It’s About Time: The Circadian Network as Time-Keeper for Cognitive Functioning, Locomotor Activity and Mental Health

A variety of organisms including mammals have evolved a 24h, self-sustained timekeeping machinery known as the circadian clock (biological clock), which enables to anticipate, respond, and adapt to environmental influences such as the daily light and dark cycles. Proper functioning of the clock plays a pivotal role in the temporal regulation of a wide range of cellular, physiological, and behavioural processes. The disruption of circadian rhythms was found to be associated with the onset and progression of several pathologies including sleep and mental disorders, cancer, and neurodegeneration. Thus, the role of the circadian clock in health and disease, and its clinical applications, have gained increasing attention, but the exact mechanisms underlying temporal regulation require further work and the integration of evidence from different research fields. In this review, we address the current knowledge regarding the functioning of molecular circuits as generators of circadian rhythms and the essential role of circadian synchrony in a healthy organism. In particular, we discuss the role of circadian regulation in the context of behaviour and cognitive functioning, delineating how the loss of this tight interplay is linked to pathological development with a focus on mental disorders and neurodegeneration. We further describe emerging new aspects on the link between the circadian clock and physical exercise-induced cognitive functioning, and its current usage as circadian activator with a positive impact in delaying the progression of certain pathologies including neurodegeneration and brain-related disorders. Finally, we discuss recent epidemiological evidence pointing to an important role of the circadian clock in mental health.Peer Reviewe

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research

Impaired differential learning of fear versus safety signs in obsessive-compulsive disorder

Pavlovian learning mechanisms are of great importance both for models of psychiatric disorders and treatment approaches, but understudied in obsessive-compulsive disorder (OCD). Using an established Pavlovian fear conditioning and reversal procedure, we studied skin conductance responses in 41 patients with OCD and in 32 matched healthy control participants. Within both groups, fear acquisition and reversal effects were evident. When comparing groups, patients showed impaired differential learning of threatening and safe stimuli, consistent with previous research. In contrast to prior findings, differential learning impairments were restricted to fear acquisition, and not observed in the reversal stage of the experiment. As previous and present fear reversal experiments in OCD differed in the use of color coding to facilitate stimulus discrimination, the studies converge to suggest that differential learning of threatening versus safe stimuli is impaired in OCD, but manifests itself differently depending on the difficulty of the association to be learned. When supported by the addition of color, patients with OCD previously appeared to acquire an association early but failed to reverse it according to changed contingencies. In absence of such color coding of stimuli, our data suggest that patients with OCD already show differential learning impairments during fear acquisition, which may relate to findings of altered coping with uncertainty previously observed in OCD. Impaired differential learning of threatening versus safe stimuli should be studied further in OCD, in order to determine whether impairments in differential learning predict treatment outcomes in patients, and whether they are etiologically relevant for OCD.Peer Reviewe

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research

Unconditioned responses and functional fear networks in human classical conditioning.

Human imaging studies examining fear conditioning have mainly focused on the neural responses to conditioned cues. In contrast, the neural basis of the unconditioned response and the mechanisms by which fear modulates inter-regional functional coupling have received limited attention. We examined the neural responses to an unconditioned stimulus using a partial-reinforcement fear conditioning paradigm and functional MRI. The analysis focused on: (1) the effects of an unconditioned stimulus (an electric shock) that was either expected and actually delivered, or expected but not delivered, and (2) on how related brain activity changed across conditioning trials, and (3) how shock expectation influenced inter-regional coupling within the fear network. We found that: (1) the delivery of the shock engaged the red nucleus, amygdale, dorsal striatum, insula, somatosensory and cingulate cortices, (2) when the shock was expected but not delivered, only the red nucleus, the anterior insular and dorsal anterior cingulate cortices showed activity increases that were sustained across trials, and (3) psycho-physiological interaction analysis demonstrated that fear led to increased red nucleus coupling to insula but decreased hippocampus coupling to the red nucleus, thalamus and cerebellum. The hippocampus and the anterior insula may serve as hubs facilitating the switch between engagement of a defensive immediate fear network and a resting network

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 +/- 3.47 in the replication set; Children, 59.8 +/- 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 +/- 3.84; Children, 48.51 +/- 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 +/- 3.97; Children, 72.45 +/- 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.ISSN:1359-4184ISSN:1476-557