Changes in cortisol but not in brain-derived neurotrophic factor modulate the association between sleep disturbances and major depression

Sleep disturbance is a symptom consistently found in major depression and is associated with a longer course of illness, reduced response to treatment, increased risk of relapse and recurrence. Chronic insomnia has been associated with changes in cortisol and serum brain-derived neurotrophic factor (BDNF) levels, which in turn are also changed in major depression. Here, we evaluated the relationship between sleep quality, salivary cortisol awakening response (CAR), and serum BDNF levels in patients with sleep disturbance and treatment-resistant major depression (n = 18), and in a control group of healthy subjects with good (n = 21) and poor (n = 18) sleep quality. We observed that the patients had the lowest CAR and sleep duration of all three groups and a higher latency to sleep than the healthy volunteers with a good sleep profile. Besides, low CAR was correlated with more severe depressive symptoms and worse sleep quality. There was no difference in serum BDNF levels between groups with distinct sleep quality. Taken together, our results showed a relationship between changes in CAR and in sleep quality in patients with treatment-resistant depression, which were correlated with the severity of disease, suggesting that cortisol could be a physiological link between sleep disturbance and major depression

Potential biomarkers of major depression diagnosis and chronicity

Background Molecular biomarkers are promising tools to be routinely used in clinical psychiatry. Among psychiatric diseases, major depression disorder (MDD) has gotten attention due to its growing prevalence and morbidity. Methods We tested some peripheral molecular parameters such as serum mature Brain-Derived Neurotrophic Factor (mBDNF), plasma C-Reactive Protein (CRP), serum cortisol (SC), and the salivary Cortisol Awakening Response (CAR), as well as the Pittsburgh sleep quality inventory (PSQI), as part of a multibiomarker panel for potential use in MDD diagnosis and evaluation of disease's chronicity using regression models, and ROC curve. Results For diagnosis model, two groups were analyzed: Patients in the first episode of major depression (MD: n = 30) and a healthy control (CG: n = 32). None of those diagnosis models tested had greater power than Hamilton Depression Rating Scale-6. For MDD chronicity, a group of patients with treatment-resistant major depression (TRD: n = 28) was tested across the MD group. The best chronicity model (p < 0.05) that discriminated between MD and TRD included four parameters, namely PSQI, CAR, SC, and mBDNF (AUC ROC = 0.99), with 96% of sensitivity and 93% of specificity. Conclusion These results indicate that changes in specific biomarkers (CAR, SC, mBDNF and PSQI) have potential on the evaluation of MDD chronicity, but not for its diagnosis. Therefore, these findings can contribute for further studies aiming the development of a stronger model to be commercially available and used in psychiatry clinical practice

Pathophysiology of major depression by clinical stages

The comprehension of the pathophysiology of the major depressive disorder (MDD) is essential to the strengthening of precision psychiatry. In order to determine the relationship between the pathophysiology of the MDD and its clinical progression, analyzed by severity of the depressive symptoms and sleep quality, we conducted a study assessing different peripheral molecular biomarkers, including the levels of plasma C-reactive protein (CRP), serum mature brain-derived neurotrophic factor (mBDNF), serum cortisol (SC), and salivary cortisol awakening response (CAR), of patients with MDD (n = 58) and a control group of healthy volunteers (n = 62). Patients with the first episode of MDD (n = 30) had significantly higher levels of CAR and SC than controls (n = 32) and similar levels of mBDNF of controls. Patients with treatment-resistant depression (TRD, n = 28) presented significantly lower levels of SC and CAR, and higher levels of mBDNF and CRP than controls (n = 30). An increased severity of depressive symptoms and worse sleep quality were correlated with levels low of SC and CAR, and with high levels of mBDNF. These results point out a strong relationship between the stages clinical of MDD and changes in a range of relevant biological markers. This can assist in the development of precision psychiatry and future research on the biological tests for depression

Time-perception network and default mode network are associated with temporal prediction in a periodic motion task

The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study we used fMRI to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex, anterior cingulate cortex, and bilateral posterior cingulate cortex/precuneus. It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may reflect the validation of prospective internal models and predictive control. Taken together, our findings suggest that continuous manipulation of temporal predictions engages representations of temporal prediction as well as task-independent updating of internal models

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.FAPESP[05/03225-7]CNPqCAPESCINAPCE[05-56447-7

Visualizando a elaboração da linguagem em surdos bilíngues por meio da ressonância magnética funcional

As línguas de sinais são línguas naturais que compartilham as propriedades das línguas orais. Este estudo buscou visualizar as áreas corticais ativadas em surdos bilíngues em Língua Brasileira de Sinais e em Língua Portuguesa. As tarefas envolveram a elaboração da linguagem. Foram feitos treze exames de Imagem por Ressonância Magnética Funcional. Foi utilizada, ainda, uma sequência de imagem ecoplanar para a aquisição BOLD (Blood Oxygen Level Dependent) e, para corregistro, foram buscadas imagens de alta resolução de todo o encéfalo. Os mapas estatísticos foram obtidos com o Modelo Geral Linear, utilizando o programa Brain Voyager.TM A tarefa de elaboração da linguagem em Língua Brasileira de Sinais proporcionou ativações mais significativas do que a tarefa de elaboração em Língua Portuguesa, sugerindo que os surdos bilíngues apresentam maior competência linguística em língua de sinais.Sign languages are natural languages that have the same properties of oral languages. This study aimed to identify the cortical areas activated in bilingual deaf subjects who communicate through Brazilian Sign Language and Portuguese Language. Tasks involved the production of language. Thirteen functional magnetic resonances imaging (fMRI) exams were performed. An Echo Planar Imaging (EPI) sequence was used to obtain the BOLD (Blood Oxygen Level Dependent), associated with a whole brain high resolution imaging for co-register. The statistical maps were obtained using the General Linear Model with Brain VoyagerTM software. The task of producing Brazilian Sign Language provided more significant activations than did the task of producing Portuguese Language, suggesting that bilingual deaf subjects present a higher language proficiency in sign language

Visualizando a elaboração da linguagem em surdos bilíngues por meio da ressonância magnética funcional

As línguas de sinais são línguas naturais que compartilham as propriedades das línguas orais. Este estudo buscou visualizar as áreas corticais ativadas em surdos bilíngues em Língua Brasileira de Sinais e em Língua Portuguesa. As tarefas envolveram a elaboração da linguagem. Foram feitos treze exames de Imagem por Ressonância Magnética Funcional. Foi utilizada, ainda, uma sequência de imagem ecoplanar para a aquisição BOLD (Blood Oxygen Level Dependent) e, para corregistro, foram buscadas imagens de alta resolução de todo o encéfalo. Os mapas estatísticos foram obtidos com o Modelo Geral Linear, utilizando o programa Brain Voyager.TM A tarefa de elaboração da linguagem em Língua Brasileira de Sinais proporcionou ativações mais significativas do que a tarefa de elaboração em Língua Portuguesa, sugerindo que os surdos bilíngues apresentam maior competência linguística em língua de sinais