Alterations in regional homogeneity of spontaneous brain activity in late-life subthreshold depression

Cortical activation for adolescent-onset minor depression and major depressive disorder: an fNIRS study

BACKGROUND

While depression is increasing worldwide, some patients are diagnosed as having Major Depressive Disorder (MDD), but others are diagnosed with minor depression, however, the potential neuro mechanism is unknown.

METHODS

Sixty-two patients with minor depression, 44 adolescents with MDD and 54 healthy adolescents participated in this study. Functional near-infrared spectroscopy (fNIRS), both HAMD and HAMA data were collected from all of the participants.

RESULTS

The result indicates the pervasively decreased activation of BA, 11, 21, 45 and 46 were observed in the MDD group and reduced activation of BA 45 was observed in the minor depression group. However, cortical activation was not observed between the minor depression or MDD groups. Cortical activation was also not correlated with the depressive/anxious score in the minor and MDD groups separately.

CONCLUSIONS

Cortical activation was pervasively decreased in the MDD group and slightly reduced in the minor depression group, which may be a potential neural mechanism. As reduced cortical activation in minor depression, interventions in the early stages of minor depression may help slow or even modify the development of the illness.

Structural and functional brain alterations in subthreshold depression: A multimodal coordinate-based meta-analysis

Imaging studies of subthreshold depression (StD) have reported structural and functional abnormalities in a variety of spatially diverse brain regions. However, there is no consensus among different studies. In the present study, we applied a multimodal meta-analytic approach, the Activation Likelihood Estimation (ALE), to test the hypothesis that StD exhibits spatially convergent structural and functional brain abnormalities compared to healthy controls. A total of 31 articles with 25 experiments were included, collectively representing 1001 subjects with StD. We found consistent differences between StD and healthy controls mainly in the left insula across studies with various neuroimaging methods. Further exploratory analyses found structural atrophy and decreased functional activities in the right pallidum and thalamus in StD, and abnormal spontaneous activity converged to the middle frontal gyrus. Coordinate-based meta-analysis found spatially convergent structural and functional impairments in StD. These findings provide novel insights for understanding the neural underpinnings of subthreshold depression and enlighten the potential targets for its early screening and therapeutic interventions in the future.

Abnormal spontaneous activity of regions related to mood regulation mediates the effect of childhood emotional neglect on major depressive disorder

This study investigated the mediating factors between childhood emotional neglect (EN) and major depressive disorder (MDD) and whether combining multi-indicator could help diagnose MDD. Regional homogeneity (ReHo) and clinical features were compared between 33 MDD patients and 36 healthy controls (HC). Mediation analysis was employed to explore whether social support or ReHo mediates the association between EN and MDD. The linear discriminant analysis model was constructed with EN, social support, and ReHo, and applied to distinguish MDD from HC in both primary and replication cohorts. We found that MDD patients experienced severer EN and poorer social support, and exhibited lower ReHo in the left middle occipital gyrus and bilateral postcentral gyrus, and higher ReHo in the right cerebellum crus1. EN could affect MDD directly and indirectly through ReHo in these discrepant brain regions and social support. Combining ReHo values of these four distinct brain regions, EN, and objective support could classify MDD patients from HC, and the 10-fold cross-validation accuracy within-study replication and in the independent cohort was 83.78 % ± 1.49 % and 82.72 % ± 2.22 %, respectively. These findings suggested that childhood EN, social support, and emotional-related regions' ReHo were associated with risks of MDD, providing new insights into the pathological mechanisms underlying MDD.

Comparative study of magnetic resonance imaging-based neuroimaging methods in older adults with depression

BACKGROUND

Older patients with depression often have accompanying physical diseases, thus, their disease situation is more complex than that of younger people. The medical community has aimed for earlier diagnosis of senile depression due to ineffective treatment and eventual cognitive impairment.

METHOD

Neuroimaging markers of senile depression were identified through the systematic analysis of multimodal data including resting-state functional MRI (rs-fMRI) and structural MRI (sMRI), and compared with clinical neural scales between older participants with and without depression.

RESULTS

Morphological analysis of gray matter by MRI showed significantly enlarged volumes in the left inferior temporal gyrus and right talus fissure, and reduced volumes in the left parahippocampal gyrus and lentiform globus pallidus in the older depression group compared with those in the control group. Comparison of fractional amplitude of low-frequency fluctuation between the groups showed increased partial brain activity in the left posterior central gyrus and right anterior central gyrus in the depression group compared with those in the control group.

CONCLUSION

Older patients with depression showed significant organic changes and significantly increased local brain activity. There was a positive correlation between the intensity of local brain activity in the superior occipital gyrus and the Hamilton Depression Rating Scale scores.

GUIDING SIGNIFICANCE

It is important to assess the organic changes and the degree of brain activity in specific brain regions in the clinical diagnosis of depression in the older adults, to adjust treatment plans early according to the incidence.

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.

Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms

BACKGROUND

Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect.

METHODS

Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms.

RESULTS

Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants (anxiety→somatic→depression: effect: 0.2785, Boot 95% CI: 0.0958-0.3729; depression→somatic→anxiety: effect: 0.0753, Boot 95% CI: 0.0232-0.1314) and participants in remission of MDD (anxiety→somatic→depression: effect: 0.2948, Boot 95% CI: 0.0357-0.7382; depression→somatic→anxiety: effect: 0.0984, Boot 95% CI: 0.0007-0.2438). Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants (effect: 0.0020, Boot 95% CI: 0.0003-0.0043). The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD (effect: 0.0437, Boot 95% CI: 0.0024-0.1190). These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected p < 0.01).

CONCLUSIONS

Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.

Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages

BACKGROUND

Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown.

METHODS

Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification.

RESULTS

Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions.

LIMITATION

The sample size is relatively small.

CONCLUSION

Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD.

Altered Intrinsic Brain Activity in Patients With Late-Life Depression: A Resting-State Functional MRI Study

Objectives

To investigate the altered intrinsic brain activity (IBA) in patients suffering from late-life depression (LLD) using a percent amplitude of fluctuation (PerAF) method.

Methods

In total, fifty patients with LLD and 40 non-depressed controls (NCs) were recruited for the present research. Participants underwent the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) test and resting-state functional MRI (rs-fMRI) scans. The RBANS test consists of 12 sub-tests that contribute to a total score and index scores across the following five domains: immediate memory, visuospatial/constructional, language, attention, and delayed memory. The PerAF method was used for data analysis to detect changes in neural activity in the relevant brain regions. A receiver operating characteristic (ROC) curve was conducted to evaluate the ability of the RBANS test and proposed the PerAF method in distinguishing the two groups. The relationships between altered IBA and neuropsychologic deficits were determined by the Pearson correlation analysis.

Results

A significant difference existed in RBANS total score, immediate memory, visuospatial/constructional, language, attention, and delayed memory between groups (P < 0.05). Compared with the NCs group, the LLD group demonstrated decreased PerAF differences in the bilateral superior frontal gyrus, orbital part (Frontal_Sup_Orb), and bilateral anterior cingulate cortex (ACC). The PerAF method and RBANS test exhibited an excellent discriminatory power with the area under curve (AUC) values in distinguishing the two groups. In addition, the attention score of the RBANS test positively correlated with the PerAF values of the bilateral Frontal_Sup_Orb and bilateral ACC.

Conclusion

The changes of PerAF in the bilateral Frontal_Sup_Orb and bilateral ACC are related to an increased risk of developing LLD. Moreover, the PerAF method could be used as an underlying sensitivity biomarker to identify the psychiatric disorder.

Discriminating subclinical depression from major depression using multi-scale brain functional features: A radiomics analysis

BACKGROUND

The diagnosis of subclinical depression (SD) currently relies exclusively on subjective clinical scores and structured interviews, which shares great similarities with major depression (MD) and increases the risk of misdiagnosis of SD and MD. This study aimed to develop a method of disease classification for SD and MD by resting-state functional features using radiomics strategy.

METHODS

Twenty-six SD, 36 MD subjects and 33 well-matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). A novel radiomics analysis was proposed to discriminate SD from MD. Multi-scale brain functional features were extracted to explore a comprehensive representation of functional characteristics. A two-level feature selection strategy and support vector machine (SVM) were employed for classification.

RESULTS

The overall classification accuracy among SD, MD and HC groups was 84.21%. Particularly, the model excellently distinguished SD from MD with 96.77% accuracy, 100% sensitivity, and 92.31% specificity. Moreover, features with high discriminative power to distinguish SD from MD showed a strong association with default mode network, frontoparietal network, affective network, and visual network regions.

LIMITATION

The sample size was relatively small, which may limit the application in clinical translation to some extent.

CONCLUSION

These findings demonstrated that a valid radiomics approach based on functional measures can discriminate SD from MD with a high classification performance, facilitating an objective and reliable diagnosis individually in clinical practice. Features with high discriminative power may provide insight into a profound understanding of the brain functional impairments and pathophysiology of SD and MD.

A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan

This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.

Alterations of Static and Dynamic Functional Connectivity of the Nucleus Accumbens in Patients With Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) is associated with dysfunction of the reward system. As an important node in the reward system, the resting-state functional connectivity of the nucleus accumbens (NAc) is related to the etiology of MDD. However, an increasing number of recent studies propose that brain activity is dynamic over time, no study to date has examined whether the NAc dynamic functional connectivity (DFC) is changed in patients with MDD. Moreover, few studies have examined the impact of the clinical characteristics of patients with MDD.

METHODS

A total of 220 MDD patients and 159 healthy controls (HCs), group-matched for age, sex, and education level, underwent resting-state functional magnetic resonance imagining (rs-fMRI) scans. Seed-based resting-state functional connectivity (RSFC) and DFC of the NAc were conducted. Two sample t-tests were performed to alter RSFC/DFC of NAc. In addition, we examined the association between altered RSFC/DFC and depressive severity using Pearson correlation. Finally, we divided patients with MDD into different subgroups according to clinical characteristics and tested whether there were differences between the subgroups.

RESULTS

Compared with the HCs, MDD patients show reduced the NAc-based RSFC with the dorsolateral prefrontal cortex (DLPFC), hippocampus, middle temporal gyrus (MTG), inferior temporal gyrus (ITG), precuneus, and insula, and patients with MDD show reduced the NAc-based DFC with the DLPFC, ventromedial prefrontal cortex (VMPFC), ventrolateral prefrontal cortex (VLPFC), MTG, ITG, and insula. MDD severity was associated with RSFC between the NAc and precentral gyrus (r = 0.288, p = 0.002, uncorrected) and insula (r = 0.272, p = 0.003, uncorrected).

CONCLUSION

This study demonstrates abnormal RSFC and DFC between the NAc and distributed cerebral regions in MDD patients, characterized by decreased RSFC and DFC of the NAc connecting with the reward, executive, default-mode, and salience network. Our results expand previous descriptions of the NAc RSFC abnormalities in MDD, and the altered RSFC/DFC may reflect the disrupted function of the NAc.

Frontal-posterior functional imbalance and aberrant function developmental patterns in schizophrenia

Schizophrenia (SZ) is a neurodevelopmental disorder. There remain significant gaps in understanding the neural trajectory across development in SZ. A major research focus is to clarify the developmental functional changes of SZ and to identify the specific timing, the specific brain regions, and the underlying mechanisms of brain alterations during SZ development. Regional homogeneity (ReHo) characterizing brain function was collected and analyzed on humans with SZ (hSZ) and healthy controls (HC) cross-sectionally, and methylazoxymethanol acetate (MAM) rats, a neurodevelopmental model of SZ, and vehicle rats longitudinally from adolescence to adulthood. Metabolomic and proteomic profiling in adult MAM rats and vehicle rats was examined and bioanalyzed. Compared to HC or adult vehicle rats, similar ReHo alterations were observed in hSZ and adult MAM rats, characterized by increased frontal (medial prefrontal and orbitofrontal cortices) and decreased posterior (visual and associated cortices) ReHo. Longitudinal analysis of MAM rats showed aberrant ReHo patterns as decreased posterior ReHo in adolescence and increased frontal and decreased posterior ReHo in adulthood. Accordingly, it was suggested that the visual cortex was a critical locus and adolescence was a sensitive window in SZ development. In addition, metabolic and proteomic alterations in adult MAM rats suggested that central carbon metabolism disturbance and mitochondrial dysfunction were the potential mechanisms underlying the ReHo alterations. This study proposed frontal-posterior functional imbalance and aberrant function developmental patterns in SZ, suggesting that the adolescent visual cortex was a critical locus and a sensitive window in SZ development. These findings from linking data between hSZ and MAM rats may have a significant translational contribution to the development of effective therapies in SZ.

The additive effect of late-life depression and olfactory dysfunction on the risk of dementia was mediated by hypersynchronization of the hippocampus/fusiform gyrus

Early detection of patients with late-life depression (LLD) with a high risk of developing dementia contributes to early intervention. Odor identification (OI) dysfunction serves as a marker for predicting dementia, but whether OI dysfunction increases the risk of dementia in LLD patients remains unclear. The present study aimed to explore the interactive effect of LLD and OI dysfunction on the risk of dementia and its underlying neuroimaging changes. One hundred and fifty-seven LLD patients and 101 normal controls were recruited, and data on their OI, cognition, activity of daily living (ADL), and resting-state functional magnetic resonance imaging were collected. Two × two factorial analyses were used to analyze the interactive effects of LLD and OI dysfunction on neuropsychological and neuroimaging abnormalities. Mediation analyses were used to explore whether abnormalities detected by neuroimaging mediated the the associations between OI and cognition/ADL. The results suggested that LLD and OI dysfunction exhibited additive effects on reduced ADL, global cognition and memory scores, as well as neuroimaging variables including (i) increased fractional amplitude of low-frequency fluctuation (fALFF) in the right orbitofrontal cortex and right precentral cortex, and (ii) increased regional homogeneity (ReHo) in the left hippocampus/fusiform gyrus, etc. In addition, these increased fALFF and ReHo values were associated with reduced neuropsychological scores (ADL, global cognition, memory, and language). Moreover, ReHo of the left hippocampus/fusiform gyrus completely mediated the relationship between OI and ADL, and partially mediated the relationship between OI and global cognition. Overall, mediated by the hypersynchronization of the left hippocampus/fusiform gyrus, OI dysfunction may increase the risk of dementia in LLD patients.

Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks

Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

Cognitive Control Network Homogeneity and Executive Functions in Late-Life Depression

BACKGROUND

Late-life depression is characterized by network abnormalities, especially within the cognitive control network. We used alternative functional connectivity approaches, regional homogeneity (ReHo) and network homogeneity, to investigate late-life depression functional homogeneity. We examined the association between cognitive control network homogeneity and executive functions.

METHODS

Resting-state functional magnetic resonance imaging data were analyzed for 33 older adults with depression and 43 healthy control subjects. ReHo was performed as the correlation between each voxel and the 27 neighbor voxels. Network homogeneity was calculated as global brain connectivity restricted to 7 networks. T-maps were generated for group comparisons. We measured cognitive performance and executive functions with the Dementia Rating Scale, Trail-Making Test (A and B), Stroop Color Word Test, and Digit Span Test.

RESULTS

Older adults with depression showed increased ReHo in the bilateral dorsal anterior cingulate cortex (dACC) and the right middle temporal gyrus, with no significant findings for network homogeneity. Hierarchical linear regression models showed that higher ReHo in the dACC predicted better performance on Trail-Making Test B (p < .001; R2 = .49), Digit Span Backward (p < .05; R2 = .23), and Digit Span Total (p < .05; R2 = .23). Used as a seed, the dACC cluster of higher ReHo showed lower functional connectivity with bilateral precuneus.

CONCLUSIONS

Higher ReHo within the dACC and right middle temporal gyrus distinguish older adults with depression from control subjects. The correlations with executive function performance support increased ReHo in the dACC as a meaningful measure of the organization of the cognitive control network and a potential compensatory mechanism. Lower functional connectivity between the dACC and the precuneus in late-life depression suggests that clusters of increased ReHo may be functionally segregated.

Major Depressive Disorder in Neuroimaging: What is Beyond Fronto-limbic Model?

Background:

The major depressive disorder (MDD) is a chronic illness with major manifestations in cognitive, social and occupational functions. The pathophysiological model is an intrigue issue for scientists to understand the origin of MDD.

Objective:

In the beginning, the cortico-limbic-striato-pallidal-thalamic model has been proposed to link the clinical symptoms with the abnormalities in brain structure and function. However, the model is still evolving due to recent advances in the neuroimaging techniques, especially for functional magnetic resonance imaging (fMRI). The recent findings in the fMRI studies in MDD showed the importance of fronto-limbic model for the modulations between cognitive function and primitive and negative emotions.

Method:

This review will focus on the literature of fMRI studies in MDD with findings not in the fronto-limbic structures.

Results:

Additional regions beyond the fronto-limbic model have been observed in some literature of MDD. Some regions in the parietal, temporal and occipital lobes have been shown with the alterations in gray matter, white matter and brain function. The importance of sensory detection, visuospatial function, language reception, motor response and emotional memories in these regions might provide the clues to understand the cognitive misinterpretations related to altered reception of outside information, behavioral responses related to biased cognition and emotional memories and clinical symptoms related to the significant alterations of interactions between different brain regions.

Conclusion:

Future studies to establish a more comprehensive model for MDD will be warranted, especially for the model beyond the fronto-limbic structures.

Within-Network Connectivity in the Salience Network After Attention Bias Modification Training in Residual Depression: Report From a Preregistered Clinical Trial

Alterations in resting state networks (RSNs) are associated with emotional- and attentional control difficulties in depressed individuals. Attentional bias modification (ABM) training may lead to more adaptive emotional processing in depression, but little is known about the neural underpinnings associated with ABM. In the current study a sample of 134 previously depressed individuals were randomized into 14 days of computerized ABM- or a closely matched placebo training regime followed by a resting state magnetic resonance imaging (MRI) scan. Using independent component analysis (ICA) we examined within-network connectivity in three major RSN's, the default mode network (DMN), the salience network (SN) and the central executive network (CEN) after 2 weeks of ABM training. We found a significant difference between the training groups within the SN, but no difference within the DMN or CEN. Moreover, a significant symptom improvement was observed in the ABM group after training. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT02931487.

Machine learning in major depression: From classification to treatment outcome prediction

AIMS

Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders.

DISCUSSIONS

In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression.

CONCLUSIONS

We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.

Stimulated left DLPFC-nucleus accumbens functional connectivity predicts the anti-depression and anti-anxiety effects of rTMS for depression

Not all depression patients effectively respond to repeated transcranial magnetic stimulation (rTMS). We tested whether the intrinsic functional connectivity (FC) strength between the stimulated left dorsolateral prefrontal cortex (DLPFC) and left nucleus accumbens (NAcc) might predict effects of rTMS. Twenty-two medication-naïve depression patients received rTMS on left DLPFC for 2 weeks and underwent baseline functional magnetic resonance imaging (fMRI). We compared the amplitude of the low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) in the stimulated target (the cortex region directly stimulated by rTMS) located in the left DLPFC, and the left NAcc, as well as the intrinsic FC of the DLPFC-NAcc between early improvers and non-improvers. We evaluated the association between the baseline brain imaging features (ALFF, ReHo, and FC) and improvements in depression and anxiety symptoms. We found that the pretreatment ALFF and ReHo in the stimulated DLPFC and left NAcc did not significantly differ between the subgroups. The early improvers displayed increased negative FC strength between the stimulated DLPFC and left NAcc with respect to non-improvers. The stimulated DLPFC-NAcc FC strength negatively correlated with improved depressive and anxious symptoms. This study is the first to demonstrate that the resting-state FC of the stimulated DLPFC-NAcc, rather than regional brain activity or local synchronization in the stimulated target, might predict the anti-depression and anti-anxiety effects of rTMS for depression.

Loneliness 5 years ante-mortem is associated with disease-related differential gene expression in postmortem dorsolateral prefrontal cortex

Subjective social isolation, loneliness, is associated with poor mental and physical health, but the underlying molecular mechanisms are poorly understood. Here we analyzed loneliness data collected on average 5 years ante-mortem and RNA gene expression at death in postmortem dorsolateral prefrontal cortex (DLPFC) from 181 participants in the Rush Memory and Aging Project (MAP), a longitudinal, prospective cohort study of common chronic conditions of aging. Our analytic protocol controlled for biographical variables (age, sex, education), psychological and health variables (depressive symptoms, interval between assessment and autopsy, slope of cognitive decline, AD pathology, presence of infarcts) and RNA integrity. Our results are based on a pre-ranked Gene Set Enrichment Analysis (GSEA) at FDR-corrected q-values <0.05, using these collections from the Molecular Signatures Database (v6.0 MSigDB): (1) Hallmarks, (2) Canonical, (3) Gene Ontology (GO), (4) Chemical and Genetic Perturbations, (5) Immunologic Signatures, (6) Oncogenic Signatures, and (7) Cancer Modules. We now report on 337 up-regulated and 43 down-regulated gene sets, among which the most significant ones were associated with Alzheimer's disease, psychiatric illness, immune dysfunction, and cancer. These gene sets constitute attractive targets for future studies into the molecular mechanisms by which loneliness exacerbates a wide range of neurodegenerative, psychiatric, and somatic illnesses.

Fronto-Limbic Alterations in Negatively Biased Attention in Young Adults with Subthreshold Depression

Attentional bias toward negative stimuli has been observed in major depression disorders (MDDs). Imaging studies suggest the engagement of fronto-limbic regions like amygdala, anterior cingulate cortex (ACC), and lateral prefrontal cortex, is related to negatively biased attention. However, neural correlates of attentional bias for negative stimuli in individuals with subthreshold depression (SubD), that is individuals who have clinically relevant depressive symptoms but do not fulfill the criteria for MDD, remain unclear. Here, we used functional neuroimaging and the dot-probe task to elucidate the neural substrates of negatively biased attention among individuals with SubD. Behavioral results found that individuals with SubD allocated more attention toward negative stimuli relative to neutral stimuli, which were not observed among non-depressed controls (NCs). Imaging results found greater amygdala and rostral ACC activity in attentional bias toward negative stimuli among participants with SubD compared to NCs; Additionally, participants with SubD showed reduced engagement of bilateral inferior frontal gyrus compared with NCs in the attentional processing of negative stimuli. Together, these results suggest that alteration of fronto-limbic systems relative to controls, known to be related to negative detection and attentional control, is associated with negatively biased attention in individuals with SubD.

Fractional amplitude of low-frequency fluctuations (fALFF) in post-stroke depression

Depression is a common outcome following stroke, associated with reduced quality of life and poorer recovery. Despite attempts to associate depression symptoms with specific lesion sites, the neural basis of post-stroke depression remains poorly understood. Resting state fMRI has provided new insights into the neural underpinnings of post-stroke depression, but has been limited to connectivity analyses exploring interregional correlations in the time-course of activity. Other aspects of resting state BOLD signal remain unexamined. Measuring the amplitude of low frequency fluctuations allows the detection of spontaneous neural activity across the whole brain. It provides complementary information about frequency-specific local neural activity. We calculated the fractional amplitude of low frequency fluctuations (fALFF) in a group of 64 participants scanned 3 months post-stroke. Twenty showed depression symptoms when assessed with the Patient Health Questionnaire (PHQ-9). We performed analyses in both the typical 0.01-0.08 Hz range, as well as separately in the slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) ranges. We found significantly higher fALFF in the depressed compared to non-depressed participants in the left dorsolateral prefrontal cortex (DLPFC) and the right precentral gyrus, and a significant association between higher depression scores and higher fALFF in the left insula. The group differences were detected in the slow-5 fluctuations, while the association with depression severity was observed in the slow-4 range. We conclude that post-stroke depression can be characterised by aberrant spontaneous local neural activity, which in small samples could be a more sensitive measure than lesion volume and location.

Probabilistic Entity-Relationship Diagram: A correlation between functional connectivity and spontaneous brain activity during resting state in major depressive disorder

BACKGROUND

Alterations of functional connectivity (FC) and spontaneous brain activity (SBA) during the resting state has been observed in subjects with major depressive disorder (MDD). Although there are many studies separately describing on the alterations of FC and SBA in major depressive disorder, their correlation are still have not been performed.

METHODS

A literature search based on Pubmed and Embase was conducted until 20 April 2016 to identify studies evaluating the correlation for the alterations between functional connectivity and spontaneous brain activity during resting-state in MDD. Meta-analyses were performed using the Probabilistic Entity-Relationship Diagram (PERD) approach to summarize the relationships among multiple factors in an intuitive manner.

RESULTS

A total of 30 studies (747 individuals with MDD and 757 healthy controls) met the inclusion criteria. In this study, we demonstrated that the functional connectivity and spontaneous brain activity, which was quantitatively measured by the primary analysis methods, was decreased in the parahippocampal gyrus, orbitofrontal cortex (OFC) and postcentral gyrus (PCG), and increased in insula and left dorsal medial prefrontal cortex (DMPFC) in MDD patients. Furthermore, we found that MDD patients presented negative correlation alterations both FC and SBA in the default mode network and the dorsal attention network, but positive correlation alterations both FC and SBA in the insular network, executive control network, the salience network and the other network.

CONCLUSIONS

Our results first suggested that there were correlation alterations between functional connectivity and spontaneous brain activity during resting-state in patients with MDD. Besides, we applied a recent meta-analysis approach (PERD) to summarize and integrate the inconsistence of the existing findings regarding the network dysfunction of MDD.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression

BACKGROUND

The incidence of major depressive disorder (MDD) rises during adolescence, yet the neural mechanisms of MDD during this key developmental period are unclear. Altered amygdala resting-state functional connectivity (RSFC) has been associated with both adolescent and adult MDD, as well as symptom improvement in response to treatment in adults. However, no study to date has examined whether amygdala RSFC is associated with changes in depressive symptom severity in adolescents.

METHOD

We examined group differences in amygdala RSFC between medication-naïve depressed adolescents (N=48) and well-matched healthy controls (N=53) cross-sectionally. We then longitudinally examined whether baseline amygdala RSFC was associated with change in depression symptoms three months later in a subset of the MDD group (N=24).

RESULTS

Compared to healthy controls, depressed adolescents showed reduced amygdala-based RSFC with the dorsolateral prefrontal cortex (DLPFC)and the ventromedial prefrontal cortex (VMPFC). Within the depressed group, more positive baseline RSFC between the amygdala and insulae was associated with greater reduction in depression symptoms three months later.

LIMITATIONS

Only a subset of depressed participants was assessed at follow-up and treatment type and delivery were not standardized.

CONCLUSIONS

Adolescent depression may be characterized by dysfunction of frontolimbic circuits (amygdala-DLPFC, amygdala-VMPFC) underpinning emotional regulation, whereas those circuits (amygdala-insula) subserving affective integration may index changes in depression symptom severity and may therefore potentially serve as a candidate biomarker for treatment response. Furthermore, these results suggest that the biomarkers of MDD presence are distinct from those associated with change in depression symptoms over time.

The alterations in regional homogeneity of parieto-cingulate and temporo-cerebellum regions of first-episode medication-naïve depression patients

This study surveyed the characteristics of the indicator for the synchrony of brain activities, regional homogeneity (ReHo), in patients who were diagnosed with major depressive disorder (MDD) without co-morbidities. Forty-four patients with MDD and twenty-seven normal controls were enrolled in our study. The ReHo outputs of patients and controls were compared by a nonparametric permutation-based method with global brain volume, age, and gender as covariates. In addition, the correlations between the clinical variables (such as depression severity, anxiety severity, illness duration) and ReHo values were also estimated in each group and across both groups. The patients with MDD had lower ReHo values than the controls for the cognitive division of right anterior cingulate cortex and the left inferior parietal lobule. In contrast, the patients had higher values of ReHo than controls for the right inferior temporal lobe and the right cerebellum. Additionally, the ReHo values were negatively correlated with the depression severity and with illness duration in the right anterior cingulate cortex. MDD patients had significant alterations in the ReHo of the parieto-cingulate and temporo-cerebellum regions with opposite trends.

Disrupted Interhemispheric Synchrony in Default Mode Network Underlying the Impairment of Cognitive Flexibility in Late-Onset Depression

The intuitive association between cognitive impairment and aberrant functional activity in the brain network has prompted interest in exploring the role of functional connectivity in late-onset depression (LOD). The relationship of altered voxel-mirrored homotopic connectivity (VMHC) and cognitive dysfunction in LOD is not yet well understood. This study was designed to examine the implicit relationship between the disruption of interhemispheric functional coordination and cognitive impairment in LOD. LOD patients (N = 31) and matched healthy controls (HCs; N = 37) underwent neuropsychological tests and functional magnetic resonance imaging (fMRI) in this study. The intergroup difference of interhemispheric coordination was determined by calculating VMHC value in the whole brain. The neuro-behavioral relevancy approach was applied to explore the association between disrupted VMHC and cognitive measures. Receiver operating characteristic (ROC) curve analysis was used to determine the capability of disrupted regional VMHC to distinguish LOD. Compared to the HC group, significantly attenuated VMHC in the superior frontal gyrus (SFG), superior temporal gyrus (STG), posterior cerebellar lobe (CePL) and post- and precentral gyri were observed in the bilateral brain of LOD patients. The interhemispheric asynchrony in bilateral CePLs was positively correlated with the performance of trail making test B (TMT-B) in LOD patients (r = 0.367, P = 0.040). ROC analysis revealed that regions with abnormal VMHC could efficiently distinguish LOD from HCs (Area Under Curve [AUC] = 0.90, P < 0.001). Altered linkage patterns of intrinsic homotopic connectivity and impaired cognitive flexibility was first investigated in LOD, and it would provide a novel clue for revealing the neural substrates underlying cognitive impairment in LOD.

Smaller gray matter volume of hippocampus/parahippocampus in elderly people with subthreshold depression: a cross-sectional study

BACKGROUND

Hippocampal/parahippocampal structural changes accompany major depressive disorders in the elderly, but whether subthreshold depression (StD) at an advanced age is also accompanied by similar changes in hippocampal/parahippocampal volumes is still unknown. By using voxel-based morphometry (VBM) analysis of the gray matter, we explored whether there are structural alterations of the hippocampus/parahippocampus and the correlations between its volume and participants' self-reported depressive symptoms.

METHODS

Participants were 19 community-dwelling older adults with StD assessed by the Center for Epidemiologic Studies Depression scale (CES-D) scores. We collected magnetic resonance images of their brain compared to images of 17 healthy aged-matched adults. We used VBM to analyze differences in gray matter volume (GMV) of the hippocampus/parahippocampus between the two groups. Moreover, we examined the correlation between the GMV of the hippocampus/parahippocampus and participants' self-reported depressive symptoms.

RESULTS

VBM revealed that elderly individuals with StD had substantially reduced volumes of the right parahippocampus compared to healthy controls. Furthermore, the volumes of the hippocampus/parahippocampus were significantly associated with participants' self-reported depressive symptoms in StD.

CONCLUSIONS

Gray matter volume alterations in the hippocampus/parahippocampus are correlated with subthreshold depression suggesting that early structural changes in the hippocampus/parahippocampus can constitute a risk indicator of depression.

Imbalanced spontaneous brain activity in orbitofrontal-insular circuits in individuals with cognitive vulnerability to depression

BACKGROUND

The hopelessness theory of depression posits that individuals with negative cognitive styles are at risk of developing depression following negative life events. The purpose of this work was to examine whether individuals with cognitive vulnerability to depression (CVD) exhibit similar spontaneous brain activity patterns as compared to patients with major depressive disorder (MDD).

METHODS

Subjects with CVD (N=32), drug-naïve first-episode patients with major depressive disorder (N=32), and sex-, age- and education-matched healthy controls (HCs; N=35) were subjected to resting state functional magnetic resonance imaging (RS-fMRI) and amplitudes of low-frequency fluctuation (ALFF) was compared between the groups. Pearson correlation analysis was performed between regional ALFFs and psychometric scores, namely the Cognitive Style Questionnaire (CSQ) and the Center for Epidemiologic Studies Depression (CES-D) scale scores.

RESULTS

Significant group differences in ALFF values were observed bilaterally in the orbitofrontal cortex (OFC) and insular cortex (IC), and in the left fusiform gyrus (FFG). Compared to HCs, CVD subjects had reduced ALFFs in the bilateral OFC and increased ALFF in the bilateral IC and the left FFG, which were similar to the differences observed between the HCs and MDD patients. Compared to MDD patients, CVD subjects showed significant reduced ALFF values in right IC. Additionally, CSQ scores for the CVD group correlated with ALFF values in the left IC.

LIMITATIONS

We did not conduct a longitudinal study. Our findings were limited in cross-sectional analysis.

CONCLUSIONS

A hypoactive OFC and hyperactive IC in a resting-state may underlie an imbalance in the spontaneous brain activity in orbitofrontal-insular circuits, and these differences may represent a trait-related marker of vulnerability to depression.

Enhanced default mode network connectivity with ventral striatum in subthreshold depression individuals

Subthreshold depression (StD) is a highly prevalent condition associated with increased service utilization and social morbidity. Nevertheless, due to limitations in current diagnostic systems that set the boundary for major depressive disorder (MDD), very few brain imaging studies on the neurobiology of StD have been carried out, and its underlying neurobiological mechanism remains unclear. In recent years, accumulating evidence suggests that the disruption of the default mode network (DMN), a network involved in self-referential processing, affective cognition, and emotion regulation, is involved in major depressive disorder. Using independent component analysis, we investigated resting-state default mode network (DMN) functional connectivity (FC) changes in two cohorts of StD patients with different age ranges (young and middle-aged, n = 57) as well as matched controls (n = 79). We found significant FC increase between the DMN and ventral striatum (key region in the reward network), in both cohorts of StD patients in comparison with controls. In addition, we also found the FC between the DMN and ventral striatum was positively and significantly associated with scores on the Center for Epidemiologic Studies Depression Scale (CES-D), a measurement of depressive symptomatology. We speculate that this enhanced FC between the DMN and the ventral striatum may reflect a self-compensation to ameliorate the lowered reward function.

Depressive symptom severity is associated with increased cortical thickness in older adults

OBJECTIVE

Structural neuroimaging studies in older adults have consistently shown volume reductions in both major and subthreshold depression. Cortical thickness, another measure of brain structure, has not been well studied in this population. We examined cortical thickness in older adults across a range of depressive symptom (DS) severity.

METHODS

Forty-three community-dwelling older adults (mean age = 68.80 ± 7.00 years) underwent magnetic resonance imaging. Based on a priori hypotheses, we examined cortical thickness in regions of interest in the rostral anterior cingulate, orbitofrontal cortex, middle frontal gyrus, and isthmus cingulate using multiple linear regressions with depression questionnaire scores as the independent variable and age, sex, and mean hemispheric thickness as covariates. We also performed an exploratory vertex-wise analysis.

RESULTS

After correction for multiple comparisons, we found an association between increased DSs and greater cortical thickness in the right isthmus cingulate (F(1, 38) = 8.09, false discovery rate corrected p = 0.028; R(2) = 35.78) in the region of interest analysis and in the left precuneus (cluster size = 413, p = 0.00002) in the vertex-wise analysis.

CONCLUSIONS

Older adults with higher DSs also have greater cortical thickness in the isthmus cingulate and precuneus, areas important for emotion regulation and self-referential processing. Additional research is needed to elucidate the mechanisms and potential clinical significance underlying this relationship.

Behavioral activation can normalize neural hypoactivation in subthreshold depression during a monetary incentive delay task

BACKGROUND

Late adolescents are under increased risk of developing depressive symptoms. Behavioral activation is an effective treatment for subthreshold depression, which can prevent the development of subthreshold depression into a major depressive disorder. However, the neural mechanisms underlying the efficacy of behavioral activation have not been clearly understood. We investigated neural responses during reward processing by individuals with subthreshold depression to clarify the neural mechanisms of behavioral activation.

METHODS

Late adolescent university students with subthreshold depression (n=15, age 18-19 years) as indicated by a high score on the Beck's Depression Inventory-ll (BDI-ll) and 15 age-matched controls with a low BDI-ll score participated in functional magnetic resonance imaging scanning conducted during a monetary incentive delay task on two occasions. The Individuals in the subthreshold depression group received five, weekly behavioral activation sessions between the two scanning sessions. Moreover, they did not receive any medication until the study was completed.

RESULTS

Behavioral activation significantly reduced depressive symptoms. Moreover, compared to the changes in brain functions in the control group, the behavioral activation group showed functional changes during loss anticipation in brain structures that mediates cognitive and emotional regulation, including the left ventrolateral prefrontal cortex and angular gyrus.

LIMITATIONS

Replication of the study with a larger sample size is required to increase the generalizability of these results.

CONCLUSIONS

Behavioral activation results in improved functioning of the fronto-parietal region during loss anticipation. These results increase our understanding of the mechanisms underlying specific psychotherapies.

Decreased Regional Homogeneity in Patients With Acute Mild Traumatic Brain Injury: A Resting-State fMRI Study

Mild traumatic brain injury (mTBI) is characterized by structural disconnection and large-scale neural network dysfunction in the resting state. However, little is known concerning the intrinsic changes in local spontaneous brain activity in patients with mTBI. The aim of the current study was to assess regional synchronization in acute mTBI patients. Fifteen acute mTBI patients and 15 sex-, age-, and education-matched healthy controls (HCs) were studied. We used the regional homogeneity (ReHo) method to map local connectivity across the whole brain and performed a two-sample t-test between the two groups. Compared with HCs, patients with acute mTBI showed significantly decreased ReHo in the left insula, left precentral/postcentral gyrus, and left supramarginal gyrus (p < 0.05, AlphaSim corrected). The ReHo index of the left insula showed a positive correlation with the Mini-Mental State Examination (MMSE) scores across all acute mTBI patients (p < 0.05, uncorrected). The ReHo method may provide an objective biomarker for evaluating the functional abnormity of mTBI in the acute setting.

Altered Resting-State Brain Activity and Connectivity in Depressed Parkinson's Disease

Depressive symptoms are common in Parkinson's disease (PD), but the neurophysiological mechanisms of depression in PD are poorly understood. The current study attempted to examine disrupted spontaneous local brain activities and functional connectivities that underlie the depression in PD. We recruited a total of 20 depressed PD patients (DPD), 40 non-depressed PD patients (NDPD) and 43 matched healthy controls (HC). All the subjects underwent neuropsychological tests and resting-state fMRI scanning. The between-group differences in the amplitude of low frequency fluctuations (ALFF) of BOLD signals were examined using post-hoc tests after the analysis of covariance. Compared with the NDPD and HC, the DPD group showed significantly increased ALFF in the left median cingulated cortex (MCC). The functional connectivity (FC) between left MCC and all the other voxels in the brain were then calculated. Compared with the HC and NDPD group, the DPD patients showed stronger FC between the left MCC and some of the major nodes of the default mode network (DMN), including the post cingulated cortex/precuneus, medial prefrontal cortex, inferior frontal gyrus, and cerebellum. Correlation analysis revealed that both the ALFF values in the left MCC and the FC between the left MCC and the nodes of DMN were significantly correlated with the Hamilton Depression Rating Scale score. Moreover, higher local activities in the left MCC were associated with increased functional connections between the MCC and the nodes of DMN in PD. These abnormal activities and connectivities of the limbic-cortical circuit may indicate impaired high-order cortical control or uncontrol of negative mood in DPD, which suggested a possible neural mechanism of the depression in PD.

Altered cortical and subcortical local coherence in PTSD: evidence from resting-state fMRI

BACKGROUND

Post-traumatic stress disorder (PTSD) is often characterized by region-specific brain activation/deactivation and functional abnormalities in corticolimbic circuitry, as elucidated by task-dependent functional neuroimaging. However, little is known about the abnormalities in the local coherence of cortical and subcortical activity occurring during the resting state.

PURPOSE

To evaluate the functional discrepancy of local coherence between cortical and subcortical regions in PTSD patients using resting-state functional magnetic resonance imaging (fMRI).

MATERIAL AND METHODS

Resting-state fMRI (RS-fMRI) was performed on 14 outpatients with PTSD, along with 14 age- and sex-matched normal control subjects. Regional homogeneity (ReHo), a measurement of the coherence of spontaneous RS-fMRI signal oscillations within spatially neighboring voxels, was examined.

RESULTS

Compared with the normal controls, PTSD patients showed increased local coherence in subcortical regions, including amygdala, hippocampus, thalamus, and putamen, and decreased local coherence in cortical regions, including medial prefrontal cortex and dorsolateral prefrontal cortex. Moreover, a correlation analysis of the ReHo measurement versus the severity of the disorder was performed, and highly positive correlation were observed in the right amygdala.

CONCLUSION

The present study identified a functional discrepancy of local coherence between cortical and subcortical regions in PTSD patients compared with normal controls. The findings revealed that resting-state abnormalities might lead to further improvement of the understanding of the neural substrates of cognitive impairment and symptoms in PTSD.

The patterns of fractional amplitude of low-frequency fluctuations in depression patients: the dissociation between temporal regions and fronto-parietal regions

OBJECTIVE

This study surveyed the characteristics of the indicator for the fluctuations of default brain activity, fractional amplitude of low-frequency fluctuations (fALFF), in patients with pure major depressive disorder (MDD).

METHODS

Forty-four patients with MDD and 27 normal controls were enrolled in our study. All the participants received the resting-state functional magnetic resonance imaging (rs-fMRI) scans, which were pre-processed by the REST toolbox (resting-state functional MRI data analysis toolbox). The group-related differences of the fALFF between patients and controls were explored by performing comparisons of the fALFF values obtained from rs-fMRI data. The fALFF outputs of patients and controls were compared with global brain volume, age and gender as covariates. In addition, the correlations between the clinical variables (such as depression severity, anxiety severity, illness duration) and fALFF values were also estimated in each group and across both groups.

RESULTS

The patients with MDD had significantly higher fALFF values than the controls, for the left temporal subgyral region. In the contrary, the patients had lower values of fALFF than controls, for the right frontal subcallosal gyrus and right parietal postcentral gyrus. In addition, the fALFF values were negatively correlated with the depression severity in the left temporal subgyral region.

CONCLUSION

MDD patients had dissociable alterations in the fALFF values of the fronto-parietal and temporal regions. The pattern of fALFF alterations might be unique for depression patients with later onset-age of young adult phase.

Anatomical and functional brain abnormalities in unmedicated major depressive disorder

BACKGROUND

Using magnetic resonance imaging (MRI) and resting-state functional magnetic resonance imaging (rsfMRI) to explore the mechanism of brain structure and function in unmedicated patients with major depressive disorder (MDD).

PATIENTS AND METHODS

Fifty patients with MDD and 50 matched healthy control participants free of psychotropic medication underwent high-resolution structural and rsfMRI scanning. Optimized diffeomorphic anatomical registration through exponentiated lie algebra and the Data Processing Assistant for rsfMRI were used to find potential differences in gray-matter volume (GMV) and regional homogeneity (ReHo) between the two groups. A Pearson correlation model was used to analyze associations of morphometric and functional changes with clinical symptoms.

RESULTS

Compared to healthy controls, patients with MDD showed significant GMV increase in the left posterior cingulate gyrus and GMV decrease in the left lingual gyrus (P<0.001, uncorrected). In ReHo analysis, values were significantly increased in the left precuneus and decreased in the left putamen (P<0.001, uncorrected) in patients with MDD compared to healthy controls. There was no overlap between anatomical and functional changes. Linear correlation suggested no significant correlation between mean GMV values within regions with anatomical abnormality and ReHo values in regions with functional abnormality in the patient group. These changes were not significantly correlated with symptom severity.

CONCLUSION

Our study suggests a dissociation pattern of brain regions with anatomical and functional alterations in unmedicated patients with MDD, especially with regard to GMV and ReHo.

Resting state networks in major depressive disorder

Resting state functional magnetic resonance imaging (fMRI) examines the spontaneous low frequency neural activity of the brain to reveal networks of correlated neural activity. A number of different methodologies, each with its own advantages and disadvantages, have been used to examine networks of neural activity that may be related to clinical presentation. Major depressive disorder (MDD) research has largely focused on the default mode network (DMN), which is most active at rest and may relate to negative rumination. However, other networks can be discerned in the resting state such as salience and affective and cognitive control networks, all of which may be relevant to MDD psychopathology. This article reviews the rapidly increasing literature on resting state networks. A number of state- and trait-dependent abnormalities have been reported in MDD in a wide variety of regions including the cerebellum, lingual gyrus, anterior cingulate cortex (ACC), middle frontal gyrus (MFG), dorsolateral prefrontal cortex (dlPFC), amygdala and insula. Current and chronic medication is often a potential confound. Few trials have examined the immediate or delayed effects of antidepressants on resting state networks. This article presents a novel approach to the analysis of drug effects, the identification of signatures of efficacy, and thus for drug development.

Altered resting-state connectivity in college students with nonclinical depressive symptoms

Background

The underlying brain basis of nonclinical depressive symptoms (nCDSs) is largely unknown. Recently, the seed-based functional connectivity (FC) approach for analyzing resting-state fMRI (rs-fMRI) data has been increasingly used to explore the neural basis of depressive disorders. Other than common seed-based FC method using an a priori seed region, we conducted FC analysis based on regions with altered spontaneous activity revealed by the fractional amplitude of low-frequency fluctuations (fALFF) approach. The aim of the present study was to provide novel insight in the underlying mechanism of nCDSs in college students.

Methodology/Principal Findings

A total number of 1105 college students were recruited to participant in a survey for assessing depressive symptoms. Subsequently, 17 individuals with nCDSs and 20 healthy controls (HCs) were enrolled to perform MR studies. Alternations of fALFF were identified in the right superior parietal lobule (SPL) and left lingual gyrus, both of which were used as ROIs for further FC analysis. With right SPL, compare with HCs, subjects with nCDSs showed reduced FCs in the bilateral dorsal lateral prefrontal cortex (DLPFC), left inferior frontal gurus (IFG), left premotor cortex (PMC), DMN network [i.e., bilateral precuneus, posterior cingulate cortex (PCC), right supramarginal gyrus (SMG), right parahippocampal gyrus (PHG), bilateral inferior temporal gurus (ITG)] and left cerebellum posterior lobe (CPL). In addition, increased FCs were observed between the left lingual gyrus and right fusiform gyrus as well as in the left precuneus.

Conclusion/Significance

Our results indicate the abnormalities of spontaneous activity in the right SPL and left lingual gyrus and their corresponding dysfunction of the brain circuits might be related to the pathophysiology of nCDSs.

Diffusion tensor imaging studies in late-life depression: systematic review and meta-analysis

OBJECTIVES

Late-life depression (LLD) is the association with more cerebrovascular susceptibilities and white matter damage that can be assessed with diffusion tensor imaging (DTI). To better understand the white matter pathological alterations in LLD, we conducted a systematic review and meta-analysis.

METHODS

We searched MEDLINE, EMBASE, PsycINFO, PubMed, and Google Scholar databases for DTI studies comparing patients with LLD and healthy controls. For each study, details regarding participants, imaging methods, and results were extracted. Fractional anisotropy, an index of white matter integrity, was the dependent variable for group comparison. Effect sizes indicating the degree of group difference were estimated by random-effects meta-analysis.

RESULTS

A total of 15 eligible studies were included in the qualitative systematic review, nine of which were suitable for quantitative meta-analyses for the dorsolateral prefrontal cortex (DLPFC), corpus callosum, cingulum, and uncinate fasciculus (UF). Compared with the healthy control group, the LLD group showed lower fractional anisotropy in the DLPFC and UF with a large and a medium effect size, respectively, although heterogeneity and publication bias were found in the DLPFC.

CONCLUSION

Diffusion tensor imaging studies of LLD consistently showed reduced anisotropy in the DLPFC and UF of patients with LLD. These damaged regions are located with the frontostriatal and limbic networks. Thus, our findings showed that the disruption of frontal and frontal-to-limbic white matter tracts contributes to the pathogenesis of LLD.

Aberrant functional connectivity of the hippocampus in older adults with subthreshold depression

Major depression disorder (MDD) is closely associated with functional and structural changes in the hippocampus (HC). Neuroimaging studies have demonstrated abnormal functional connectivity (FC) of the HC in patients with MDD, but it remains unknown whether this abnormal hippocampal FC pattern occurs in individuals with subthreshold depression (StD) who are at high risk of MDD. Resting-state functional magnetic resonance imaging data were collected from 19 elderly individuals with StD and 18 normal controls. Whole brain voxel-wise FC analyses were conducted to investigate the hippocampal FC pattern by selecting the HC as the region of interest, and correlation analyses were performed to explore the association of altered FC of the HC with self-reported depressive symptoms. The results showed that elderly individuals with StD had substantially decreased FC of the HC to the prefrontal and cuneus cortices compared with healthy normal controls. Moreover, the strength of HC-cuneus connectivity was correlated with self-reported depressive symptoms in elderly individuals with StD. These findings suggest that dysfunctional integration within the HC and cortical regions may occur at an early stage of depression.

Neuroimaging studies of depressive disorders in China since 2000

Summary

This paper reviews neuroimaging studies of depressive disorders conducted in Chinese populations since 2000. Both cross-sectional and longitudinal studies using structural and functional imaging techniques have compared different types of depressed individuals, with and without specific genotypes, and the characteristics of depressed individuals before and after treatment with antidepressants. Many of the findings are unstable – probably because most of the studies are underpowered – but there have been some important contributions to the international literature. Future studies in China need to use standardized methods, longitudinal designs, and joint application of both structural and functional MRI.

Altered local activity and functional connectivity of the anterior cingulate cortex in elderly individuals with subthreshold depression

The anterior cingulate cortex (ACC) is recognized as a key structure in the pathogenesis of depression. This study aimed to investigate the resting-state regional activity and functional connectivity of the ACC in a community sample of elderly individuals with subthreshold depression (StD). We employed resting-state functional magnetic resonance imaging to acquire data from 19 elderly subjects with StD and 18 normal controls. We used a regional amplitude of low-frequency fluctuation (ALFF) analysis and a correlation-based functional connectivity (FC) approach to explore changes in local activity and remote connectivity of the ACC in StD. Compared to controls, the StD group demonstrated increased ALFF in the anterior portion of the dorsal ACC (adACC). The adACC also displayed increased FC with the dorsolateral prefrontal cortex and supplementary motor area and decreased FC with several subcortical regions. The FC levels of the adACC displayed a trending correlation with self-reported depressive symptoms. This study is the first to reveal the ACC changes in resting-state activity and connectivity in the elderly with StD, suggesting that altered ALFF/FC of the adACC is an important feature of StD.

Regional homogeneity of intrinsic brain activity in happy and unhappy individuals

Background

Why are some people happier than others? This question has intrigued many researchers. However, limited work has addressed this question within a neuroscientific framework.

Methods

The present study investigated the neural correlates of trait happiness using the resting-state functional magnetic resonance imaging (rs-fMRI) approach. Specifically, regional homogeneity (ReHo) was examined on two groups of young adults: happy and unhappy individuals (N = 25 per group).

Results

Decreased ReHo in unhappy relative to happy individuals was observed within prefrontal cortex, medial temporal lobe, superior temporal lobe, and retrosplenial cortex. In contrast, increased ReHo in unhappy relative to happy individuals was observed within the dorsolateral prefrontal cortex, middle cingulate gyrus, putamen, and thalamus. In addition, the ReHo within the left thalamus was negatively correlated with Chinese Happiness Inventory (CHI) score within the happy group.

Limitations

As an exploratory study, we examined how general trait happiness is reflected in the regional homogeneity of intrinsic brain activity in a relatively small sample. Examining other types of happiness in a larger sample using a multitude of intrinsic brain activity indices are warranted for future work.

Conclusions

The local synchronization of BOLD signal is altered in unhappy individuals. The regions implicated in this alteration partly overlapped with previously identified default mode network, emotional circuitry, and rewarding system, suggesting that these systems may be involved in happiness.