Visual experience modulates whole‐brain connectivity dynamics: A resting‐state fMRI study using the model of radiologists

• Published in: Human brain mapping Vol. 42; no. 14; pp. 4538 - 4554
• Main Authors: Wang, Yue, Jin, Chenwang, Yin, Zhongliang, Wang, Hongmei, Ji, Ming, Dong, Minghao, Liang, Jimin
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2021
• Subjects: Adult; Brain; Brain mapping; brain plasticity; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiology; Circuits; Clustering; Cognition & reasoning; Cognitive ability; College students; Connectivity analysis; Connectome; Decision making; Domains; Experiments; Female; Functional integration; Functional magnetic resonance imaging; Humans; Image processing; Information processing; Integration; Interactive control; Internship and Residency; Magnetic Resonance Imaging; Male; Medical imaging; Medical research; Neural networks; Neuronal Plasticity - physiology; Performance evaluation; Radiologists; Radiology; Radiology - education; resting state fMRI; Segmentation; Semantic analysis; Semantics; Software; spontaneous dynamic interactions; Visual cortex; visual expertise; Visual pathways; Visual Perception - physiology; Visual system; Young Adult; resting state fMRI; visual expertise; radiologists; brain plasticity; spontaneous dynamic interactions
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25563

Visual expertise refers to proficiency in visual recognition. It is attributed to accumulated visual experience in a specific domain and manifests in widespread neural activities that extend well beyond the visual cortex to multiple high‐level brain areas. An extensive body of studies has centered on the neural mechanisms underlying a distinctive domain of visual expertise, while few studies elucidated how visual experience modulates resting‐state whole‐brain connectivity dynamics. The current study bridged this gap by modeling the subtle alterations in interregional spontaneous connectivity patterns with a group of superior radiological interns. Functional connectivity analysis was based on functional brain segmentation, which was derived from a data‐driven clustering approach to discriminate subtle changes in connectivity dynamics. Our results showed there was radiographic visual experience accompanied with integration within brain circuits supporting visual processing and decision making, integration across brain circuits supporting high‐order functions, and segregation between high‐order and low‐order brain functions. Also, most of these alterations were significantly correlated with individual nodule identification performance. Our results implied that visual expertise is a controlled, interactive process that develops from reciprocal interactions between the visual system and multiple top‐down factors, including semantic knowledge, top‐down attentional control, and task relevance, which may enhance participants' local brain functional integration to promote their acquisition of specific visual information and modulate the activity of some regions for lower‐order visual feature processing to filter out nonrelevant visual details. The current findings may provide new ideas for understanding the central mechanism underlying the formation of visual expertise. The current study investigated whole‐brain connectivity dynamics modulated by visual expertise using functional connectivity analysis and the model of radiologists consisted of 20 trained superior intern radiologists and well‐matched normal controls. Our results showed there was radiographic visual experience accompanied with integration within brain circuits supporting visual processing and decision making, integration across brain circuits supporting high‐order functions, and segregation between high‐order and low‐order brain functions. The results implied that visual expertise may enhance subjects' local brain functional integration to promote their acquisition of specific visual information and modulate the activity of some regions for lower‐order visual feature processing to filter out non‐relevant visual details.