- Email: [email protected]
Cortical networks associated with sensorimotor integration
NemoImage
11, Number
5, 2OOO, Part 2 of 2 Parts 10
E al”
SENSORIMOTOR
Cortical Networks Associated With Sensorimotor Integration W. Richard Staine.s*t!$*, William E. McIlroyt#*, *Department
Simon J. Graham#$*,
Sandra E. Black*$*
of Medicine (Neurology),
i-Graduate Department of Rehabilitation Science $Department of Medical Imaging 5 University of Toronto ‘Sunnybrook and Women’s College HSC Performance of natural movement requires a complex integration of sensory information from multiple modalities. initiation of a visuomotor task, the motor output must be continually adjusted to integrate feedback information proprioceptive and visual inputs in order to maintain accurate movement. The purpose of the present study was to obtain understanding of the cortical networks involved in such sensorimotor integration tasks, using functional magnetic imaging (FMRI).
Following from both a detailed resonance
Methods Six normal, healthy volunteer subjects (5 right handed, 1 left handed) performed 4 variations of a visuomotor tracking task during FMRI. During each variation a vertical bar was presented on a visual display. A computer controlled the horizontal displacement of this target bar. Subjects held a plastic cylinder wrapped with a thin force sensitive resistor (FSR) in their dominant hand. Force applied by the thumb to the FSR controlled the horizontal movement of a second vertical bar (tracking bar) which was also displayed on the screen. This arrangement provided the capability, therefore, to track the trajectory of the target bar by moving the tracking bar via isometric thumb contractions to vary the force applied to the FSR. Increased forces moved the bar to the right, decreased forces moved it to the left. Force measurements were stored on computer for subsequent analysis of behavioural performance. Subjects performed three variations of the tracking task in which the trajectory of the target bar was unpredictable (random waveform, mean frequency 0.8 Hz). Tracking was alternated with the following tasks in a block design (30s tracking / 30s alternate task; 7 repeats): (1) rest, no thumb movement, no visual input, (2) visual display of target movement, no thumb movement, and (3) no visual input, rhythmic thumb movement (no tracking). The final variation consisted of tracking a predictable target movement (sinusoidal waveform, mean frequency 0.8 Hz) alternated with (2) visual display of target movement, no thumb force. Each variation was presented separately during acquisition of single-shot spiral gradient-echo FMRI (1.5T GE scanner; 80” flip angle; TBTR40/1500ms; acq. matrix90*90; 18 slices; 7 mm thick; 20 cm FOV). Activation maps were superimposed on high-resolution 3D Tl-weighted anatomical scans (35” flip angle; TE-3.4ms; acq. matrix-256*256; 124 slices; 1.2 mm thick; 22 cm FOV). Results: The main findings from preliminary analyses indicate that a network of activity associated with visuomotor tracking includes both bilateral premotor and inferior patietal cortices (&o&arm areas 6 and 40 respectively). These areas were consistently active across subjects regardless of the alternate task. Although bilateral, the activation within these areas appears stronger on the right. While the Figure 1: Vasuandw liacking pattern of activity is similar between predictable and unpredictable target movement, the magnitude of activation appears to be greater for tracking of predictable targets (shown for 1 subject in Figure 1). Other task-related differences emerged with considerable between-subject variability in the activated foci. When the alternating task included no visual information, activation of occipital cortex was observed along with a heightened activation of the inferior parietal cortex. Similarly, primary motor cortex activation was apparent when tracking with the thumb was contrasted to task conditions with no movement. Conclusions: Preliminary results from the present study reinforce the importance of the networks connecting premotor and inferior parietal cortices likely needed for the analysis and use of multimodal sensory information. Detailed understanding of the neural networks underlying such complex sensorimotor transformations is crucial for interpretation of data from patients undergoing functional motor recovery from stroke. Similar visuomotor or somatosensory-motor tracking tasks will prove useful in assessing patients with control deficiency specific to certain stages of the underlying sensorimotor transformations.
S849
11, Number
5, 2OOO, Part 2 of 2 Parts 10
E al”
SENSORIMOTOR
Cortical Networks Associated With Sensorimotor Integration W. Richard Staine.s*t!$*, William E. McIlroyt#*, *Department
Simon J. Graham#$*,
Sandra E. Black*$*
of Medicine (Neurology),
i-Graduate Department of Rehabilitation Science $Department of Medical Imaging 5 University of Toronto ‘Sunnybrook and Women’s College HSC Performance of natural movement requires a complex integration of sensory information from multiple modalities. initiation of a visuomotor task, the motor output must be continually adjusted to integrate feedback information proprioceptive and visual inputs in order to maintain accurate movement. The purpose of the present study was to obtain understanding of the cortical networks involved in such sensorimotor integration tasks, using functional magnetic imaging (FMRI).
Following from both a detailed resonance
Methods Six normal, healthy volunteer subjects (5 right handed, 1 left handed) performed 4 variations of a visuomotor tracking task during FMRI. During each variation a vertical bar was presented on a visual display. A computer controlled the horizontal displacement of this target bar. Subjects held a plastic cylinder wrapped with a thin force sensitive resistor (FSR) in their dominant hand. Force applied by the thumb to the FSR controlled the horizontal movement of a second vertical bar (tracking bar) which was also displayed on the screen. This arrangement provided the capability, therefore, to track the trajectory of the target bar by moving the tracking bar via isometric thumb contractions to vary the force applied to the FSR. Increased forces moved the bar to the right, decreased forces moved it to the left. Force measurements were stored on computer for subsequent analysis of behavioural performance. Subjects performed three variations of the tracking task in which the trajectory of the target bar was unpredictable (random waveform, mean frequency 0.8 Hz). Tracking was alternated with the following tasks in a block design (30s tracking / 30s alternate task; 7 repeats): (1) rest, no thumb movement, no visual input, (2) visual display of target movement, no thumb movement, and (3) no visual input, rhythmic thumb movement (no tracking). The final variation consisted of tracking a predictable target movement (sinusoidal waveform, mean frequency 0.8 Hz) alternated with (2) visual display of target movement, no thumb force. Each variation was presented separately during acquisition of single-shot spiral gradient-echo FMRI (1.5T GE scanner; 80” flip angle; TBTR40/1500ms; acq. matrix90*90; 18 slices; 7 mm thick; 20 cm FOV). Activation maps were superimposed on high-resolution 3D Tl-weighted anatomical scans (35” flip angle; TE-3.4ms; acq. matrix-256*256; 124 slices; 1.2 mm thick; 22 cm FOV). Results: The main findings from preliminary analyses indicate that a network of activity associated with visuomotor tracking includes both bilateral premotor and inferior patietal cortices (&o&arm areas 6 and 40 respectively). These areas were consistently active across subjects regardless of the alternate task. Although bilateral, the activation within these areas appears stronger on the right. While the Figure 1: Vasuandw liacking pattern of activity is similar between predictable and unpredictable target movement, the magnitude of activation appears to be greater for tracking of predictable targets (shown for 1 subject in Figure 1). Other task-related differences emerged with considerable between-subject variability in the activated foci. When the alternating task included no visual information, activation of occipital cortex was observed along with a heightened activation of the inferior parietal cortex. Similarly, primary motor cortex activation was apparent when tracking with the thumb was contrasted to task conditions with no movement. Conclusions: Preliminary results from the present study reinforce the importance of the networks connecting premotor and inferior parietal cortices likely needed for the analysis and use of multimodal sensory information. Detailed understanding of the neural networks underlying such complex sensorimotor transformations is crucial for interpretation of data from patients undergoing functional motor recovery from stroke. Similar visuomotor or somatosensory-motor tracking tasks will prove useful in assessing patients with control deficiency specific to certain stages of the underlying sensorimotor transformations.
S849