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Ping A, Wang J, Ángel García-Cabezas M, Li L, Zhang J, Gothard KM, Zhu J, Roe AW. Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala. Natl Sci Rev 2025; 12:nwae473. [PMID: 40170996 PMCID: PMC11960096 DOI: 10.1093/nsr/nwae473] [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: 06/20/2024] [Revised: 11/29/2024] [Accepted: 12/03/2024] [Indexed: 04/03/2025] Open
Abstract
The primate amygdala serves to evaluate the emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal, lateral and central nuclei, respectively. Recent evidence has suggested the mesoscale (millimeter-scale) nature of intra-amygdala functional organization. However, the connectivity patterns by which these mesoscale regions interact with brainwide networks remain unclear. Using infrared neural stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7-T functional magnetic resonance imaging, we have discovered that these mesoscale sites exert influence over a surprisingly extensive scope of the brain. Our findings strongly indicate that mesoscale sites within the amygdala modulate brainwide networks through a 'one-to-many' (integral) way. Meanwhile, these connections exhibit a point-to-point (focal) topography. Our work provides new insights into the functional architecture underlying emotional and social behavioral networks, thereby opening up possibilities for individualized modulation of psychological disorders.
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Affiliation(s)
- An Ping
- Department of Neurosurgery of the Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 310009, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
- School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianbao Wang
- Department of Neurosurgery of the Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 310009, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
- School of Medicine, Zhejiang University, Hangzhou 310058, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310012, China
| | - Miguel Ángel García-Cabezas
- Department of Anatomy, Histology, and Neuroscience, School of Medicine, Autónoma University of Madrid, Madrid 28049, Spain
| | - Lihui Li
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Jianmin Zhang
- Department of Neurosurgery of the Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Katalin M Gothard
- Departments of Physiology and Neuroscience, University of Arizona, Tucson 85721, USA
| | - Junming Zhu
- Department of Neurosurgery of the Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Anna Wang Roe
- Department of Neurosurgery of the Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 310009, China
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
- School of Medicine, Zhejiang University, Hangzhou 310058, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310012, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
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Ai H, Lin W, Liu C, Chen N, Zhang P. Mesoscale functional organization and connectivity of color, disparity, and naturalistic texture in human second visual area. eLife 2025; 13:RP93171. [PMID: 40111254 PMCID: PMC11925451 DOI: 10.7554/elife.93171] [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] [Indexed: 03/22/2025] Open
Abstract
Although parallel processing has been extensively studied in the low-level geniculostriate pathway and the high-level dorsal and ventral visual streams, less is known at the intermediate-level visual areas. In this study, we employed high-resolution fMRI at 7T to investigate the columnar and laminar organizations for color, disparity, and naturalistic texture in the human secondary visual cortex (V2), and their informational connectivity with lower- and higher-order visual areas. Although fMRI activations in V2 showed reproducible interdigitated color-selective thin and disparity-selective thick 'stripe' columns, we found no clear evidence of columnar organization for naturalistic textures. Cortical depth-dependent analyses revealed the strongest color-selectivity in the superficial layers of V2, along with both feedforward and feedback informational connectivity with V1 and V4. Disparity selectivity was similar across different cortical depths of V2, which showed significant feedforward and feedback connectivity with V1 and V3ab. Interestingly, the selectivity for naturalistic texture was strongest in the deep layers of V2, with significant feedback connectivity from V4. Thus, while local circuitry within cortical columns is crucial for processing color and disparity information, feedback signals from V4 are involved in generating the selectivity for naturalistic textures in area V2.
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Affiliation(s)
- Hailin Ai
- Department of Psychological and Cognitive Sciences, Tsinghua UniversityBeijingChina
| | - Weiru Lin
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesChangshaChina
| | - Chengwen Liu
- Department of Psychology and Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal UniversityHunanChina
- Center for Mind & Brain Sciences, Hunan Normal UniversityChangshChina
| | - Nihong Chen
- Department of Psychological and Cognitive Sciences, Tsinghua UniversityBeijingChina
- THU-IDG/McGovern Institute for Brain Research, Tsinghua UniversityBeijingChina
| | - Peng Zhang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesChangshaChina
- Institute of Artificial Intelligence, Hefei Comprehensive National Science CenterHefeiChina
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Tian F, Liu Y, Chen M, Schriver KE, Roe AW. Selective activation of mesoscale functional circuits via multichannel infrared stimulation of cortical columns in ultra-high-field 7T MRI. CELL REPORTS METHODS 2025; 5:100961. [PMID: 39874948 PMCID: PMC11840946 DOI: 10.1016/j.crmeth.2024.100961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Revised: 11/13/2024] [Accepted: 12/26/2024] [Indexed: 01/30/2025]
Abstract
To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable. In addition, its magnetic resonance (MR) compatibility (INS-fMRI) permits systematic mapping of brain-wide circuits. In the MRI, we illustrate (1) the single-point activation of functionally specific networks, (2) shifting cortical networks activated via shifting points of stimulation, and (3) "moving dot" stimulation-evoked activation of higher-order motion-selective areas. We suggest that, by mimicking patterns of columnar activation normally activated by visual stimuli, a columnar VCP opens doors for the planned activation of feature-specific circuits and their associated visual percepts.
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Affiliation(s)
- Feiyan Tian
- Department of Neurosurgery of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310029, China; Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China; National Key Laboratory of Brain and Computer Intelligence, Zhejiang University, Hangzhou 310058, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Yipeng Liu
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
| | - Meixuan Chen
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China
| | - Kenneth Edward Schriver
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310012, China
| | - Anna Wang Roe
- Department of Neurosurgery of the Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310029, China; Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou 310029, China; National Key Laboratory of Brain and Computer Intelligence, Zhejiang University, Hangzhou 310058, China; Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310012, China.
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Tian F, Zhang Y, Schriver KE, Hu JM, Roe AW. A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation. Nat Commun 2024; 15:6528. [PMID: 39095351 PMCID: PMC11297274 DOI: 10.1038/s41467-024-50375-0] [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: 06/10/2023] [Accepted: 07/09/2024] [Indexed: 08/04/2024] Open
Abstract
Cutting edge advances in electrical visual cortical prosthetics have evoked perception of shapes, motion, and letters in the blind. Here, we present an alternative optical approach using pulsed infrared neural stimulation. To interface with dense arrays of cortical columns with submillimeter spatial precision, both linear array and 100-fiber bundle array optical fiber interfaces were devised. We deliver infrared stimulation through these arrays in anesthetized cat visual cortex and monitor effects by optical imaging in contralateral visual cortex. Infrared neural stimulation modulation of response to ongoing visual oriented gratings produce enhanced responses in orientation-matched domains and suppressed responses in non-matched domains, consistent with a known higher order integration mediated by callosal inputs. Controls include dynamically applied speeds, directions and patterns of multipoint stimulation. This provides groundwork for a distinct type of prosthetic targeted to maps of visual cortical columns.
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Affiliation(s)
- Feiyan Tian
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, 310029, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Ying Zhang
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, 310029, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Kenneth E Schriver
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, 310029, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310012, China
| | - Jia Ming Hu
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, 310029, China.
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310012, China.
| | - Anna Wang Roe
- Department of Neurosurgery of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, 310029, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310012, China.
- National Key Laboratory of Brain and Computer Intelligence, Zhejiang University, Hangzhou, 310058, China.
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Shipp S. Computational components of visual predictive coding circuitry. Front Neural Circuits 2024; 17:1254009. [PMID: 38259953 PMCID: PMC10800426 DOI: 10.3389/fncir.2023.1254009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.
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Affiliation(s)
- Stewart Shipp
- Institute of Ophthalmology, University College London, London, United Kingdom
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Cai X, Xu H, Han C, Li P, Wang J, Zhang R, Tang R, Fang C, Yan K, Song Q, Liang C, Lu HD. Mesoscale functional connectivity in macaque visual areas. Neuroimage 2023; 271:120019. [PMID: 36914108 DOI: 10.1016/j.neuroimage.2023.120019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/13/2023] Open
Abstract
Studies of resting-state functional connectivity (rsFC) have provided rich insights into the structures and functions of the human brain. However, most rsFC studies have focused on large-scale brain connectivity. To explore rsFC at a finer scale, we used intrinsic signal optical imaging to image the ongoing activity of the anesthetized macaque visual cortex. Differential signals from functional domains were used to quantify network-specific fluctuations. In 30-60 min resting-state imaging, a series of coherent activation patterns were observed in all three visual areas we examined (V1, V2, and V4). These patterns matched the known functional maps (ocular dominance, orientation, color) obtained in visual stimulation conditions. These functional connectivity (FC) networks fluctuated independently over time and exhibited similar temporal characteristics. Coherent fluctuations, however, were observed from orientation FC networks in different areas and even across two hemispheres. Thus, FC in the macaque visual cortex was fully mapped both on a fine scale and over a long range. Hemodynamic signals can be used to explore mesoscale rsFC in a submillimeter resolution.
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Affiliation(s)
- Xingya Cai
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Haoran Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Chao Han
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Peichao Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Jiayu Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Rui Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Rendong Tang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Chen Fang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Kun Yan
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Qianling Song
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Chen Liang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China
| | - Haidong D Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xin Jie Kou Wai Street, Beijing 100875, China.
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