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Sasaki R, Kinoshita M, Osugi N, Tamura K, Nagata K, Nakagawa I. Differential electroencephalogram findings depending on type of praxis in reflex epilepsy. Neurophysiol Clin 2025; 55:103077. [PMID: 40339547 DOI: 10.1016/j.neucli.2025.103077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2025] [Revised: 04/18/2025] [Accepted: 04/29/2025] [Indexed: 05/10/2025] Open
Affiliation(s)
- Ryota Sasaki
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan; Department of Neurosurgery, National Hospital Organization Nara Medical Center, Nara, Japan
| | - Masako Kinoshita
- Department of Neurology, National Hospital Organization Utano National Hospital, Kyoto, Japan.
| | - Nahomi Osugi
- Department of Clinical Laboratory, National Hospital Organization Nara Medical Center, Nara, Japan
| | - Kentaro Tamura
- Department of Neurosurgery, National Hospital Organization Nara Medical Center, Nara, Japan
| | - Kiyoshi Nagata
- Department of Neurosurgery, National Hospital Organization Nara Medical Center, Nara, Japan
| | - Ichiro Nakagawa
- Department of Neurosurgery, Nara Medical University, Kashihara, Nara, Japan
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Wen P, Thompson LW, Rosenberg A, Landy MS, Rokers B. Single-Trial fMRI Decoding of 3D Motion with Stereoscopic and Perspective Cues. J Neurosci 2025; 45:e0044252025. [PMID: 40262902 PMCID: PMC12121709 DOI: 10.1523/jneurosci.0044-25.2025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Revised: 03/31/2025] [Accepted: 04/03/2025] [Indexed: 04/24/2025] Open
Abstract
How does the brain process 3D motion? We focused on the human motion complex (hMT+), extending insights from monkey studies. Using 3D-motion stimuli containing perspective and/or stereoscopic cues, we investigated the hierarchy within the motion complex in humans of both sexes to understand the neural mechanisms underlying motion perception. On each trial we decoded 3D motion direction (toward/away) based on the BOLD response in primary visual cortex (V1), and regions within hMT+ including the middle temporal (MT) and medial superior temporal (MST) areas, and the fundus of the superior temporal sulcus (FST). We found that 3D-motion direction could be reliably decoded from all four areas but accuracy depended on cue content. MT and FST showed greatest decoding accuracy with perspective and stereoscopic cues, respectively. Decoding of motion direction in V1 and MST could be explained by retinotopic biases in the BOLD response to motion stimuli. MT and FST were less impacted by such biases. We also identified significant behavioral differences between participants: some were proficient at using stereoscopic cues and others performed near chance. Good behavioral performance with stereoscopic cues was accompanied by better decoding performance in FST but not MT. A control experiment that eliminated 3D motion percepts for stereoscopic stimuli, but not perspective stimuli, revealed that unlike MT, decoding accuracy in FST was influenced by perceptual components of 3D motion. Our findings support that MT and FST play distinct roles in the analysis of visual motion and are key in the transformation of retinal input into perceptual report.
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Affiliation(s)
- Puti Wen
- Psychology, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Lowell W Thompson
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Ari Rosenberg
- Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin 53705
| | - Michael S Landy
- Department of Psychology and Center for Neural Science, New York University, New York, New York 10003
| | - Bas Rokers
- Psychology, New York University Abu Dhabi, Abu Dhabi, UAE
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Herrera-Esposito D, Burge J. Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli. J Neurosci 2025; 45:e0490242024. [PMID: 39592236 PMCID: PMC11841760 DOI: 10.1523/jneurosci.0490-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 10/30/2024] [Accepted: 11/06/2024] [Indexed: 11/28/2024] Open
Abstract
Estimating the motion of objects in depth is important for behavior and is strongly supported by binocular visual cues. To understand both how the brain should estimate motion in depth and how natural constraints shape and limit performance in two local 3D motion tasks, we develop image-computable ideal observers from a large number of binocular video clips created from a dataset of natural images. The observers spatiotemporally filter the videos and nonlinearly decode 3D motion from the filter responses. The optimal filters and decoder are dictated by the task-relevant image statistics and are specific to each task. Multiple findings emerge. First, two distinct filter subpopulations are spontaneously learned for each task. For 3D speed estimation, filters emerge for processing either changing disparities over time or interocular velocity differences, cues that are used by humans. For 3D direction estimation, filters emerge for discriminating either left-right or toward-away motion. Second, the filter responses, conditioned on the latent variable, are well-described as jointly Gaussian, and the covariance of the filter responses carries the information about the task-relevant latent variable. Quadratic combination is thus necessary for optimal decoding, which can be implemented by biologically plausible neural computations. Finally, the ideal observer yields nonobvious-and in some cases counterintuitive-patterns of performance like those exhibited by humans. Important characteristics of human 3D motion processing and estimation may therefore result from optimal information processing in the early visual system.
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Affiliation(s)
| | - Johannes Burge
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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Ruan J, Yuan Y, Qiao Y, Qiu M, Dong X, Cui Y, Wang J, Liu N. Connectional differences between humans and macaques in the MT+ complex. iScience 2025; 28:111617. [PMID: 39834863 PMCID: PMC11743884 DOI: 10.1016/j.isci.2024.111617] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 10/16/2024] [Accepted: 12/13/2024] [Indexed: 01/22/2025] Open
Abstract
MT+ is pivotal in the dorsal visual stream, encoding tool-use characteristics such as motion speed and direction. Despite its conservation between humans and monkeys, differences in MT+ spatial location and organization may lead to divergent, yet unexplored, connectivity patterns and functional characteristics. Using diffusion tensor imaging, we examined the structural connectivity of MT+ subregions in macaques and humans. We also employed graph-theoretical analyses on the constructed homologous tool-use network to assess their functional roles. Our results revealed location-dependent connectivity in macaques, with MST, MT, and FST predominantly connected to dorsal, middle, and ventral surfaces, respectively. Humans showed similar connectivity across all subregions. Differences in connectivity between MST and FST are more pronounced in macaques. In humans, the entire MT+ region, especially MST, exhibited stronger information transmission capabilities. Our findings suggest that the differences in tool use between humans and macaques may originate earlier than previously thought, particularly within the MT+ region.
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Affiliation(s)
- Jianxiong Ruan
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230026, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Ye Yuan
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Yicheng Qiao
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Minghao Qiu
- National Resource Center for Non-Human Primates and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Xueda Dong
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Sino-Danish Centre for Education and Research, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Yue Cui
- Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Jianhong Wang
- National Resource Center for Non-Human Primates and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Ning Liu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China
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Sangoi A, Hajebrahimi F, Gohel S, Scheiman M, Alvarez TL. Efferent compared to afferent neural substrates of the vergence eye movement system evoked via fMRI. Front Neurosci 2025; 18:1497326. [PMID: 39844855 PMCID: PMC11750780 DOI: 10.3389/fnins.2024.1497326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Accepted: 12/12/2024] [Indexed: 01/24/2025] Open
Abstract
Introduction The vergence neural system was stimulated to dissect the afferent and efferent components of symmetrical vergence eye movement step responses. The hypothesis tested was whether the afferent regions of interest would differ from the efferent regions to serve as comparative data for future clinical patient population studies. Methods Thirty binocularly normal participants participated in an oculomotor symmetrical vergence step block task within a functional MRI experiment compared to a similar sensory task where the participants did not elicit vergence eye movements. Results For the oculomotor vergence task, functional activation was observed within the parietal eye field, supplemental eye field, frontal eye field, and cerebellar vermis, and activation in these regions was significantly diminished during the sensory task. Differences between the afferent sensory and efferent oculomotor experiments were also observed within the visual cortex. Discussion Differences between the vergence oculomotor and sensory tasks provide a protocol to delineate the afferent and efferent portion of the vergence neural circuit. Implications with clinical populations and future therapeutic intervention studies are discussed.
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Affiliation(s)
- Ayushi Sangoi
- Vision and Neural Engineering Laboratory, Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Farzin Hajebrahimi
- Vision and Neural Engineering Laboratory, Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Suril Gohel
- Department of Health Informatics, Rutgers University School of Health Professions, Newark, NJ, United States
| | - Mitchell Scheiman
- Pennsylvania College of Optometry at Drexel University, Philadelphia, PA, United States
| | - Tara L. Alvarez
- Vision and Neural Engineering Laboratory, Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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Wen P, Ezzo R, Thompson LW, Rosenberg A, Landy MS, Rokers B. Functional localization of visual motion area FST in humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.19.614014. [PMID: 39345389 PMCID: PMC11430136 DOI: 10.1101/2024.09.19.614014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
The fundus of the superior temporal sulcus (FST) in macaques is implicated in the processing of complex motion signals, yet a human homolog remains elusive. Here we considered potential localizers and evaluated their effectiveness in delineating putative FST (pFST), from hMT and MST, two nearby motion-sensitive areas in humans. Nine healthy participants underwent scanning sessions with 2D and 3D motion localizers, as well as population receptive field (pRF) mapping. We observed consistent anterior and inferior activation relative to hMT and MST in response to stimuli that contained coherent 3D, but not 2D, motion. Motion opponency and myelination measures further validated the functional and structural distinction between pFST and hMT/MST. At the same time, standard pRF mapping techniques that reveal the visual field organization of hMT/MST proved suboptimal for delineating pFST. Our findings provide a robust framework for localizing pFST in humans, and underscore its distinct functional role in motion processing.
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Affiliation(s)
- Puti Wen
- Psychology, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Rania Ezzo
- Psychology, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Lowell W Thompson
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ari Rosenberg
- Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, WI 53705, USA
| | - Michael S Landy
- Department of Psychology and Center for Neural Science, New York University, New York, NY 10003, USA
| | - Bas Rokers
- Psychology, New York University Abu Dhabi, Abu Dhabi, UAE
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