We present a case report on visual brain plasticity after total blindness acquired in adulthood. SH lost her sight when she was 27. Despite having been totally blind for 43 years, she reported to strongly rely on her vivid visual imagery. Three-Tesla magnetic resonance imaging (MRI) of SH and age-matched controls was performed. The MRI sequence included anatomical MRI, resting-state functional MRI, and task-related functional MRI where SH was instructed to imagine colours, faces, and motion. Compared to controls, voxel-based analysis revealed white matter loss along SH's visual pathway as well as grey matter atrophy in the calcarine sulci. Yet we demonstrated activation in visual areas, including V1, using functional MRI. Of the four identified visual resting-state networks, none showed alterations in spatial extent; hence, SH's preserved visual imagery seems to be mediated by intrinsic brain networks of normal extent. Time courses of two of these networks showed increased correlation with that of the inferior posterior default mode network, which may reflect adaptive changes supporting SH's strong internal visual representations. Overall, our findings demonstrate that conscious visual experience is possible even after years of absence of extrinsic input.
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http://dx.doi.org/10.1007/s00429-015-1010-2 | DOI Listing |
Sci Rep
December 2024
Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Pasteura 3, Warsaw, 02-093, Poland.
Patients with major depressive disorder (MDD) and borderline personality disorder (BPD) are reported to have disrupted autobiographical memory (AM). Using functional magnetic resonance imaging we investigated behavioral and neural processing of the recall of emotional (sad and happy) memories in 30 MDD, 18 BPD, and 34 healthy control (HC) unmedicated women. The behavioral results showed that the MDD group experienced more sadness than the HC after the sad recall, while BPD participants experienced less happiness than HC after the happy recall.
View Article and Find Full Text PDFJ Imaging
December 2024
Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada.
This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with Z axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red-green-blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities.
View Article and Find Full Text PDFGeriatrics (Basel)
December 2024
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Background: Hand dexterity is affected by normal aging and neuroinflammatory processes in the brain. Understanding the relationship between hand dexterity and brain structure in neurotypical older adults may be informative about prodromal pathological processes, thus providing an opportunity for earlier diagnosis and intervention to improve functional outcomes.
Methods: this study investigates the associations between hand dexterity and brain measures in neurotypical older adults (≥65 years) using the Nine-Hole Peg Test (9HPT) and magnetic resonance imaging (MRI).
Conscious Cogn
December 2024
School of Kinesiology, University of British Columbia, 210-6081 University Boulevard, Vancouver, BC V6T 1Z1, Canada. Electronic address:
Motor imagery (MI) is a cognitive process believed to rely on the representation developed through experience. The equivalence between MI and execution has been questioned and the relationship between experience types and MI is unclear. We tested how observational and physical practice of hand gesture sequences impacted visual and kinesthetic MI and transfer to the unpracticed effector.
View Article and Find Full Text PDFJ Neural Eng
December 2024
West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.
Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
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