Functional and structural brain imaging has identified neural and neurotransmitter systems involved in schizophrenia and their link to cognitive and behavioural disturbances such as psychosis. ...Mapping such abnormalities in patients, however, cannot fully capture the strong neurodevelopmental component of schizophrenia that pre-dates manifest illness. A recent strategy to address this issue has been to focus on mechanisms of disease risk. Imaging genetics techniques have made it possible to define neural systems that mediate heritable risk linked to candidate and genome-wide-supported common variants, and mechanisms for environmental risk and gene-environment interactions are emerging. Characterizing the neural risk architecture of schizophrenia provides a translational research strategy for future treatments.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Mental health and social life are intimately inter-related, as demonstrated by the frequent social deficits of psychiatric patients and the increased rate of psychiatric disorders in people exposed ...to social environmental adversity. Here, we review emerging evidence that combines epidemiology, social psychology and neuroscience to bring neural mechanisms of social risk factors for mental illness into focus. In doing so, we discuss existing evidence on the effects of common genetic risk factors in social neural pathways and outline the need for integrative approaches to identify the converging mechanisms of social environmental and genetic risk in brain.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
5.
Deep learning for small and big data in psychiatry Koppe, Georgia; Meyer-Lindenberg, Andreas; Durstewitz, Daniel
Neuropsychopharmacology (New York, N.Y.),
01/2021, Letnik:
46, Številka:
1
Journal Article
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Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored ...treatments. To this end, it appears that the analysis of 'small' experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather 'small' samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.
Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant ...frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have not been investigated so far. In this study, resting‐state fMRI data from 58 right‐handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within‐network correlation of the sensorimotor, visual, and default‐mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM‐IV‐TR (n = 44), there was a significant correlation between increased within FNC in cortico‐striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states.
The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states.
The evolutionarily highly conserved neuropeptide oxytocin is a key mediator of social and emotional behavior in mammals, including humans. A common variant (rs53576) in the oxytocin receptor gene ...(OXTR) has been implicated in social-behavioral phenotypes, such as maternal sensitivity and empathy, and with neuropsychiatric disorders associated with social impairment, but the intermediate neural mechanisms are unknown. Here, we used multimodal neuroimaging in a large sample of healthy human subjects to identify structural and functional alterations in OXTR risk allele carriers and their link to temperament. Activation and interregional coupling of the amygdala during the processing of emotionally salient social cues was significantly affected by genotype. In addition, evidence for structural alterations in key oxytocinergic regions emerged, particularly in the hypothalamus. These neural characteristics predicted lower levels of reward dependence, specifically in male risk allele carriers. Our findings identify sex-dependent mechanisms impacting the structure and function of hypothalamic-limbic circuits that are of potential clinical and translational significance.
One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the ...initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12‐year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism‐based interventions. These goals will be achieved by: (i) using mobile health (m‐health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real‐life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal‐directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.
The losing and regaining control over drug intake (ReCoDe) framework to study losing and regaining control over drug intake over a 12‐year perspective: Our 19 projects are divided into three research domains
The neuropeptides oxytocin (OXT) and arginine vasopressin (AVP) are evolutionarily highly conserved mediators in the regulation of complex social cognition and behaviour. Recent studies have ...investigated the effects of OXT and AVP on human social interaction, the genetic mechanisms of inter-individual variation in social neuropeptide signalling and the actions of OXT and AVP in the human brain as revealed by neuroimaging. These data have advanced our understanding of the mechanisms by which these neuropeptides contribute to human social behaviour. OXT and AVP are emerging as targets for novel treatment approaches--particularly in synergistic combination with psychotherapy--for mental disorders characterized by social dysfunction, such as autism, social anxiety disorder, borderline personality disorder and schizophrenia.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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