Sensory Integration Mapping in Early Blindness
Poster No:
2112
Submission Type:
Abstract Submission
Authors:
Wei Wei1, Victoria Shevchenko1, R. Austin Benn1, Ulysse Klatzmann1, Francesco Alberti1, Jonathan Smallwood2, Olivier Collignon3,4, Daniel Margulies1
Institutions:
1Université Paris Cité, INCC UMR 8002, CNRS, Paris, France, 2Department of Psychology, Queen’s University, Ontario, Canada, 3Institutes for Research in Psychology (IPSY) & Neuroscience (IoNS), UCLouvain, Louvain-la-Neuve, Belgium, 4HES-SO Valais-Wallis, The Sense Innovation and Research Center, Lausanne and Sion, Swaziland
First Author:
Co-Author(s):
Olivier Collignon
Institutes for Research in Psychology (IPSY) & Neuroscience (IoNS), UCLouvain|HES-SO Valais-Wallis, The Sense Innovation and Research Center
Louvain-la-Neuve, Belgium|Lausanne and Sion, Swaziland
Institutes for Research in Psychology (IPSY) & Neuroscience (IoNS), UCLouvain|HES-SO Valais-Wallis, The Sense Innovation and Research Center
Louvain-la-Neuve, Belgium|Lausanne and Sion, Swaziland
Introduction:
Multisensory information integrates as signals propagate from lower- to higher-order brain regions (Mesulam, 1998, Calvert, 2001; Beauchamp et al., 2004; Beauchamp, 2005; Driver & Noesselt, 2008). However, it is unclear how early-onset blindness alters the organization of sensory processing. This study compared early-blind and sighted controls using a sensory integration model, which represents cortical organization by relative relationships to primary sensory areas. With this work, we aimed to enhance our understanding of cortical reorganization in the absence of visual experience.
Methods:
We included 16 early-blind participants (EB, 10F, mean age = 32.8 yrs) and 22 sighted controls (SC, 11F, mean age = 31.3 yrs) from a previously published dataset (Xu et al., 2023). SC participants underwent blindfolding during the MRI scan. The resting-state functional and T1 weighted structural MRI data were processed using Micapipe (Rodriguez-Cruces et al., 2022), with additional surface-based smoothing (FWHM = 4).
The sensory integration model was established as follows (Fig.1a). Sensory-related information at each vertex was derived using a non-negative linear model, employing time series from the primary visual, sensorimotor, and auditory cortex (V1, S1, and A1, delineated in Glasser et al., 2016) as predictors. The coefficients obtained from the linear model were converted into angles through hue transformations, representing the relative combination of primary signals. The variance explained by the predictors was ordered and rescaled from 0 to 1 to derive magnitude, which reflects the dependence on signals from the primary sensory cortex.
Between-group similarities in magnitudes and angles were assessed using Spearman and circular correlation (Jammalamadaka and Sengupta 2001, p176), respectively. Vertex-level comparisons involved evaluating magnitudes through a two-sample t-test and angles using the Watson-Williams test. The cluster-based permutation test (n = 5000, p < 0.05) was used to correct all statistical results for multiple comparisons.
The sensory integration model was established as follows (Fig.1a). Sensory-related information at each vertex was derived using a non-negative linear model, employing time series from the primary visual, sensorimotor, and auditory cortex (V1, S1, and A1, delineated in Glasser et al., 2016) as predictors. The coefficients obtained from the linear model were converted into angles through hue transformations, representing the relative combination of primary signals. The variance explained by the predictors was ordered and rescaled from 0 to 1 to derive magnitude, which reflects the dependence on signals from the primary sensory cortex.
Between-group similarities in magnitudes and angles were assessed using Spearman and circular correlation (Jammalamadaka and Sengupta 2001, p176), respectively. Vertex-level comparisons involved evaluating magnitudes through a two-sample t-test and angles using the Watson-Williams test. The cluster-based permutation test (n = 5000, p < 0.05) was used to correct all statistical results for multiple comparisons.
Results:
The polar and surface projections of the sensory integration model are depicted in Fig.1b for early-blind (left) and sighted controls (right). The network-wise distributions of magnitude exhibit overall similarity between the two groups (Fig.1c). Along the ventral visual stream, both groups demonstrate patterns transitioning from periphery to core, but the angular distribution of EB shows more alignment to visual domain than SC (Fig.1d).
The between-group correlations of the global angles and magnitudes are 0.718 and 0.926. A significant between-group difference (vertex-level p < 0.001 and cluster-level p < 0.05) is observed in angles within the extrastriate cortex (Fig. 2a) and in magnitudes within left parietal cortex (area PFm) (Fig. 2c). The pattern in the extrastriate cortex of EB is located close to the V1 anchoring angle (0°), whereas the pattern of SC is closer to the S1 anchoring angle (120°) (Fig.2b). Although both groups were scanned without visual stimulus, the signal from V1 still plays a great role in the activity of surrounding regions of EB but not for SC. The pattern in left PFm of EB is more peripheral (Fig. 2d), suggesting a greater dependence on primary signals than SC.
The between-group correlations of the global angles and magnitudes are 0.718 and 0.926. A significant between-group difference (vertex-level p < 0.001 and cluster-level p < 0.05) is observed in angles within the extrastriate cortex (Fig. 2a) and in magnitudes within left parietal cortex (area PFm) (Fig. 2c). The pattern in the extrastriate cortex of EB is located close to the V1 anchoring angle (0°), whereas the pattern of SC is closer to the S1 anchoring angle (120°) (Fig.2b). Although both groups were scanned without visual stimulus, the signal from V1 still plays a great role in the activity of surrounding regions of EB but not for SC. The pattern in left PFm of EB is more peripheral (Fig. 2d), suggesting a greater dependence on primary signals than SC.
Conclusions:
The sensory integration model delineates distinct areas along the sensory processing stream that significantly differ between early-blind and sighted controls, especially within regions related to visual processing. The nature of the cortical reorganization in early blindness has been partially captured in the extrastriate cortex through the relative combination of signals from the primary sensory cortex and in area PFm through the dependence on these signals. Although further exploration is needed to validate and refine these findings, they provide a novel insight to investigate the stability and flexibility of cortical organization and how they change based on experience with visual input.
Modeling and Analysis Methods:
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Perception, Attention and Motor Behavior:
Perception: Multisensory and Crossmodal 2
Perception: Visual
Keywords:
Cortex
FUNCTIONAL MRI
Modeling
Perception
Vision
Other - sensory processing
1|2Indicates the priority used for review
Provide references using author date format
Beauchamp, M. S., et al. (2004). 'Integration of Auditory and Visual Information about Objects in Superior Temporal Sulcus'. Neuron, 41(5), 809–823.
Calvert, G. A. (2001). 'Crossmodal Processing in the Human Brain: Insights from Functional Neuroimaging Studies'. Cerebral Cortex, 11(12),
Driver, J., & Noesselt, T. (2008). 'Multisensory Interplay Reveals Crossmodal Influences on ‘Sensory-Specific’ Brain Regions, Neural Responses, and Judgments'. Neuron, 57(1), 11–23.
Glasser, M. F., et al. (2016). 'A multi-modal parcellation of human cerebral cortex'. Nature, 536(7615), 171–178.
Jammalamadaka, S. R., & Sengupta, A. (2001). 'Topics in circular statistics' (Vol. 5). world scientific.
Mesulam, M. (1998). 'From sensation to cognition'. Brain, 121(6), 1013–1052.
Rodriguez-Cruces, R., et al. (2022). 'Micapipe: A pipeline for multimodal neuroimaging and connectome analysis'. Neuroscience.
Yeo, B. T. T., et al. (2011). 'The organization of the human cerebral cortex estimated by intrinsic functional connectivity'. Journal of Neurophysiology, 106(3), 1125–1165.
Xu, Y., et al. (2023). 'Similar object shape representation encoded in the inferolateral occipitotemporal cortex of sighted and early blind people'. PLOS Biology, 21(7), e3001930.