Dysfunction of the anterior and intermediate hippocampal functional network in major depressive disorders across the adult lifespan

Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents

BACKGROUND

Depression has frequently been associated with smaller hippocampal volume. The hippocampus varies in function along its anterior-posterior axis, with the anterior hippocampus more strongly associated with stress and emotion processing. The goals of this study were to examine the associations among parental history of anxiety/depression, polygenic risk scores for depression (PGS-DEP), and anterior and posterior hippocampal volumes in children and adolescents. To examine specificity to PGS-DEP, we examined associations of educational attainment polygenic scores (PGS-EA) with anterior and posterior hippocampal volume.

METHODS

Participants were 350 3- to 21-year-olds (46 % female). PGS-DEP and PGS-EA were computed based on recent, large-scale genome-wide association studies. High-resolution, T1-weighted magnetic resonance imaging (MRI) data were acquired, and a semi-automated approach was used to segment the hippocampus into anterior and posterior subregions.

RESULTS

Children and adolescents with higher polygenic risk for depression were more likely to have a parent with a history of anxiety/depression. Higher polygenic risk for depression was significantly associated with smaller anterior but not posterior hippocampal volume. PGS-EA was not associated with anterior or posterior hippocampal volumes.

LIMITATIONS

Participants in these analyses were all of European ancestry.

CONCLUSIONS

Polygenic risk for depression may lead to smaller anterior but not posterior hippocampal volume in children and adolescents, and there may be specificity of these effects to PGS-DEP rather than PGS-EA. These findings may inform the earlier identification of those in need of support and the design of more effective, personalized treatment strategies.

DECLARATIONS OF INTEREST

none.

DECLARATIONS OF INTEREST

None.

Aberrant intrinsic hippocampal and orbitofrontal connectivity in drug-naive adolescent patients with major depressive disorder

Alterations in resting-state functional connectivity (rsFC) of hippocampus and orbitofrontal cortex (OFC) have been highly implicated in major depressive disorder (MDD) and the researches have penetrated to the subregional level. However, relatively little is known about the intrinsic connectivity patterns of these two regions in adolescent MDD (aMDD), especially that of their functional subregions. Therefore, in the current study, we recruited 68 first-episode drug-naive aMDD patients and 43 matched typically developing controls (TDC) to characterize the alterations of whole-brain rsFC patterns in hippocampus and OFC at both regional and subregional levels in aMDD. The definition of specific functional subregions in hippocampus and OFC were based on the prior functional clustering-analysis results. Furthermore, the relationship between rsFC alterations and clinical features was also explored. Compared to TDC group, aMDD patients showed decreased connectivity of the left whole hippocampus with bilateral OFC and right inferior temporal gyrus at the regional level and increased connectivity between one of the right hippocampal subregions and right posterior insula at the subregional level. Reduced connectivity of OFC was only found in the subregion of left OFC with left anterior insula extending to lenticula in aMDD patients relative to TDC group. Our study identifies that the aberrant hippocampal and orbitofrontal rsFC was predominantly located in the insular cortex and could be summarized as an altered hippo-orbitofrontal-insular circuit in aMDD, which may be the unique features of brain network dysfunction in depression at this particular age stage. Moreover, we observed the distinct rsFC alterations in adolescent depression at the subregional level, especially the medial and lateral OFC.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BACKGROUND

Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS

We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS

We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION

We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder

Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.

Linking external stimuli with internal drives: A role for the ventral hippocampus

The ventral hippocampus (vHPC) has long been thought of as the "emotional" hippocampus. Over the past several years, the complexity of vHPC has come to light, highlighting the diversity of cell types, inputs, and outputs that coordinate a constellation of positively and negatively motivated behaviors. Here, we review recent work on how vCA1 contributes to a network that associates external stimuli with internal motivational drive states to promote the selection of adaptive behavioral responses. We propose a model of vHPC function that emphasizes its role in the integration and transformation of internal and external cues to guide behavioral selection when faced with multiple potential outcomes.

Resting-state functional connectivity correlates of anxiety co-morbidity in major depressive disorder

Major depressive disorder (MDD) is frequently co-morbid with anxiety disorders. The co-morbid state has poorer functional outcomes and greater resistance to first line treatments, highlighting the need for novel treatment targets. This systematic review examined differences in resting-state brain connectivity associated with anxiety comorbidity in young- and middle-aged adults with MDD, with the aim of identifying novel targets for neuromodulation treatments, as these treatments are thought to work partly by altering dysfunctional connectivity pathways. Twenty-one studies met inclusion criteria, including a total of 1292 people with MDD. Only two studies included people with MDD and formally diagnosed co-morbid anxiety disorders; the remainder included people with MDD with dimensional anxiety measurement. The quality of most studies was judged as fair. Results were heterogeneous, partly due to a focus on a small set of connectivity relationships within individual studies. There was evidence for dysconnectivity between the amygdala and other brain networks in co-morbid anxiety, and an indication that abnormalities of default mode network connectivity may play an underappreciated role in this condition.