A brain network model for depression: From symptom understanding to disease intervention

EEG coherences of the default mode network among patients comorbid with major depressive disorder and anxiety symptoms

BACKGROUND

Higher functional connectivity within the default mode network (DMN) has been found in functional magnetic resonance imaging (fMRI) studies of major depressive disorder (MDD). We used electroencephalogram (EEG) coherence as an index of functional connectivity to examine group differences in DMN between the MDD and healthy control (HC) groups during the resting state.

METHODS

MDD patients with comorbid anxiety symptoms (n = 154) and healthy controls (n = 165) completed the questionnaires of depression, anxiety, and rumination. A 19-channel EEG recording was measured under resting state for all participants. EEG coherences of the delta, theta, alpha, beta, and high beta in the anterior DMN (aDMN), posterior DMN (pDMN), aDMN-pDMN, DMN-parahippocampal gyrus (PHG), and DMN-temporal gyrus were compared between the two groups. The correlations between rumination, anxiety, and DMN coherence were examined in the MDD group.

RESULTS

(1) No difference was found in the delta, theta, alpha, and beta within the DMN brain regions between the two groups; the MDD group showed higher high beta coherence within DMN brain regions than the HC group. (2) Rumination was negatively correlated with theta coherence of aDMN, and positively correlated with beta coherence of aDMN and with alpha coherence of pDMN and DMN-PHG. (3) Anxiety was positively correlated with high beta coherence of aDMN, pDMN, and DMN-PHG.

CONCLUSIONS

MDD patients with comorbid anxiety symptoms exhibited hypercoherence within the DMN brain regions. Hypercoherences were related to symptoms of rumination, and anxiety may be a biomarker for MDD patients with comorbid anxiety symptoms.

Altered sleep onset transition in depression: Evidence from EEG activity and EEG functional connectivity analyses

OBJECTIVE

Sleep disorders constitute a principal diagnostic criterion for depression, potentially reflecting the abnormal persistence of brain activity during the sleep onset (SO) transition. Here, we sought to explore the differences in the dynamic changes in the EEG activity and the EEG functional connectivity (FC) during the SO transition in depressed patients.

METHODS

Overnight polysomnography recordings were obtained from thirty-two depressed patients and thirty-three healthy controls. The multiscale permutation entropy (MSPE) and EEG relative power were extracted to characterize EEG activity, and weighted phase lag index (WPLI) was calculated to characterize EEG FC.

RESULTS

The intergroup differences in EEG activity of relative power and MSPE were reversed near SO, which attributed to slower rates of change among depressed patients. Regarding the characteristics of the EEG FC network, depressed patients exhibited significantly higher inter-hemispheric and interregional WPLI values in both delta and alpha bands throughout the SO transition, concomitant with different dynamic properties in the delta band FC. During the process after SO, patients exhibited increased inter-hemispheric long-range links, whereas controls showed more intra-hemispheric ones. Finally, we found significant correlations in the dynamic changes between different EEG measures.

CONCLUSIONS

Our research revealed that the abnormal changes during the SO transition in depressed patients were manifested in both homeostatic and dynamic aspects, which were reflected in EEG FC and EEG activity, respectively.

SIGNIFICANCE

These findings may elucidate the mechanism underlying sleep disorders in depression from the perspective of neural activity.

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Effect of Low-Intensity Transcranial Focused Ultrasound Stimulation in Patients With Major Depressive Disorder: A Randomized, Double-Blind, Sham-Controlled Clinical Trial

OBJECTIVE

Low-intensity transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive brain stimulation modality with high spatial selectivity and the ability to reach deep brain areas. The present study aimed to investigate the safety and effectiveness of low-intensity tFUS in treating major depressive disorder.

METHODS

Participants were recruited in an outpatient clinic and randomly assigned to either the verum tFUS or sham stimulation group. The intervention group received six sessions of tFUS stimulation to the left dorsolateral prefrontal cortex over two weeks. Neuropsychological assessments were conducted before and after the sessions. Resting-state functional magnetic resonance imaging (rsfMRI) was also performed to evaluate changes in functional connectivity (FC). The primary outcome measure was the change in depressive symptoms, assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS).

RESULTS

The tFUS stimulation sessions were well tolerated without any undesirable side effects. The analysis revealed a significant main effect of session sequence on the MADRS scores and significant interactions between the session sequences and groups. The rsfMRI analysis showed a higher FC correlation between the right superior part of the subgenual anterior cingulate cortex (sgACC) and several other brain regions in the verum group compared with the sham group.

CONCLUSION

Our results reveal that tFUS stimulation clinically improved MADRS scores with network-level modulation of a sgACC subregion. This randomized, sham-controlled clinical trial, the first study of its kind, demonstrated the safety and probable efficacy of tFUS stimulation for the treatment of depression.

Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.

Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

Clinical characteristics of post-stroke basal ganglia aphasia and the study of language-related white matter tracts based on diffusion spectrum imaging

BACKGROUND

Stroke often damages the basal ganglia, leading to atypical and transient aphasia, indicating that post-stroke basal ganglia aphasia (PSBGA) may be related to different anatomical structural damage and functional remodeling rehabilitation mechanisms. The basal ganglia contain dense white matter tracts (WMTs). Hence, damage to the functional tract may be an essential anatomical structural basis for the development of PSBGA.

METHODS

We first analyzed the clinical characteristics of PSBGA in 28 patients and 15 healthy controls (HCs) using the Western Aphasia Battery and neuropsychological test batteries. Moreover, we investigated white matter injury during the acute stage using diffusion magnetic resonance imaging scans for differential tractography. Finally, we used multiple regression models in correlation tractography to analyze the relationship between various language functions and quantitative anisotropy (QA) of WMTs.

RESULTS

Compared with HCs, patients with PSBGA showed lower scores for fluency, comprehension (auditory word recognition and sequential commands), naming (object naming and word fluency), reading comprehension of sentences, Mini-Mental State Examination, and Montreal Cognitive Assessment, along with increased scores in Hamilton Anxiety Scale-17 and Hamilton Depression Scale-17 within 7 days after stroke onset (P < 0.05). Differential tractography revealed that patients with PSBGA had damaged fibers, including in the body fibers of the corpus callosum, left cingulum bundles, left parietal aslant tracts, bilateral superior longitudinal fasciculus II, bilateral thalamic radiation tracts, left fornix, corpus callosum tapetum, and forceps major, compared with HCs (FDR < 0.02). Correlation tractography highlighted that better comprehension was correlated with a higher QA of the left inferior fronto-occipital fasciculus (IFOF), corpus callosum forceps minor, and left extreme capsule (FDR < 0.0083). Naming was positively associated with the QA of the left IFOF, forceps minor, left arcuate fasciculus, and uncinate fasciculus (UF) (FDR < 0.0083). Word fluency of naming was also positively associated with the QA of the forceps minor, left IFOF, and thalamic radiation tracts (FDR < 0.0083). Furthermore, reading was positively correlated with the QA of the forceps minor, left IFOF, and UF (FDR < 0.0083).

CONCLUSION

PSBGA is primarily characterized by significantly impaired word fluency of naming and preserved repetition abilities, as well as emotional and cognitive dysfunction. Damaged limbic pathways, dorsally located tracts in the left hemisphere, and left basal ganglia pathways are involved in PSBGA pathogenesis. The results of connectometry analysis further refine the current functional localization model of higher-order neural networks associated with language functions.

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, placebo-controlled and delayed-start, neuroimaging trial in depression

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide across domains of health and cognition, affecting overall quality of life. Approximately one third of individuals with depression do not fully respond to treatments (e.g., conventional antidepressants, psychotherapy) and alternative strategies are needed. Recent early phase trials suggest psilocybin may be a safe and efficacious intervention with rapid-acting antidepressant properties. Psilocybin is thought to exert therapeutic benefits by altering brain network connectivity and inducing neuroplastic changes that endure for weeks post-treatment. Although early clinical results are encouraging, psilocybin's acute neurobiological effects on neuroplasticity have not been fully investigated. We aim to examine for the first time how psilocybin acutely (intraday) and subacutely (weeks) alters functional brain networks implicated in depression.

METHODS

Fifty participants diagnosed with MDD or persistent depressive disorder (PDD) will be recruited from a tertiary mood disorders clinic and undergo 1:1 randomization into either an experimental or control arm. Participants will be given either 25 mg psilocybin or 25 mg microcrystalline cellulose (MCC) placebo for the first treatment. Three weeks later, those in the control arm will transition to receiving 25 mg psilocybin. We will investigate whether treatments are associated with changes in arterial spin labelling and blood oxygenation level-dependent contrast neuroimaging assessments at acute and subacute timepoints. Primary outcomes include testing whether psilocybin demonstrates acute changes in (1) cerebral blood flow and (2) functional brain activity in networks associated with mood regulation and depression when compared to placebo, along with changes in MADRS score over time compared to placebo. Secondary outcomes include changes across complementary clinical psychiatric, cognitive, and functional scales from baseline to final follow-up. Serum peripheral neurotrophic and inflammatory biomarkers will be collected at baseline and follow-up to examine relationships with clinical response, and neuroimaging measures.

DISCUSSION

This study will investigate the acute and additive subacute neuroplastic effects of psilocybin on brain networks affected by depression using advanced serial neuroimaging methods. Results will improve our understanding of psilocybin's antidepressant mechanisms versus placebo response and whether biological measures of brain function can provide early predictors of treatment response.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT06072898. Registered on 6 October 2023.

Rationale and design of a double-blinded, randomized placebo-controlled trial of 40 Hz light neurostimulation therapy for depression (FELIX)

BACKGROUND

Major depressive disorder (MDD) is a debilitating condition that affects more than 300 million people worldwide. Current treatments are based on a trial-and-error approach, and reliable biomarkers are needed for more informed and personalized treatment solutions. One of the potential biomarkers, gamma-frequency (30-80 Hz) brainwaves, are hypothesized to originate from the excitatory-inhibitory interaction between the pyramidal cells and interneurons. The imbalance between this interaction is described as a crucial pathological mechanism in neuropsychiatric conditions, including MDD, and the modulation of this pathological interaction has been investigated as a potential target. Previous studies attempted to induce gamma activity in the brain using rhythmic light and sound stimuli (GENUS - Gamma Entrainment Using Sensory stimuli) that resulted in neuroprotective effects in Alzheimer's disease (AD) patients and animal models. Here, we investigate the antidepressant, cognitive, and electrophysiological effects of the novel light therapy approach using 40 Hz masked flickering light for patients diagnosed with MDD.

METHODS AND DESIGN

Sixty patients with a current diagnosis of a major depressive episode will be enrolled in a randomized, double-blinded, placebo-controlled trial. The active treatment group will receive 40 Hz masked flickering light stimulation while the control group will receive continuous light matched in color temperature and brightness. Patients in both groups will get daily light treatment in their own homes and will attend four follow-up visits to assess the symptoms of depression, including depression severity measured by Hamilton Depression Rating Scale (HAM-D17), cognitive function, quality of life and sleep, and electroencephalographic changes. The primary endpoint is the mean change from baseline to week 6 in depression severity (HAM-D6 subscale) between the groups.

EEG Microstates in Mood and Anxiety Disorders: A Meta-analysis

To reduce the psycho-social burden increasing attention has focused on brain abnormalities in the most prevalent and highly co-occurring neuropsychiatric disorders, such as mood and anxiety. However, high inter-study variability in these patients results in inconsistent and contradictory alterations in the fast temporal dynamics of large-scale networks as measured by EEG microstates. Thus, in this meta-analysis, we aim to investigate the consistency of these changes to better understand possible common neuro-dynamical mechanisms of these disorders.In the systematic search, twelve studies investigating EEG microstate changes in participants with mood and anxiety disorders and individuals with subclinical depression were included in this meta-analysis, adding up to 787 participants.The results suggest that EEG microstates consistently discriminate mood and anxiety impairments from the general population in patients and subclinical states. Specifically, we found a small significant effect size for B microstates in patients compared to healthy controls, with larger effect sizes for increased B presence in unmedicated patients with comorbidity. In a subgroup meta-analysis of ten mood disorder studies, microstate D showed a significant effect size for decreased presence. When investigating only the two anxiety disorder studies, we found a significantly small effect size for the increased microstate A and a medium effect size for decreased microstate E (one study). However, more studies are needed to elucidate whether these findings are diagnostic-specific markers.Results are discussed in relation to the functional meaning of microstates and possible contribution to an explanatory mechanism of overlapping symptomatology of mood and anxiety disorders.

Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders

BACKGROUND

Resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them.

METHODS

To uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders.

RESULTS

Hypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD.

CONCLUSIONS

Findings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.

Botulinum Toxin Injections for Psychiatric Disorders: A Systematic Review of the Clinical Trial Landscape

Botulinum toxin type A (BONT-A) has shown promise in improving the mood-related symptoms of psychiatric disorders by targeting muscles linked to the expression of negative emotions. We conducted a systematic review of past and ongoing efficacy trials of BONT-A therapy for psychiatric disorders to identify relevant trends in the field and discuss the refinement of therapeutic techniques. A comprehensive search for published clinical trials using BONT-A injections for psychiatric disorders was performed on 4 May 2023 through OVID databases (MEDLINE, Embase, APA PsycINFO). Unpublished clinical trials were searched through the ClinicalTrials.gov and International Clinical Trial Registry Platform public registries. The risk of bias was assessed using the JBI Critical Appraisal tools for use in systematic reviews. We identified 21 studies (17 published, 4 unpublished clinical trials) involving 471 patients. The studies focused on evaluating the efficacy of BONT-A for major depressive, borderline personality, social anxiety, and bipolar disorders. BONT-A was most commonly injected into the glabellar area, with an average dose ranging between 37.75 U and 44.5 U in published studies and between 32.7 U and 41.3 U in unpublished trials. The results indicated significant symptom reductions across all the studied psychiatric conditions, with mild adverse effects. Thus, BONT-A appears to be safe and well-tolerated for psychiatric disorders of negative affectivity. However, despite the clinical focus, there was a noted shortage of biomarker-related assessments. Future studies should focus on pursuing mechanistic explorations of BONT-A effects at the neurobiological level.

Weakened effective connectivity between salience network and default mode network during resting state in adolescent depression

Adolescent major depressive disorder (MDD) is associated with altered resting-state connectivity between the default mode network (DMN) and the salience network (SN), which are involved in self-referential processing and detecting and filtering salient stimuli, respectively. Using spectral dynamical causal modelling, we investigated the effective connectivity and input sensitivity between key nodes of these networks in 30 adolescents with MDD and 32 healthy controls while undergoing resting-state fMRI. We found that the DMN received weaker inhibition from the SN and that the medial prefrontal cortex and the anterior cingulate cortex showed reduced self-inhibition in MDD, making them more prone to external influences. Moreover, we found that selective serotonin reuptake inhibitor (SSRI) intake was associated with decreased and increased self-inhibition of the SN and DMN, respectively, in patients. Our findings suggest that adolescent MDD is characterized by a hierarchical imbalance between the DMN and the SN, which could affect the integration of emotional and self-related information. We propose that SSRIs may help restore network function by modulating excitatory/inhibitory balance in the DMN and the SN. Our study highlights the potential of prefrontal-amygdala interactions as a biomarker and a therapeutic target for adolescent depression.

Deep brain stimulation for refractory major depressive disorder: a comprehensive review

Deep brain stimulation (DBS) has emerged as a promising treatment for select patients with refractory major depressive disorder (MDD). The clinical effectiveness of DBS for MDD has been demonstrated in meta-analyses, open-label studies, and a few controlled studies. However, randomized controlled trials have yielded mixed outcomes, highlighting challenges that must be addressed prior to widespread adoption of DBS for MDD. These challenges include tracking MDD symptoms objectively to evaluate the clinical effectiveness of DBS with sensitivity and specificity, identifying the patient population that is most likely to benefit from DBS, selecting the optimal patient-specific surgical target and stimulation parameters, and understanding the mechanisms underpinning the therapeutic benefits of DBS in the context of MDD pathophysiology. In this review, we provide an overview of the latest clinical evidence of MDD DBS effectiveness and the recent technological advancements that could transform our understanding of MDD pathophysiology, improve the clinical outcomes for MDD DBS, and establish a path forward to develop more effective neuromodulation therapies to alleviate depressive symptoms.

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods.

Early-life participation in cognitively stimulating activities and risk of depression and anxiety in late life

BACKGROUND

Early-life stressful experiences are associated with increased risk of adverse psychological outcomes in later life. However, much less is known about associations between early-life positive experiences, such as participation in cognitively stimulating activities, and late-life mental health. We investigated whether greater engagement in cognitively stimulating activities in early life is associated with lower risk of depression and anxiety in late life.

METHODS

We surveyed former participants of the St. Louis Baby Tooth study, between 22 June 2021 and 25 March 2022 to collect information on participants' current depression/anxiety symptoms and their early-life activities (N = 2187 responded). A composite activity score was created to represent the early-life activity level by averaging the frequency of self-reported participation in common cognitively stimulating activities in participants' early life (age 6, 12, 18), each rated on a 1 (least frequent) to 5 (most frequent) point scale. Depression/anxiety symptoms were measured by Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder Screener (GAD-7). We used logistic regressions to estimate odds ratios (OR) and 95% confidence intervals (CI) of outcome risk associated with frequency of early-life activity.

RESULTS

Each one-point increase in the early-life composite cognitive activity score was associated with an OR of 0.54 (95% CI 0.38-0.77) for late-life depression and an OR of 0.94 (95% CI 0.61-1.43) for late-life anxiety, adjusting for age, sex, race, parental education, childhood family structure, and socioeconomic status.

CONCLUSIONS

More frequent participation in cognitively stimulating activities during early life was associated with reduced risk of late-life depression.

Brain network hierarchy reorganization in subthreshold depression

BACKGROUND

Hierarchy is the organizing principle of human brain network. How network hierarchy changes in subthreshold depression (StD) is unclear. The aim of this study was to investigate the altered brain network hierarchy and its clinical significance in patients with StD.

METHODS

A total of 43 patients with StD and 43 healthy controls matched for age, gender and years of education participated in this study. Alterations in the hierarchy of StD brain networks were depicted by connectome gradient analysis. We assessed changes in network hierarchy by comparing gradient scores in each network in patients with StD and healthy controls. The study compared different brain subdivisions if there was a different network. Finally, we analysed the relationship between the altered gradient scores and clinical characteristics.

RESULTS

Patients with StD had contracted network hierarchy and suppressed cortical range gradients. In the principal gradient, the gradient scores of default mode network were significantly reduced in patients with StD compared to controls. In the default network, the subdivisions of reduced gradient scores were mainly located in the precuneus, superior temporal gyrus, and anterior and posterior cingulate gyrus. Reduced gradient scores in the default mode network, the anterior and posterior cingulate gyrus were correlated with severity of depression.

CONCLUSIONS

The network hierarchy of the StD changed and was significantly correlated with depressive symptoms and severity. These results provided new insights into further understanding of the neural mechanisms of StD.

Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities

The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.

The role of serotonin in depression-A historical roundup and future directions

Depression is one of the most common psychiatric disorders worldwide, affecting approximately 280 million people, with probably much higher unrecorded cases. Depression is associated with symptoms such as anhedonia, feelings of hopelessness, sleep disturbances, and even suicidal thoughts. Tragically, more than 700 000 people commit suicide each year. Although depression has been studied for many decades, the exact mechanisms that lead to depression are still unknown, and available treatments only help a fraction of patients. In the late 1960s, the serotonin hypothesis was published, suggesting that serotonin is the key player in depressive disorders. However, this hypothesis is being increasingly doubted as there is evidence for the influence of other neurotransmitters, such as noradrenaline, glutamate, and dopamine, as well as larger systemic causes such as altered activity in the limbic network or inflammatory processes. In this narrative review, we aim to contribute to the ongoing debate on the involvement of serotonin in depression. We will review the evolution of antidepressant treatments, systemic research on depression over the years, and future research applications that will help to bridge the gap between systemic research and neurotransmitter dynamics using biosensors. These new tools in combination with systemic applications, will in the future provide a deeper understanding of the serotonergic dynamics in depression.

Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study

Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.

Neurophysiological evidence that frontoparietal connectivity and GABA-A receptor changes underpin the antidepressant response to ketamine

Revealing the acute cortical pharmacodynamics of an antidepressant dose of ketamine in humans with depression is key to determining the specific mechanism(s) of action for alleviating symptoms. While the downstream effects are characterised by increases in plasticity and reductions in depressive symptoms-it is the acute response in the brain that triggers this cascade of events. Computational modelling of cortical interlaminar and cortico-cortical connectivity and receptor dynamics provide the opportunity to interrogate this question using human electroencephalography (EEG) data recorded during a ketamine infusion. Here, resting-state EEG was recorded in a group of 30 patients with major depressive disorder (MDD) at baseline and during a 0.44 mg/kg ketamine dose comprising a bolus and infusion. Fronto-parietal connectivity was assessed using dynamic causal modelling to fit a thalamocortical model to hierarchically connected nodes in the medial prefrontal cortex and superior parietal lobule. We found a significant increase in parietal-to-frontal AMPA-mediated connectivity and a significant decrease in the frontal GABA time constant. Both parameter changes were correlated across participants with the antidepressant response to ketamine. Changes to the NMDA receptor time constant and inhibitory intraneuronal input into superficial pyramidal cells did not survive correction for multiple comparisons and were not correlated with the antidepressant response. These results provide evidence that the antidepressant effects of ketamine may be mediated by acute fronto-parietal connectivity and GABA receptor dynamics. Furthermore, it supports the large body of literature suggesting the acute mechanism underlying ketamine's antidepressant properties is related to GABA-A and AMPA receptors rather than NMDA receptor antagonism.

Input-output relation of midbrain connectomics in a rodent model of depression

BACKGROUND

The symptoms associated with depression are believed to arise from disruptions in information processing across brain networks. The ventral tegmental area (VTA) influences reward-based behavior, motivation, addiction, and psychiatric disorders, including depression. Deep brain stimulation (DBS) of the medial forebrain bundle (MFB), is an emerging therapy for treatment-resistant depression. Understanding the depression associated anatomical networks crucial for comprehending its antidepressant effects.

METHODS

Flinders Sensitive Line (FSL), a rodent model of depression and Sprague-Dawley rats (n = 10 each) were used in this study. We used monosynaptic tracing to map inputs of VTA efferent neurons: VTA-to-NAc nucleus accumbens (NAc) (both core and shell) and VTA-to-prefrontal cortex (PFC). Quantitative analysis explored afferent diversity and strengths.

RESULTS

VTA efferent neurons receive a variety of afferents with varying input weights and predominant neuromodulatory representation. Notably, NAc-core projecting VTA neurons showed stronger afferents from dorsal raphe, while NAc shell-projecting VTA neurons displayed lower input strengths from cortex, thalamus, zona incerta and pretectal area in FSL rats. NAc shell-projecting VTA neurons showed the most difference in connectivity across the experimental groups.

LIMITATIONS

Lack of functional properties of the anatomical connections is the major limitation of this study. Incomplete labeling and the cytotoxicity of the rabies virus should be made aware of.

CONCLUSIONS

These findings provide the first characterization of inputs to different VTA ascending projection neurons, shedding light on critical differences in the connectome of the midbrain-forebrain system. Moreover, these differences support potential network effects of these circuits in the context of MFB DBS neuromodulation for depression.

The potential role of gray matter volume differences in the association between smoking and depression: A narrative review

Tobacco use and major depression are both leading contributors to the global burden of disease and are also highly comorbid. Previous research indicates bi-directional causality between tobacco use and depression, but the mechanisms that underlie this causality are unclear, especially for the causality from tobacco use to depression. Here we narratively review the available evidence for a potential causal role of gray matter volume in the association. We summarize the findings of large existing neuroimaging meta-analyses, studies in UK Biobank, and the Enhancing NeuroImaging Genetics through MetaAnalysis (ENIGMA) consortium and assess the overlap in implicated brain areas. In addition, we review two types of methods that allow us more insight into the causal nature of associations between brain volume and depression/smoking: longitudinal studies and Mendelian Randomization studies. While the available evidence suggests overlap in the alterations in brain volumes implicated in tobacco use and depression, there is a lack of research examining the underlying pathophysiology. We conclude with recommendations on (genetically-informed) causal inference methods useful for studying these associations.

Neurosteroids: mechanistic considerations and clinical prospects

Like other classes of treatments described in this issue's section, neuroactive steroids have been studied for decades but have risen as a new class of rapid-acting, durable antidepressants with a distinct mechanism of action from previous antidepressant treatments and from other compounds covered in this issue. Neuroactive steroids are natural derivatives of progesterone but are proving effective as exogenous treatments. The best understood mechanism is that of positive allosteric modulation of GABAA receptors, where subunit selectivity may promote their profile of action. Mechanistically, there is some reason to think that neuroactive steroids may separate themselves from liabilities of other GABA modulators, although research is ongoing. It is also possible that intracellular targets, including inflammatory pathways, may be relevant to beneficial actions. Strengths and opportunities for further development include exploiting non-GABAergic targets, structural analogs, enzymatic production of natural steroids, precursor loading, and novel formulations. The molecular mechanisms of behavioral effects are not fully understood, but study of brain network states involved in emotional processing demonstrate a robust influence on affective states not evident with at least some other GABAergic drugs including benzodiazepines. Ongoing studies with neuroactive steroids will further elucidate the brain and behavioral effects of these compounds as well as likely underpinnings of disease.

Neuroproteomics: Unveiling the Molecular Insights of Psychiatric Disorders with a Focus on Anxiety Disorder and Depression

Anxiety and depression are two of the most common mental disorders worldwide, with a lifetime prevalence of up to 30%. These disorders are complex and have a variety of overlapping factors, including genetic, environmental, and behavioral factors. Current pharmacological treatments for anxiety and depression are not perfect. Many patients do not respond to treatment, and those who do often experience side effects. Animal models are crucial for understanding the complex pathophysiology of both disorders. These models have been used to identify potential targets for new treatments, and they have also been used to study the effects of environmental factors on these disorders. Recent proteomic methods and technologies are providing new insights into the molecular mechanisms of anxiety disorder and depression. These methods have been used to identify proteins that are altered in these disorders, and they have also been used to study the effects of pharmacological treatments on protein expression. Together, behavioral and proteomic research will help elucidate the factors involved in anxiety disorder and depression. This knowledge will improve preventive strategies and lead to the development of novel treatments.

Subjective and neural reactivity during savoring and rumination

Repetitive thinking about negative emotions or events is strongly associated with worse mental health, whereas repetitive positive thought is generally believed to be beneficial. This observation is at odds with the idea that all forms of repetitive thinking share underlying neural mechanisms. To resolve this apparent discrepancy, the present study examined relationships between subjective affect and neural mechanisms during periods of sustained processing of positive (savoring) and negative (rumination) emotion. We also examined potential common moderators of savoring and rumination including memory specificity and sleep quality. Results indicated that individuals who experience high positive affect during savoring also are likely to experience more intense negative affect during rumination. fMRI-derived brain activity revealed common mechanisms of rumination and savoring. Memory specificity had common effects on neural correlates of rumination and savoring; sleep quality was not associated with mechanisms of savoring or rumination. These results suggest that repetitive engagement with positive and negative affect is similar both subjectively and mechanistically. Clinical interventions for rumination may benefit from capitalizing on preserved capacity for savoring.

Correlation between hemoglobin levels and depression in late-stage cancer patients with irritability as mediating variable

PURPOSE

In late-stage cancer, the cancer itself or the side effects of cancer treatment are known to affect the hemoglobin (Hgb) levels or emotions of patients. We to investigate the relationship between Hgb levels and depression in late-stage cancer patients and verified whether irritability has a mediating effect on this relationship.

METHOD

The research tools included a patient basic information form, the Irritability Scale-Initial Version (TISi), and the Hamilton Depression Rating Scale (HAMD). We first compared the Hgb levels, HAMD scores, and TISi scores of the cancer patients with different attributes, performed multiple hierarchical regression analysis, and then analyzed the mediating effects of TISi scores using the Sobel test.

RESULTS

In the 117 late-stage cancer patients, Hgb levels of patients with a BMI<18.5 kg/m2 were lower than those of the patients with a BMI 24.0 kg/m2. Hgb levels had a negative influence on both TISi scores (B = -2.74, p = .001) and HAMD scores (B = -0.75, p = .010). TISi scores mediated the relationship between Hgb levels and HAMD scores (Z = 2.06, p = .040).

CONCLUSIONS

Irritability is a mediating variable of the influence of Hgb levels on depression, meaning that lower Hgb levels in late-stage cancer patients may be detrimental to emotional stability, induce irritability, and thereby cause depression. Thus, in the psychological care of late-stage cancer patients, medical teams should be more vigilant in monitoring Hgb levels and anemia treatment.

Depression recurrence is accompanied by longer periods in default mode and more frequent attentional and reward processing dynamic brain-states during resting-state activity

Recurrence in major depressive disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain's dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode. Sixty two drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying Leading Eigenvector Dynamics Analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset. After the transition into a depressive state, basal ganglia-anterior cingulate cortex (ACC) and visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the basal ganglia-ACC areas underlying the reward network, were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). No significant changes were observed in the matched-remitted patients who were scanned twice while in remission. These findings characterize changes that may be associated with the transition from remission to recurrence and provide initial evidence of altered dynamical exploration of the brain's repertoire of functional networks when a recurrent depressive episode occurs.

Decreased cortical gyrification in major depressive disorder

BACKGROUND

Early neurodevelopmental deviations, such as abnormal cortical folding patterns, are candidate biomarkers of major depressive disorder (MDD). We aimed to investigate the association of MDD with the local gyrification index (LGI) in each cortical region at the whole-brain level, and the association of the LGI with clinical characteristics of MDD.

METHODS

We obtained T1-weighted images from 234 patients with MDD and 215 healthy controls (HCs). The LGI values from 66 cortical regions in the bilateral hemispheres were automatically calculated according to the Desikan-Killiany atlas. We compared the LGI values between the MDD and HC groups using analysis of covariance, including age, sex, and years of education as covariates. The association between the clinical characteristics and LGI values was investigated in the MDD group.

RESULTS

Compared with HCs, patients with MDD showed significantly decreased LGI values in the cortical regions, including the bilateral ventrolateral and dorsolateral prefrontal cortices, medial and lateral orbitofrontal cortices, insula, right rostral anterior cingulate cortex, and several temporal and parietal regions, with the largest effect size in the left pars triangularis (Cohen's f2 = 0.361; p = 1.78 × 10-13). Regarding the association of clinical characteristics with LGIs within the MDD group, recurrence and longer illness duration were associated with increased gyrification in several occipital and temporal regions, which showed no significant difference in LGIs between the MDD and HC groups.

CONCLUSIONS

These findings suggest that the LGI may be a relatively stable neuroimaging marker associated with MDD predisposition.

Advances in Deep Brain Stimulation: From Mechanisms to Applications

Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.

Functional dysconnectivity in youth depression: Systematic review, meta-analysis, and network-based integration

Youth depression has been associated with heterogenous patterns of aberrant brain connectivity. To make sense of these divergent findings, we conducted a systematic review encompassing 19 resting-state fMRI seed-to-whole-brain studies (1400 participants, comprising 795 youths with major depression and 605 matched healthy controls). We incorporated separate meta-analyses of connectivity abnormalities across the levels of the most commonly seeded brain networks (default-mode and limbic networks) and, based on recent additions to the literature, an updated meta-analysis of amygdala dysconnectivity in youth depression. Our findings indicated broad and distributed findings at an anatomical level, which could not be captured by conventional meta-analyses in terms of spatial convergence. However, we were able to parse the complexity of region-to-region dysconnectivity by considering constituent regions as components of distributed canonical brain networks. This integration revealed dysconnectivity centred on central executive, default mode, salience, and limbic networks, converging with findings from the adult depression literature and suggesting similar neurobiological underpinnings of youth and adult depression.

Lateral habenula deep brain stimulation alleviates depression-like behaviors and reverses the oscillatory pattern in the nucleus accumbens in an animal model of depression

Depression is a series of symptoms that influence mood, thinking, and behavior and create unpleasant emotions like hopelessness and apathy. Treatment-resistant depression (TRD) affects 30 % of depression patients despite the availability of several non-invasive therapies. Deep brain stimulation (DBS) is a novel therapy for TRD. The aim of the current study was to evaluate the effect of LHb-DBS by recording local field potentials (LFP) and conducting behavioral experiments. Thirty-two mature male Wistar rats were randomly divided into four groups: control, chronic mild stress (CMS), CMS+DBS, and DBS. After surgery and electrode placement in the lateral habenula (LHb), nucleus accumbens (NAc), and prelimbic cortex (PrL), the CMS protocol was applied for 3 weeks to create depression-like models. The open field test (OFT), sucrose preference test (SPT), and forced swim test (FST) were also performed. In the DBS groups, the LHb area was stimulated for four consecutive days. Finally, on the 22nd day, LFP was recorded from the NAc and PrL and analyzed using MATLAB software. Analyzing the findings using ANOVA and P-values ≤ 0.05 was considered. LHb-DBS alleviated depression-like behaviors in chronic moderate stress model rats (P ≤ 0.05). Three weeks of CMS enhanced almost all band powers in the NAc, while LHb-DBS decreased the power of the theta, alpha, beta, and gamma bands in the NAc (P ≤ 0.05), and the low-gamma band in the PrL. CMS also boosted the NAc-PrL coherence in low-frequency bands, while LHb-DBS increased beta and low gamma band coherence (P ≤ 0.05). In sum, the results of the present study showed that depression enhances low-frequency coherence between NAc and PrL cortex. Depression also potentiates many brain oscillations in the NAc, which can be mainly reversed by LHb-DBS.

Trait- and state-like co-activation pattern dynamics in current and remitted major depressive disorder

BACKGROUND

Distinguishing between trait- and state-like neural alternations in major depressive disorder (MDD) may advance our understanding of this recurring disorder. We aimed to investigate dynamic functional connectivity alternations in unmedicated individuals with current or past MDD using co-activation pattern analyses.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from individuals with first-episode current MDD (cMDD, n = 50), remitted MDD (rMDD, n = 44), and healthy controls (HCs, n = 64). Using a data-driven consensus clustering technique, four whole-brain states of spatial co-activation were identified and associated metrics (dominance, entries, transition frequency) were analyzed with respect to clinical characteristics.

RESULTS

Relative to rMDD and HC, cMDD showed increased dominance and entries of state 1 (primarily involving default mode network (DMN)), and decreased dominance of state 4 (mostly involving frontal-parietal network (FPN)). Among cMDD, state 1 entries correlated positively with trait rumination. Conversely, relative to cMDD and HC, individuals with rMDD were characterized by increased state 4 entries. Relative to HC, both MDD groups showed increased state 4-to-1 (FPN to DMN) transition frequency but reduction in state 3 (spanning visual attention, somatosensory, limbic networks), with the former metric specifically related to trait rumination.

LIMITATIONS

Further confirmation with longitudinal studies are required.

CONCLUSIONS

Regardless of symptoms, MDD was characterized by increased FPN-to-DMN transitions and reduced dominance of a hybrid network. State-related effect emerged in regions critically implicated in repetitive introspection and cognitive control. Asymptomatic individuals with past MDD were uniquely linked to increased FPN entries. Our findings identify trait-like brain network dynamics that might increase vulnerability to future MDD.

Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING)

Treatment-resistant depression (TRD) represents a severe clinical condition with high social and economic costs. Esketamine Nasal Spray (ESK-NS) has recently been approved for TRD by EMA and FDA, but data about predictors of response are still lacking. Thus, a tool that can predict the individual patients' probability of response to ESK-NS is needed. This study investigates sociodemographic and clinical features predicting responses to ESK-NS in TRD patients using machine learning techniques. In a retrospective, multicentric, real-world study involving 149 TRD subjects, psychometric data (Montgomery-Asberg-Depression-Rating-Scale/MADRS, Brief-Psychiatric-Rating-Scale/BPRS, Hamilton-Anxiety-Rating-Scale/HAM-A, Hamilton-Depression-Rating-Scale/HAMD-17) were collected at baseline and at one month/T1 and three months/T2 post-treatment initiation. We trained three different random forest classifiers, able to predict responses to ESK-NS with accuracies of 68.53% at T1 and 66.26% at T2 and remission at T2 with 68.60% of accuracy. Features like severe anhedonia, anxious distress, mixed symptoms as well as bipolarity were found to positively predict response and remission. At the same time, benzodiazepine usage and depression severity were linked to delayed responses. Despite some limitations (i.e., retrospective study, lack of biomarkers, lack of a correct interrater-reliability across the different centers), these findings suggest the potential of machine learning in personalized intervention for TRD.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Effects of Exercise Training on Executive Functioning in Adults with Depression: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Exercise is a promising nonpharmacological intervention to improve executive function (EF). However, results from randomized trials and meta-analyses examining the effects of exercise on working memory in adults with depression are mixed, and the influence of exercise on EF, as well as the key moderators of the relationship, remain inconclusive.

OBJECTIVE

The present systematic review with meta-analysis examined the influence of exercise interventions on EF in adults with depression, and the influence of key moderating variables.

METHODS

Electronic searches were conducted using Embase, Cochrane Central, Scopus, Ovid MEDLINE, PubMed, Web of Science, China National Knowledge Infrastructure, Wanfang Database, and Weipu Database up to 25 June 2022, and updated on 16 January 2023. Randomized controlled trials (RCTs) examining the effects of exercise training on EF in adults with depression were included. A three-level meta-analysis based on a random-effects model was applied in R. Study quality was assessed using the Physiotherapy Evidence Database (PEDro) scale.

RESULTS

A total of 14 RCTs that evaluated 1201 adults with depression were included. The results indicated that exercise significantly improved global EF [g = 0.180; 95% confidence intervals (CI) = 0.038, 0.323], and the subdomains of working memory (g = 0.182; 95% CI = 0.015, 0.350), cognitive flexibility (g = 0.222; 95% CI = 0.048, 0.395), and reasoning/planning (g = 0.889; 95% CI = 0.571, 1.206). In subgroup analyses, significant improvements in EF were only observed for aerobic exercise (g = 0.203; 95% CI = 0.023, 0.382), moderate-to-vigorous intensity exercise (g = 0.200; 95% CI = 0.022, 0.379), exercise performed three or more times per week (g = 0.207; 95% CI = 0.026, 0.388), in sessions ≤ 60 min (g = 0.173; 95% CI = 0.003, 0.343), and in program durations lasting at least 13 weeks (g = 0. 248; 95% CI = 0.034, 0.462).

CONCLUSIONS

This meta-analysis demonstrates the benefits of exercise training for improving EF and the subdomains of working memory, cognitive flexibility, and reasoning/planning in adults with depression. Future randomized clinical trials are warranted to determine the therapeutic effects of exercise training on EF and cognitive symptoms in depressed patients.

Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

Morphometric similarity network alterations in COVID-19 survivors correlate with behavioral features and transcriptional signatures

OBJECTIVES

To explore the differences in the cortical morphometric similarity network (MSN) between COVID-19 survivors and healthy controls, and the correlation between these differences and behavioralfeatures and transcriptional signatures.

MATERIALS & METHODS

39 COVID-19 survivors and 39 age-, sex- and education years-matched healthy controls (HCs) were included. All participants underwent MRI and behavioral assessments (PCL-17, GAD-7, PHQ-9). MSN analysis was used to compute COVID-19 survivors vs. HCs differences across brain regions. Correlation analysis was used to determine the associations between regional MSN differences and behavioral assessments, and determine the spatial similarities between regional MSN differences and risk genes transcriptional activity.

RESULTS

COVID-19 survivors exhibited decreased regional MSN in insula, precuneus, transverse temporal, entorhinal, para-hippocampal, rostral middle frontal and supramarginal cortices, and increased regional MSN in pars triangularis, lateral orbitofrontal, superior frontal, superior parietal, postcentral, and inferior temporal cortices. Regional MSN value of lateral orbitofrontal cortex was positively associated with GAD-7 and PHQ-9 scores, and rostral middle frontal was negatively related to PHQ-9 scores. The analysis of spatial similarities showed that seven risk genes (MFGE8, MOB2, NUP62, PMPCA, SDSL, TMEM178B, and ZBTB11) were related to regional MSN values.

CONCLUSION

The MSN differences were associated with behavioral and transcriptional signatures, early psychological counseling or intervention may be required to COVID-19 survivors. Our study provided a new insight into understanding the altered coordination of structure in COVID-19 and may offer a new endophenotype to further investigate the brain substrate.

Late life depression and concepts of aging: an emerging paradigm

Late life depression (LLD) is an emerging challenge, and recognized as a significant barrier to long-term healthy aging. Viewed within the context of the medical/biological model, advances in brain sciences over the last several decades have led to a deeper understanding of the biology of LLD. These advances in current knowledge include the description of aging brain pathophysiology; the biology and biochemistry of neurotransmitters; the correspondence between changes in neurological structure, function, and neural network; the description of neural, hormonal and inflammatory biomarkers; and identification of typical phenotypic subtypes of LLD. Despite these advances, current treatment of LLD, which remains largely pharmacological with accompanying cognitive and behavioral interventions, has poor success rate for long-term remission among older people. A wider perspective, in keeping with several emerging aging concepts, is suggested as an alternative framework within which to view LLD. A growing body of research supports the important role in LLD of frailty, resilience, intrinsic capacity, and functional integrity. Similarly, important social determinants need to be addressed in the etiology of LLD, rooted largely in negative stereotypes of aging, with consequent repercussions of reduced participation and inclusion, growing social isolation, with loss of identity, meaning and hope. This perspective suggests the importance of a wider integrative conceptualization of depression, set against a background of emerging aging concepts.

Effective connectivity during face processing in major depression - distinguishing markers of pathology, risk, and resilience

BACKGROUND

Aberrant brain connectivity during emotional processing, especially within the fronto-limbic pathway, is one of the hallmarks of major depressive disorder (MDD). However, the methodological heterogeneity of previous studies made it difficult to determine the functional and etiological implications of specific alterations in brain connectivity. We previously reported alterations in psychophysiological interaction measures during emotional face processing, distinguishing depressive pathology from at-risk/resilient and healthy states. Here, we extended these findings by effective connectivity analyses in the same sample to establish a refined neural model of emotion processing in depression.

METHODS

Thirty-seven patients with MDD, 45 first-degree relatives of patients with MDD and 97 healthy controls performed a face-matching task during functional magnetic resonance imaging. We used dynamic causal modeling to estimate task-dependent effective connectivity at the subject level. Parametric empirical Bayes was performed to quantify group differences in effective connectivity.

RESULTS

MDD patients showed decreased effective connectivity from the left amygdala and left lateral prefrontal cortex to the fusiform gyrus compared to relatives and controls, whereas patients and relatives showed decreased connectivity from the right orbitofrontal cortex to the left insula and from the left orbitofrontal cortex to the right fusiform gyrus compared to controls. Relatives showed increased connectivity from the anterior cingulate cortex to the left dorsolateral prefrontal cortex compared to patients and controls.

CONCLUSIONS

Our results suggest that the depressive state alters top-down control of higher visual regions during face processing. Alterations in connectivity within the cognitive control network present potential risk or resilience mechanisms.

Triple network model of brain connectivity changes related to adverse mood effects in an oral contraceptive placebo-controlled trial

Combined oral contraceptives (COC) are among the most commonly used contraceptive methods worldwide, and mood side effects are the major reason for discontinuation of treatment. We here investigate the directed connectivity patterns associated with the mood side effects of an androgenic COC in a double-blind randomized, placebo-controlled trial in women with a history of affective COC side effects (n = 34). We used spectral dynamic causal modeling on a triple network model consisting of the default mode network (DMN), salience network (SN) and executive control network (ECN). Within this framework, we assessed the treatment-related changes in directed connectivity associated with adverse mood side effects. Overall, during COC use, we found a pattern of enhanced connectivity within the DMN and decreased connectivity within the ECN. The dorsal anterior cingulate cortex (SN) mediates an increased recruitment of the DMN by the ECN during treatment. Mood lability was the most prominent COC-induced symptom and also arose as the side effect most consistently related to connectivity changes. Connections that were related to increased mood lability showed increased connectivity during COC treatment, while connections that were related to decreased mood lability showed decreased connectivity during COC treatment. Among these, the connections with the highest effect size could also predict the participants' treatment group above chance.

Electroconvulsive therapy triggers a reversible decrease in brain N-acetylaspartate

Introduction

Based on previous research on electroconvulsive therapy (ECT) we have proposed a model where disruption, potentiation, and rewiring of brain networks occur in sequence and serve as the underlying therapeutic mechanism of ECT. This model implies that a temporary disturbance of neuronal networks (disruption) is followed by a trophic effect (potentiation), which enables the rewiring of neuronal circuits to a more euthymic functioning brain. We hypothesized that disruption of neuronal networks could trigger biochemical alterations leading to a temporary decrease in N-acetylaspartate (tNAA, considered a marker of neuronal integrity), while choline (a membrane component), myo-Inositol (mI, astroglia marker), and glutamate/glutamine (Glx, excitatory neurotransmitter) were postulated to increase. Previous magnetic resonance spectroscopy studies, reporting diverse findings, have used two different referencing methods - creatine ratios and tissue corrected values referenced to water - for the quantification of brain metabolites. Changes in creatine during ECT have also been reported, which may confound estimates adopting this as an internal reference.

Methods

Using MR spectroscopy, we investigated 31 moderately to severely depressed patients and 19 healthy controls before, during, and after ECT or at similar time points (for controls). We tested whether biochemical alterations in tNAA, choline, mI, and Glx lend support to the disrupt, potentiate, and rewire hypothesis. We used both creatine ratios and water-scaled values for the quantification of brain metabolites to validate the results across referencing methods.

Results

Levels of tNAA in the anterior cingulate cortex decreased after an ECT treatment series (average 10.6 sessions) by 6% (p = 0.007, creatine ratio) and 3% (p = 0.02, water referenced) but returned to baseline 6 months after ECT. Compared to after treatment series tNAA levels at 6-month follow-up had increased in both creatine ratio (+6%, p < 0.001) and water referenced data (+7%, p < 0.001). Findings for other brain metabolites varied and could not be validated across referencing methods.

Discussion

Our findings suggest that prior research must be interpreted with care, as several referencing and processing methods have been used in the past. Yet, the results for tNAA were robust across quantification methods and concur with relevant parts of the disrupt, potentiate, and rewire model.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium

BACKGROUND

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices.

METHODS

Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations.

RESULTS

VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network.

CONCLUSIONS

Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

An MRI-based morphometric and structural covariance network study of Brazilian adolescents stratified by depression risk

OBJECTIVE

To explore differences in regional cortical morphometric structure between adolescents at risk for depression or with current depression.

METHODS

We analyzed cross-sectional structural neuroimaging data from a sample of 150 Brazilian adolescents classified as low-risk (n=50) or high-risk for depression (n=50) or with current depression (n=50) through a vertex-based approach with measurements of cortical volume, surface area and thickness. Differences between groups in subcortical volumes and in the organization of networks of structural covariance were also explored.

RESULTS

No significant differences in brain structure between groups were observed in whole-brain vertex-wise cortical volume, surface area or thickness. Also, no significant differences in subcortical volume were observed between risk groups. In relation to the structural covariance network, there was an indication of an increase in the hippocampus betweenness centrality index in the high-risk group network compared to the low-risk and current depression group networks. However, this result was only statistically significant when applying false discovery rate correction for nodes within the affective network.

CONCLUSION

In an adolescent sample recruited using an empirically based composite risk score, no major differences in brain structure were detected according to the risk and presence of depression.

Structural and functional brain correlates of suicidal ideation and behaviors in depression: A scoping review of MRI studies

Identifying and integrating the neural correlates of suicidal ideation and behaviors is crucial to expand the knowledge and develop targeted strategies to prevent suicide. This review aimed to describe the neural correlates of suicidal ideation, behavior and the transition between them, using different magnetic resonance imaging (MRI) modalities, providing an up-to-date overview of the literature. To be included, the observational, experimental, or quasi-experimental studies must include adult patients currently diagnosed with major depressive disorder and investigate the neural correlates of suicidal ideation, behavior and/or the transition using MRI. The searches were conducted on PubMed, ISI Web of Knowledge and Scopus. Fifty articles were included in this review: 22 on suicidal ideation, 26 on suicide behaviors and two on the transition between them. The qualitative analysis of the included studies suggested alterations in the frontal, limbic and temporal lobes in suicidal ideation associated with deficits in emotional processing and regulation, and in the frontal, limbic, parietal lobes, and basal ganglia in suicide behaviors associated with impairments in decision-making. Gaps in the literature and methodological concerns were identified and might be addressed in future studies.

Transcranial focused ultrasound modulates the emergence of learned helplessness via midline theta modification

BACKGROUND

Helplessness and hopelessness are states closely related to depressive disorders. They ensue following the anticipated absence of valid behavioral options to alleviate an aversive state. One neural structure involved in the detection of such options, is the cingulate cortex (CC), which conveys the evaluation of behavior against the value of its outcome. Accordingly, CC-related EEG measures are thought to correlate to feedback evaluation but also to anxious and conflict-related states, signaling the need for further adaptation to current requirements. Against this background, this study investigated the role of CC functioning in the emergence and prevention of helpless/hopeless states by applying low-intensity transcranial focused ultrasound (litFUS) neuromodulation prior to a learned helplessness task.

METHOD

In a randomized controlled double blind experimental setup with 55 participants, litFUS was used to inhibit the right lateral prefrontal cortex (lPFC), a region closely connected to CC modulation. Participants were instructed to play 8 games of chess against a computer that was unbeatable to them, while an EEG was recorded. Theta oscillation in midline electrodes, playing performance and self-rate measures on cognitive, affective and physiological parameters were assessed.

RESULTS

The results show a considerable influence of litFUS neuromodulation of the lPFC on midline theta activity (Fz and Pz electrode position) which in turn correlated to several psychological variables including self-report data on emotion, cognition, and arousal as well as behavioral measures (playing performance).

LIMITATIONS

Due to the small sample size and sample characteristics, results cannot be generalized to the field of clinical application. A replication of results in larger samples and in context of other sonication parameters is needed to increase the robustness of results and to maximize the efficacy of litFUS application.

CONCLUSIONS

We conclude that the development of learned helplessness/hopelessness could be positively influenced in its course by litFUS neuromodulation of the right lPFC. In line with previous results, especially the posterior midline electrode Pz seems to be an interesting target for further research in this field as theta activity at this electrode is correlated to control perception and motivated behavior. To our knowledge, this is the first study to use neuromodulation to monitor and manipulate the development of helplessness in the laboratory.

Hippocampal BAIAP2 prevents chronic mild stress-induced depression-like behaviors in mice

Background

The pathogenesis of depression is closely related to changes in hippocampal synaptic plasticity; however, the underlying mechanism is still unclear. Brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2), a postsynaptic scaffold protein in excitatory synapses important for synaptic plasticity, is highly expressed in the hippocampus and has been implicated in several psychiatric disorders. However, the role of BAIAP2 in depression remains poorly understood.

Methods

In the present study, a mouse model of depression was established via exposure to chronic mild stress (CMS). An adeno-associated virus (AAV) vector expressing BAIAP2 was injected into the hippocampal brain region of mice and a BAIAP2 overexpression plasmid was transfected into HT22 cells to upregulate BAIAP2 expression. Depression- and anxiety-like behaviors and dendritic spine density were examined in mice using behavioral tests and Golgi staining, respectively. In vitro, hippocampal HT22 cells were treated with corticosterone (CORT) to simulate the stress state, and the effect of BAIAP2 on CORT-induced cell injury was explored. Reverse transcription-quantitative PCR and western blotting were employed to determine the expression levels of BAIAP2 and those of the synaptic plasticity-related proteins glutamate receptor ionotropic, AMPA 1 (GluA1), and synapsin 1 (SYN1).

Results

Mice exposed to CMS exhibited depression- and anxiety-like behaviors accompanied by decreased levels of BAIAP2 in the hippocampus. In vitro, the overexpression of BAIAP2 increased the survival rate of CORT-treated HT22 cells and upregulated the expression of GluA1 and SYN1. Consistent with the in vitro data, the AAV-mediated overexpression of BAIAP2 in the hippocampus of mice significantly inhibited CMS-induced depression-like behavior, concomitant with increases in dendritic spine density and the expression of GluA1 and SYN1 in hippocampal regions.

Conclusion

Our findings indicate that hippocampal BAIAP2 can prevent stress-induced depression-like behavior and may be a promising target for the treatment of depression or other stress-related diseases.

Heterogeneity of depression across the socioeconomic spectrum

Why is lower socioeconomic status associated with higher rates of depression? And, is the surplus of depression at lower SES just more of the same type as depression found at higher levels, or is it distinctive? We addressed these questions by examining the relations among SES, amygdala volume, and symptoms of depression in healthy young adults. Amygdala volume, a risk factor for depression, does not synergize with SES in a diathesis-stress relation, nor does it mediate the relation of SES to depression. Rather, SES and amygdala volume are independent, additive risk factors. They are also associated with different depression symptoms and, whereas perceived stress fully mediates the relation of SES to depression, it has no relation to amygdala volume. These findings suggest heterogeneity of depression across the socioeconomic spectrum, with implications for treatment selection as well as for future genetic and brain studies.