Differences in prefrontal cortex activation in Chinese college students with different severities of depressive symptoms: A large sample of functional near-infrared spectroscopy (fNIRS) findings.

BACKGROUND: Previous studies proposed that functional near-infrared spectroscopy (fNIRS) can be used to distinguish between not only different severities of depressive symptoms but also different subgroups of depression, such as anxious and non-anxious depression, bipolar and unipolar depression, and melancholia and non-melancholia depression. However, the differences in brain haemodynamic activation between depression subgroups (such as confirmed depression [CD] and suspected depression [SD]) with different symptom severities and the possible correlation between symptom severity and haemodynamic activation in specific brain regions using fNIRS have yet to be clarified. METHODS: The severity of depression symptoms was classified using the Hospital Anxiety and Depression scale (HADS) and the Mini International Neuropsychiatric Interview by psychiatrists. We recruited 654 patients with depression who had varying severities of depressive symptoms, including 276 with SD and 378 with CD, and 317 with HCs from among Chinese college students. The 53-channel fNIRS was used to detect the cerebral hemodynamic difference of the three groups during the VFT (verbal fluency task). RESULTS: Compared with the HC, region-specific fNIRS leads indicate CD patients had significant lower haemodynamic activation in three particular prefrontal regions: 1) right dorsolateral prefrontal cortex (DLPFC), 2) bilateral frontopolar cortex (FPC), and 3) right Broca's area (BA). SD vs. HC comparisons revealed only significant lower haemodynamic activation in the right FPC area. Compared to SD patients, CD patients exhibited decreased hemodynamic activation changes in the right DLPFC and the right BA. Correlation analysis established a significant negative correlation between the hemodynamic changes in the bilateral FPC and the severity of depressive symptoms. CONCLUSIONS: The right DLPFC and right BA are expected to be physiological mechanisms to distinguish depression subgroups (CD, SD) with different symptom severities. The haemodynamic changes in the bilateral FPC was nagatively associated with the symptom severity of depression.

Abnormal caudate nucleus activity in patients with depressive disorder: Meta-analysis of task-based functional magnetic resonance imaging studies with behavioral domain.

During task-based functional magnetic resonance imaging (t-fMRI) patients with depressive disorder (DD) have shown abnormal caudate nucleus activation. There have been no meta-analyses that are conducted on the caudate nucleus using Activation Likelihood Estimation (ALE) in patients with DD, and the relationships between abnormal caudate activity and different behavior domains in patients with DD remain unclear. There were 24 previously published t-fMRI studies included in the study with the caudate nucleus as the region of interest. Meta-analyses were performed using the method of ALE. Included five ALE meta-analyses: (1) the hypoactivated caudate nucleus relative to healthy controls (HCs); (2) the hyper-activated caudate nucleus; (3) the abnormal activation in the caudate nucleus in the emotion domain; (4) the abnormal activation in cognition domain; (5) the abnormal activation in the affective cognition domain. Results revealed that the hypo-/hyper-activity in the caudate subregions is mainly located in the caudate body and head, while the relationships between abnormal caudate subregions and different behavior domains are complex. The hypoactivation of the caudate body and head plays a key role in the emotions which indicates there is a positive relationship between the decreased caudate activity and depressed emotional behaviors in patients with DD.

Serum glial fibrillary acidic protein and neurofilament light chain in treatment-naïve patients with unipolar depression.

BACKGROUND: Unipolar depression has been associated with increased levels of glial dysfunction and neurodegeneration biomarkers, such as Glial Fibrillary Acidic Protein (GFAP) and Neurofilament light chain (NfL). However, previous studies were conducted on patients taking psychotropic medication and did not monitor longitudinal associations between disease status and GFAP/NfL. METHODS: Treatment-naïve patients with unipolar depression (n = 110) and healthy controls (n = 33) were included. GFAP/NfL serum levels were analyzed by Single Molecule Array at baseline and 3-month follow-up. The primary endpoint was GFAP/NfL levels in patients with depression compared with healthy controls. The secondary endpoint was the associations between GFAP/NfL with depression severity and cognitive function. RESULTS: The patients' mean HAM-D17 score was 18.9 (SD 3.9) at baseline and improved by 7.9 (SD 6.8) points during follow-up. GFAP/NfL was quantified in all individuals. At baseline, the adjusted GFAP levels were -16.8 % (95 % CI: -28.8 to -1.9, p = 0.03) lower among patients with depression compared to healthy controls, while NfL levels were comparable between the groups (p = 0.57). In patients with depression, mean NfL levels increased from baseline to follow-up (0.68 pg/ml, p = 0.03), while GFAP levels were unchanged (p = 0.24). We did not find consistent associations between NfL/GFAP with depression scores or cognitive function. CONCLUSION: This largest study of serum NfL/GFAP levels in patients with depression did not support previous findings of elevated GFAP/NfL in patients with depression or positive associations with depression severity. Although limited by a small control group, our study may support the presence of glial dysfunction but not damage to neurons in depression.

Real-time fMRI neurofeedback compared to cognitive behavioral therapy in a pilot study for the treatment of mild and moderate depression.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback was found to reduce depressive symptoms. However, no direct comparison of drug-free patients with an active psychotherapy control group is available. The present study compared rt-fMRI neurofeedback with cognitive behavioral therapy, as the standard treatment in patients declining anti-depressants. Twenty adult, drug-free patients with mild or moderate depression were non-randomly assigned either to a course of eight half-hour sessions of neurofeedback targeting the left medial prefrontal cortex (N = 12) or to a 16-session course of cognitive behavioral therapy (N = 8). Montgomery-Asberg Depression Rating Scale was introduced at baseline, mid-treatment, and end-treatment points. In each group, 8 patients each remained in the study to a mid-treatment evaluation and 6 patients each to the study end-point. ANOVA revealed a depression reduction with a significant effect of Time (F(3,6) = 19.0, p < 0.001, η2 = 0.76). A trend to greater improvement in the cognitive behavioral therapy group compared to neurofeedback emerged (Group × Time; p = 0.078). Percent signal change in the region of interest between up- and down-regulation conditions was significantly correlated with session number (Pearson's r = 0.85, p < 0.001) indicating a learning effect. As limitations, small sample size could lead to insufficient power and non-random allocation to selection bias. Both neurofeedback and cognitive behavioral therapy improved mild and moderate depression. Neurofeedback was not superior to cognitive behavioral therapy. Noteworthy, the neurofeedback training course was associated with continuous improvement in the self-regulation skill, without plateau. This study delivers data to plan clinical trials comparing neurofeedback with cognitive behavioral interventions.

Transcranial Sonography as a Diagnostic Tool for Depressive Disorders.

BACKGROUND: Transcranial sonography (TCS) is an available and noninvasive neuroimaging method that has been found to reduce the echogenicity of the brainstem raphe (BR) in patients with depression. Applying the criteria of the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV), we performed a meta-analysis of the diagnostic accuracy of TCS. METHODS: A systematic search was conducted in PubMed, EMBASE, The Cochrane Library, and Web of Science. The databases were searched from inception to December 2021. The quality of the included literature was assessed using the QUADAS-2. Heterogeneity analysis was performed. A summary receiver operating characteristic (SROC) curve was generated to evaluate the diagnostic accuracy of TCS. RESULTS: We included 12 studies with 809 patients. The pooled sensitivity was 0.66 (95% confidence interval [CI]: 0.61-0.71), and the specificity was 0.84 (95% CI: 0.80-0.87). The combined positive likelihood ratio (LR) was 3.84 (95% CI: 2.68-5.51), the negative LR was 0.41 (95% CI: 0.29-0.57), and the diagnostic odds ratio (DOR) was 11.45 (95% CI: 5.57-23.02). The area under the curve (AUC) of the plotted SROC curve was 0.86 (95% CI: 0.83-0.89). The meta-regression and subgroup analyses found no source of heterogeneity. CONCLUSION: TCS has high potential and efficacy in diagnosing depression and may be a reasonable test to perform clinically for the assessment of depression.

Resting-state neural signal variability in women with depressive disorders.

Aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions is well-documented in depression. Recent neuroimaging research suggests that altered variability in the blood oxygen level-dependent (BOLD) signal may disrupt normal network integration and be an important novel predictor of psychopathology. However, no studies have yet determined the relationship between resting-state BOLD signal variability and depressive disorders nor applied BOLD signal variability features to the classification of depression history using machine learning (ML). We collected resting-state fMRI data for 79 women with different depression histories: no history, past history, and current depressive disorder. We tested voxelwise differences in BOLD signal variability related to depression group and severity. We also investigated whether BOLD signal variability of DMN, FPN, and SN regions could predict depression history group using a supervised random forest ML model. Results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex (pFWE <0.05). Furthermore, greater depression severity was also associated with reduced BOLD signal variability in the cerebellum. A random forest model classified participant depression history with 74% accuracy, with the ventral anterior cingulate cortex of the DMN as the most important variable in the model. These findings provide novel support for resting-state BOLD signal variability as a marker of neural dysfunction in depression and implicate decreased neural signal variability in the pathophysiology of depression.

Divergent and Convergent Imaging Markers Between Bipolar and Unipolar Depression Based on Machine Learning.

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain "high-order functional connectivity (HOFC)" network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex "chronnectome" metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthycontrols (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.

Link between structural connectivity of the medial forebrain bundle, functional connectivity of the ventral tegmental area, and anhedonia in unipolar depression.

The ventral tegmental area (VTA), nucleus accumbens (NAcc), and prefrontal cortex (PFC) are essential for experiencing pleasure and initiating motivated behaviour. The VTA, NAcc, and PFC are connected through the medial forebrain bundle (MFB). In humans, two branches have been described: an infero-medial branch (imMFB) and a supero-lateral branch (slMFB). This study aimed to explore the associations between structural connectivity of the MFB, functional connectivity (FC) of the VTA, anhedonia, and depression severity in patients with depression. Fifty-six patients with unipolar depression and 22 healthy controls matched for age, sex, and handedness were recruited at the University Hospital of Psychiatry and Psychotherapy in Bern, Switzerland. Diffusion-weighted imaging and resting-state functional magnetic resonance imaging scans were acquired. Using manual tractography, the imMFB and slMFB were reconstructed bilaterally for each participant. Seed-based resting-state FC was computed from the VTA to the PFC. Hedonic tone was assessed using the Fawcett-Clark Pleasure Scale. We identified reduced tract volume and reduced number of tracts in the left slMFB. There was an increase in FC between the VTA and right medial PFC in patients with depression. Depression severity was associated with reduced tract volume and fewer tracts in the left slMFB. Reduced hedonic tone was associated with reduced tract volume. Conversely, reduced hedonic tone was associated with increased FC between the VTA and the PFC. In conclusion, our results suggest reduced structural connectivity of the slMFB in patients with depression. Increases in FC between the VTA and PFC may be associated with anhedonia or compensatory hyperactivity.

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review.

OBJECTIVES: Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs. METHODS: We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature. RESULTS: We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression. CONCLUSIONS: When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.

Resting-state functional connectivity in adolescents experiencing subclinical and clinical symptoms of depression: a mini-review of recent evidence.

Adolescence is a developmental period associated with major neural reorganization and the onset of many psychological disorders. Depression in particular is prevalent and impairing in adolescents and rates have been rising in recent years. Recent advances in the neurobiology of adolescent depression contribute to a better understanding of functional connectivity among neural networks and represent a promising start for determining biomarkers of depression and potential areas of intervention.

Distinct Basal Brain Functional Activity and Connectivity in the Emotional-Arousal Network and Thalamus in Patients With Functional Constipation Associated With Anxiety and/or Depressive Disorders.

OBJECTIVE: Functional constipation (FC) is a common gastrointestinal disorder. Anxiety and/or depressive disorders are common in patients with FC (FCAD). Brain dysfunction may play a role in FC, but the contribution of comorbid anxiety and/or depression in patients with FC is poorly understood. METHODS: Sixty-five FC patients and 42 healthy controls (HCs) were recruited, and a hierarchical clustering algorithm was used to classify FC patients into FCAD and patients without anxiety/depressive status (FCNAD) based on neuropsychological assessment. Resting-state functional magnetic resonance imaging measures including fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity were used to investigate brain functional differences. RESULTS: Thirty-seven patients were classified as FCAD, and 28 patients were classified as FCNAD; as compared with HC, both groups showed decreased activity (fALFF) in the perigenual anterior cingulate cortex (pACC), dorsomedial prefrontal cortex (DMPFC), and precuneus; enhanced precentral gyrus-thalamus connectivity and attenuated precuneus-thalamus connectivity in FCAD/FCNAD highlighted the thalamus as a critical connectivity node in the brain network (pFWE < .05). In comparison with FCNAD/HC, the FCAD group also had decreased fALFF in the orbitofrontal cortex (OFC) and thalamus, and increased OFC-hippocampus connectivity. In the FCNAD group, brain activities (pACC/DMPFC) and connection (precuneus-thalamus) had correlations only with symptoms; in the FCAD group, brain activities (OFC, pACC/DMPFC) and connectivities (OFC-hippocampus/precentral gyrus-thalamus) showed correlations with both constipation symptoms and anxiety/depressive status ratings. Mediation analysis indicated that the relationship between abdominal distension and OFC activity was completely mediated by anxiety in FCAD. CONCLUSIONS: These findings provide evidence of differences in brain activity and functional connectivity between FCAD and FCNAD, potentially providing important clues for improving treatment strategies.

Abnormal prediction error processing in schizophrenia and depression.

To make adaptive decisions under uncertainty, individuals need to actively monitor the discrepancy between expected outcomes and actual outcomes, known as prediction errors. Reward-based learning deficits have been shown in both depression and schizophrenia patients. For this study, we compiled studies that investigated prediction error processing in depression and schizophrenia patients and performed a series of meta-analyses. In both groups, positive t-maps of prediction error tend to yield striatum activity across studies. The analysis of negative t-maps of prediction error revealed two large clusters within the right superior and inferior frontal lobes in schizophrenia and the medial prefrontal cortex and bilateral insula in depression. The concordant posterior cingulate activity was observed in both patient groups, more prominent in the depression group and absent in the healthy control group. These findings suggest a possible role in dopamine-rich areas associated with the encoding of prediction errors in depression and schizophrenia.

The stage-specifically accelerated brain aging in never-treated first-episode patients with depression.

Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never-treated first-episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel-based morphometry derived from T1-weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never-treated first-episode patients with depression (N = 195). The brain-predicted age difference (brain-PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age-, gender-, educational level-matched HCs in Dataset 1. Overall, patients presented higher brain-PAD scores suggesting patients with depression having an "older" brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data-driven method and significant correlation between brain-PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage-dependent phenomenon in depression.

Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk of suicide to a greater extent than clinician assessment. The present study aimed to use deep learning of structural magnetic resonance imaging (MRI) to create an algorithm for detecting suicidal ideation and suicidal attempts. METHODS: We recruited 4 groups comprising a total of 186 participants: 33 depressive patients with suicide attempt (SA), 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (DP), and 58 healthy controls (HCs). The confirmation of depressive disorder, SA and SI was based on psychiatrists' diagnosis and Mini-International Neuropsychiatric Interview (MINI) interviews. In the generalized q-sampling imaging (GQI) dataset, indices of generalized fractional anisotropy (GFA), the isotropic value of the orientation distribution function (ISO), and normalized quantitative anisotropy (NQA) were separately trained in convolutional neural network (CNN)-based deep learning and DenseNet models. RESULTS: From the results of 5-fold cross-validation, the best accuracies of the CNN classifier for predicting SA, SI, and DP against HCs were 0.916, 0.792, and 0.589, respectively. In SA-ISO, DenseNet outperformed the simple CNNs with a best accuracy from 5-fold cross-validation of 0.937. In SA-NQA, the best accuracy was 0.915. CONCLUSIONS: The results showed that a deep learning method based on structural MRI can effectively detect individuals at different levels of suicide risk, from depression to suicidal ideation and attempted suicide. Further studies from different populations, larger sample sizes, and prospective follow-up studies are warranted to confirm the utility of deep learning methods for suicide prevention and intervention.

White matter abnormalities in adults with bipolar disorder type-II and unipolar depression.

Discerning distinct neurobiological characteristics of related mood disorders such as bipolar disorder type-II (BD-II) and unipolar depression (UD) is challenging due to overlapping symptoms and patterns of disruption in brain regions. More than 60% of individuals with UD experience subthreshold hypomanic symptoms such as elevated mood, irritability, and increased activity. Previous studies linked bipolar disorder to widespread white matter abnormalities. However, no published work has compared white matter microstructure in individuals with BD-II vs. UD vs. healthy controls (HC), or examined the relationship between spectrum (dimensional) measures of hypomania and white matter microstructure across those individuals. This study aimed to examine fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD) across BD-II, UD, and HC groups in the white matter tracts identified by the XTRACT tool in FSL. Individuals with BD-II (n = 18), UD (n = 23), and HC (n = 24) underwent Diffusion Weighted Imaging. The categorical approach revealed decreased FA and increased RD in BD-II and UD vs. HC across multiple tracts. While BD-II had significantly lower FA and higher RD values than UD in the anterior part of the left arcuate fasciculus, UD had significantly lower FA and higher RD values than BD-II in the area of intersections between the right arcuate, inferior fronto-occipital and uncinate fasciculi and forceps minor. The dimensional approach revealed the depression-by-spectrum mania interaction effect on the FA, RD, and AD values in the area of intersection between the right posterior arcuate and middle longitudinal fasciculi. We propose that the white matter microstructure in these tracts reflects a unique pathophysiologic signature and compensatory mechanisms distinguishing BD-II from UD.

Impact of the KCNQ2/3 Channel Opener Ezogabine on Reward Circuit Activity and Clinical Symptoms in Depression: Results From a Randomized Controlled Trial.

OBJECTIVE: Preclinical studies point to the KCNQ2/3 potassium channel as a novel target for the treatment of depression and anhedonia, a reduced ability to experience pleasure. The authors conducted the first randomized placebo-controlled trial testing the effect of the KCNQ2/3 positive modulator ezogabine on reward circuit activity and clinical outcomes in patients with depression. METHODS: Depressed individuals (N=45) with elevated levels of anhedonia were assigned to a 5-week treatment period with ezogabine (900 mg/day; N=21) or placebo (N=24). Participants underwent functional MRI during a reward flanker task at baseline and following treatment. Clinical measures of depression and anhedonia were collected at weekly visits. The primary endpoint was the change from baseline to week 5 in ventral striatum activation during reward anticipation. Secondary endpoints included depression and anhedonia severity as measured using the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Snaith-Hamilton Pleasure Scale (SHAPS), respectively. RESULTS: The study did not meet its primary neuroimaging endpoint. Participants in the ezogabine group showed a numerical increase in ventral striatum response to reward anticipation following treatment compared with participants in the placebo group from baseline to week 5. Compared with placebo, ezogabine was associated with a significantly larger improvement in MADRS and SHAPS scores and other clinical endpoints. Ezogabine was well tolerated, and no serious adverse events occurred. CONCLUSIONS: The study did not meet its primary neuroimaging endpoint, although the effect of treatment was significant on several secondary clinical endpoints. In aggregate, the findings may suggest that future studies of the KCNQ2/3 channel as a novel treatment target for depression and anhedonia are warranted.

Structural abnormalities of cingulate cortex in patients with first-episode drug-naïve schizophrenia comorbid with depressive symptoms.

Depressive symptoms are common in patients with first-episode psychosis. However, the neural mechanisms underlying the comorbid depression in schizophrenia are still unknown. The main purpose of this study was to characterize the structural abnormalities of first-episodes drug-naïve (FEDN) schizophrenia comorbid with depression by utilizing both volume-based and surface-based morphometric measurements. Forty-two patients with FEDN schizophrenia and 29 healthy controls were recruited. The 24-item Hamilton Depression Rating Scale (HAMD-24) was administrated to divide all patients into depressive patients (DP) and non-depressive patients (NDP). Compared with NDP, DP had a significantly larger volume and surface area in the left isthmus cingulate cortex and also had a greater volume in the left posterior cingulate cortex. Correlation analysis showed that HAMD total score was positively correlated with the surface area of the left isthmus cingulate and gray matter volume of the left isthmus cingulate cortex. In addition, gray matter volume of the left isthmus cingulate was also correlated with the PANSS general psychopathology or total score. The findings suggest that prominent structural abnormalities of gray matter are mainly concentrated on the cingulate cortex in FEDN schizophrenia patients comorbid with depression, which may contribute to depressive symptoms and psychopathological symptoms.

Differentiating between bipolar and unipolar depression using prefrontal activation patterns: Promising results from functional near infrared spectroscopy (fNIRS) findings.

BACKGROUND: Bipolar depression (BD) is a unique, severe and prevalent mental illness that shares many similarities in symptoms with unipolar depression (UD). Improving precision of their diagnoses would enhance treatment outcome and prognosis for both conditions. This study aims to provide evidence from functional Near-Infrared Spectroscopy (fNIRS) as a potential tool to differentiate UD and BD based on their differences in hemodynamic change in the prefrontal cortex during verbal fluency tasks (VFT). METHODS: We enrolled 179 participants with clinically confirmed diagnoses, including 69 UD patients, 68 BD patients and 42 healthy controls(HC). Every participant was assessed using a 45-channel fNIRS and various clinical scales. FINDINGS: Compared with HC, region-specific fNIR leads show UD patients had significant lower hemodynamic activation in 4 particular pre-frontal regions: 1) the left dorsolateral prefrontal cortex (DLPFC), 2) orbitofrontal cortex (OFC), 3) bilateral ventrolateral prefrontal cortex (VLPFC) and 4) left inferior frontal gyrus (IFG). In contrast, BD vs. HC comparisons showed only significant lower hemodynamic activation in the LIFG area. Furthermore, compared to BD patients, UD patients showed decreased hemodynamic activation changes in the VLPFC region. CONCLUSION: Our results show significant frontal lobe activation pattern differences between UD and BD groups. fNIRS can be a potential tool to increase diagnostic precision for these conditions. In particular, the VLPFC area holds promise to be a useful site for such differentiation for further investigations.

Effects of Early Life Stress on Bone Homeostasis in Mice and Humans.

Bone pathology is frequent in stressed individuals. A comprehensive examination of mechanisms linking life stress, depression and disturbed bone homeostasis is missing. In this translational study, mice exposed to early life stress (MSUS) were examined for bone microarchitecture (µCT), metabolism (qPCR/ELISA), and neuronal stress mediator expression (qPCR) and compared with a sample of depressive patients with or without early life stress by analyzing bone mineral density (BMD) (DXA) and metabolic changes in serum (osteocalcin, PINP, CTX-I). MSUS mice showed a significant decrease in NGF, NPYR1, VIPR1 and TACR1 expression, higher innervation density in bone, and increased serum levels of CTX-I, suggesting a milieu in favor of catabolic bone turnover. MSUS mice had a significantly lower body weight compared to control mice, and this caused minor effects on bone microarchitecture. Depressive patients with experiences of childhood neglect also showed a catabolic pattern. A significant reduction in BMD was observed in depressive patients with childhood abuse and stressful life events during childhood. Therefore, future studies on prevention and treatment strategies for both mental and bone disease should consider early life stress as a risk factor for bone pathologies.

Volumetric brain differences in clinical depression in association with anxiety: a systematic review with meta-analysis.

Background: Structural differences associated with depression have not been confirmed in brain regions apart from the hippocampus. Comorbid anxiety has been inconsistently assessed, and may explain discrepancies in previous findings. We investigated the link between depression, comorbid anxiety and brain structure. Methods: We followed Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines (PROSPERO CRD42018089286). We searched the Cochrane Library, MEDLINE, PsycInfo, PubMed and Scopus, from database inception to Sept. 13, 2018, for MRI case-control studies that reported brain volumes in healthy adults and adults with clinical depression. We summarized mean volumetric differences using meta-analyses, and we assessed demographics, depression factors and segmentation procedure as moderators using meta-regressions. Results: We included 112 studies in the meta-analyses, assessing 4911 healthy participants and 5934 participants with depression (mean age 49.8 yr, 68.2% female). Volume effects were greater in late-onset depression and in multiple episodes of depression. Adults with depression and no comorbidity showed significantly lower volumes in the putamen, pallidum and thalamus, as well as significantly lower grey matter volume and intracranial volume; the largest effects were in the hippocampus (6.8%, p < 0.001). Adults with depression and comorbid anxiety showed significantly higher volumes in the amygdala (3.6%, p < 0.001). Comorbid anxiety lowered depression effects by 3% on average. Sex moderated reductions in intracranial volume. Limitations: High heterogeneity in hippocampus effects could not be accounted for by any moderator. Data on symptom severity and medication were sparse, but other factors likely made significant contributions. Conclusion: Depression-related differences in brain structure were modulated by comorbid anxiety, chronicity of symptoms and onset of illness. Early diagnosis of anxiety symptomatology will prove crucial to ensuring effective, tailored treatments for improving long-term mental health and mitigating cognitive problems, given the effects in the hippocampus.