Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism.

Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730 Japanese adults, aiming to capture neural signatures that reflect pathophysiology at the functional network level, neurotransmitters, and clinical symptoms of the autistic brain. Our adult ASD classifier was successfully generalized to adults in the United States, Belgium, and Japan. The classifier further demonstrated its successful transportability to children and adolescents. The classifier contained 141 functional connections (FCs) that were important for discriminating individuals with ASD from typically developing controls. These FCs and their terminal brain regions were associated with difficulties in social interaction and dopamine and serotonin, respectively. Finally, we mapped attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD) onto the biological axis defined by the ASD classifier. ADHD and SCZ, but not MDD, were located proximate to ASD on the biological dimensions. Our results revealed functional signatures of the ASD brain, grounded in molecular characteristics and clinical symptoms, achieving generalizability and transportability applicable to the evaluation of the biological continuity of related diseases.

Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification.

Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

The status of MRI databases across the world focused on psychiatric and neurological disorders.

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Generalizable brain network markers of major depressive disorder across multiple imaging sites.

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Examining the usefulness of the brain network marker program using fMRI for the diagnosis and stratification of major depressive disorder: a non-randomized study protocol.

BACKGROUND: Although many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study. METHODS: This is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers. DISCUSSION: In this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the "brain network marker for MDD diagnosis" reflects treatment effects in patients with MDD and whether treatment effects can be predicted by "brain network markers for MDD stratification". Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification. TRIAL REGISTRATION: Japan Registry of Clinical Trials ( jRCTs062220063 ). Registered 12/10/2022.

Day-to-day regularity and diurnal switching of physical activity reduce depression-related behaviors: a time-series analysis of wearable device data.

BACKGROUND: Wearable devices have been widely used in research to understand the relationship between habitual physical activity and mental health in the real world. However, little attention has been paid to the temporal variability in continuous physical activity patterns measured by these devices. Therefore, we analyzed time-series patterns of physical activity intensity measured by a wearable device and investigated the relationship between its model parameters and depression-related behaviors. METHODS: Sixty-six individuals used the wearable device for one week and then answered a questionnaire on depression-related behaviors. A seasonal autoregressive integral moving average (SARIMA) model was fitted to the individual-level device data and the best individual model parameters were estimated via a grid search. RESULTS: Out of 64 hyper-parameter combinations, 21 models were selected as optimal, and the models with a larger number of affiliations were found to have no seasonal autoregressive parameter. Conversely, about half of the optimal models indicated that physical activity on any given day fluctuated due to the previous day's activity. In addition, both irregular rhythms in day-to-day activity and low-level of diurnal variability could lead to avoidant behavior patterns. CONCLUSION: Automatic and objective physical activity data from wearable devices showed that diurnal switching of physical activity, as well as day-to-day regularity rhythms, reduced depression-related behaviors. These time-series parameters may be useful for detecting behavioral issues that lie outside individuals' subjective awareness.

Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders.

AIM: Increasing evidence suggests that psychiatric disorders are linked to alterations in the mesocorticolimbic dopamine-related circuits. However, the common and disease-specific alterations remain to be examined in schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorder (ASD). Thus, this study aimed to examine common and disease-specific features related to mesocorticolimbic circuits. METHODS: This study included 555 participants from four institutes with five scanners: 140 individuals with SCZ (45.0% female), 127 individuals with MDD (44.9%), 119 individuals with ASD (15.1%), and 169 healthy controls (HC) (34.9%). All participants underwent resting-state functional magnetic resonance imaging. A parametric empirical Bayes approach was adopted to compare estimated effective connectivity among groups. Intrinsic effective connectivity focusing on the mesocorticolimbic dopamine-related circuits including the ventral tegmental area (VTA), shell and core parts of the nucleus accumbens (NAc), and medial prefrontal cortex (mPFC) were examined using a dynamic causal modeling analysis across these psychiatric disorders. RESULTS: The excitatory shell-to-core connectivity was greater in all patients than in the HC group. The inhibitory shell-to-VTA and shell-to-mPFC connectivities were greater in the ASD group than in the HC, MDD, and SCZ groups. Furthermore, the VTA-to-core and VTA-to-shell connectivities were excitatory in the ASD group, while those connections were inhibitory in the HC, MDD, and SCZ groups. CONCLUSION: Impaired signaling in the mesocorticolimbic dopamine-related circuits could be an underlying neuropathogenesis of various psychiatric disorders. These findings will improve the understanding of unique neural alternations of each disorder and will facilitate identification of effective therapeutic targets.

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.

When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.

Left DLPFC activity is associated with plasma kynurenine levels and can predict treatment response to escitalopram in major depressive disorder.

AIM: To establish treatment response biomarkers that reflect the pathophysiology of depression, it is important to use an integrated set of features. This study aimed to determine the relationship between regional brain activity at rest and blood metabolites related to treatment response to escitalopram to identify the characteristics of depression that respond to treatment. METHODS: Blood metabolite levels and resting-state brain activity were measured in patients with moderate to severe depression (n = 65) before and after 6-8 weeks of treatment with escitalopram, and these were compared between Responders and Nonresponders to treatment. We then examined the relationship between blood metabolites and brain activity related to treatment responsiveness in patients and healthy controls (n = 36). RESULTS: Thirty-two patients (49.2%) showed a clinical response (>50% reduction in the Hamilton Rating Scale for Depression score) and were classified as Responders, and the remaining 33 patients were classified as Nonresponders. The pretreatment fractional amplitude of low-frequency fluctuation (fALFF) value of the left dorsolateral prefrontal cortex (DLPFC) and plasma kynurenine levels were lower in Responders, and the rate of increase of both after treatment was correlated with an improvement in symptoms. Moreover, the fALFF value of the left DLPFC was significantly correlated with plasma kynurenine levels in pretreatment patients with depression and healthy controls. CONCLUSION: Decreased resting-state regional activity of the left DLPFC and decreased plasma kynurenine levels may predict treatment response to escitalopram, suggesting that it may be involved in the pathophysiology of major depressive disorder in response to escitalopram treatment.

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training.

Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.

White matter microstructural alterations across four major psychiatric disorders: mega-analysis study in 2937 individuals.

Identifying both the commonalities and differences in brain structures among psychiatric disorders is important for understanding the pathophysiology. Recently, the ENIGMA-Schizophrenia DTI Working Group performed a large-scale meta-analysis and reported widespread white matter microstructural alterations in schizophrenia; however, no similar cross-disorder study has been carried out to date. Here, we conducted mega-analyses comparing white matter microstructural differences between healthy comparison subjects (HCS; N = 1506) and patients with schizophrenia (N = 696), bipolar disorder (N = 211), autism spectrum disorder (N = 126), or major depressive disorder (N = 398; total N = 2937 from 12 sites). In comparison with HCS, we found that schizophrenia, bipolar disorder, and autism spectrum disorder share similar white matter microstructural differences in the body of the corpus callosum; schizophrenia and bipolar disorder featured comparable changes in the limbic system, such as the fornix and cingulum. By comparison, alterations in tracts connecting neocortical areas, such as the uncinate fasciculus, were observed only in schizophrenia. No significant difference was found in major depressive disorder. In a direct comparison between schizophrenia and bipolar disorder, there were no significant differences. Significant differences between schizophrenia/bipolar disorder and major depressive disorder were found in the limbic system, which were similar to the differences in schizophrenia and bipolar disorder relative to HCS. While schizophrenia and bipolar disorder may have similar pathological characteristics, the biological characteristics of major depressive disorder may be close to those of HCS. Our findings provide insights into nosology and encourage further investigations of shared and unique pathophysiology of psychiatric disorders.

Brain changes associated with impaired attention function in chronic pain.

Attention function is thought to be important in chronic pain, with the pathology of chronic pain closely associated with cognitive-emotional components. However, there have been few neuroimaging studies of the relationship between attention function and chronic pain. We used the method of functional connectivity analysis for resting-state fMRI (rs-fMRI) data and the Attention Network Test-Revision (ANT-R) to clarify the attention-related pathology of chronic pain. We performed rs-fMRI and ANT-R on a group of 26 chronic pain (somatoform pain disorder) patients and 28 age-matched healthy controls. A significant group difference in validity effects, a component of ANT-R, emerged (F1,46 = 5.91, p = 0.019), and the chronic pain group exhibited slower reaction times. Decreased brain connectivity of the left insula and left frontal regions was confirmed in chronic pain patients (pFWE < 0.05), and connectivity was negatively correlated with validity effects (r = -0.29, permutation test p = 0.033). Further, decreased functional connectivity strength of the right insula and left temporal gyrus in the chronic pain group were confirmed (pFWE < 0.05). We conclude that poor control of attention function results from deficits of functional connectivity in the left insula and left frontal regions in chronic pain.

Age-related white matter changes revealed by a whole-brain fiber-tracking method in bipolar disorder compared to major depressive disorder and healthy controls.

AIM: Several studies have reported altered age-associated changes in white matter integrity in bipolar disorder (BD). However, little is known as to whether these age-related changes are illness-specific. We assessed disease-specific effects by controlling for age and investigated age-associated changes and Group × Age interactions in white matter integrity among major depressive disorder (MDD) patients, BD patients, and healthy controls. METHODS: Healthy controls (n = 96; age range, 20-77 years), MDD patients (n = 101; age range, 25-78 years), and BD patients (n = 58; age range, 22-76 years) participated in this study. Fractional anisotropy (FA) derived from diffusion tensor imaging in 54 white matter tracts were compared after controlling for the linear and quadratic effect of age using a generalized linear model. Age-related effects and Age × Group interactions were also assessed in the model. RESULTS: The main effect of group was significant in the left column and body of the fornix after controlling for both linear and quadratic effects of age, and in the left body of the corpus callosum after controlling for the quadratic effect of age. BD patients exhibited significantly lower FA relative to other groups. There was no Age × Group interaction in the tracts. CONCLUSION: Significant FA reductions were found in BD patients after controlling for age, indicating that abnormal white matter integrity in BD may occur at a younger age rather than developing progressively with age.

Distinctive Neuroanatomical Substrates for Depression in Bipolar Disorder versus Major Depressive Disorder.

No neuroanatomical substrates for distinguishing between depression of bipolar disorder (dBD) and major depressive disorder (dMDD) are currently known. The aim of the current multicenter study was to identify neuroanatomical patterns distinct to depressed patients with the two disorders. Further analysis was conducted on an independent sample to enable generalization of results. We directly compared MR images of these subjects using voxel-based morphometry (VBM) and a support vector machine (SVM) algorithm using 1531 participants. The VBM analysis showed significantly reduced gray matter volumes in the bilateral dorsolateral prefrontal (DLPFC) and anterior cingulate cortices (ACC) in patients with dBD compared with those with dMDD. Patients with the two disorders shared small gray matter volumes for the right ACC and left inferior frontal gyrus when compared with healthy subjects. Voxel signals in these regions during SVM analysis contributed to an accurate classification of the two diagnoses. The VBM and SVM results in the second cohort also supported these results. The current findings provide new evidence that gray matter volumes in the DLPFC and ACC are core regions in displaying shared and distinct neuroanatomical substrates and can shed light on elucidation of neural mechanism for depression within the bipolar/major depressive disorder continuum.

Neural circuitry of emotion regulation: Effects of appraisal, attention, and cortisol administration.

Psychosocial well-being requires effective regulation of emotional responding in context of threat or stress. Neuroimaging studies have focused on instructed, volitional regulation (e.g., reappraisal or distancing), largely ignoring implicit regulation that does not involve purposeful effort to alter emotional experience. These implicit processes may or may not involve the same neural pathways as explicit regulatory strategies. We examined the neurobiology of implicit emotional regulation processes and the impact of the stress hormone cortisol on these processes. Our study task employed composite pictures of faces and places to examine neural activity during implicit emotional processing (of emotional faces), while these responses were implicitly regulated by attention shift away from the emotionally evocative stimuli, and while subjects reflectively appraised their own emotional response to them. Subjects completed the task in an fMRI scanner after random assignment to receive placebo or hydrocortisone (HCT), an orally administered version of cortisol. Implicit emotional processing activated insula/IFG, dACC/dMPFC, midbrain and amygdala. With attention shifting, we saw diminished signal in emotion generating/response regions (e.g., amygdala) and increased activations in task specific attention regions like parahippocampus. With appraisal of emotions, we observed robust activations in medial prefrontal areas, where activation is also seen in instructed reappraisal studies. We observed no main effects of HCT administration on brain, but males and females showed opposing neural effects in prefrontal areas. The data suggest that different types of emotion regulation utilize overlapping circuits, but with some strategy specific activation. Further study of the dimorphic sex response to cortisol is needed.

Increased amygdala reactivity following early life stress: a potential resilience enhancer role.

BACKGROUND: Amygdala hyper-reactivity is sometimes assumed to be a vulnerability factor that predates depression; however, in healthy people, who experience early life stress but do not become depressed, it may represent a resilience mechanism. We aimed to test these hypothesis examining whether increased amygdala activity in association with a history of early life stress (ELS) was negatively or positively associated with depressive symptoms and impact of negative life event stress in never-depressed adults. METHODS: Twenty-four healthy participants completed an individually tailored negative mood induction task during functional magnetic resonance imaging (fMRI) assessment along with evaluation of ELS. RESULTS: Mood change and amygdala reactivity were increased in never-depressed participants who reported ELS compared to participants who reported no ELS. Yet, increased amygdala reactivity lowered effects of ELS on depressive symptoms and negative life events stress. Amygdala reactivity also had positive functional connectivity with the bilateral DLPFC, motor cortex and striatum in people with ELS during sad memory recall. CONCLUSIONS: Increased amygdala activity in those with ELS was associated with decreased symptoms and increased neural features, consistent with emotion regulation, suggesting that preservation of robust amygdala reactions may reflect a stress buffering or resilience enhancing factor against depression and negative stressful events.

Disrupted Brain Activation and Deactivation Pattern during Semantic Verbal Fluency Task in Patients with Major Depression.

BACKGROUND: Patients with major depressive disorder (MDD) exhibit cognitive impairment, and evidence suggests that the semantic version of the verbal fluency task is a reliable cognitive marker of the disorder. Here, using functional magnetic resonance imaging (fMRI), we investigated the dysfunction of neural processing in acute depression and examined the effects of a 6-week pharmacological intervention. METHODS: Sixteen patients with MDD participated in 2 fMRI sessions, and 16 healthy control (HC) subjects participated in 1 fMRI session. During each fMRI session, the participants performed a semantic verbal fluency task. Brain activity during the task was compared between groups (MDD 1st fMRI vs. HC) and times (MDD 1st fMRI vs. 2nd fMRI). RESULTS: Significant brain hypoactivation was observed in MDD patients at the prefrontal, lateral parietal, and limbic regions compared to HC, and MDD patients exhibited hyperactivation at the left precuneus compared to HC. Hypoactivity of the left dorsolateral prefrontal cortex (DLPFC) and hyperactivity of the precuneus were normalized with treatment. CONCLUSIONS: Hypoactivation of the left DLPFC and hyperactivation of the precuneus should be considered as dysregulation of anticorrelated brain networks during a cognitive demanding task. This failure of network regulation may be an important factor in the pathophysiology of MDD.

[Neuroimaging studies of depression: Current status and future direction.]

Recent advances in neuroimaging studies enable us to measure brain function non-invasively. Over the past several decades, neuroimaging measurement has been substantially applied to elucidate the neurobiological mechanisms underlying major depressive disorder (MDD). Since MDD is a wide spectrum which consists of various symptoms such as low mood or loss of interest and is considered to affect a biologically het- erogeneous population, we have not yet elucidated the biological underpinnings of MDD. It seems to be more promising to consider MDD as an integral network abnormality instead of deficits in specific brain regions or neurotransmitters. We firstly provide a comprehensive review of the extant neuroimaging literature including structural and functional magnetic resonance imaging, magnetic resonance spectroscopy, single pho- ton emission computed tomography, and positron emission tomography studies. Then we show our neuroimaging studies for MDD toward po- tential application as a biomarker for diagnosis and treatment. We believe it will facilitate the development, of a more integrative model of neural dysfunction in MDD.

Direct and indirect influences of childhood abuse on depression symptoms in patients with major depressive disorder.

BACKGROUND: It is known that the onset, progression, and prognosis of major depressive disorder are affected by interactions between a number of factors. This study investigated how childhood abuse, personality, and stress of life events were associated with symptoms of depression in depressed people. METHODS: Patients with major depressive disorder (N = 113, 58 women and 55 men) completed the Beck Depression Inventory-II (BDI-II), the Neuroticism Extroversion Openness Five Factor Inventory (NEO-FFI), the Child Abuse and Trauma Scale (CATS), and the Life Experiences Survey (LES), which are self-report scales. Results were analyzed with correlation analysis and structural equation modeling (SEM), by using SPSS AMOS 21.0. RESULTS: Childhood abuse directly predicted the severity of depression and indirectly predicted the severity of depression through the mediation of personality. Negative life change score of the LES was affected by childhood abuse, however it did not predict the severity of depression. CONCLUSIONS: This study is the first to report a relationship between childhood abuse, personality, adulthood life stresses and the severity of depression in depressed patients. Childhood abuse directly and indirectly predicted the severity of depression. These results suggest the need for clinicians to be receptive to the possibility of childhood abuse in patients suffering from depression. SEM is a procedure used for hypothesis modeling and not for causal modeling. Therefore, the possibility of developing more appropriate models that include other variables cannot be excluded.