Structural network integrity of the central executive network is associated with the therapeutic effect of rTMS in treatment resistant depression

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies

Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.

Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Effects of gonadal steroids on reward circuitry function and anhedonia in women with a history of postpartum depression

BACKGROUND

Reward system dysfunction is evident across neuropsychiatric conditions. Here we present data from a double-blinded pharmaco-fMRI study investigating the triggering of anhedonia and reward circuit activity in women.

METHODS

The hormonal states of pregnancy and parturition were simulated in euthymic women with a history of postpartum depression (PPD+; n = 15) and those without such a history (PPD-; n = 15) by inducing hypogonadism, adding back estradiol and progesterone for 8 weeks ("addback"), and then withdrawing both steroids ("withdrawal"). Anhedonia was assessed using the Inventory of Depression and Anxiety Symptoms (IDAS) during each hormone phase. Those who reported a 30 % or greater increase in IDAS anhedonia, dysphoria, or ill temper during addback or withdrawal, compared with pre-treatment, were identified as hormone sensitive (HS+) and all others were identified as non-hormone sensitive (HS-). The monetary incentive delay (MID) task was administered during fMRI sessions at pre-treatment and during hormone withdrawal to assess brain activation during reward anticipation and feedback.

RESULTS

On average, anhedonia increased during addback and withdrawal in PPD+ but not PPD-. During reward feedback, both HS+ (n = 10) and HS- (n = 18) showed decreased activation in clusters in the right putamen (p < .031, FWE-corrected) and left postcentral and supramarginal gyri (p < .014, FWE-corrected) at the withdrawal scans, relative to pre-treatment scans.

LIMITATIONS

A modest sample size, stringent exclusion criteria, and relative lack of diversity in study participants limit the generalizability of results.

CONCLUSION

Although results do not explain differential hormone sensitivity in depression, they demonstrate significant effects of reproductive hormones on reward-related brain function in women.

Parietal-hippocampal rTMS improves cognitive function in Alzheimer's disease and increases dynamic functional connectivity of default mode network

Parietal-hippocampal repetitive transcranial magnetic stimulation (rTMS) improves cognitive function in Alzheimer's disease (AD), however, the underlying therapeutic mechanism has not been elucidated. A double-blind, randomized, sham-controlled parietal-hippocampal rTMS trial (five sessions/week for a total of 10 sessions) of mild-to-moderate AD patients was conducted in the study. High-frequency rTMS was applied to a subject-specific left lateral parietal region with the highest functional connectivity with the hippocampus based on resting-state fMRI. A multimodal MRI scan and a complete neuropsychological battery of tests were conducted at baseline, immediately after the intervention and 12-week follow-up after the rTMS treatment. Compared to sham treatment (n = 27), patients undergoing active rTMS treatment (n = 29) showed higher Mini Mental State Examination (MMSE) score and dynamic functional connectivity (dFC) magnitude of the default mode network (DMN) after two weeks of rTMS treatment, but not at 12-week follow-up. A significant positive correlation was observed between changes in MMSE and changes in the dFC magnitude of DMN in patients who underwent active-rTMS treatment, but not in those who received sham-rTMS treatment. The findings of the current study indicate that fMRI-guided rTMS treatment improves cognitive function of AD patients in the short term, and DMN functional connectivity contributes to therapeutic effectiveness of rTMS.

Large-scale structural network change correlates with clinical response to rTMS in depression

Response to repetitive transcranial magnetic stimulation (rTMS) among individuals with major depressive disorder (MDD) varies widely. The neural mechanisms underlying rTMS are thought to involve changes in large-scale networks. Whether structural network integrity and plasticity are associated with response to rTMS therapy is unclear. Structural MRIs were acquired from a series of 70 adult healthy controls and 268 persons with MDD who participated in two arms of a large randomized, non-inferiority trial, THREE-D, comparing intermittent theta-burst stimulation to high-frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). Patients were grouped according to percentage improvement on the 17-item Hamilton Depression Rating Score at treatment completion. For the entire sample and then for each treatment arm, multivariate analyses were used to characterize structural covariance networks (SCN) from cortical gray matter thickness, volume, and surface area maps from T1-weighted MRI. The association between SCNs and clinical improvement was assessed. For both study arms, cortical thickness and volume SCNs distinguished healthy controls from MDD (p = 0.005); however, post-hoc analyses did not reveal a significant association between pre-treatment SCN expression and clinical improvement. We also isolated an anticorrelated SCN between the left DLPFC rTMS target site and the subgenual anterior cingulate cortex across cortical measures (p = 0.0004). Post-treatment change in cortical thickness SCN architecture was associated with clinical improvement in treatment responders (p = 0.001), but not in non-responders. Structural network changes may underpin clinical response to rTMS, and SCNs are useful for understanding the pathophysiology of depression and neural mechanisms of plasticity and response to circuit-based treatments.

Decreased salience network fMRI functional connectivity following a course of rTMS for treatment-resistant depression

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is a treatment shown to be effective in treating major depressive disorder (MDD). However, the effect of rTMS therapy on functional connectivity within the brains of patients being treated for MDD remains poorly understood. Few studies have investigated the effects of a course of rTMS on resting-state network activity.

METHODS

In an open-label naturalistic study, resting-state fMRI was collected prior to and following a four-week course of rTMS in 24 participants with MDD and 2 with bipolar disorder. Montgomery-Asberg depression rating scale scores showed a response rate of 42%.

RESULTS

Clinical response to rTMS was correlated with reduced functional connectivity from baseline to post-rTMS within the salience network (SN). This indicates SN connectivity may be functionally relevant to how rTMS produces antidepressant effects. In an exploratory inter-network analysis, connectivity between the SN and posterior default mode network (pDMN) was higher following treatment. However this difference was not correlated with the antidepressant response. Local BOLD activity within these networks was also assessed using the fractional amplitude of low-frequency fluctuations (fALFF) technique. Local activity increased in both the SN and pDMN following rTMS. However this increase was also not correlated with antidepressant response.

LIMITATIONS

The sample population was heterogeneous, continuing current use of medications, and the study lacked a healthy control or sham stimulation comparison group.

CONCLUSIONS

Together, these results provide evidence for the involvement of the SN in the antidepressant response to rTMS treatment.

Decreased gray matter volume is associated with theory of mind deficit in adolescents with schizophrenia

Schizophrenia patients often suffer from deficit in theory of mind (TOM). Prior neuroimaging studies revealed neuroimaging correlates of TOM deficit in adults with schizophrenia, neuroimaging correlates of TOM in adolescents is less well established. This study aimed to investigate gray matter volume (GMV) abnormalities and TOM deficits in schizophrenic adolescents, and examine the relationship between them. Twenty adolescent schizophrenic patients and 25 age, sex-matched healthy controls underwent T1-weighted magnetic resonance imaging (MRI) scans, and were examined for TOM based on the Reading the Mind in the Eyes test (RMET). Univariate voxel-based morphometry (VBM) and multivariate source-based morphometry (SBM) were employed to examine alterations of two GMV phenotypes in schizophrenic adolescents: voxel-wise GMV and covarying structural brain patterns (SBPs). Compared with controls, our results revealed a significant deficit in RMET performance of the patients, Voxel-wise VBM analysis revealed that patients exhibited decreased GMV in bilateral insula, orbitofrontal cortex, and right rolandic operculum, and GMV of these brain regions were positively correlated with RMET performance. Multivariate SBM analysis identified a significantly different between-group SBP comprising of bilateral insula and inferior frontal cortex, bilateral superior temporal cortex, and bilateral lateral parietal cortex and right rolandic operculum. The loading scores of this SBP was positively correlated with RMET performance. This study revealed impairment of TOM ability in schizophrenic adolescents and revealed an association between TOM deficit and decreased GMV in regions which are crucial for social cognition, thereby provided insight and possible target regions for understanding the neural pathology and normalizing TOM deficit in adolescent schizophrenia patients.

Baseline Clinical and Neuroimaging Biomarkers of Treatment Response to High-Frequency rTMS Over the Left DLPFC for Resistant Depression

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) has proven to be an efficient treatment option for patients with treatment-resistant depression (TRD). However, the success rate of this method is still low, and the treatment outcome is unpredictable. The objective of this study was to explore clinical and structural neuroimaging factors as potential biomarkers of the efficacy of high-frequency (HF) rTMS (20 Hz) over the left dorso-lateral pre-frontal cortex (DLPFC).

METHODS

We analyzed the records of 131 patients with mood disorders who were treated with rTMS and were assessed at baseline at the end of the stimulation and at 1 month after the end of the treatment. The response is defined as a 50% decrease in the MADRS score between the first and the last assessment. Each of these patients underwent a T1 MRI scan of the brain, which was subsequently segmented with FreeSurfer. Whole-brain analyses [Query, Design, Estimate, Contrast (QDEC)] were conducted and corrected for multiple comparisons. Additionally, the responder status was also analyzed using binomial multivariate regression models. The explored variables were clinical and anatomical features of the rTMS target obtained from T1 MRI: target-scalp distance, DLPFC gray matter thickness, and various cortical measures of interest previously studied.

RESULTS

The results of a binomial multivariate regression model indicated that depression type (p = 0.025), gender (p = 0.010), and the severity of depression (p = 0.027) were found to be associated with response to rTMS. Additionally, the resistance stage showed a significant trend (p = 0.055). Whole-brain analyses on volume revealed that the average volume of the left part of the superior frontal and the caudal middle frontal regions is associated with the response status. Other MRI-based measures are not significantly associated with response to rTMS in our population.

CONCLUSION

In this study, we investigated the clinical and neuroimaging biomarkers associated with responsiveness to high-frequency rTMS over the left DLPFC in a large sample of patients with TRD. Women, patients with bipolar depressive disorder (BDD), and patients who are less resistant to HF rTMS respond better. Responders present a lower volume of the left part of the superior frontal gyrus and the caudal middle frontal gyrus. These findings support further investigation into the use of clinical variables and structural MRI as possible biomarkers of rTMS treatment response.

Structural covariance pattern abnormalities of insula in major depressive disorder: A CAN-BIND study report

BACKGROUND AND METHODS

Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs.

RESULTS

The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network.

CONCLUSIONS

Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.

Treating cocaine and opioid use disorder with transcranial magnetic stimulation: A path forward

Developing new, effective treatments for substance use disorders (SUDs), especially cocaine and opioid use disorders (CUD and OUD), are of immense importance. These are chronic, relapsing brain diseases characterized by dysregulated circuits manifesting from neuroplastic change brought on by repeated exposure to substances of abuse. A potential treatment is therapeutically inducing neuroplastic change in targeted dysregulated circuits. One such intervention, repetitive transcranial magnetic stimulation (rTMS) has gained traction over the past two decades as a method of noninvasively stimulating cortical structures in order to induce subcortical neuroplastic change. By doing so, rTMS ameliorates symptoms that are consequent of dysregulations in disease-related circuits, such as craving, and reduces drug use. Although rTMS has been successfully applied as a treatment for other clinical disorders, progress toward treatment applications for SUDs has been stymied by what we dub "known unknowns". These are fundamental lines of research within the rTMS-SUD field that have yet to be systematically understood which could help to optimize TMS as an intervention for SUDs. Because progress in treatment for CUD and OUD is imperative given the widespread severity of OUD and the lack of treatment for CUD, it is necessary to critically reflect on the ways in which rTMS research for these disorders can most effectively move forward to help patients. We articulate six "known unknowns" and outline a direction of research to address each. Briefly, the "known unknowns" in the field are: 1) Cortical target selection, 2) subcortical circuit engagement, 3) optimizing rTMS sequences, 4) rTMS as an adjuvant to existing interventions, 5) manipulating brain state, and 6) selecting outcome measures. We also outline research design approaches to address these "known unknowns" in the rTMS-SUDs field. Unification of efforts across research laboratories is necessary to develop empirically validated treatments that will benefit patients in a timely fashion.

Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder

The neuroimaging is an important tool for understanding the biomarkers and predicting treatment responses in major depressive disorder (MDD). The potential biomarkers and prediction of treatment response in MDD will be addressed in the review article. The brain regions of cognitive control and emotion regulation, such as the frontal and limbic regions, might represent the potential targets for MDD biomarkers. The potential targets of frontal lobes might include anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). For the limbic system, hippocampus and amygdala might be the potentially promising targets for MDD. The potential targets of fronto-limbic regions have been found in the studies of several major neuroimaging modalities, such as the magnetic resonance imaging, near-infrared spectroscopy, electroencephalography, positron emission tomography, and single-photon emission computed tomography. Additional regions, such as brainstem and midbrain, might also play a part in the MDD biomarkers. For the prediction of treatment response, the gray matter volumes, white matter tracts, functional representations and receptor bindings of ACC, DLPFC, OFC, amygdala, and hippocampus might play a role in the prediction of antidepressant responses in MDD. For the response prediction of psychotherapies, the fronto-limbic, reward regions, and insula will be the potential targets. For the repetitive transcranial magnetic stimulation, the DLPFC, ACC, limbic, and visuospatial regions might represent the predictive targets for treatment. The neuroimaging targets of MDD might be focused in the fronto-limbic regions. However, the neuroimaging targets for the prediction of treatment responses might be inconclusive and beyond the fronto-limbic regions.

Sex effects on cortical morphological networks in healthy young adults

Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.

Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance

Noninvasive brain stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are promising add-on treatments for a number of psychiatric conditions. Yet, some of the initial excitement is wearing off. Randomized controlled trials (RCT) have found inconsistent results. This inconsistency is suspected to be the consequence of variation in treatment effects and solvable by identifying responders in RCTs and individualizing treatment. However, is there enough evidence from RCTs that patients respond differently to treatment? This question can be addressed by comparing the variability in the active stimulation group with the variability in the sham group. We searched MEDLINE/PubMed and included all double-blinded, sham-controlled RCTs and crossover trials that used TMS or tDCS in adults with a unipolar or bipolar depression, bipolar disorder, schizophrenia spectrum disorder, or obsessive compulsive disorder. In accordance with the PRISMA guidelines to ensure data quality and validity, we extracted a measure of variability of the primary outcome. A total of 130 studies with 5748 patients were considered in the analysis. We calculated variance-weighted variability ratios for each comparison of active stimulation vs sham and entered them into a random-effects model. We hypothesized that treatment effect variability in TMS or tDCS would be reflected by increased variability after active compared with sham stimulation, or in other words, a variability ratio greater than one. Across diagnoses, we found only a minimal increase in variability after active stimulation compared with sham that did not reach statistical significance (variability ratio = 1.03; 95% CI, 0.97, 1.08, P = 0.358). In conclusion, this study found little evidence for treatment effect variability in brain stimulation, suggesting that the need for personalized or stratified medicine is still an open question.

Neuroimaging mechanisms of high-frequency repetitive transcranial magnetic stimulation for treatment of amnestic mild cognitive impairment: a double-blind randomized sham-controlled trial

Individuals with amnestic mild cognitive impairment (aMCI) have a high risk of developing Alzheimer’s disease. Although repetitive transcranial magnetic stimulation (rTMS) is considered a potentially effective treatment for cognitive impairment in patients with aMCI, the neuroimaging mechanisms are poorly understood. Therefore, we performed a double-blind randomized sham-controlled trial in which rTMS was applied to the left dorsolateral prefrontal cortex of aMCI patients recruited from a community near the Third Hospital Affiliated to Sun Yat-sen University, China. Twenty-four patients with aMCI were randomly assigned to receive true rTMS (treatment group, n = 12, 6 men and 6 women; age 65.08 ± 4.89 years) or sham stimulation (sham group, n = 12, 5 men and 7 women; age 64.67 ± 4.77 years). rTMS parameters included a stimulation frequency of 10 Hz, stimulation duration of 2 seconds, stimulation interval of 8 seconds, 20 repetitions at 80% of the motor threshold, and 400 pulses per session. rTMS/sham stimulation was performed five times per week over a period of 4 consecutive weeks. Our results showed that compared with baseline, Montreal Cognitive Assessment scores were significantly increased and the value of the amplitude of low-frequency fluctuation (ALFF) was significantly increased at the end of treatment and 1 month after treatment. Compared with the sham group, the ALFF values in the right inferior frontal gyrus, triangular part of the inferior frontal gyrus, right precuneus, left angular gyrus, and right supramarginal gyrus were significantly increased, and the ALFF values in the right superior frontal gyrus were significantly decreased in the treatment group. These findings suggest that high-frequency rTMS can effectively improve cognitive function in aMCI patients and alter spontaneous brain activity in cognitive-related brain areas. This study was approved by the Ethics Committee of Shenzhen Baoan Hospital of Southern Medical University, China (approval No. BYL20190901) on September 3, 2019, and registered in the Chinese Clinical Trials Registry (registration No. ChiCTR1900028180) on December 14, 2019.

Phenotype Network and Brain Structural Covariance Network of Major Depression

Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.

SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multiscanner Studies

BACKGROUND AND PURPOSE

Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy.

METHODS

In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients' and healthy controls' GMV from two scanners.

RESULTS

The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs, whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences.

CONCLUSIONS

SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multiscanner study.

An update on the clinical use of repetitive transcranial magnetic stimulation in the treatment of depression

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an increasingly used treatment for patients with depression. The use of rTMS in depression is supported by over 20 years of clinical trials. There has been a significant increase in knowledge around the use of rTMS in recent years.

OBJECTIVE

The aim of this paper was to review the use of rTMS in depression to provide an update for rTMS practitioners and clinicians interested in the clinical use of this treatment.

METHODS

A targeted review of the literature around the use of rTMS treatment of depression with a specific focus on studies published in the last 3 years.

RESULTS

High-frequency rTMS applied to the left dorsolateral prefrontal cortex is an effective treatment for acute episodes of major depressive disorder. There are several additional methods of rTMS delivery that are supported by clinical trials and meta-analyses but no substantive evidence that any one approach is any more effective than any other. rTMS is effective in unipolar depression and most likely bipolar depression. rTMS courses may be repeated in the management of depressive relapse but there is less evidence for the use of rTMS in the maintenance phase.

CONCLUSIONS

The science around the use of rTMS is rapidly evolving and there is a considerable need for practitioners to remain abreast of the current state of this literature and its implications for clinical practice. rTMS is an effective antidepressant treatment but its optimal use should be continually informed by knowledge of the state of the art.

Two-week rTMS-induced neuroimaging changes measured with fMRI in depression

OBJECTIVE

To study the neuroimaging mechanisms of repetitive transcranial magnetic stimulation (rTMS) in treating major depressive disorder (MDD).

METHODS

Twenty-seven treatment-naive patients with major depressive disorder (MDD) and 27 controls were enrolled. All of them were scanned with resting-state functional magnetic resonance imaging (fMRI) at baseline, and 15 patients were rescanned after two-week rTMS. The amplitude of low frequency fluctuation (ALFF) and functional connection degree (FCD), based on voxels and 3 brain networks (default mode network [DMN], central executive network [CEN], salience network[SN]),were used as imaging indicators to analyze. The correlations of brain imaging changes after rTMS with clinical efficacy were calculated.

RESULTS

At baseline, patients groups showed increased ALFF in the right orbital frontal cortex (OFC) and decreased ALFF in the left striatal cortex and medial prefrontal cortex (PFC), while increased FCD in the right dorsal anterior cingulate cortex and OFC and decreased FCD in the right inferior parietal lobe and in the CEN. After rTMS, patients showed increased ALFF in the left dorsolateral prefrontal cortex (DLPFC)and superior frontal gyrus, FCD in the right dorsal anterior cingulate cortex, superior temporal gyrus and CEN, as well as decreased FCD in the bilateral lingual gyrus than pre-rTMS . These rTMS induced neuroimaging changes did not significantly correlated with clinical effecacy.

CONCLUSIONS

This study indicated that rTMS resulted in changes of ALFF and FCD in some brain regions and CEN. But we could not conclude this is the neuroimaging mechanism of rTMS according to the correlation analysis.

Efficacy and tolerability of deep transcranial magnetic stimulation for treatment-resistant depression: A systematic review and meta-analysis

OBJECTIVES

This study aimed to investigate the efficacy of deep transcranial magnetic stimulation (dTMS) for treatment-resistant depression (TRD).

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Medline, PsycINFO, Embase, and Cochrane Library were systematically searched from the time of their inception until July 17, 2019. Data were pooled using a random-effects model. Primary outcomes were mean change of depression and anxiety severity. Secondary outcomes were response and remission rate of depression.

RESULTS

Fifteen studies including three randomized controlled trials (RCTs) (n = 417, mean age: 50.6 years) and twelve uncontrolled clinical trials (n = 284, mean age: 46.4 years) were included. dTMS significantly improved the depressive (Hedges' g = -1.323, 95% CI = -1.651 to -0.995, p < .001) and anxiety symptoms (Hedges' g = -1.282, 95% CI = -1.514 to -1.051, p < .001) in patients with TRD. Subgroup analysis showed that non-RCTs had a larger effect size than RCTs (-1.461 vs -0.756) on depression severity. Although the response and remission rates of the dTMS group were high, only studies using both dTMS and antidepressant medications achieved significance. The anxiolytic effect of dTMS was more heterogeneous, and the results were obtained mainly from non-RCTs. Importantly, the dTMS group showed favorable tolerability without major adverse events.

CONCLUSIONS

dTMS is a safe and effective intervention in patients with TRD. Studies combining dTMS and antidepressant medications seemed to show greater therapeutic effects. Future studies are needed to address the interaction effect of dTMS with different classes of antidepressant medications.

The Inter-Regional Connectivity Within the Default Mode Network During the Attentional Processes of Internal Focus and External Focus: An fMRI Study of Continuous Finger Force Feedback

Sustained attention involves two distinct processes, i.e., external focus and internal focus. Some recent neuroimaging studies employed the instruction of experimenters or the self-report from participants to generate the two attentional processes, and observed that the default mode network (DMN) was also responding to the external focus. These observations challenged the general view that the DMN accounts for the internally directed cognition, e.g., unfocused mind wandering, task independent-thoughts and internally focused events. Notably, the instruction or self-report may not effectively ensure the participants engage in the external focus/internal focus, and thus, the functional significance of the DMN for the externally focused process remains to be verified. In the present study, a new task paradigm, i.e., real/sham continuous feedback of finger force, was employed to generate the attentional process of external focus/internal focus, and the functional connectivity among the node regions of the DMN was further investigated in the two processes respectively. We found that two regions of the DMN, posterior cingulate cortex and left inferior parietal cortex/angular gyrus showed stronger inter-regional connectivity in the externally focused process than it in the internally focused process. Intriguingly, this functional connectivity was closely related to the behavioral performance in the process of external focus. These findings implicated that the functional significance of the DMN in sustained attention was more than responding to the internally directed cognition, and the task paradigm of continuous finger force feedback could benefit for the future studies on the externally focused/internally focused process of sustained attention.

Functional disconnectivity of the hippocampal network and neural correlates of memory impairment in treatment-resistant depression

BACKGROUND

Major depressive disorder (MDD) is a disabling neuropsychiatric condition associated with cognitive impairment. Neuroimaging studies have consistently linked memory deficits with hippocampal atrophy in MDD patients. However, there has been a paucity of research examining how the hippocampus functionally contributes to memory impairments in MDD. The present study examined whether hippocampal networks distinguish treatment-resistant depression (TRD) patients from healthy controls (HCs), and whether these networks underlie declarative memory deficits in TRD. We hypothesized that functional connectivity (FC) of the posterior hippocampus would correlate preferentially with memory in patients, whereas FC pattern of the anterior and intermediate hippocampus would correlate with emotion-mediated regions and show a significant correlation with memory.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) scans were acquired in 56 patients and 42 age- and sex-matched HCs. We parcellated the hippocampus into three subregions based on a sparse representation-based method recently developed by our group. FC networks of hippocampal subregions were compared between patients and HCs and correlated with clinical measures and cognitive performance.

RESULTS

Decreased connectivity of the right intermediate hippocampus (RIH) with the limbic regions was a distinguishing feature between TRD and HCs. These functional abnormalities were present in the absence of structural volumetric differences. Furthermore, lower right amygdalar connectivity to the RIH related to a longer current depressive episode. Declarative memory deficits in TRD were significantly associated with left posterior and right intermediate hippocampal FC patterns.

LIMITATIONS

Our patient samples were treatment-resistant, the conclusions from this study cannot be generalized to all MDD patients directly. Task-based imaging studies are needed to demonstrate hippocampal engagement in the memory deficits of patients. Finally, our findings are strongly in need of replication in independent validation samples.

CONCLUSIONS

These findings demonstrate a transitional property of the intermediate hippocampal subregion between its anterior and posterior counterparts in TRD patients, and provide new insights into the neural network-level dysfunction of the hippocampus in TRD.