Antisocial behaviours such as disobedience, lying, stealing, destruction of property, and aggression towards others are common to multiple disorders of childhood and adulthood, including conduct disorder, oppositional defiant disorder, psychopathy, and antisocial personality disorder. These disorders have a significant negative impact for individuals and for society, but whether they represent clinically different phenomena, or simply different approaches to diagnosing the same underlying psychopathology is highly debated. Computational psychiatry, with its dual focus on identifying different classes of disorder and health (data-driven) and latent cognitive and neurobiological mechanisms (theory-driven), is well placed to address these questions. The elucidation of mechanisms that might characterise latent processes across different disorders of antisocial behaviour can also provide important advances. In this review, we critically discuss the contribution of computational research to our understanding of various antisocial behaviour disorders, and highlight suggestions for how computational psychiatry can address important clinical and scientific questions about these disorders in the future.
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http://dx.doi.org/10.1016/j.neubiorev.2022.104995 | DOI Listing |
Npj Ment Health Res
January 2025
Department of Psychiatry, University Hospital Infanta Leonor, 28031, Madrid, Spain.
Attention-deficit/hyperactivity disorder (ADHD) presents with symptoms like impulsiveness, inattention, and hyperactivity, often affecting children's academic and social functioning. Non-pharmacological interventions, such as digital cognitive therapy, are emerging as complementary treatments for ADHD. The randomized controlled trial explored the impact of an AI-driven digital cognitive program on impulsiveness, inattentiveness, and neurophysiological markers in 41 children aged 8-12 with ADHD.
View Article and Find Full Text PDFCommun Med (Lond)
January 2025
Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
Background: Alzheimer's disease (AD) is a major neurodegenerative disorder with significant environmental factors, including diet and lifestyle, influencing its onset and progression. Although previous studies have suggested that certain diets may reduce the incidence of AD, the underlying mechanisms remain unclear.
Method: In this post-hoc analysis of a randomized crossover study of 20 elderly adults, we investigated the effects of a modified Mediterranean ketogenic diet (MMKD) on the plasma lipidome in the context of AD biomarkers, analyzing 784 lipid species across 47 classes using a targeted lipidomics platform.
Sci Rep
January 2025
Department of Cognitive Sciences, University of California, 2201 Social & Behavioral Sciences Gateway, Irvine, CA, 92697, USA.
In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior rather than noise or other irrelevant factors.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Computational Brain Research and Intervention (C-Brain) Lab, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, California, USA.
Introduction: Amyloid beta (Aβ) plaques and hyperphosphorylated tau in the entorhinal regions are key Alzheimer's disease (AD) markers, but the spatial Aβ pathways influencing tau pathology remain unclear.
Methods: We applied predictive modeling to identify Aβ standardized uptake value ratio (SUVR) spatial patterns that predict entorhinal tau levels, future hippocampal volume, and Preclinical Alzheimer's Cognitive Composite (PACC) scores at 5-year follow-up. The model was trained on Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 237), incorporating amyloid-PET (positron emission tomography), tau-PET, magnetic resonance imaging (MRI), and cognitive data, and validated on Harvard Aging Brain Study (HABS) (N = 276).
Int J Methods Psychiatr Res
March 2025
Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Background: Large Language Models (LLMs) hold promise in enhancing psychiatric research efficiency. However, concerns related to bias, computational demands, data privacy, and the reliability of LLM-generated content pose challenges. GAP: Existing studies primarily focus on the clinical applications of LLMs, with limited exploration of their potentials in broader psychiatric research.
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