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Obsessive-compulsive disorder is among the most disabling psychiatric disorders. Although deep brain stimulation is considered an effective treatment, its use in clinical practice is not fully established. This is, at least in part, due to ambiguity about the best suited target and insufficient knowledge about underlying mechanisms. Recent advances suggest that changes in broader brain networks are responsible for improvement of obsessions and compulsions, rather than local impact at the stimulation site. These findings were fueled by innovative methodological approaches using brain connectivity analyses in combination with neuromodulatory interventions. Such a connectomic approach for neuromodulation constitutes an integrative account that aims to characterize optimal target networks. In this critical review, we integrate findings from connectomic studies and deep brain stimulation interventions to characterize a neural network presumably effective in reducing obsessions and compulsions. To this end, we scrutinize methodologies and seemingly conflicting findings with the aim to merge observations to identify common and diverse pathways for treating obsessive-compulsive disorder. Ultimately, we propose a unified network that-when modulated by means of cortical or subcortical interventions-alleviates obsessive-compulsive symptoms.
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http://dx.doi.org/10.1016/j.biopsych.2021.07.010 | DOI Listing |
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFLancet Reg Health West Pac
September 2025
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
Background: There is ongoing controversy as to whether surgical intervention to haematoma evacuation benefits patients with acute intracerebral haemorrhage (ICH). This study aimed to evaluate the association of surgical intervention to evacuate the haematoma and 6-month functional outcome in participants of the third Intensive Care Bundle with Blood Pressure Reduction in Acute Cerebral Haemorrhage Trial (INTERACT3).
Methods: This was a secondary analysis of INTERACT3, which enrolled adults (age ≥18 years) spontaneous ICH patients within 6 h after onset.
Neuropsychiatr Dis Treat
September 2025
Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People's Republic of China.
Parkinson's disease (PD) represents a progressive neurodegenerative disorder with escalating global burden, with mechanistic studies revealing α-synuclein propagation through gut-brain axis, mitochondrial defects, and neuroinflammatory cascades driven by genetic-environmental interplay. Recent advancements in diagnostic paradigms have successfully combined α-synuclein seed amplification assays with multimodal neuroimaging techniques, achieving an impressive diagnostic accuracy of 92% during the prodromal stages of disease. Phase II trials highlight disease-modifying potential of α-synuclein-targeting immunotherapies (40% reduction in motor decline) and LRRK2 kinase inhibitors showing blood-brain barrier penetration.
View Article and Find Full Text PDFMed Phys
September 2025
School of Computer, Electronics and Information, Guangxi University, Nanning, China.
Background: Deformable medical image registration is a critical task in medical imaging-assisted diagnosis and treatment. In recent years, medical image registration methods based on deep learning have made significant success by leveraging prior knowledge, and the registration accuracy and computational efficiency have been greatly improved. Models based on Transformers have achieved better performance than convolutional neural network methods (ConvNet) in image registration.
View Article and Find Full Text PDFComput Biol Med
September 2025
Postgraduate Program in Computing, Center for Technological Development, Federal University of Pelotas, Pelotas, 96010-610, Rio Grande do Sul, Brazil.
In the task of image classification for emotion recognition, facial expression data is commonly used. However, electrical brain signals generated by neural activity provide data with greater integrity. We can capture these signals non-invasively using electroencephalogram (EEG) recording devices.
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