhanced hippocampal connectivity at an average sense.
In contrast, decreased occurrence of dFC state 4 was identified after ECT treatment. State 4 is the most commonly identified dynamic state, with weak and diffuse connectivity patterns. A previous study of dFC in children revealed that the occurrence of such a weak connectivity state is related to the content of self-focused thought (
). Taken together, our result reveals that ECT might normalize the activity of self-focusing by decreasing the occurrence into a brain state that is involved in self-referential processing.
Another interesting finding is that metastate dynamism is responsive to ECT with an increasing trend after ECT. This result provides further evidence of the potential relationships between dynamic fluidity and dFC state occurrences. Specifically, ECT may normalize the dynamic range and fluidity of the brain by decreasing the time that DEPs stay trapped in poorly defined dFC states, where the dynamic interplay between regions is less marked (
). Prominent theories of ECT response have highlighted the disconnections of FC between brain systems in treatment-resistant depression, which can be normalized by ECT treatment (
Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: Validation in two independent samples.
Ketamine plus propofol-electroconvulsive therapy (ECT) transiently improves the antidepressant effects and the associated brain functional alterations in patients with propofol-ECT-resistant depression.
), we argue that metastate dynamism can provide a potential explanation of these disparities. Previous imaging investigations have indicated ECT to be a potent stimulator of neuroplasticity of the human brain (
). We speculate that ECT normalizes the neuroplasticity of the brain, reflecting by increasing dynamic range and fluidity rather than regulating FC directly. The enhanced neuroplasticity would allow the diseased brain a better ability to change and reorganize its FC for purpose of adapting to different situations.
Associations Between Dynamic Characteristics and Treatment Outcomes and Side Effects
For ECT responders and ECT remitters, we found that the change in the dwell time of state 1 is correlated with therapeutic outcomes. This finding indicates that the change in the continuous staying time of a state with the strongest dFC patterns might play a mechanistic role in the efficacy of ECT treatment for DEPs. We also found that the decreased occurrence of state 4 induced by ECT is correlated with memory recall decline in DEPs after ECT treatment. As discussed earlier, state 4 corresponds with the content of self-focus, and decreased occurrence in this state might reflect a reduction of self-focus in DEPs. Previous research has shown that high levels of ruminative analytic thinking in depression might be critical for the maintenance of overgeneral memory, and reducing self-focus will reduce autobiographical memory (
). Our result might build a bridge between the decline in memory recall and self-focus from a dynamic perspective. Specifically, ECT will alleviate ruminative self-focus in patients by reducing their time spent in the self-focused state, which therefore exerts an adverse effect on memory recall (
We further show that memory recall decline after ECT is significantly correlated with increased dynamic range measured by metastate number and span. These findings are in line with a previous observation of negative associations between the variability of dFC and cognitive performance (
). Based on this and other prior work, we posit that the dynamic range of brain FC might be related to the degree of mind wandering, and ECT would increase the dynamic range. This in turn facilitates mind wandering during the resting state, leading to the more responsive time of the brain once memory recall is demanded (
). As opposed to dynamic range, we also found that increased dynamic fluidity is associated with memory rise after ECT. The metastate changes and distance reflect the flexibility of the brain to shift between different network configurations during different tasks and rest conditions. Studies have shown that such flexibility might index the varying degrees of cognitive impairment related to brain diseases (
). We argue that although ECT increased both dynamic range and fluidity in DEPs, these dynamic characteristics could be different indicators of memory decline and rise, underlying different neural mechanisms that are related to ECT.
Acknowledgments and Disclosures
This work was supported by the National Institutes of Health (Grant Nos. R01MH118695, R01EB020407, and R01MH117107 [to VDC], U01MH111826 [to CCA], and R61MH125126 [to CCA]), the China Natural Science Foundation (Grant No. 61773380 [to JS]), and the Beijing Municipal Science and Technology Commission (Grant No. Z181100001518005 [to JS]).
The authors report no biomedical financial interests or potential conflicts of interest.
Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013.
Amygdala and ventromedial prefrontal cortex are inversely coupled during regulation of negative affect and predict the diurnal pattern of cortisol secretion among older adults.
Decreased functional coupling of the amygdala and supragenual cingulate is related to increased depression in unmedicated individuals with current major depressive disorder.
Altered static and dynamic functional network connectivity in Alzheimer’s disease and subcortical ischemic vascular disease: Shared and specific brain connectivity abnormalities.
Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: Validation in two independent samples.
Ketamine plus propofol-electroconvulsive therapy (ECT) transiently improves the antidepressant effects and the associated brain functional alterations in patients with propofol-ECT-resistant depression.
Electroconvulsive therapy (ECT) is a highly effective, noninvasive, and safe medical treatment that is used primarily in treatment-resistant depression (TRD) but also in manic episodes of bipolar disorder or treatment-resistant schizophrenia or schizoaffective disorder, according to international clinical guidelines. In depression with psychotic features or severe forms of catatonia, ECT is frequently recommended as the first-choice treatment option. Furthermore, ECT may improve cognitive impairment due to depression, with improved general cognition and memory after ECT.
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