Applying dimensional psychopathology: transdiagnostic associations among regional homogeneity, leptin and depressive symptoms

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders

BACKGROUND

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

METHODS

We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort.

RESULTS

Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable.

CONCLUSIONS

Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Dynamic Changes in Local Brain Connectivity and Activity: A Longitudinal Study in Adolescent Anorexia Nervosa

BACKGROUND

Resting-state functional connectivity analysis has been used to study disruptions in neural circuitries underlying eating disorder symptoms. Research has shown resting-state functional connectivity to be altered during the acute phase of anorexia nervosa (AN), but little is known about the biological mechanisms underlying neural changes associated with weight restoration. The goal of the current study was to investigate longitudinal changes in regional homogeneity (ReHo) among neighboring voxels, degree centrality (DC) (a voxelwise whole brain correlation coefficient), voxel-mirrored homotopic connectivity (VMHC) (measuring the synchronization between hemispheres), and the fractional amplitude of low-frequency fluctuations associated with weight gain during AN treatment.

METHODS

Resting-state functional connectivity data were acquired and analyzed from a sample of 174 female volunteers: 87 underweight patients with AN that were scanned before treatment and again after at least 12% body mass index increase, as well as 87 age-matched healthy control participants.

RESULTS

Longitudinal changes in ReHo, DC, VMHC, and the fractional amplitude of low-frequency fluctuations were observed in most regions identified to differ between patients with AN before treatment and healthy control participants. However, the degree of normalization varied for each parameter, ranging from 9% of all clusters in DC to 66% in VMHC. Longitudinal changes in ReHo and VMHC showed a linear association weight gain.

CONCLUSIONS

Resting-state functional magnetic resonance imaging measures, including ReHo, DC, VMHC, and the fractional amplitude of low-frequency fluctuations, show varying degrees of recovery after short-term weight restoration. Although only some of these changes were related to weight gain, our results provide an overall positive message, suggesting that weight restoration is associated with changes in functional brain measures that point toward normalization.

Visual cortex repetitive transcranial magnetic stimulation (rTMS) reversing neurodevelopmental impairments in adolescents with major psychiatric disorders (MPDs): A cross-species translational study

AIMS

Neurodevelopmental impairments are closely linked to the basis of adolescent major psychiatric disorders (MPDs). The visual cortex can regulate neuroplasticity throughout the brain during critical periods of neurodevelopment, which may provide a promising target for neuromodulation therapy. This cross-species translational study examined the effects of visual cortex repetitive transcranial magnetic stimulation (rTMS) on neurodevelopmental impairments in MPDs.

METHODS

Visual cortex rTMS was performed in both adolescent methylazoxymethanol acetate (MAM) rats and patients with MPDs. Functional magnetic resonance imaging (fMRI) and brain tissue proteomic data in rats and fMRI and clinical symptom data in patients were analyzed.

RESULTS

The regional homogeneity (ReHo) analysis of fMRI data revealed an increase in the frontal cortex and a decrease in the posterior cortex in the MAM rats, representing the abnormal neurodevelopmental pattern in MPDs. In regard to the effects of rTMS, similar neuroimaging changes, particularly reduced frontal ReHo, were found both in MAM rats and adolescent patients, suggesting that rTMS may reverse the abnormal neurodevelopmental pattern. Proteomic analysis revealed that rTMS modulated frontal synapse-associated proteins, which may be the underpinnings of rTMS efficacy. Furthermore, a positive relationship was observed between frontal ReHo and clinical symptoms after rTMS in patients.

CONCLUSION

Visual cortex rTMS was proven to be an effective treatment for adolescent MPDs, and the underlying neural and molecular mechanisms were uncovered. Our study provides translational evidence for therapeutics targeting the neurodevelopmental factor in MPDs.

Applying dimensional psychopathology: transdiagnostic prediction of executive cognition using brain connectivity and inflammatory biomarkers

BACKGROUND

The association between executive dysfunction, brain dysconnectivity, and inflammation is a prominent feature across major psychiatric disorders (MPDs), schizophrenia, bipolar disorder, and major depressive disorder. A dimensional approach is warranted to delineate their mechanistic interplay across MPDs.

METHODS

This single site study included a total of 1543 participants (1058 patients and 485 controls). In total, 1169 participants underwent diffusion tensor and resting-state functional magnetic resonance imaging (745 patients and 379 controls completed the Wisconsin Card Sorting Test). Fractional anisotropy (FA) and regional homogeneity (ReHo) assessed structural and functional connectivity, respectively. Pro-inflammatory cytokine levels [interleukin (IL)-1β, IL-6, and tumor necrosis factor-α] were obtained in 325 participants using blood samples collected with 24 h of scanning. Group differences were determined for main measures, and correlation and mediation analyses and machine learning prediction modeling were performed.

RESULTS

Executive deficits were associated with decreased FA, increased ReHo, and elevated IL-1β and IL-6 levels across MPDs, compared to controls. FA and ReHo alterations in fronto-limbic-striatal regions contributed to executive deficits. IL-1β mediated the association between FA and cognition, and IL-6 mediated the relationship between ReHo and cognition. Executive cognition was better predicted by both brain connectivity and cytokine measures than either one alone for FA-IL-1β and ReHo-IL-6.

CONCLUSIONS

Transdiagnostic associations among brain connectivity, inflammation, and executive cognition exist across MPDs, implicating common neurobiological substrates and mechanisms for executive deficits in MPDs. Further, inflammation-related brain dysconnectivity within fronto-limbic-striatal regions may represent a transdiagnostic dimension underlying executive dysfunction that could be leveraged to advance treatment.

Evidence of functional abnormalities in the default mode network in bipolar depression: A coordinate-based activation likelihood estimation meta-analysis

BACKGROUND

The default mode network (DMN) is thought to be involved in the pathophysiology of bipolar depression (BD). However, the findings of prior studies on DMN alterations in BD are inconsistent. Thus, this study aimed to systematically investigate functional abnormalities of the DMN in BD patients.

METHODS

We systematically searched PubMed, Ovid, and Web of Science for functional neuroimaging studies on regional homogeneity, amplitude of low frequency fluctuations (ALFF), and functional connectivity of the DMN in BD patients published before March 18, 2022. The stereotactic coordinates of the reported altered brain regions were extracted and incorporated into a brain map using the coordinate-based activation likelihood estimation approach.

RESULTS

A total of 43 original research studies were included in the meta-analysis. BD patients showed specific changes in the DMN including decreased ALFF/fractional ALFF in the left cingulate gyrus (CG) and bilateral precuneus (PCUN); increased functional connectivity (FC) in the left CG, left posterior CG, left PCUN, bilateral medial frontal gyrus, and bilateral superior frontal gyrus; and decreased FC in the left CG, left PCUN, left inferior parietal lobule, and left postcentral gyrus.

LIMITATIONS

Conclusions are limited by the small number of studies, additional meta-analyses are needed to obtain more data in BD subgroup.

CONCLUSION

This meta-analysis supports specific changes in DMN activity and FC in BD patients, which may be powerful biomarkers for the diagnosis of BD. The CG and PCUN were the most affected regions and are thus potential targets for clinical interventions to delay BD progression.

Appetite-regulating hormones in bipolar disorder: A systematic review and meta-analysis

Impaired hormonal regulation of appetite may contribute to higher cardiovascular risk in bipolar disorder (BD). We performed a systematic review and meta-analysis of studies investigating peripheral blood levels of appetite-regulating hormones in BD and controls. A total of 32 studies were included. Leptin and insulin levels were significantly elevated in patients with BD during euthymia, but not in other mood states. Greater differences in the number of male participants between patients with BD and healthy controls were associated with higher effect size estimates for the levels of insulin. There were significant positive correlations of effect size estimates for the levels of adiponectin with the percentage of individuals with type I BD and duration of BD. Our findings point to the mechanisms underlying high rates of cardiometabolic comorbidities in BD. Moreover, they suggest that investigating hormonal regulation of appetite might help to understand differences in the neurobiology of BD types.

A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods

BACKGROUND

The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD.

METHODS

The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared.

RESULTS

The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex.

LIMITATIONS

First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected.

CONCLUSION

The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.