Deep neural networks in psychiatry DOI Creative Commons
Daniel Durstewitz, Georgia Koppe, Andreas Meyer‐Lindenberg

и другие.

Molecular Psychiatry, Год журнала: 2019, Номер 24(11), С. 1583 - 1598

Опубликована: Фев. 15, 2019

Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success impact in many commercial research settings. They are powerful tools for large scale data analysis, prediction classification, especially very data-rich environments ("big data"), started to find their way into medical applications. Here we will first give an overview machine a focus on recurrent neural networks, relation statistics, the principles behind them. We then discuss review directions along which (deep) networks can be, or already been, context psychiatry, try delineate future potential this area. also comment emerging area that so far has much less well explored: by embedding semantically interpretable computational models brain dynamics behavior statistical context, insights dysfunction beyond mere classification may be gained. Especially marriage inference offer behavioral mechanisms could open completely novel avenues psychiatric treatment.

Язык: Английский

Dynamic models of large-scale brain activity DOI
Michael Breakspear

Nature Neuroscience, Год журнала: 2017, Номер 20(3), С. 340 - 352

Опубликована: Фев. 23, 2017

Язык: Английский

Процитировано

1054

Building better biomarkers: brain models in translational neuroimaging DOI
Choong‐Wan Woo, Luke J. Chang, Martin A. Lindquist

и другие.

Nature Neuroscience, Год журнала: 2017, Номер 20(3), С. 365 - 377

Опубликована: Фев. 23, 2017

Язык: Английский

Процитировано

969

Studying and modifying brain function with non-invasive brain stimulation DOI
Rafael Polanìa, Michael A. Nitsche, Christian C. Ruff

и другие.

Nature Neuroscience, Год журнала: 2018, Номер 21(2), С. 174 - 187

Опубликована: Янв. 8, 2018

Язык: Английский

Процитировано

870

Active interoceptive inference and the emotional brain DOI Creative Commons
Anil K. Seth, Karl Friston

Philosophical Transactions of the Royal Society B Biological Sciences, Год журнала: 2016, Номер 371(1708), С. 20160007 - 20160007

Опубликована: Окт. 11, 2016

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference the brain. The notion interoceptive means that bodily states are regulated by autonomic reflexes enslaved descending predictions from deep generative models our internal external milieu. This re-conceptualization illuminates several issues cognitive clinical neuroscience with implications for experiences selfhood emotion. first contextualize terms active (Bayesian) brain, highlighting enactivist (embodied) aspects. then consider key role uncertainty or precision how this might translate into neuromodulation. next examine understanding functional anatomy emotional surveying observations on agranular cortex. Finally, we turn theoretical issues, namely, shaping sense embodied self feelings. will draw links between physiological homoeostasis allostasis, early cybernetic ideas predictive control processing. explanatory scope ranges explanations autism depression, through consciousness. offer brief survey these exciting developments. article is part themed issue ‘Interoception beyond homeostasis: affect, cognition mental health’.

Язык: Английский

Процитировано

766

The Predictive Coding Account of Psychosis DOI Creative Commons
Philipp Sterzer, Rick A. Adams, Paul C. Fletcher

и другие.

Biological Psychiatry, Год журнала: 2018, Номер 84(9), С. 634 - 643

Опубликована: Май 25, 2018

Fueled by developments in computational neuroscience, there has been increasing interest the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding current state world are made combining prior beliefs with incoming sensory signals. Mismatches between signals constitute prediction errors that drive new learning. Psychosis suggested to result from a decreased precision encoding relative data, thereby garnering maladaptive inferences. we review evidence for aberrant discuss challenges this canonical account For example, hallucinations delusions may relate distinct alterations coding, despite their common co-occurrence. More broadly, some studies implicate weakened psychosis, others find stronger priors. These might be answered more nuanced view coding. Different priors specified different modalities integration, deficits each modality need not uniform. Furthermore, hierarchical organization critical. Altered processes at lower levels hierarchy linearly related higher (and vice versa). Finally, theories do highlight active inference—the process through which effects our actions on sensations anticipated minimized. It is possible conflicting findings reconciled considering these complexities, portending framework psychosis equipped deal its many manifestations.

Язык: Английский

Процитировано

721

Support vector machine DOI
Derek Pisner, David M. Schnyer

Machine learning, Год журнала: 2019, Номер unknown, С. 101 - 121

Опубликована: Ноя. 15, 2019

Язык: Английский

Процитировано

708

Machine Learning for Precision Psychiatry: Opportunities and Challenges DOI
Danilo Bzdok, Andreas Meyer‐Lindenberg

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Год журнала: 2017, Номер 3(3), С. 223 - 230

Опубликована: Дек. 6, 2017

Язык: Английский

Процитировано

689

The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research DOI
Donald J. Robinaugh, Ria H. A. Hoekstra, Emma R. Toner

и другие.

Psychological Medicine, Год журнала: 2019, Номер 50(3), С. 353 - 366

Опубликована: Дек. 26, 2019

Abstract The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade is now a full-fledged area psychiatric research. In this article, we provide an overview critical analysis 363 articles produced research program, with focus on key theoretical, methodological, empirical contributions. addition, turn our attention next propose avenues for future each these domains. We argue program will best served by working toward two overarching aims: (a) identification robust phenomena (b) development formal theories explain those phenomena. recommend specific steps forward within broad framework are necessary if develop into progressive capable producing cumulative body knowledge about how operate systems.

Язык: Английский

Процитировано

587

Supervised Machine Learning: A Brief Primer DOI
Tammy Jiang, Jaimie L. Gradus, Anthony J. Rosellini

и другие.

Behavior Therapy, Год журнала: 2020, Номер 51(5), С. 675 - 687

Опубликована: Май 16, 2020

Язык: Английский

Процитировано

531

Cognitive and behavioural flexibility: neural mechanisms and clinical considerations DOI Creative Commons
Lucina Q. Uddin

Nature reviews. Neuroscience, Год журнала: 2021, Номер 22(3), С. 167 - 179

Опубликована: Фев. 3, 2021

Cognitive and behavioural flexibility permit the appropriate adjustment of thoughts behaviours in response to changing environmental demands. Brain mechanisms enabling have been examined using non-invasive neuroimaging approaches humans alongside pharmacological lesion studies animals. This work has identified large-scale functional brain networks encompassing lateral orbital frontoparietal, midcingulo-insular frontostriatal regions that support across lifespan. Flexibility can be compromised early-life neurodevelopmental disorders, clinical conditions emerge during adolescence late-life dementias. We critically evaluate evidence for enhancement through cognitive training, physical activity bilingual experience. is critical optimal adaptation actions under circumstances. In this Review, Uddin summarizes research processes neural systems supporting discusses ways improve

Язык: Английский

Процитировано

470