Yusuke Kyuragi’s research while affiliated with Kyoto University and other places

Electroconvulsive therapy (ECT) is one of the most effective treatments for depression. ECT induces volume changes in the amygdala, a key center of anxiety. However, the clinical relevance of ECT-induced changes in amygdala volume remains uncertain. We hypothesized that nuclei-specific amygdala volumes and anxiety symptoms in depression could explain the clinical correlates of ECT-induced volume changes. To test this hypothesis, we enrolled patients with depression who underwent ECT (N = 20) in this multicenter observational study and collected MRI data at three time points: before and after treatment and a 6-month follow-up. Patients who received medication (N = 52), cognitive behavioral therapy (N = 63), or transcranial magnetic stimulation (N = 20), and healthy participants (N = 147) were included for comparison. Amygdala nuclei were identified using FreeSurfer and clustered into three subdivisions to enhance reliability and interpretability. Anxiety symptoms were quantified using the anxiety factor scores derived from the Hamilton Depression Rating Scale. Before treatment, basolateral and basomedial subdivisions of the right amygdala were smaller than those of healthy controls. The volumes of the amygdala subdivisions increased after ECT and decreased during the follow-up period, but the volumes at 6-month follow-up were larger than those observed before treatment. These volume changes were specific to ECT. Long-term volume changes in the right basomedial amygdala correlated with improvements in anxiety symptoms. Baseline volumes in the right basolateral amygdala correlated with long-term improvements in anxiety symptoms. These findings demonstrate that clinical correlates of ECT-induced amygdala volume changes are existent, but in a nucleus and symptom-specific manner.

Exploring the neurobiological effects of various treatments for depression is pivotal in elucidating shared and unique mechanisms of action that may explain commonalities and differences in treatment outcomes. The amygdala, consisting of subdivisions with distinct roles in emotional processing, is thought to be implicated in the pathophysiology of depression. However, how various treatments alter nuclei-specific resting-state functional connectivity (rs-FC) of the amygdala remains unclear. Here, we hypothesized that treatments restore nuclei-specific rs-FC abnormalities of the amygdala, with shared and unique changes. To test this hypothesis, this multicenter observational study analyzed functional MRI data of patients with depression (N = 187) and healthy controls (N = 113). Patients were scheduled to undergo either cognitive behavioral therapy (CBT; N = 63), medication therapy (N = 43), electroconvulsive therapy (ECT; N = 32), or transcranial magnetic stimulation (TMS; N = 26). Nine amygdala subregions were delineated using the FreeSurfer segmentation module. To enhance interpretability and reliability, these subregions were further clustered by applying k-means + + clustering to resting-state functional MRI time courses, resulting in three clusters referred to as the basolateral (BL), basomedial (BM), and centromedial (CM) subdivisions based on their anatomical locations. At baseline, the BL and BM subdivisions of patients with depression exhibited hyperconnectivity with the mediodorsal thalamus (MDTH), periaqueductal gray, perigenual anterior cingulate cortex, dorsal raphe nucleus, and dorsomedial prefrontal cortex. Following treatment, there were no shared rs-FC changes across treatment types; however, BL-MDTH connectivity decreased to the level of healthy controls following ECT. These results demonstrate that ECT-specific restoration of rs-FC exists while multiple treatments do not share common rs-FC changes in the amygdala.

Alterations in gray matter (GM) and white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). While previous neuroimaging studies have primarily focused on WM microstructural changes, limited attention has been given to GM alterations. Additionally, many of these studies concentrate on region-specific associations with symptoms rather than a comprehensive assessment of broader microstructural changes. In this study, we employed neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI) to investigate GM and WM microstructural changes at both whole-brain and regional levels. Data were collected from 159 MDD patients and 112 healthy controls across multiple centers. Our findings revealed significantly increased mean free water fraction (FWF) in GM, elevated mean orientation dispersion index (ODI) in WM, and decreased fractional anisotropy (FA) in WM among MDD patients compared to healthy controls. Furthermore, the mean FA of WM exhibited a negative correlation, and the mean ODI of WM showed a positive correlation with illness duration. No significant correlations were observed between diffusion indices and Hamilton Depression Rating Scale (HAMD-17) scores. Gray matter-based spatial statistics demonstrated increased FWF in several GM regions, including the frontal lobes, temporal lobes, and limbic system. Tract-based spatial statistics revealed widespread reductions in FA across WM in MDD patients. These findings suggest that neuroinflammation and myelin abnormalities may underlie the observed microstructural changes, emphasizing the need for future research to link neuroimaging findings with underlying biological mechanisms of MDD.

Background The habenula is involved in the pathophysiology of depression. However, its small structure limits the accuracy of segmentation methods, and the findings regarding its volume have been inconsistent. This study aimed to create a highly accurate habenula segmentation model using deep learning, test its generalizability to clinical magnetic resonance imaging, and examine differences between healthy participants and patients with depression. Methods This multicenter study included 382 participants (patients with depression: N = 234, women 47.0%; healthy participants: N = 148, women 37.8%). A 3-dimensional residual U-Net was used to create a habenula segmentation model on 3T magnetic resonance images. The reproducibility and generalizability of the predictive model were tested on various validation cohorts. Thereafter, differences between the habenula volume of healthy participants and that of patients with depression were examined. Results A Dice coefficient of 86.6% was achieved in the derivation cohort. The test-retest dataset showed a mean absolute percentage error of 6.66, indicating sufficiently high reproducibility. A Dice coefficient of >80% was achieved for datasets with different imaging conditions, such as magnetic field strengths, spatial resolutions, and imaging sequences, by adjusting the threshold. A significant negative correlation with age was observed in the general population, and this correlation was more pronounced in patients with depression (p < 10⁻⁷, r = −0.59). Habenula volume decreased with depression severity in women even when the effects of age and scanner were excluded (p = .019, η² = 0.099). Conclusions Habenula volume could be a pathophysiologically relevant factor and diagnostic and therapeutic marker for depression, particularly in women.

Background: Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses. Methods: We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression. Results: We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment. Limitations: First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference. Conclusions: These results show the specific properties of functional brain connectivity.

Background The Generalized Problematic Internet Use Scale 2 (GPIUS2) is a self-administered questionnaire that evaluates problematic internet use (PIU) from a multidimensional perspective. We analysed the psychometric properties and adequacy of the theoretical model of Japanese version of the GPIUS2. Methods This study included 291 healthy Japanese adults (median age = 25 years; interquartile range 22–43 years; 128 women) who completed the GPIUS2 and several other questionnaires evaluating the degree of PIU, self-esteem, depression, and impulsivity. Results Exploratory factor analysis (EFA) revealed a similar factor structure between the original and Japanese versions of the GPIUS2, with only minor differences in item composition. Higher-order confirmatory factor analyses revealed a good overall fit for the factorial model suggested by EFA, indicating adequate construct validity. The model showed acceptable internal consistency. Partial correlation analyses between GPIUS2 and other measures, with age as a control variable, revealed good convergent validity. Finally, structural equation modelling showed a good fit to the data, supporting the cognitive-behavioural model of Caplan (2010). Conclusions The Japanese version of the GPIUS2 has good psychometric properties and the theoretical model of the original GPIUS2 is applicable to Japanese adults.