Brain wide activation and connectivity analysis in awake mouse fMRI during forepaw force control
Poster No:
2348
Submission Type:
Abstract Submission
Authors:
VISHWAS JINDAL1,2, Daniel Wesson3, David Vaillancourt1,2, Shahab Vahdat1,2
Institutions:
1Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, 2McKnight Brain Institute, University of Florida, Gainesville, FL, 3Department of Pharmacology & Therapeutics, University of Florida, Gainesville, FL
First Author:
VISHWAS JINDAL, MS
Department of Applied Physiology and Kinesiology, University of Florida|McKnight Brain Institute, University of Florida
Gainesville, FL|Gainesville, FL
Department of Applied Physiology and Kinesiology, University of Florida|McKnight Brain Institute, University of Florida
Gainesville, FL|Gainesville, FL
Co-Author(s):
Daniel Wesson, Ph.D.
Department of Pharmacology & Therapeutics, University of Florida
Gainesville, FL
Department of Pharmacology & Therapeutics, University of Florida
Gainesville, FL
David Vaillancourt, Ph.D.
Department of Applied Physiology and Kinesiology, University of Florida|McKnight Brain Institute, University of Florida
Gainesville, FL|Gainesville, FL
Department of Applied Physiology and Kinesiology, University of Florida|McKnight Brain Institute, University of Florida
Gainesville, FL|Gainesville, FL
Shahab Vahdat
Department of Applied Physiology and Kinesiology, University of Florida|McKnight Brain Institute, University of Florida
Gainesville, FL|Gainesville, FL
Department of Applied Physiology and Kinesiology, University of Florida|McKnight Brain Institute, University of Florida
Gainesville, FL|Gainesville, FL
Introduction:
Controlling the level of upper limb force is crucial for performing daily activities. Despite knowing the areas involved in motor skill learning [1-3], the neural mechanisms involved in learning appropriate force control are not well understood.
Functional magnetic resonance imaging (fMRI) in rodents allows unbiased tracking of whole-brain activation maps during learning to pinpoint key cortical, subcortical, and brainstem structures. However, there are no published fMRI studies performed during a motor task in mice. To fill this gap, we developed a novel-MR compatible head fixation apparatus for awake mouse fMRI during the forepaw force control task. This allowed us to shape mice during a motor behavior while minimizing noise and motion artifacts.
Functional magnetic resonance imaging (fMRI) in rodents allows unbiased tracking of whole-brain activation maps during learning to pinpoint key cortical, subcortical, and brainstem structures. However, there are no published fMRI studies performed during a motor task in mice. To fill this gap, we developed a novel-MR compatible head fixation apparatus for awake mouse fMRI during the forepaw force control task. This allowed us to shape mice during a motor behavior while minimizing noise and motion artifacts.
Methods:
We built an accurate (resolution 0.005 N) MR-compatible miniature force transducer, as well as a 3D-printed head fixation system to shape and allow mice to engage in the forelimb force control task. We also designed and built a saddle linear MRI coil to fit our head fixation system. The training paradigm involves wild-type water-deprived mice undergoing a reward-based forepaw press task. The mice were shaped to press the force transducer in a water-motivated press/no-press task for 7-14 days. After the training days, mice underwent an event-related awake SE-EPI fMRI scan in an 11T Bruker scanner while performing the right forepaw press task (resolution 0.35x0.35x0.5 mm3, TR = 2s). T2w anatomical scans (resolution 0.1x0.1x0.35 mm3) were also acquired for registration to the template.
In aggregating activation maps (Fig. 1), an average was computed across 15 animals, each having a minimum of 2 functional scans. Subsequently, the average activation within each distinct brain region was computed to analyze the functional connectivity among them (Fig. 2).
In aggregating activation maps (Fig. 1), an average was computed across 15 animals, each having a minimum of 2 functional scans. Subsequently, the average activation within each distinct brain region was computed to analyze the functional connectivity among them (Fig. 2).
Results:
Our fMRI activation maps show (Fig. 1) significant (p<0.05, corrected) brain-wide activation related to forelimb force control across different cortical, subcortical, and cerebellar regions (Figure 1). The cortical structures mainly included M1, M2, ACC, RSG, and S1. The visible activation in subcortical structures included STR, hypothalamus, THL-VPL, THL-VL, SC & and IC, while, in the cerebellum, significant activation clusters were observed in Crus1 and the SIM lobules. We found that, although the activation was bilateral, it was more dominant on the contralateral side of the forepaw. The average motion during functional scans was minimal (less than 0.25 mm in all 3 directions), and additional motion correction parameters from the FSL software package were included as a confound in the regression model to ensure decoupling the effects of body motion from the activation maps.
We used partial correlation analysis, by removing the effects of task paradigm, to assess functional connectivity. The functional connectivity analysis (Fig. 2) between the cortical, subcortical, and cerebellum areas showed 1) a significant correlation (p < 0.05, * Holm-Bonferroni correction) between various cortical and subcortical sensorimotor areas, including thalamus and striutum. 2) It is observed that, even though the results didn't reach statistical significance, a positive correlation exists between the cortical and cerebellar structures (SIM, AN, PRM with M1, M2, and ACC).
We used partial correlation analysis, by removing the effects of task paradigm, to assess functional connectivity. The functional connectivity analysis (Fig. 2) between the cortical, subcortical, and cerebellum areas showed 1) a significant correlation (p < 0.05, * Holm-Bonferroni correction) between various cortical and subcortical sensorimotor areas, including thalamus and striutum. 2) It is observed that, even though the results didn't reach statistical significance, a positive correlation exists between the cortical and cerebellar structures (SIM, AN, PRM with M1, M2, and ACC).
Conclusions:
Overall, our proposed method shows the feasibility of awake mouse fMRI in forelimb motor control and provides evidence for a widespread network of cortical and subcortical areas activated during force control. The functional connectivity analysis further helps us in determining functional interaction across various cortical, subcortical, and brainstrem regions. This method can further be used to study brain network reotganization following movement disorders and stroke in mice.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Motion Correction and Preprocessing
Motor Behavior:
Motor Planning and Execution
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Brainstem
Cerebellum
Cortex
FUNCTIONAL MRI
Learning
Motor
Somatosensory
Sub-Cortical
Thalamus
Other - Force control,Sensorimotor system, Rodents
1|2Indicates the priority used for review
Provide references using author date format
Yang W, Kanodia H, Arber S (2023) Structural and functional map for forelimb movement phases between cortex and medulla. Cell 186:162-177.e18
Yin HH, Mulcare SP, Hilário MRF, Clouse E, Holloway T, Davis MI, Hansson AC, Lovinger DM, Costa RM (2009) Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill. Nat Neurosci 12:333–34.