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Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3946642 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0091090 | PLOS |
J Neuroradiol
September 2025
Department of Physical Therapy, Yeungnam University College, 170 Hyeonchung-ro, Nam-gu, Daegu, Republic of Korea. Electronic address:
Visuospatial perception, which is based on the comprehension of objects and space, requires spatial attention to the surrounding environment. Stimulus-related elements that affect visuospatial tasks include object geometry, familiarity, complexity, and picture plane versus depth rotation. The dorsal stream pathway from the visual cortex, which is implicated in spatial processing, reflects the spatial component needed to orient the focus of attention to the location of the expected target stimulus.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
View Article and Find Full Text PDFCereb Cortex
August 2025
Section of Brain Function Information, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, Japan.
This study aimed to identify brain activity modulations associated with different types of visual tracking using advanced functional magnetic resonance imaging techniques developed by the Human Connectome Project (HCP) consortium. Magnetic resonance imaging data were collected from 27 healthy volunteers using a 3-T scanner. During a single run, participants either fixated on a stationary visual target (fixation block) or tracked a smoothly moving or jumping target (smooth or saccadic tracking blocks), alternating across blocks.
View Article and Find Full Text PDFCereb Cortex
August 2025
School of Psychology, University of Surrey, Stag Hill, Guildford, Surrey, GU2 7XH, United Kingdom.
Alpha oscillations have been implicated in the maintenance of working memory representations. Notably, when memorised content is spatially lateralised, the power of posterior alpha activity exhibits corresponding lateralisation during the retention interval, consistent with the retinotopic organisation of the visual cortex. Beyond power, alpha frequency has also been linked to memory performan ce, with faster alpha rhythms associated with enhanced retention.
View Article and Find Full Text PDFBrain Behav
September 2025
Radiology Department, Yantaishan Hospital, Yantai, Shandong, China.
Objective: To investigate the characteristics of brain structures in patients with noise-induced hearing loss (NIHL) using source-based morphometry (SBM) and to evaluate the correlation between abnormal brain regions and clinical data.
Methods: High-resolution 3D T1 structural images were acquired from 81 patients with NIHL and 74 age- and education level-matched healthy controls (HCs). The clinical data of all subjects were collected, including noise exposure time, monaural hearing threshold weighted values (MTWVs), Mini-Mental State Examination (MMSE), and Hamilton Anxiety Scale (HAMA) scores.