Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.

BACKGROUND: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS: Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS: Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Psychiatric disorders have different prevalence between sexes, with women being twice as likely to develop depression and anxiety across the lifespan. Previous studies have investigated sex differences in brain structure that might contribute to this prevalence but have mostly focused on a single-structure level, potentially overlooking the interplay between brain regions. Sex differences in structures responsible for emotional regulation (limbic system), affected in many psychiatric disorders, have been previously reported. Here, we apply a machine learning model to obtain an estimate of brain sex for each participant based on the volumes of multiple brain regions. Particularly, we compared the estimates obtained with a model based solely on limbic structures with those obtained with a non-limbic model (entire brain except limbic structures) and a whole brain model. To investigate the genetic determinants of the models, we assessed the heritability of the estimates between identical twins and fraternal twins. The estimates of all our models were heritable, suggesting a genetic component contributing to brain sex. Finally, to investigate the association with mental health, we compared brain sex estimates in healthy subjects and in a depressed population. We found an association between depression and brain sex in females for the limbic model, but not for the non-limbic model. No effect was found in males. Overall, our results highlight the potential utility of machine learning models of brain sex based on relevant structures to better understand the link between sex differences in the brain and mental disorders.

Subjective experience of the environment determines serotoninergic antidepressant treatment outcome in male mice.

BACKGROUND: The effects of the selective serotonin reuptake inhibitors (SSRIs), the first-line antidepressant treatment, have been proposed to be affected, at least in part, by the living environment. Since the quality of the environment depends not only on its objective features, but also on the subjective experience, we hypothesized that the latter plays a key role in determining SSRI treatment outcome. METHODS: We chronically administered the SSRI fluoxetine to two groups of adult CD-1 male mice that reportedly show distinct subjective experiences of the environment measured as consistent and significantly different responses to the same emotional and social stimuli. These distinct socioemotional profiles were generated by rearing mice either in standard laboratory conditions (SN) or in a communal nest (CN) where three dams breed together their offspring, sharing caregiving behavior. RESULTS: At adulthood, CN mice displayed higher levels of agonistic and anxiety-like behaviors than SN mice, indicating that they experience the environment as more socially challenging and potentially dangerous. We then administered fluoxetine, which increased offensive and anxious response in SN, while producing opposite effects in CN mice. BDNF regulation was modified by the treatment accordingly. LIMITATIONS: Subjective experience in mice was assessed as behavioral response to the environment. CONCLUSIONS: These results show that the subjective experience of the environment determines fluoxetine outcome. In a translational perspective, our findings suggest considering not only the objective quality, but also the subjective appraisal, of the patient's living environment for developing effective personalized therapeutic approaches in psychiatry.

Selecting antidepressants according to a drug-by-environment interaction: A comparison of fluoxetine and minocycline effects in mice living either in enriched or stressful conditions.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder. It has been recently proposed that these drugs, by enhancing neural plasticity, amplify the influences of the living conditions on mood. Consequently, SSRI outcome depends on the quality of the environment, improving symptomatology mainly in individuals living in favorable conditions. In adverse conditions, drugs with a different mechanism of action might have higher efficacy. The antibiotic minocycline, with neuroprotective and anti-inflammatory properties, has been recently proposed as a novel potential antidepressant treatment. To explore the drug-by-environment interaction, we compared the effects on depressive-like behavior and neural plasticity of the SSRI fluoxetine and minocycline in enriched and stressful conditions. We first exposed C57BL/6 adult female mice to 14 days of chronic unpredictable mild stress to induce a depressive-like profile. Afterward, mice received vehicle, fluoxetine, or minocycline for 21 days, while exposed to either enriched or stressful conditions. During the first five days, fluoxetine led to an improvement in enrichment but not in stress. By contrast, minocycline led to an improvement in both conditions. After 21 days, all groups showed a significant improvement in enrichment while fluoxetine worsened the depressive like behavior in stress. The effects of the drugs on neural plasticity, measured as long-term potentiation, were also environment-dependent. Overall, we show that the environment affects fluoxetine but not minocycline outcome, indicating that the latter represents a potential alternative to SSRIs to treat depressed patients living in adverse conditions. From a translation perspective, our finding call for considering the drug-by-environment interaction to select the most effective pharmacological treatment.

Combined Fluoxetine and Metformin Treatment Potentiates Antidepressant Efficacy Increasing IGF2 Expression in the Dorsal Hippocampus.

An increasing number of studies show that selective serotonin reuptake inhibitors (SSRIs) exert their therapeutic action, at least in part, by amplifying the influence of the living environment on mood. As a consequence, when administered in a favorable environment, SSRIs lead to a reduction of symptoms, but in stressful conditions, they show limited efficacy. Therefore, novel therapeutic approaches able to neutralize the influence of the stressful environment on treatment are needed. The aim of our study was to test whether, in a mouse model of depression, the combined administration of SSRI fluoxetine and metformin, a drug able to improve the metabolic profile, counteracts the limited efficacy of fluoxetine alone when administered in stressful conditions. Indeed, metabolic alterations are associated to both the onset of major depression and the antidepressant efficacy. To this goal, adult C57BL/6 male mice were exposed to stress for 6 weeks; the first two weeks was aimed at generating a mouse model of depression. During the remaining 4 weeks, mice received one of the following treatments: vehicle, fluoxetine, metformin, or a combination of fluoxetine and metformin. We measured liking- and wanting-type anhedonia as behavioral phenotypes of depression and assessed the expression levels of selected genes involved in major depressive disorder and antidepressant response in the dorsal and ventral hippocampus, which are differently involved in the depressive symptomatology. The combined treatment was more effective than fluoxetine alone in ameliorating the depressive phenotype after one week of treatment. This was associated to an increase in IGF2 mRNA expression and enhanced long-term potentiation, specifically in the dorsal hippocampus, at the end of treatment. Overall, the present results show that, when administered in stressful conditions, the combined fluoxetine and metformin treatment may represent a more effective approach than fluoxetine alone in a short term. Finally, our findings highlight the relevance of polypharmacological strategy as effective interventions to increase the efficacy of the antidepressant drugs currently available.