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Importance: The continuum view of the psychosis spectrum (PS) implies that, in population-based samples, PS symptoms should be associated with neural abnormalities similar to those found in help-seeking clinical risk individuals and in schizophrenia. To our knowledge, functional neuroimaging has not previously been applied in large population-based PS samples and can help us understand the neural architecture of psychosis more broadly and identify brain phenotypes beyond symptoms that are associated with the extended psychosis phenotype.
Objective: To examine the categorical and dimensional relationships of PS symptoms to prefrontal hypoactivation during working memory and to amygdala hyperactivation during threat emotion processing.
Design, Setting, And Participants: The Philadelphia Neurodevelopmental Cohort is a genotyped, prospectively accrued, population-based sample of almost 10,000 youths who received a structured psychiatric evaluation and a computerized neurocognitive battery. The study was conducted at an academic and children's hospital health care network, between November 1, 2009 to November 30, 2011. A subsample of 1445 youths underwent neuroimaging, including functional magnetic resonance imaging tasks examined herein. Participants were youth aged 11 to 22 years old identified through structured interview as having PS features (PS group) (n = 260) and typically developing (TD) comparison youth without significant psychopathology (TD group) (n = 220).
Main Outcomes And Measures: Two functional magnetic resonance imaging paradigms were used: a fractal n-back working memory task probing executive system function and an emotion identification task probing amygdala responses to threatening faces.
Results: In the n-back task, working memory evoked lower activation in the PS group than the TD group throughout the executive control circuitry, including dorsolateral prefrontal cortex (cluster-corrected P < .05). Within the PS group, dorsolateral prefrontal cortex activation correlated with cognitive deficits (r = .32, P < .001), but no correlation was found with positive symptom severity. During emotion identification, PS demonstrated elevated responses to threatening facial expressions in amygdala, as well as left fusiform cortex and right middle frontal gyrus (cluster-corrected P < .05). The response in the amygdala correlated with positive symptom severity (r = .16, P = .01) but not with cognitive deficits.
Conclusions And Relevance: The pattern of functional abnormalities observed in the PS group is similar to that previously found in schizophrenia and help-seeking risk samples. Specific circuit dysfunction during cognitive and emotion-processing tasks is present early in the development of psychopathology and herein could not be attributed to chronic illness or medication confounds. Hypoactivation in executive circuitry and limbic hyperactivation to threat could reflect partly independent risk factors for PS symptoms, with the former relating to cognitive deficits that increase the risk for developing psychotic symptoms and the latter contributing directly to positive psychotic symptoms.
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http://dx.doi.org/10.1001/jamapsychiatry.2014.3169 | DOI Listing |
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School of Intelligent Rehabilitation Engineering, Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China.
Background: Prosthetic socket is a key component of the prosthesis for clinical application; its performance directly affects the adaptation of the residual limb to the prosthetic socket. There are long-term and short-term volume fluctuation of the residual limb. The volume fluctuation of the residual limb will lead to the pressure mismatch at the interface of the residual limb and the prosthetic socket, which will cause a series of skin and fit problems.
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December 2024
Department of Research, Innlandet Hospital Trust, Brumunddal, Norway.
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Methods: Adults ( = 65, = 25.4) with schizophrenia spectrum disorders participating in a clinical trial (registered at clinicaltrials.
Front Psychiatry
December 2024
Department of Anatomy and Neurosciences, Amsterdam University Medical Center, Amsterdam, Netherlands.
Introduction: Improved effectiveness and treatment adherence is needed in smoking cessation (SC) therapies. Another important challenge is to disrupt maladaptive drug-related memories. To achieve these goals, we developed a novel treatment strategy on the basis of motion-assisted memory desensitization and reprocessing (3MDR).
View Article and Find Full Text PDFWater Res X
January 2025
Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment of treated effluents to discharge quality standards. To aid in real-time decision-making for stable WWTP operations, this study developed a probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity of producing probability predictions, to enhance the robustness of real-time WWTP effluent quality prediction under such events. The developed probabilistic encoder-decoder LSTM (P-ED-LSTM) model was tested in an actual WWTP, where bihourly effluent quality prediction of total nitrogen was performed and compared with classical deep learning models, including LSTM, gated recurrent unit (GRU) and Transformer.
View Article and Find Full Text PDFHeliyon
December 2024
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia.
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas.
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