Shared and disease-sensitive dysfunction across bipolar and unipolar disorder during depressive episodes: a transdiagnostic study

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BACKGROUND

The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume.

METHODS

We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors.

RESULTS

In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination.

CONCLUSIONS

Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Shared and specific neurobiology in bipolar disorder and unipolar disorder: Evidence based on the connectome gradient and a transcriptome-connectome association study

BACKGROUND

Distinguishing bipolar disorder (BD) and unipolar disorder (UD) remains challenging. To identify the common and diagnosis-specific neuropathological alterations and their potential molecular mechanisms in patients with UD and BD (with a current depressive episode).

METHODS

Resting-state functional magnetic resonance imaging was obtained from 279 participants (95 BD patients, 107 UD patients and 77 health controls). Connectome gradients analysis was performed to explore the shared and diagnosis-specific gradient alterations in BD and UD. The Allen Human Brain Atlas data was used to explore the potential gene mechanisms of the gradient alterations.

RESULTS

BD and UD had shared hierarchical disorganisation, including downgrading and contraction from the unimodal sensory networks (vision and sensorimotor) to the transmodal cognitive networks (limbic, frontoparietal, dorsal attention, and default) (all P < 0.05, FDR corrected) in gradient 1 and gradient 2. The BD patients had specific connectome gradient dysfunction in the subcortical network. Moreover, the hierarchical disorganisation was closely correlated with profiles of gene expression specific to the neuroglial cells in the prefrontal cortex in BD and UD, while the most correlated gene ontology biological processes and function were concentrated in synaptic signalling, calcium ion binding, and transmembrane transporter activity.

CONCLUSION

These findings reveal the shared and diagnosis-specific neurobiological mechanism underlying BD and UD patients, which advances our understanding of the neuromechanisms of these disorders.

The sex differences in anhedonia in major depressive disorder: A resting-state fMRI study

OBJECTIVE

The external behavioural manifestations and internal neural mechanisms of anhedonia are sexually dimorphic. This study aimed to explore the sex differences in the regional brain neuroimaging features of anhedonia in the context of major depressive disorder (MDD).

METHOD

The resting-fMRI by applying amplitude of low-frequency fluctuation (ALFF) method was estimated in 414 patients with MDD (281 high anhedonia [HA], 133 low anhedonia [LA]) and 213 healthy controls (HC). The effects of two factors in patients with MDD were analysed using a 2 (sex: male, female) × 2 (group: HA, LA) ANOVA concerning the brain regions in which statistical differences were identified between patients with MDD and HC. We followed up with patients with HA at baseline, and 43 patients completed a second fMRI scan in remission. Paired t-test was performed to compare the ALFF values of anhedonia-related brain regions between the baseline and remission periods.

RESULTS

For the sex-by-group interaction, the bilateral insula, right hippocampus, right post cingulum cortex, and left putamen showed significant differences. Furthermore, the abnormally elevated ALFF values in anhedonia-related brain regions at baseline decreased in remission.

CONCLUSION

Our findings point to the fact that the females showed unique patterns of anhedonia-related brain activity compared to males, which may have clinical implications for interfering with the anhedonia symptoms in MDD. Using task fMRI, we can further examine the distinct characteristics between consumption anhedonia and anticipation anhedonia in MDD.

A synopsis of multitarget therapeutic effects of anesthetics on depression

Depression is a profound mental disorder that dampens the mood and undermines volition, which exhibited an increased incidence over the years. Although drug-based interventions remain the primary approach for depression treatment, the available medications still can't satisfy the patients. In recent years, the newly discovered therapeutic targets such as N-methyl-D-aspartate (NMDA) receptor, α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptor, and tyrosine kinase B (TrkB) have brought new breakthroughs in the development of antidepressant drugs. Moreover, it has come to light that certain anesthetics possess pharmacological mechanisms intricately linked to the aforementioned therapeutic targets for depression. At present, numerous preclinical and clinical studies have explored the therapeutic effects of anesthetic drugs such as ketamine, isoflurane, N2O, and propofol, on depression. These investigations suggested that these drugs can swiftly ameliorate patients' depression symptoms and engender long-term effects. In this paper, we provide a comprehensive review of the research progress and potential molecular mechanisms of various anesthetic drugs for depression treatment. By shedding light on this subject, we aim to facilitate the development and clinical implementation of new antidepressant drugs based on anesthetic medications.