Ultrafast resting state fMRI unveils layer-specific feedforward and feedback interactions
Poster No:
1101
Submission Type:
Abstract Submission
Authors:
Joana Carvalho1, Francisca Fernandes1, Mafalda Valente1, Koen Haak2, Noam Shemesh1
Institutions:
1Champalimaud Foundation, Lisbon, Lisbon, 2Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Gelderland
First Author:
Co-Author(s):
Koen Haak
Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour
Nijmegen, Gelderland
Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour
Nijmegen, Gelderland
Introduction:
Decoding the intricate directionality of information flow within cortical circuits is crucial for deciphering brain dynamics and understanding the driving forces behind learning, sensory training and neuroplasticity following brain damage. Despite its scientific and clinical relevance, a model describing layer-specific feedforward and feedback interactions in health and disease is still lacking. Here, we applied a layer based connective field (lCF) model [1] to ultrafast fMRI and diffusion (d)fMRI resting-state (RS) data to reveal the feedback vs feedback layer specific fingerprints in healthy controls and brain damage animals.
Methods:
All experiments were preapproved by the competent authorities. N=15 adult Long Evan rats were scanned on a 9.4T Biospec scanner, under medetomidine sedation. Two different acquisitions were performed: BOLD (N=9) and dfMRI (N=6). BOLD: 6 healthy controls and 3 cortical blind (CB, V1 lesioned), animals were scanned in two scanning sessions: retinotopic mapping and ultrafast RS (3 RS datasets were obtained using GE-EPI acquisitions from: a multislice set; a visual cortex and visual pathway slice sets). V1 Lesioning: Bilateral V1 lesions were performed in 3 animals using a 1% solution of ibotenic acid, Fig2A. The CB animals were scanned 2 weeks post-lesioning. Diffusion fMRI: One scanning session with a visual stimulation and RS paradigms (b= 1.2 ms/μm2 and b=0.05 ms/μm2.) Preprocessing: Images were NORDIC denoised, slice-timing and motion corrected, coregistered, normalized to the SIGMA atlas. Population receptive field mapping and lCF models were implemented in in-house python scripts and build on the work of [1,3].
Results:
The lCF position maps estimated from ultrafast RS data show retinotopic organization, Fig1A. LCF size, which reflects the source layer sampling extent, revealed that deeper layers have larger lCF sizes than superficial layers, Fig.1B. This is in agreement with the idea that deeper layers receive feedback information from higher visual areas. Furthermore intracortical lCF between layers of VC showed two different lCF size profiles: feedforward (L4 projecting to all the other layers of the cortex) with inverse U shape with the larger lCF sizes at layer 5 and feedback with a U shape with the larger CF sizes at superficial (L1) and deeper layers (L6), Fig1C. Similar lCF size profiles were also found between V1 and V2 Fig1D. To confirm that larger CF sizes are associated with feedback signals, we computed CFs during visual stimulation and RS. LCFs estimated during visual stimulation (reflecting predominantly feedforward connections) have significantly smaller size than lCFs estimated during RS (spontaneous activity is thought to be feedback dominated) Fig 1F. Fig 1E shows the reliability of the CF model, CF estimated from functionally linked areas (i,.e V1 to other visual areas) have a higher variance explained than areas that are not connected.
LCF when applied to CB animals shows increased V2 sampling from visual areas that provide direct input to V1 (LGN and LP) but not to visual areas indirectly connected with V1 (SC), consistent with idea that residual vision is mediated by V1-bypassing circuits [4], Fig2D. Furthermore, also in CB animals deep layers of V2 have the larger lCFs, Fig2C.
In addition, lCF estimates obtained from the RS ADC signal show smaller lCF size than the ones obtained form BOLD, and they seem to be more layer specific, Fig2 E,F.
LCF when applied to CB animals shows increased V2 sampling from visual areas that provide direct input to V1 (LGN and LP) but not to visual areas indirectly connected with V1 (SC), consistent with idea that residual vision is mediated by V1-bypassing circuits [4], Fig2D. Furthermore, also in CB animals deep layers of V2 have the larger lCFs, Fig2C.
In addition, lCF estimates obtained from the RS ADC signal show smaller lCF size than the ones obtained form BOLD, and they seem to be more layer specific, Fig2 E,F.
Conclusions:
The application of lCF model to ultrafast RS and dfMRI data shows: 1) A bypass of V1 in CB animals mediated by feedback to deep layers of V2; 2) functional connectivity reflects visuotopic organization in the absence of visual input; 3) the size profiles of lCFs enable to disentangle feedback and feedforward signals. Our findings align with the idea that in the visual system layers 5 and 6 carry the feedback information to layer 4, and with the underlying neural architecture [5,6].
Learning and Memory:
Neural Plasticity and Recovery of Function 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
Computational Neuroscience
Cortical Layers
fMRI CONTRAST MECHANISMS
HIGH FIELD MR
Modeling
Plasticity
1|2Indicates the priority used for review
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2. Barrière, D. A. et al. The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization. Nat. Commun. 10, 5699 (2019).
3. Dumoulin, S. O. & Wandell, B. A. Population receptive field estimates in human visual cortex. Neuroimage 39, 647–660 (2008).
4. Rima, S. & Christoph Schmid, M. V1-bypassing thalamo-cortical visual circuits in blindsight and developmental dyslexia. Current Opinion in Physiology 16, 14–20 (2020).
5. Semedo, J. D. et al. Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat. Commun. 13, 1099 (2022).
6. Kok, P., Bains, L. J., van Mourik, T., Norris, D. G. & de Lange, F. P. Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback. Curr. Biol. 26, 371–376 (2016).