Accelerated aging of the putamen in patients with major depressive disorder

Beyond the Buzz: Cortical and subcortical brain changes in patients with pulsatile tinnitus

Pulsatile tinnitus (PT) can be a debilitating condition characterized by rhythmic, heartbeat-synchronous sounds, which can severely impact patients' quality of life. Understanding the neuroanatomical changes in PT patients may provide critical insights into the impacts of this condition. This study aimed to investigate potential differences in cortical and subcortical brain volume between adults with PT and age-matched controls (60 to 70 years of age). A retrospective, cross-sectional analysis of imaging and medical records was conducted, with data collected from January 2015 to December 2021. The study was conducted in a tertiary referral center with a specialized tinnitus clinic. A total of 135 adults diagnosed with PT and 135 age-matched controls were included. All participants were screened for PT and relevant medical history, with consecutive sampling used for selection. Cortical and subcortical brain volume differences between PT patients and controls were measured using Freesurfer. PT patients (n = 79, after exclusion of patients with inadequate imaging data) exhibited significant decreases in cortical thickness in the anterior cingulate and entorhinal cortex, and decreased volume in the left putamen, compared to age-matched controls (n = 135). PT patients also demonstrated significant increased volume in frontal and occipital lobe structures, the cerebellum, hippocampi, and ventral pallidum. In conclusion, our findings suggest that individuals with PT may have structural differences in brain regions related to auditory processing, and depression, which provides additional evidence of the psychiatric sequalae of PT. These findings demonstrate that there are neuroanatomical alterations in patients with PT, emphasizing the value in evaluating and treating this disease to prevent these neuroanatomical differences from developing.

Accelerated biological brain aging in major depressive disorder

Major depressive disorder (MDD) patients commonly encounter multiple types of functional disabilities, such as social, physical, and role functioning. MDD is related to an accreted risk of brain atrophy, aging-associated brain diseases, and mortality. Based on recently available studies, there are correlations between notable biological brain aging and MDD in adulthood. Despite several clinical and epidemiological studies that associate MDD with aging phenotypes, the underlying mechanisms in the brain remain unknown. The key areas in the study of biological brain aging in MDD are structural brain aging, impairment in functional connectivity, and the impact on cognitive function and age-related disorders. Various measurements have been used to determine the severity of brain aging, such as the brain age gap estimate (BrainAGE) or brain-predicted age difference (BrainPAD). This review summarized the current results of brain imaging data on the similarities between the manifestation of brain structural changes and the age-associated processes in MDD. This review also provided recent evidence of BrainPAD or BrainAGE scores in MDD, brain structural abnormalities, and functional connectivity, which are commonly observed between MDD and age-associated processes. It serves as a basis of current reference for future research on the potential areas of investigation for diagnostic, preventive, and potentially therapeutic purposes for brain aging in MDD.

Molecular signatures of premature aging in Major Depression and Substance Use Disorders

Major depressive disorder (MDD) and substance-use disorders (SUDs) often lead to premature aging, increasing vulnerability to cognitive decline and other forms of dementia. This study utilized advanced systems bioinformatics to identify aging "signatures" in MDD and SUDs and evaluated the potential for known lifespan-extending drugs to target and reverse these signatures. The results suggest that inhibiting the transcriptional activation of FOS gene family members holds promise in mitigating premature aging in MDD and SUDs. Conversely, antidepressant drugs activating the PI3K/Akt/mTOR pathway, a common mechanism in rapid-acting antidepressants, may accelerate aging in MDD patients, making them unsuitable for those with comorbid aging-related conditions like dementia and Alzheimer's disease. Additionally, this innovative approach identifies potential anti-aging interventions for MDD patients, such as Deferoxamine, Resveratrol, Estradiol valerate, and natural compounds like zinc acetate, genistein, and ascorbic acid, regardless of comorbid anxiety disorders. These findings illuminate the premature aging effects of MDD and SUDs and offer insights into treatment strategies for patients with comorbid aging-related conditions, including dementia and Alzheimer's disease.

Larger putamen in individuals at risk and with manifest bipolar disorder

BACKGROUND

Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

METHODS

In 410 male and female participants aged 17-35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

RESULTS

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

CONCLUSIONS

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

Neuroinflammatory Responses Occur in Brain Lesions During Alzheimer's Disease: Postmortem Case Report

BACKGROUND

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. It is characterized by a gradual decrease in cognitive function and is considered a disorder in which the intensifying neuronal loss. The autopsy is considered the gold standard for the diagnosis of AD and non-AD dementia.

OBJECTIVE

Our study aims to clarify the involvement of neuroinflammation processes in brain lesions of AD.

METHODS

The defunct was admitted to the forensic medicine department of Issad Hassani Hospital (Algeria). In order to recover the brain, an autopsy was performed within 24 hours of death and then immediately fixed in formaldehyde to maintain structural brain integrity for histological and immunohistochemical analysis.

RESULTS

Our findings indicate the presence of tissue lesions in the specific brain regions: right middle frontal gyrus, right cingulate gyrus, right putamen and globus pallidus, right caudate nucleus, right hippocampus, inferior parietal lobule, left parahippocampal gyrus, and left hippocampus. Notably, there is a predominant occurrence of lesions: granulovacuolar degeneration, Hirano bodies, cotton-wool, and neuritic plaques. The causes of neurodegenerative processes are probably related to TNF-α, IL-1β, and TGF-β production and iNOS expression by the NF-κB activation pathway in the R-HP, inducing necroptosis.

CONCLUSIONS

The occurrence of neuroinflammatory responses is linked to tissue lesions in AD. The production of inflammatory cytokines is the basis of this process, which ultimately leads to the necroptosis, which is triggered by neuroinflammation amplification. The inhibition of neuroinflammation by targeting TNF-α/iNOS could stop tissue damage, this may be a promising therapeutic pathway.

Predicting treatment response in adolescents and young adults with major depressive episodes from fMRI using graph isomorphism network

BACKGROUND

Major depressive episode (MDE) is the main clinical feature of mood disorders (major depressive disorder and bipolar disorder) in adolescents and young adults and accounts for most of the disease course. However, 30%-40% of MDE patients not responding to clinical first-line interventions. It is crucial to predict treatment response in the early stages and identify biomarkers associated with treatment response. Graph Isomorphism Network (GIN), a deep learning method, is promising for predicting treatment response for individual MDE patients with more powerful representation ability to capture the features of brain functional connectivity.

METHODS

In this study, GIN was used to predict individual treatment response in 198 adolescents and young adults with MDE. The most discriminating regions were also identified for the treatment response prediction.

RESULTS

Using GIN approach, the baseline functional connectivity could predict 79.8% responders and 67.4% non-responders to treatment (accuracy 74.24%). Furthermore, the most discriminating brain regions were mainly involved in paralimbic and subcortical areas.

CONCLUSIONS

GIN has shown potential in predicting treatment response for individual patients, which may enable personalized treatment decisions. Furthermore, targeted interventions focused on modulating the activity and connectivity within paralimbic and subcortical regions could potentially improve treatment outcomes and enable personalized interventions for adolescents and young adults with MDE.

Differences in White Matter Structural Networks in Family Risk of Major Depressive Disorder and Suicidality: A Connectome Analysis

Background

Depression and suicide are leading global causes of disability and death and are highly familial. Family and individual history of depression are associated with neurobiological differences including decreased white matter connectivity; however, this has only been shown for individual regions. We use graph theory models to account for the network structure of the brain with high levels of specialization and integration and examine whether they differ by family history of depression or of suicidality within a three-generation longitudinal family study with well-characterized clinical histories.

Methods

Clinician interviews across three generations were used to classify family risk of depression and suicidality. Then, we created weighted network models using 108 cortical and subcortical regions of interest for 96 individuals using diffusion tensor imaging derived fiber tracts. Global and local summary measures (clustering coefficient, characteristic path length, and global and local efficiencies) and network-based statistics were utilized for group comparison of family history of depression and, separately, of suicidality, adjusted for personal psychopathology.

Results

Clustering coefficient (connectivity between neighboring regions) was lower in individuals at high family risk of depression and was associated with concurrent clinical symptoms. Network-based statistics showed hypoconnected subnetworks in individuals with high family risk of depression and of suicidality, after controlling for personal psychopathology. These subnetworks highlighted cortical-subcortical connections including between the superior frontal cortex, thalamus, precuneus, and putamen.

Conclusions

Family history of depression and of suicidality are associated with hypoconnectivity between subcortical and cortical regions, suggesting brain-wide impaired information processing, even in those personally unaffected.

Regions with Altered Degree Centrality and Their Functional Connectivity in First-Episode Drug-Naïve Major Depressive Disorder: A Resting-State Functional Magnetic Resonance Imaging Study

Objective

The aim of this study was to identify regions with altered degrees of centrality (DC) and changes in their functional connectivity (FC) in first-episode drug-naïve major depressive disorder (FEDN-MDD) patients using resting-state functional magnetic resonance imaging (fMRI).

Methods

The study included 74 FEDN-MDD patients who met the study criteria and 41 healthy controls (HCs). All had undergone fMRI scanning in the resting condition. To evaluate differences between FEDN-MDD patients and HCs, we first compared the DC between the 2 groups. The DC regions with the most significant differences were then taken as seeds, and their FC was calculated.

Results

Right posterior cingulum cortex (PCC.R), right precuneus (PCUN.R), and right putamen (PUT.R) all showed significantly different DC values (P < .001) between FEDN-MDD patients and HC groups, which helped in distinguishing these groups. The PUT.R in FEDN-MDD patients showed increased FC (P < .001) with the right inferior temporal gyrus and right inferior occipital gyrus compared to HC. Moreover, the PCUN.R in FEDN-MDD patients showed decreased FC (P < .001) with bilateral cerebellum crus I, left cerebellum crus II, bilateral orbital medial frontal gyrus, right superior medial frontal gyrus, left precuneus, left posterior cingulum cortex, right superior frontal gyrus, and PCC.R compared with the HC group. The P-values for cluster testing were .050, while for voxel testing they were .001.

Conclusion

These findings imply that PUT.R, PCUN.R, and PCC.R serve as the core brain net hub in FEDN-MDD patients, and their FC displays aberrant function. This may involve a specific psychiatric neuropathology associated with FEDN-MDD.

Brain morphometric changes in fibromyalgia and the impact of psychometric and clinical factors: a volumetric and diffusion-tensor imaging study

BACKGROUND

Previous studies have repeatedly found distinct brain morphometric changes in patients with fibromyalgia (FM), mainly affecting gray and white matter abnormalities in areas related to sensory and affective pain processing. However, few studies have thus far linked different types of structural changes and not much is known about behavioral and clinical determinants that might influence the emergence and progression of such changes.

METHODS

We used voxel-based morphometry (VBM) and diffusion-tensor imaging (DTI) to detect regional patterns of (micro)structural gray (GM) and white matter (WM) alterations in 23 patients with FM compared to 21 healthy controls (HC), while considering the influence of demographic, psychometric, and clinical variables (age, symptom severity, pain duration, heat pain threshold, depression scores).

RESULTS

VBM and DTI revealed striking patterns of brain morphometric changes in FM patients. Bilateral middle temporal gyrus (MTG), parahippocampal gyrus, left dorsal anterior cingulate cortex (dACC), right putamen, right caudate nucleus, and left dorsolateral prefrontal cortex (DLPFC) showed significantly decreased GM volumes. In contrast, increased GM volume was observed in bilateral cerebellum and left thalamus. Beyond that, patients displayed microstructural changes of WM connectivity within the medial lemniscus, corpus callosum, and tracts surrounding and connecting the thalamus. Sensory-discriminative aspects of pain (pain severity, pain thresholds) primarily showed negative correlations with GM within bilateral putamen, pallidum, right midcingulate cortex (MCC), and multiple thalamic substructures, whereas the chronicity of pain was negatively correlated with GM volumes within right insular cortex and left rolandic operculum. Affective-motivational aspects of pain (depressive mood, general activity) were related to GM and FA values within bilateral putamen and thalamus.

CONCLUSIONS

Our results suggest a variety of distinct structural brain changes in FM, particularly affecting areas involved in pain and emotion processing such as the thalamus, putamen, and insula.

Associations of lesion location, structural disconnection, and functional diaschisis with depressive symptoms post stroke

Introduction

Post-stroke depressive symptoms (PSDS) are common and relevant for patient outcome, but their complex pathophysiology is ill understood. It likely involves social, psychological and biological factors. Lesion location is a readily available information in stroke patients, but it is unclear if the neurobiological substrates of PSDS are spatially localized. Building on previous analyses, we sought to determine if PSDS are associated with specific lesion locations, structural disconnection and/or localized functional diaschisis.

Methods

In a prospective observational study, we examined 270 patients with first-ever stroke with the Hospital Anxiety and Depression Scale (HADS) around 6 months post-stroke. Based on individual lesion locations and the depression subscale of the HADS we performed support vector regression lesion-symptom mapping, structural-disconnection-symptom mapping and functional lesion network-symptom-mapping, in a reanalysis of this previously published cohort to infer structure-function relationships.

Results

We found that depressive symptoms were associated with (i) lesions in the right insula, right putamen, inferior frontal gyrus and right amygdala and (ii) structural disconnection in the right temporal lobe. In contrast, we found no association with localized functional diaschisis. In addition, we were unable to confirm a previously described association between depressive symptom load and a network damage score derived from functional disconnection maps.

Discussion

Based on our results, and other recent lesion studies, we see growing evidence for a prominent role of right frontostriatal brain circuits in PSDS.

The influence of season on glutamate and GABA levels in the healthy human brain investigated by magnetic resonance spectroscopy imaging

Seasonal changes in neurotransmitter systems have been demonstrated in imaging studies and are especially noticeable in diseased states such as seasonal affective disorder (SAD). These modulatory neurotransmitters, such as serotonin, are influencing glutamatergic and GABAergic neurotransmission. Furthermore, central components of the circadian pacemaker are regulated by GABA (the suprachiasmatic nucleus) or glutamate (e.g., the retinohypothalamic tract). Therefore, we explored seasonal differences in the GABAergic and glutamatergic system in 159 healthy individuals using magnetic resonance spectroscopy imaging with a GABA-edited 3D-MEGA-LASER sequence at 3T. We quantified GABA+/tCr, GABA+/Glx, and Glx/tCr ratios (GABA+, GABA+ macromolecules; Glx, glutamate + glutamine; tCr, total creatine) in five different subcortical brain regions. Differences between time periods throughout the year, seasonal patterns, and stationarity were tested using ANCOVA models, curve fitting approaches, and unit root and stationarity tests, respectively. Finally, Spearman correlation analyses between neurotransmitter ratios within each brain region and cumulated daylight and global radiation were performed. No seasonal or monthly differences, seasonal patterns, nor significant correlations could be shown in any region or ratio. Unit root and stationarity tests showed stable patterns of GABA+/tCr, GABA+/Glx, and Glx/tCr levels throughout the year, except for hippocampal Glx/tCr. Our results indicate that neurotransmitter levels of glutamate and GABA in healthy individuals are stable throughout the year. Hence, despite the important correction for age and gender in the analyses of MRS derived GABA and glutamate, a correction for seasonality in future studies does not seem necessary. Future investigations in SAD and other psychiatric patients will be of high interest.

Altered functional coupling between the cerebellum and cerebrum in patients with amnestic mild cognitive impairment

Cognitive processing relies on the functional coupling between the cerebrum and cerebellum. However, it remains unclear how the 2 collaborate in amnestic mild cognitive impairment (aMCI) patients. With functional magnetic resonance imaging techniques, we compared cerebrocerebellar functional connectivity during the resting state (rsFC) between the aMCI and healthy control (HC) groups. Additionally, we distinguished coupling between functionally corresponding and noncorresponding areas across the cerebrum and cerebellum. The results demonstrated decreased rsFC between both functionally corresponding and noncorresponding areas, suggesting distributed deficits of cerebrocerebellar connections in aMCI patients. Increased rsFC was also observed, which were between functionally noncorresponding areas. Moreover, the increased rsFC was positively correlated with attentional scores in the aMCI group, and this effect was absent in the HC group, supporting that there exists a compensatory mechanism in patients. The current study contributes to illustrating how the cerebellum adjusts its coupling with the cerebrum in individuals with cognitive impairment.

Differences in gray matter volumes of subcortical nuclei between major depressive disorder with and without persistent depressive disorder

OBJECTIVE

This study aimed to compare the differences in gray matter volumes (GMVs) of subcortical nuclei between major depressive disorder (MDD) patients with and without persistent depressive disorder (PDD) at long-term follow-up.

METHODS

114 and 94 subjects with MDD, including 48 and 41 with comorbid PDD, were enrolled to undergo high-resolution T1-weighted imaging at first (FIP) and second (three years later, SIP) investigation points, respectively. FreeSurfer was used to extract the GMVs of seven subcortical nuclei, and Generalized Estimating Equation models were employed to estimate the differences in GMVs of subcortical nuclei between the two subgroups.

RESULTS

The PDD subgroup had a significantly greater depressive severity and a higher percentage of patients undergoing pharmacotherapy at the FIP as compared with the non-PDD subgroup. These differences became insignificant at the SIP. The PDD subgroup had a significantly (p < 0.003) smaller GMV in the right putamen at the SIP and in the right nucleus accumbens (NAc) at the FIP and SIP as compared with the non-PDD subgroup. After controlling for clinical variables, PDD was independently associated with smaller GMVs in the right putamen and NAc.

LIMITATIONS

Imaging was not performed at baseline and pharmacotherapy was not controlled at the FIP and SIP.

CONCLUSIONS

MDD with PDD was associated with smaller GMVs in the right putamen and NAc as compared with MDD without PDD. Whether the two regions are biomarkers related to a poor prognosis and the chronicity of depression requires further study.

Electrophysiological biomarkers and age characterize phenotypic heterogeneity among individuals with major depressive disorder

Introduction: Despite the high need for effective treatments for major depressive disorder (MDD), the development of novel medicines is hampered by clinical, genetic and biological heterogeneity, unclear links between symptoms and neural dysfunction, and tenuous biomarkers for clinical trial contexts of use. Methods: In this study, we examined the International Study to Predict Optimized Treatment in Depression (iSPOT-D) clinical trial database for new relationships between auditory event-related potential (ERP) responses, demographic features, and clinical symptoms and behavior, to inform strategies for biomarker-driven patient stratification that could be used to optimize future clinical trial design and drug development strategy in MDD. Results: We replicate findings from previous analyses of the classic auditory oddball task in the iSPOT-D sample showing smaller than typical N1 and P300 response amplitudes and longer P300 latencies for target and standard stimuli in patients with MDD, suggesting altered bottom-up sensory and top-down attentional processes. We further demonstrate that age is an important contributor to clinical group differences, affecting both topographic distribution of the clinically informative ERP responses and the types of the stimuli sensitive to group differences. In addition, the observed brain-behavior associations indicate that levels of anxiety and stress are major contributing factors to atypical sensory and attentional processing among patients with MDD, particularly in the older subgroups. Discussion: Our novel findings support the possibility of accelerated cognitive aging in patients with MDD and identify the frontal P300 latency as an additional candidate biomarker of MDD. These results from a large, well-phenotyped sample support the view that heterogeneity of the clinical population with MDD can be systematically characterized based on age and neural biomarkers of sensory and attentional processing, informing patient stratification strategies in the design of clinical trials.

Family income buffers the relationship between childhood adverse experiences and putamen volume

Adverse experiences and family income in childhood have been associated with altered brain development. While there is a large body of research examining these associations, it has primarily used cross-sectional data sources and studied adverse experiences and family income in isolation. However, it is possible that low family income and adverse experiences represent dissociable and potentially interacting profiles of risk. To address this gap in the literature, we examined brain structure as a function of adverse experiences in childhood and family income in 158 youths with up to five waves of MRI data. Specifically, we assessed the interactive effect of these two risk factors on six regions of interest: hippocampus, putamen, amygdala, nucleus accumbens, caudate, and thalamus. Adverse experiences and family income interacted to predict putamen volume (B = 0.086, p = 0.011) but only in participants with family income one standard deviation below the mean (slope estimate = -0.11, p = 0.03). These results suggest that adverse experiences in childhood result in distinct patterns of brain development across the socioeconomic gradient. Given previous findings implicating the role of the putamen in psychopathology-related behaviors, these results emphasize the importance of considering life events and socioeconomic context when evaluating markers of risk. Future research should include interactive effects of environmental exposures and family income to better characterize risk for psychopathology in diverse samples.

Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants

Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.

Quantitative Susceptibility Mapping of Brain Iron Deposition in Patients With Recurrent Depression

OBJECTIVE

Recurrence is the most significant feature of depression and the relationship between iron and recurrent depression is still lack of direct evidence in vivo.

METHODS

Twenty-one patients with depression and twenty control subjects were included. Gradient-recalled echo, T1 and T2 images were acquired using a 3.0T MRI system. After quantitative susceptibility mapping were reconstructed and standardized, a whole-brain and the regions of interest were respectively analyzed.

RESULTS

Significant increases in susceptibility were found in multiple recurrent depression patients, which involved several brain regions (frontal lobes, temporal lobe structures, occipital lobes hippocampal regions, putamen, thalamus, cingulum, and cerebellum). Interestingly, no susceptibility changes after treatment compared to pre-treatment (all p>0.05) and no significant correlation between susceptibility and Hamilton Depression Rating Scale were found. Besides, it was close to significance that those with a higher relapse frequency or a longer mean duration of single episode had a higher susceptibility in the putamen, thalamus, and hippocampus. Further studies showed susceptibility across the putamen (ρ2=0.27, p<0.001), thalamus (ρ2=0.21, p<0.001), and hippocampus (ρ2=0.19, p<0.001) were strongly correlated with total course of disease onset.

CONCLUSION

Brain iron deposition is related to the total course of disease onset, but not the severity of depression, which suggest that brain iron deposition may be a sign of brain damage in multiple recurrent depression.

Timing and Type of Early Psychopathology Symptoms Predict Longitudinal Change in Cortical Thickness From Middle Childhood Into Early Adolescence

BACKGROUND

Early-life experiences have profound effects on functioning in adulthood. Altered cortical development may be one mechanism through which early-life experiences, including poverty and psychopathology symptoms, affect outcomes. However, there is little prospective research beginning early in development that combines clinician-rated psychopathology symptoms and multiwave magnetic resonance imaging to examine when these relationships emerge.

METHODS

Children from the Preschool Depression Study who completed diagnostic interviews at three different developmental stages (preschool, school age, early adolescent) and up to three magnetic resonance imaging scans beginning in middle childhood participated in this study (N = 138). Multilevel models were used to calculate intercepts and slopes of cortical thickness within a priori cortical regions of interest. Linear regressions probed how early-life poverty and psychopathology (depression, anxiety, and externalizing symptoms at separate developmental periods) related to intercept/slope.

RESULTS

Collectively, experiences during the preschool period predicted reduced cortical thickness, via either reduced intercept or accelerated thinning (slope). Early-life poverty predicted intercepts within sensory and sensory-motor integration regions. Beyond poverty, preschool anxiety symptoms predicted intercepts within the insula, subgenual cingulate, and inferior parietal cortex. Preschool externalizing symptoms predicted accelerated thinning within prefrontal and parietal cortices. Depression and anxiety/externalizing symptoms at later ages were not significant predictors.

CONCLUSIONS

Early childhood is a critical period of risk; experiences at this developmental stage specifically have the potential for prolonged influence on brain development. Negative early experiences collectively predicted reduced cortical thickness, but the specific neural systems affected aligned with those typically implicated in these individual disorders/experiences.

Neural substrates of rewarding and punishing self representations in depressed suicide-attempting adolescents

Adolescence is a period of plasticity in neural substrates underpinning self-processing. Such substrates are worth studying in depressed youth at risks for suicide because altered neurobiology of self-processing might partially explain differences between suicide attempting youth versus youth who contemplate but do not attempt suicide. Understanding altered substrates of self-processing among depressed adolescents with suicide attempts is critical for developing targeted prevention and treatment. Healthy youth (N = 40), youth with depression and low (N = 33) or high suicide ideation (N = 28), and youth with depression and past suicide attempt (N = 28) heard positive or negative self-descriptors during fMRI and evaluated them from their own, their mother's, classmates', and best friend's perspectives. Lower bilateral caudate activity during positive self-processing distinguished suicide attempting adolescents from all other youth. Higher bilateral caudate activity during negatively valenced self-processing tended to distinguish youth with depression. Blunted reward circuitry during positive vs. negative self-related material tended to distinguish suicide attempting youth, reflecting potentially enhanced behavioral preparedness for punishing vs. rewarding self-relevant cues.

Assessing the joint effects of brain aging and gut microbiota on the risks of psychiatric disorders

We aim to explore the potential interaction effects of brain aging and gut microbiota on the risks of sleep, anxiety and depression disorders. The genome-wide association study (GWAS) datasets of brain aging (N = 21,407) and gut microbiota (N = 3,890) were obtained from published studies. Individual level genotype and phenotype data of psychiatric traits (including sleep, anxiety and depression) were all from the UK Biobank (N = 107,947-374,505). We first calculated the polygenic risk scores (PRS) of 62 brain aging modes and 114 gut microbiota taxa as the instrumental variables, and then constructed linear and logistic regression analyses to systematically explore the potential interaction effects of brain aging and gut microbiota on psychiatric disorders. We observed the interaction effects of brain aging and gut microbiota on sleep, anxiety and depression disorders, such as Putamen/caudate T2* vs. Rhodospirillales (β = -0.012, P = 8.4 × 10^-4) was negatively associated with chronotype, Fornix MD vs. Holdemanella (β = -0.007, P = 1.76 × 10^-2) was negatively related to general anxiety disorder (GAD) scores, and White matter lesions vs. Acidaminococcaceae (β = 0.019, P = 1.29 × 10^-3) was positively correlated with self-reported depression. Interestingly, Putamen volume vs. Intestinibacter was associated with all three psychiatric disorders, including chronotype (negative correlation), GAD scores (positive correlation) and self-reported depression (positive correlation). Our study results suggest the significant impacts of brain aging and gut microbiota on the development of sleep, anxiety and depression disorders, providing new clues for clarifying the pathogenesis of these disorders.

Age-Related Decrease in Default-Mode Network Functional Connectivity Is Accelerated in Patients With Major Depressive Disorder

Major depressive disorder (MDD) is a common psychiatric disorder which is associated with an accelerated biological aging. However, little is known whether such process would be reflected by a more rapid aging of the brain function. In this study, we tested the hypothesis that MDD would be characterized by accelerated aging of the brain's default-mode network (DMN) functions. Resting-state functional magnetic resonance imaging data of 971 MDD patients and 902 healthy controls (HCs) was analyzed, which was drawn from a publicly accessible, multicenter dataset in China. Strength of functional connectivity (FC) and temporal variability of dynamic functional connectivity (dFC) within the DMN were calculated. Age-related effects on FC/dFC were estimated by linear regression models with age, diagnosis, and diagnosis-by-age interaction as variables of interest, controlling for sex, education, site, and head motion effects. The regression models revealed (1) a significant main effect of age in the predictions of both FC strength and dFC variability; and (2) a significant main effect of diagnosis and a significant diagnosis-by-age interaction in the prediction of FC strength, which was driven by stronger negative correlation between age and FC strength in MDD patients. Our results suggest that (1) both healthy participants and MDD patients experience decrease in DMN FC strength and increase in DMN dFC variability along age; and (2) age-related decrease in DMN FC strength may occur at a faster rate in MDD patients than in HCs. However, further longitudinal studies are still needed to understand the causation between MDD and accelerated aging of brain.

Brain age in mood and psychotic disorders: a systematic review and meta-analysis

OBJECTIVE

To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders.

METHODS

A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder.

RESULTS

A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals.

CONCLUSION

Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.

Statistical shape analysis of putamen in early-onset Parkinson's disease

OBJECTIVE

To investigate the shape differences in the putamen of early-onset Parkinson's patients compared with healthy controls and to assess and to assess sub-regional brain abnormalities.

METHODS

This study was conducted using the 3-T MRI scans of 23 early-onset Parkinson's patients and age and gender matched control subjects. Landmark coordinate data obtained and Procrustes analysis was used to compare mean shapes. The relationships between the centroid sizes of the left and right putamen, and the durations of disease examined using growth curve models.

RESULTS

While there was a significant difference between the right putamen shape of control and patient groups, there was not found a significant difference in terms of left putamen. Sub-regional analyses showed that for the right putamen, the most prominent deformations were localized in the middle-posterior putamen and minimal deformations were seen in the anterior putamen.

CONCLUSION

Although they were not as pronounced as those in the right putamen, the deformations in the left putamen mimic the deformations in the right putamen which are found mainly in the middle-posterior putamen and at a lesser extend in the anterior putamen.

Exploring the Impact of Cerebrovascular Disease and Major Depression on Non-diseased Human Tissue Transcriptomes

Background

The development of complex diseases is contributed by the combination of multiple factors and complicated interactions between them. Inflammation has recently been associated with many complex diseases and may cause long-term damage to the human body. In this study, we examined whether two types of complex disease, cerebrovascular disease (CVD) or major depression (MD), systematically altered the transcriptomes of non-diseased human tissues and whether inflammation is linked to identifiable molecular signatures, using post-mortem samples from the Genotype-Tissue Expression (GTEx) project.

Results

Following a series of differential expression analyses, dozens to hundreds of differentially expressed genes (DEGs) were identified in multiple tissues between subjects with and without a history of CVD or MD. DEGs from these disease-associated tissues-the visceral adipose, tibial artery, caudate, and spinal cord for CVD; and the hypothalamus, putamen, and spinal cord for MD-were further analyzed for functional enrichment. Many pathways associated with immunological events were enriched in the upregulated DEGs of the CVD-associated tissues, as were the neurological and metabolic pathways in DEGs of the MD-associated tissues. Eight gene-tissue pairs were found to overlap with those prioritized by our transcriptome-wide association studies, indicating a potential genetic effect on gene expression for circulating cytokine phenotypes.

Conclusion

Cerebrovascular disease and major depression cause detectable changes in the gene expression of non-diseased tissues, suggesting that a possible long-term impact of diseases, lifestyles and environmental factors may together contribute to the appearance of "transcriptomic scars" on the human body. Furthermore, inflammation is probably one of the systemic and long-lasting effects of cerebrovascular events.

Aberrant Development of Cross-Frequency Multiplex Functional Connectome in First-Episode, Drug-Naive Major Depressive Disorder and Schizophrenia

BACKGROUND

Major depressive disorder (MDD) and schizophrenia (SCH) are both characterized by neurodevelopmental abnormalities; however, transdiagnostic and diagnosis-specific patterns of such abnormalities have rarely been examined, particularly in large-scale functional brain networks via advanced multilayer network models.

METHODS

Here we collected resting-state functional MRI data from 45 MDD patients, 64 SCH patients and 48 healthy controls (13-45 years old), and constructed functional networks in different frequency intervals. The frequency-dependent networks were then fused by multiplex network models, followed by graph-based topological analyses.

RESULTS

We found that functional networks of the patients showed common neurodevelopmental abnormalities in the right ventromedial parietooccipital sulcus (opposite correlations with age to healthy controls), while functional networks of the MDD patients exhibited specific alterations in the left superior parietal lobule and right precentral gyrus with respect to cross-frequency interactions. These findings were quite different from those from brain networks within each frequency interval, which revealed SCH-specific neurodevelopmental abnormalities in the right superior temporal gyrus (opposite correlations with age to the other two groups) in 0.027-0.073 Hz, and SCH-specific alterations in the left superior temporal gyrus and bilateral insula in 0.073-0.198 Hz. Finally, multivariate analysis of age prediction revealed that the subcortical network lost predict ability in both patient groups, while the visual network exhibited additional prediction ability in the MDD patients.

DISCUSSION AND CONCLUSION

Altogether, these findings demonstrate transdiagnostic and diagnosis-specific neurodevelopmental abnormalities and alterations in large-scale functional brain networks between MDD and SCH, which have important implications for understanding shared and unique neural mechanisms underlying the diseases.

Accelerated aging signatures in subjects with schizophrenia and their unaffected siblings

Schizophrenia (SZ) is a chronic debilitating disease. Subjects with SZ have significant shorter life expectancy. Growing evidence suggests that a process of pathological accelerated aging occurs in SZ, leading to early development of severe clinical diseases and worse morbimortality. Furthermore, unaffected relatives can share certain endophenotypes with subjects with SZ. We aim to characterize accelerated aging as a possible endophenotype of schizophrenia by using a machine learning (ML) model of peripheral biomarkers to accurately differentiate subjects with SZ (n = 35), their unaffected siblings (SB, n = 36) and healthy controls (HC, n = 47). We used a random forest algorithm that included biomarkers related to aging: eotaxins CCL-11 and CCL-24; the oxidative stress markers thiobarbituric acid-reactive substances (TBARS), protein carbonyl content (PCC), glutathione peroxidase (GPx); and telomere length (TL). The ML algorithm of biomarkers was able to distinguish individuals with SZ from HC with prediction accuracy of 79.7%, SZ from SB with 62.5% accuracy and SB from HC with 75.5% accuracy. These results support the hypothesis that a pathological accelerated aging might occur in SZ, and this pathological aging could be an endophenotype of the disease, once this profile was also observed in SB, suggesting that SB might suffer from an accelerated aging in some level.

Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder

Background

Major depressive disorder (MDD) treatment is characterized by low remission rate and often involves weeks to months of treatment. Identification of pretreatment biomarkers of response may play a critical role in novel drug development, in enhanced prognostic predictions, and perhaps in providing more personalized medicine. Using a network restricted strength predictive modeling (NRS-PM) approach, the goal of the current study was to identify pretreatment functional connectome fingerprints (CFPs) that (1) predict symptom improvement regardless of treatment modality and (2) predict treatment specific improvement.

Methods

Functional magnetic resonance imaging and behavioral data from unmedicated patients with MDD (n = 200) were investigated. Participants were randomized to daily treatment of sertraline or placebo for 8 weeks. NRS-PM with 1000 iterations of 10 cross-validation were implemented to identify brain connectivity signatures that predict percent improvement in depression severity at week-8.

Results

The study identified a pretreatment CFP that significantly predicts symptom improvement independent of treatment modality but failed to identify a treatment specific CFP. Regardless of treatment modality, improved antidepressant response was predicted by high pretreatment connectivity between modules in the default mode network and the rest of the brain, but low external connectivity in the executive network. Moreover, high pretreatment internal nodal connectivity in the bilateral caudate predicted better response.

Conclusions

The identified CFP may contribute to drug development and ultimately to enhanced prognostic predictions. However, the results do not assist with providing personalized medicine, as pretreatment functional connectivity failed to predict treatment specific response.

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.

Multi-dimensional predictions of psychotic symptoms via machine learning

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.

Age-related atrophy of cortical thickness and genetic effect of ANK3 gene in first episode MDD patients

Brain ageing is thought to be related to geriatric depression, but the relationship between ageing and depression among middle aged individuals is unknown. The present study aimed to evaluate whether the age-related reduction of brain cortical thickness (CT) can be found in adult first-episode MDD patients, as well as to identify the possible genetic effect of the ANK3 gene polymorphism age-relates CT reduction. This study recruited 153 first-episode MDD patients with a disease duration < 2 years and 276 healthy controls (HC), and the CT of 68 whole brain regions and two ANK3 SNPs (rs1994336 and rs10994359) were analyzed. The results showed that although the CT of both groups was negative correlated with age, the MDD group had significant greater age-related decrease in CT than the HC group (–9.35 × 10⁻³ mm/year for MDD vs. –1.23 × 10⁻³ mm/year for HC in the left lateral orbitofrontal lobe). The multivariate analysis of covariance (MANCOVA) results yielded significant interactions of diagnosis × age, genotype × age and diagnosis × genotype interaction for rs10994359. In HC, the C allele showed a protective effect on age-related CT reduction. The reduction in CT with age was several times as greater in non-C carriers as in C carriers (–3.54 × 10⁻³ vs.–0.15 × 10⁻³ mm/year in left supramarginal gyrus) for HC. However, this protective effect disappeared in patients with MDD. We did not find a clear effect of rs1994336 on the age-related CT reduction. The findings indicate that the widespread accelerated brain ageing occurs early in adult-onset depression and this ageing may be a pathological mechanisms of depression rather than an outcome of the disease. The ANK3 rs10994359 polymorphism may partially affect regional cortical ageing in MDD.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Repetitive Transcranial Magnetic Stimulation Improves Amygdale Functional Connectivity in Major Depressive Disorder

Emotional abnormality in major depressive disorder (MDD) is generally regarded to be associated with functional dysregulation in the affective network (AN). The present study examined the changes in characteristics of AN connectivity of MDD patients before and after repetitive transcranial magnetic stimulation (rTMS) treatment over the left dorsolateral prefrontal cortex, and to further assess how these connectivity changes are linked to clinical characteristics of patients. Functional connectivity (FC) in the AN defined by placing seeds in the bilateral amygdale was calculated in 20 patients with MDD before and after rTMS, and in 20 healthy controls (CN). Furthermore, a linear regression model was used to obtain correlations between FC changes and Hamilton depression scale (HAMD) changes in MDD before and after rTMS. Before rTMS, compared with CN, MDD exhibited significantly lower FC between left insula (INS.L), right superior and inferior frontal gyrus (SFG.R and IFG.R), right inferior parietal lobule (IPL.R), and amygdala, and showed an increment of FC between the bilateral precuneus and amygdala in AN. After rTMS, MDD exhibited a significant increase in FC in the INS.L, IFG.R, SFG.R, IPL.R, and a significant reduction in FC in the precuneus. Interestingly, change in FC between INS.L and left amygdala was positively correlated with change in HAMD scores before and after rTMS treatment. rTMS can enhance affective network connectivity in MDD patients, which is linked to emotional improvement. This study further suggests that the insula may be a potential target region of clinical efficacy for MDD to design rationale strategies for therapeutic trials.

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group

Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Association of anxiety with subcortical amyloidosis in cognitively normal older adults

Late-life anxiety has been associated with increased progression from normal cognition to amnestic MCI, suggesting that anxiety may be a neuropsychiatric symptom of Alzheimer's disease (AD) pathological changes and a possible marker of anatomical progression in preclinical AD. This study examined whether cortical or subcortical amyloidosis, indicating earlier or later stages of preclinical AD, was associated with greater self-reported anxiety among 118 cognitively normal volunteers, aged 65-90 years, and whether this association was stronger in APOEε4 carriers. Participants underwent Pittsburgh Compound B Positron Emission Tomography (PiB-PET) to assess fibrillar amyloid-β burden in cortical and subcortical regions, and measurement of anxiety using the Hospital Anxiety and Depression Scale-anxiety subscale. Higher PiB-PET measures in the subcortex (striatum, amygdala, and thalamus), but not in the cortex, were associated with greater anxiety, adjusting for demographics, cognition, and depression. Findings were similar using a cortico-striatal staging system and continuous PET measurements. Anxiety was highest in APOEε4 carriers with subcortical amyloidosis. This work supports in vivo staging of amyloid-β deposition in both cortical and subcortical regions as a promising approach to the study of neuropsychiatric symptoms such as anxiety in cognitively normal older individuals. Elevated anxiety symptoms in combination with high-risk biological factors such as APOEε4 and subcortical amyloid-β may identify participants closest to MCI for secondary prevention trials.

The rise and fall of MRI studies in major depressive disorder

Structural and functional brain alterations are common in patients with major depressive disorder (MDD). In this review, we assessed the recent literature (1995-2018) on the structural and functional magnetic resonance imaging (MRI) studies of MDD. Despite the growing number of MRI studies on MDD, reverse inference is not possible as MRI scans cannot be used to aid in the diagnosis or treatment planning of patients with MDD. Hence, researchers must develop "bridges" to overcome the reverse inference fallacy in order to build effective tools for MDD diagnostics. From our findings, we proposed that the "bridges" may be built using multidisciplinary technologies, such as artificial intelligence, multimodality imaging, and nanotheranostics, allowing for the further study of MDD at the biological level. In return, the "bridges" will aid in the development of future diagnostics for MDD and other mental disorders.

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging

Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the BrainAGE (brain age estimation gap) framework allows machine-learning based detection of a gap between age estimated from high-resolution MRI scans an chronological age, and thus an indicator of systems-level accelerated aging. We analysed 3T high-resolution structural MRI scans in 38 major depression patients (without co-morbid axis I or II disorders) and 40 healthy controls using the BrainAGE method to test the hypothesis of accelerated aging in (non-psychotic) major depression. We found no significant difference (or trend) for elevated BrainAGE in this pilot sample. Unlike previous findings in schizophrenia (and partially bipolar disorder), unipolar depression per se does not seem to be associated with accelerated aging patterns across the brain. However, given the limitations of the sample, further study is needed to test for effects in subgroups with comorbidities, as well as longitudinal designs.

Abnormal coupling among spontaneous brain activity metrics and cognitive deficits in major depressive disorder

BACKGROUND

A variety of functional metrics derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been employed to explore spontaneous brain activity changes in major depressive disorder (MDD) and have enjoyed significant success in unraveling the neurobiological mechanisms underlying this disorder. However, it is unclear whether spatial and temporal coupling relationships among these rs-fMRI metrics are altered in MDD.

METHODS

50 patients with MDD and 36 well-matched healthy controls underwent rs-fMRI scans. A dynamic analysis was applied to compute multiple frequently used metrics including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity. Kendall's W was used to calculate volume-wise (across voxels) and voxel-wise (across time windows) concordance among these metrics. Inter-group differences in the concordance and their associations with clinical and cognitive variables were tested.

RESULTS

Compared to healthy controls, patients with MDD showed decreased whole gray matter volume-wise concordance. Despite similar spatial distributions, quantitative comparison analysis revealed that MDD patients exhibited reduced voxel-wise concordance in multiple cortical and subcortical regions. Moreover, the lower concordance was associated with worse performances in prospective memory and sustained attention in the MDD group.

LIMITATIONS

The study design of fairly modest sample size did not allow us to perform a full analysis of the potential effects of medication and illness duration.

CONCLUSIONS

Our findings suggest that spatial and temporal decoupling of multiple resting-state brain activity metrics may help elucidate the neural mechanisms of cognitive deficits in depression.

Putamen gray matter volumes in neuropsychiatric and neurodegenerative disorders

Putamen is enriched with dopamine and associated with dopamine-related phenotypes including many neuropsychiatric and neurodegenerative disorders that manifest with motor impairment, impulsive behavior, and cognitive deficits. The gray matter volume of the putamen is age-dependent and genetically controlled. In most neuropsychiatric and neurodegenerative disorders, including Parkinson's spectrum disorders, Huntington's disease, dementia with Lewy bodies, Alzheimer's disease, multiple sclerosis, attention deficit hyperactivity disorder, developmental dyslexia, and major depression, the putamen volume is significantly reduced. On the other hand, in individuals with bipolar disorder, schizophrenia spectrum disorders, especially neuroleptics-medicated patients with schizophrenia, autism spectrum disorders, obsessive-compulsive spectrum disorders, and cocaine/amphetamine dependence, the putamen volume is significantly enlarged. Therefore, the putamen volume may serve as a structural neural marker for many neuropsychiatric and neurodegenerative disorders and a predictor of treatment outcomes in individuals afflicted with these conditions. We provided an overview of the genetic bases of putamen volume and explored potential mechanisms whereby altered putamen volume manifests in these neuropsychiatric and neurodegenerative conditions, with a specific focus on dopaminergic processes.

Accelerated aging in schizophrenia and related disorders: Future research

Several lines of evidence suggest schizophrenia is a segmental progeria, that is, some but not all aspects of accelerated aging may be present. However, the evidence has not been consistent. Problems with matching and confounding may account for some of these discrepancies. Given the etiopathophysiological heterogeneity of schizophrenia, it is possible that only a specific pathophysiological group within schizophrenia is associated with progeroid features, while others are not, or that one group is associated with a particular segment of aging features, while other progeroid features are found in another pathophysiological subgroup. In the aging research field, significant progress has been made in identifying the molecular pathways that confer aging: epigenetic changes, inflammation, proteostasis, adult stem cell function, metabolic changes, and adaptation to stress, and macromolecular damage. In addition to replication and clarification of existing kinds of evidence, examining these aging pathways would improve our understanding of progeria in schizophrenia.

Diffusion MRI and MR spectroscopy reveal microstructural and metabolic brain alterations in chronic mild stress exposed rats: A CMS recovery study

Chronic mild stress (CMS) induced depression elicits several debilitating symptoms and causes a significant economic burden on society. High variability in the symptomatology of depression poses substantial impediment to accurate diagnosis and therapy outcome. CMS exposure induces significant metabolic and microstructural alterations in the hippocampus (HP), prefrontal cortex (PFC), caudate-putamen (CP) and amygdala (AM), however, recovery from these maladaptive changes are limited and this may provide negative effects on the therapeutic treatment and management of depression. The present study utilized anhedonic rats from the unpredictable CMS model of depression to study metabolic recovery in the ventral hippocampus (vHP) and microstructural recovery in the HP, AM, CP, and PFC. The study employed 1H MR spectroscopy (1H MRS) and in-vivo diffusion MRI (d-MRI) at the age of week 18 (week 1 post CMS exposure) week 20 (week 3 post CMS) and week 25 (week 8 post CMS exposure) in the anhedonic group, and at the age of week 18 and week 22 in the control group. The d-MRI data have provided an array of diffusion tensor metrics (FA, MD, AD, and RD), and fast kurtosis metrics (MKT, WL and WT). CMS exposure induced a significant metabolic alteration in vHP, and significant microstructural alterations were observed in the HP, AM, and PFC in comparison to the age match control and within the anhedonic group. A significantly high level of N-acetylaspartate (NAA) was observed in vHP at the age of week 18 in comparison to age match control and week 20 and week 25 of the anhedonic group. HP and AM showed significant microstructural alterations up to the age of week 22 in the anhedonic group. PFC showed significant microstructural alterations only at the age of week 18, however, most of the metrics showed significantly higher value at the age of week 20 in the anhedonic group. The significantly increased NAA concentration may indicate impaired catabolism due to astrogliosis or oxidative stress. The significantly increased WL in the AM and HP may indicate hypertrophy of AM and reduced volume of HP. Such metabolic and microstructural alterations could be useful in disease diagnosis and follow-up treatment intervention in depression and similar disorders.

Putamen Volume Differences Among Older Adults: Depression Status, Melancholia, and Age

BACKGROUND

Individuals with major depressive disorder (MDD) may exhibit smaller striatal volumes reflecting deficits in the reward circuit. Deficits may change with age and be more pronounced among the melancholic subtype. Limited research has investigated striatal volume differences in older adults and by depression subtypes.

METHOD

We used baseline data from the Neurocognitive Outcomes of Depression in the Elderly study. We examined volumetric differences in the putamen and caudate nucleus among older adults (60 years and older), comparing healthy control participants (n = 134) to depressed participants (n = 226), and comparing nonmelancholic depressed participants (n = 93) to melancholic depressed participants (n = 133). Group-by-age interactions were examined.

RESULTS

There were no significant group differences for the caudate nucleus. For the left putamen, investigation of the significant group-by-age interaction revealed that volume size was greater for the healthy controls compared to the depressed participants but only at younger ages (60-65 years); group differences diminished with increasing age. Examining volume by depression subtype revealed that the melancholic depressed participants had a smaller left putamen compared to the nonmelancholic depressed participants. Anhedonia symptoms were related to both smaller left and right putamen.

CONCLUSION

Structural abnormalities in reward regions may underlie the anhedonic phenotype. Volume loss associated with MDD may attenuate in older age.

Distance-dependent alterations in local functional connectivity in drug-naive major depressive disorder

Previous studies using resting-state functional magnetic resonance imaging (fMRI) have found abnormal functional connectivity in patients with major depressive disorder (MDD). Yet, effect of distance thresholds on local functional connectivity changes in MDD is largely unknown. Here, we used resting-state fMRI data and functional connectivity strength (FCS) method to test local functional connectivity differences at different distance thresholds between 47 drug-naive patients with MDD and 47 healthy controls. For the distribution of functional brain hubs with high local FCS, the overall changing trend from distance thresholds of 10mm to 100mm was from lateral to medial. Compared to controls, MDD patients exhibited decreased local FCS independent of distance threshold in the sensorimotor system (postcentral gyrus, paracentral lobule, and supplementary motor area). MDD Patients exhibited increased local FCS in the inferior temporal gyrus at two lower distance thresholds (20mm and 30mm) and a higher distance threshold (100mm). In addition, MDD patients showed increased local FCS in the putamen at higher distance thresholds (80-100mm). These findings suggest that local functional connectivity abnormalities in MDD are dependent on distance thresholds and that future studies should take the distance thresholds into account when measuring local functional connectivity in MDD.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

Quantitative Susceptibility Mapping Reveals an Association between Brain Iron Load and Depression Severity

Previous studies have detected abnormal serum ferritin levels in patients with depression; however, the results have been inconsistent. This study used quantitative susceptibility mapping (QSM) for the first time to examine brain iron concentration in depressed patients and evaluated whether it is related to severity. We included three groups of age- and gender-matched participants: 30 patients with mild-moderate depression (MD), 14 patients with major depression disorder (MDD) and 20 control subjects. All participants underwent MR scans with a 3D gradient-echo sequence reconstructing for QSM and performed the 17-item Hamilton Depression Rating Scale (HDRS) test. In MDD, the susceptibility value in the bilateral putamen was significantly increased compared with MD or control subjects. In addition, a significant difference was also observed in the left thalamus in MDD patients compared with controls. However, the susceptibility values did not differ between MD patients and controls. The susceptibility values positively correlated with the severity of depression as indicated by the HDRS scores. Our results provide evidence that brain iron deposition may be associated with depression and may even be a biomarker for investigating the pathophysiological mechanism of depression.

Myelination of the brain in Major Depressive Disorder: An in vivo quantitative magnetic resonance imaging study

Evidence from post-mortem, genetic, neuroimaging, and non-human animal research suggests that Major Depressive Disorder (MDD) is associated with abnormalities in brain myelin content. Brain regions implicated in this research, and in MDD more generally, include the nucleus accumbens (NAcc), lateral prefrontal cortex (LPFC), insula, subgenual anterior cingulate cortex (sgACC), and medial prefrontal cortex (mPFC). We examined whether MDD is characterized by reduced myelin at the whole-brain level and in NAcc, LPFC, insula, sgACC, and mPFC. Quantitative magnetic resonance imaging (qMRI) permits the assessment of myelin content, in vivo, in the human brain through the measure of R1. In this study we used qMRI to measure R1 in 40 MDD and 40 healthy control (CTL) participants. We found that the MDD participants had lower levels of myelin than did the CTL participants at the whole-brain level and in the NAcc, and that myelin in the LPFC was reduced in MDD participants who had experienced a greater number of depressive episodes. Although further research is needed to elucidate the role of myelin in affecting emotional, cognitive, behavioral, and clinical aspects of MDD, the current study provides important new evidence that a fundamental property of brain composition, myelin, is altered in this disorder.