Jakub Späti

Estimation of human circadian phase via a multi-channel ambulatory monitoring system and a multiple regression model

Reliable detection of circadian phase in humans using noninvasive ambulatory measurements in real-life conditions is challenging and still an unsolved problem. The masking effects of everyday behavior and environmental input such as physical activity and light on the measured variables need to be considered critically. Here, we aimed at developing techniques for estimating circadian phase with the lowest subject burden possible, that is, without the need of constant routine (CR) laboratory conditions or without measuring the standard circadian markers, (rectal) core body temperature (CBT), and melatonin levels. In this validation study, subjects (N = 16) wore multi-channel ambulatory monitoring devices and went about their daily routine for 1 week. The devices measured a large number of physiological, behavioral, and environmental variables, including CBT, skin temperatures, cardiovascular and respiratory function, movement/posture, ambient temperature, and the spectral composition and intensity of light received at eye level. Sleep diaries were logged electronically. After the ambulatory phase, subjects underwent a 32-h CR procedure in the laboratory for measuring unmasked circadian phase based on the “midpoint” of the salivary melatonin profile. To overcome the complex masking effects of confounding variables during ambulatory measurements, multiple regression techniques were applied in combination with the cross-validation approach to subject-independent prediction of circadian phase. The most accurate estimate of circadian phase was achieved using skin temperatures, irradiance for ambient light in the blue spectral band, and motion acceleration as predictors with lags of up to 24 h. Multiple regression showed statistically significant improvement of variance of prediction error over the traditional approaches to determining circadian phase based on single predictors (motion acceleration or sleep log), although CBT was intentionally not included as the predictor. Compared to CBT alone, our method resulted in a 40% smaller range of prediction errors and a nonsignificant reduction of error variance. The proposed noninvasive measurement method could find applications in sleep medicine or in other domains where knowing the exact endogenous circadian phase is important (e.g., for the timing of light therapy).

An Improved Method for Estimating Human Circadian Phase Derived From Multichannel Ambulatory Monitoring and Artificial Neural Networks

Recently, we developed a novel method for estimating human circadian phase with noninvasive ambulatory measurements combined with subject-independent multiple regression models and a curve-fitting approach. With this, we were able to estimate circadian phase under real-life conditions with low subject burden, i.e., without need of constant routine (CR) laboratory conditions, and without measuring standard circadian markers, such as core body temperature (CBT) or pineal hormone melatonin rhythms. The precision of ambulatory-derived estimated circadian phase was within an error of 12 ± 41 min (mean ± SD) in comparison to melatonin phase during a CR protocol. The physiological measures could be reduced to a triple combination: skin temperatures, irradiance in the blue spectral band of ambient light, and motion acceleration. Here, we present a nonlinear regression model approach based on artificial neural networks for a larger data set (25 healthy young males), including both the original data and additional data collected in the same protocol and using the same equipment. Throughout our validation study, subjects wore multichannel ambulatory monitoring devices and went about their daily routine for 1 wk. The devices collected a large number of physiological, behavioral, and environmental variables, including CBT, skin temperatures, cardiovascular and respiratory functions, movement/posture, ambient temperature, spectral composition and intensity of light perceived at eye level, and sleep logs. After the ambulatory phase, study volunteers underwent a 32-h CR protocol in the laboratory for measuring unmasked circadian phase (i.e., “midpoint” of the nighttime melatonin rhythm). To overcome the complex masking effects of many different confounding variables during ambulatory measurements, neural network–based nonlinear regression techniques were applied in combination with the cross-validation approach to subject-independent prediction of circadian phase. The most accurate estimate of circadian phase with a prediction error of −3 ± 23 min (mean ± SD) was achieved using only two types of the measured variables: skin temperatures and irradiance for ambient light in the blue spectral band. Compared to our previous linear multiple regression modeling approach, motion acceleration data can be excluded and prediction accuracy, nevertheless, improved. Neural network regression showed statistically significant improvement of variance of prediction error over traditional approaches in determining circadian phase based on single predictors (CBT, motion acceleration, or sleep logs), even though none of these variables was included as predictor. We, therefore, have identified two sets of noninvasive measures that, combined with the prediction model, can provide researchers and clinicians with a precise measure of internal time, in spite of the masking effects of daily behavior. This method, here validated in healthy young men, requires testing in a clinical or shiftwork population suffering from circadian sleep-wake disorders. (Author correspondence: vitaliy.kolodyazhniy@sbg.ac.at)

Prefrontal Thinning Affects Functional Connectivity and Regional Homogeneity of the Anterior Cingulate Cortex in Depression

Major depressive disorder (MDD) is associated with structural and functional alterations in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Enhanced ACC activity at rest (measured using various imaging methodologies) is found in treatment-responsive patients and is hypothesized to bolster treatment response by fostering adaptive rumination. However, whether structural changes influence functional coupling between fronto-cingulate regions and ACC regional homogeneity (ReHo) and whether these functional changes are related to levels of adaptive rumination and treatment response is still unclear. Cortical thickness and ReHo maps were calculated in 21 unmedicated depressed patients and 35 healthy controls. Regions with reduced cortical thickness defined the seeds for the subsequent functional connectivity (FC) analyses. Patients completed the Response Style Questionnaire, which provided a measure of adaptive rumination associated with better response to psychotherapy. Compared with controls, depressed patients showed thinning of the right anterior PFC, increased prefrontal connectivity with the supragenual ACC (suACC), and higher ReHo in the suACC. The suACC clusters of increased ReHo and FC spatially overlapped. In depressed patients, suACC ReHo scores positively correlated with PFC thickness and with FC strength. Moreover, stronger fronto-cingulate connectivity was related to higher levels of adaptive rumination. Greater suACC ReHo and connectivity with the right anterior PFC seem to foster adaptive forms of self-referential processing associated with better response to psychotherapy, whereas prefrontal thinning impairs the ability of depressed patients to engage the suACC during a major depressive episode. Bolstering the function of the suACC may represent a potential target for treatment.

Functional lateralization of the anterior insula during feedback processing

Effective adaptive behavior rests on an appropriate understanding of how much responsibility we have over outcomes in the environment. This attribution of agency to ourselves or to an external event influences our behavioral and affective response to the outcomes. Despite its special importance to understanding human motivation and affect, the neural mechanisms involved in self‐attributed rewards and punishments remain unclear. Previous evidence implicates the anterior insula (AI) in evaluating the consequences of our own actions. However, it is unclear if the AI has a general role in feedback evaluation (positive and negative) or plays a specific role during error processing. Using functional magnetic resonance imaging and a motion prediction task, we investigate neural responses to self‐ and externally attributed monetary gains and losses. We found that attribution effects vary according to the valence of feedback: significant valence × attribution interactions in the right AI, the anterior cingulate cortex (ACC), the midbrain, and the right ventral putamen. Self‐attributed losses were associated with increased activity in the midbrain, the ACC and the right AI, and negative BOLD response in the ventral putamen. However, higher BOLD activity to self‐attributed feedback (losses and gains) was observed in the left AI, the thalamus, and the cerebellar vermis. These results suggest a functional lateralization of the AI. The right AI, together with the midbrain and the ACC, is mainly involved in processing the salience of the outcome, whereas the left is part of a cerebello‐thalamic‐cortical pathway involved in cognitive control processes important for subsequent behavioral adaptations. Hum Brain Mapp 35:4428–4439, 2014.

Neural representation and clinically relevant moderators of individualised self-criticism in healthy subjects

Many people routinely criticise themselves. While self-criticism is largely unproblematic for most individuals, depressed patients exhibit excessive self-critical thinking, which leads to strong negative affects. We used functional magnetic resonance imaging in healthy subjects (N = 20) to investigate neural correlates and possible psychological moderators of self-critical processing. Stimuli consisted of individually selected adjectives of personally negative content and were contrasted with neutral and negative non-self-referential adjectives. We found that confrontation with self-critical material yielded neural activity in regions involved in emotions (anterior insula/hippocampus-amygdala formation) and in anterior and posterior cortical midline structures, which are associated with self-referential and autobiographical memory processing. Furthermore, contrasts revealed an extended network of bilateral frontal brain areas. We suggest that the co-activation of superior and inferior lateral frontal brain regions reflects the recruitment of a frontal top-down pathway, representing cognitive reappraisal strategies for dealing with evoked negative affects. In addition, activation of right superior frontal areas was positively associated with neuroticism and negatively associated with cognitive reappraisal. Although these findings may not be specific to negative stimuli, they support a role for clinically relevant personality traits in successful regulation of emotion during confrontation with self-critical material.

Amygdala response to self-critical stimuli and symptom improvement in psychotherapy for depression.

BACKGROUND Cognitive-behavioural therapy is efficacious in the treatment of major depressive disorder but response rates are still far from satisfactory. AIMS To better understand brain responses to individualised emotional stimuli and their association with outcome, to enhance treatment. METHOD Functional magnetic resonance imaging data were collected prior to individual psychotherapy. Differences in brain activity during passive viewing of individualised self-critical material in 23 unmedicated out-patients with depression and 28 healthy controls were assessed. The associations between brain activity, cognitive and emotional change, and outcome were analysed in 21 patients. RESULTS Patients showed enhanced activity in the amygdala and ventral striatum compared with the control group. Non-response to therapy was associated with enhanced activity in the right amygdala compared with those who responded, and activity in this region was negatively associated with outcome. Emotional but not cognitive changes mediated this association. CONCLUSIONS Amygdala hyperactivity may lessen symptom improvement in psychotherapy for depression through attenuating emotional skill acquisition.