Intra-V1 functional networks and classification of observed stimuli

• Published in: Frontiers in neuroinformatics Vol. 18; p. 1080173
• Main Authors: Ontivero-Ortega, Marlis, Iglesias-Fuster, Jorge, Perez-Hidalgo, Jhoanna, Marinazzo, Daniele, Valdes-Sosa, Mitchell, Valdes-Sosa, Pedro
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 11.03.2024; Frontiers Media S.A
• Subjects: Cerebral hemispheres; Classification; Cognitive ability; Experiments; Eye movements; fMRI; Functional magnetic resonance imaging; functional networks; Navon task; Network topologies; Neuroscience; SVM-classifier; weight-maps; Navon task; fMRI; SVM-classifier; V1; functional networks; weight-maps
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2024.1080173

Previous studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations. The latter approach shifts the balance towards participant-idiosyncratic activity. Here, we used multivariate pattern analysis of intra-V1 correlation matrices to predict the Level or Shape of observed Navon letters employing the types of correlations described above. We assessed accuracy in inter-subject prediction of specific conjunctions of properties, and attempted intra-subject cross-classification of stimulus properties (i.e., prediction of one feature despite changes in the other). Weight maps from successful classifiers were projected onto the visual field. A control experiment investigated eye-movement patterns during stimuli presentation. All inter-subject classifiers accurately predicted the Level and Shape of specific observed stimuli. However, successful intra-subject cross-classification was achieved only for stimulus Level, but not Shape, regardless of preprocessing scheme. Weight maps for successful Level classification differed between inter-subject correlations and deconvolved correlations. The latter revealed asymmetries in visual field link strength that corresponded to known perceptual asymmetries. Post-hoc measurement of eyeball fMRI signals did not find differences in gaze between stimulus conditions, and a control experiment (with derived simulations) also suggested that eye movements do not explain the stimulus-related changes in V1 topology. Our findings indicate that both inter-subject common responses and participant-specific activity contribute to the information in intra-V1 co-fluctuations, albeit through distinct sub-networks. Deconvolution, that enhances subject-specific activity, highlighted interhemispheric links for Global stimuli. Further exploration of intra-V1 networks promises insights into the neural basis of attention and perceptual organization.