RESUMEN
An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.
Asunto(s)
Biomarcadores , Trastorno Depresivo Mayor , Trastorno Depresivo Resistente al Tratamiento , Humanos , Trastorno Depresivo Mayor/diagnóstico por imagen , Trastorno Depresivo Mayor/inmunología , Femenino , Masculino , Adulto , Trastorno Depresivo Resistente al Tratamiento/diagnóstico por imagen , Persona de Mediana Edad , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen por Resonancia Magnética/métodos , Neuroimagen/métodos , Sustancia Gris/diagnóstico por imagen , Sustancia Gris/patología , Aprendizaje Automático , Experiencias Adversas de la Infancia , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patologíaRESUMEN
The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.
Asunto(s)
Encéfalo , Imagen por Resonancia Magnética , Humanos , Imagen por Resonancia Magnética/métodos , Encéfalo/diagnóstico por imagen , Mapeo Encefálico , Gemelos/genética , Descanso , Red NerviosaRESUMEN
BACKGROUND: Deficits in cognitive functioning, including attention, memory, and executive functions, along with impairments in language production, are present in patients with bipolar disorder (BD) patients during mood phases, but also during euthymia.Verbal fluency tasks (VFTs), being able to evaluate integrity of a wide range of cognitive domains and represent, can be used to screen for these disturbances. Neuroimaging studies, including Near-InfraRed Spectroscopy (NIRS), have repeatedly showed widespread alterations in the prefrontal and temporal cortex during the performance of VFTs in BD patients. This review aims to summarize the results of NIRS studies that evaluated hemodynamic responses associated with the VFTs in prefrontal and temporal regions in BD patients. METHODS: We performed a scoping review of studies evaluating VFT-induced activation in prefrontal and temporal regions in BD patients, and the relationship between NIRS data and various clinical variables. RESULTS: 15 studies met the inclusion criteria. In BD patients, compared to healthy controls, NIRS studies showed hypoactivation of the dorsolateral prefrontal cortex, ventrolateral prefrontal cortex and anterior temporal regions. Moreover, clinical variables, such as depressive and social adaptation scores, were negatively correlated with hemodynamic responses in prefrontal and temporal regions, while a positive correlation were reported for measures of manic symptoms and impulsivity. LIMITATIONS: The heterogeneity of the studies in terms of methodology, study design and clinical characteristics of the samples limited the comparability of the findings. CONCLUSIONS: Given its non-invasiveness, good time-resolution and no need of posturalconstraint, NIRS technique could represent a useful tool for the evaluation of prefrontal and temporal haemodynamic correlates of cognitive performances in BD patients.