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Deficient grip force control in schizophrenia: behavioral and modeling evidence for altered motor inhibition and motor noise.

Teremetz M, Amado I, Bendjemaa N, Krebs MO, Lindberg PG, Maier MA - PLoS ONE (2014)

Bottom Line: Three behavioral variables were significantly higher in both patient groups: tracking error (by 50%), coefficient of variation of force (by 57%) and duration of force release (up by 37%).Behavioral performance did not differ between patient groups.Computational simulation successfully replicated these findings and predicted that decreased motor inhibition, together with an increased signal-dependent motor noise, are sufficient to explain the observed motor deficits in patients.

View Article: PubMed Central - PubMed

Affiliation: Université Paris Descartes, FR3636 CNRS, Sorbonne Paris Cité, 75006, Paris, France.

ABSTRACT
Whether upper limb sensorimotor control is affected in schizophrenia and how underlying pathological mechanisms may potentially intervene in these deficits is still being debated. We tested voluntary force control in schizophrenia patients and used a computational model in order to elucidate potential cerebral mechanisms underlying sensorimotor deficits in schizophrenia. A visuomotor grip force-tracking task was performed by 17 medicated and 6 non-medicated patients with schizophrenia (DSM-IV) and by 15 healthy controls. Target forces in the ramp-hold-and-release paradigm were set to 5 N and to 10% maximal voluntary grip force. Force trajectory was analyzed by performance measures and Principal Component Analysis (PCA). A computational model incorporating neural control signals was used to replicate the empirically observed motor behavior and to explore underlying neural mechanisms. Grip task performance was significantly lower in medicated and non-medicated schizophrenia patients compared to controls. Three behavioral variables were significantly higher in both patient groups: tracking error (by 50%), coefficient of variation of force (by 57%) and duration of force release (up by 37%). Behavioral performance did not differ between patient groups. Computational simulation successfully replicated these findings and predicted that decreased motor inhibition, together with an increased signal-dependent motor noise, are sufficient to explain the observed motor deficits in patients. PCA also suggested altered motor inhibition as a key factor differentiating patients from control subjects: the principal component representing inhibition correlated with clinical severity. These findings show that schizophrenia affects voluntary sensorimotor control of the hand independent of medication, and suggest that reduced motor inhibition and increased signal-dependent motor noise likely reflect key pathological mechanisms of the sensorimotor deficit.

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Task and behavioral results.Visuomotor grip force tracking. A. Setup for visual grip force tracking. The subject holds a grip force manipulandum in each hand and performs the task with the tracking hand, while holding the other manipulandum with the resting hand. Inset: Power grip manipulandum (www.sensix.com). B. Single-trial grip force-tracking example for a control subject at the 5N level. C. Corresponding example for a medicated patient. Gray stippled line: target force trajectory; black solid line: actual grip force of the tracking hand. Gray continuous line: force of the resting hand. Note larger deviation from the target in the patient compared to the control subject. D. Relative error (mean ±SD over the ramp and hold period) for the three groups: control subjects, medicated patients, and non-medicated patients (NMP). E. CV of force (mean ±SD over the ramp and hold period) for the three groups. Note that relative error and CV were higher for the low force condition (5N) since both measures are relative to target force level (c.f.28). F. Release duration (mean ±SD) for the three groups. Significant differences were found between controls and both groups of patients (see Results) in all three variables. No difference was found in force tracking variables between medicated and non-medicated patients.
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pone-0111853-g001: Task and behavioral results.Visuomotor grip force tracking. A. Setup for visual grip force tracking. The subject holds a grip force manipulandum in each hand and performs the task with the tracking hand, while holding the other manipulandum with the resting hand. Inset: Power grip manipulandum (www.sensix.com). B. Single-trial grip force-tracking example for a control subject at the 5N level. C. Corresponding example for a medicated patient. Gray stippled line: target force trajectory; black solid line: actual grip force of the tracking hand. Gray continuous line: force of the resting hand. Note larger deviation from the target in the patient compared to the control subject. D. Relative error (mean ±SD over the ramp and hold period) for the three groups: control subjects, medicated patients, and non-medicated patients (NMP). E. CV of force (mean ±SD over the ramp and hold period) for the three groups. Note that relative error and CV were higher for the low force condition (5N) since both measures are relative to target force level (c.f.28). F. Release duration (mean ±SD) for the three groups. Significant differences were found between controls and both groups of patients (see Results) in all three variables. No difference was found in force tracking variables between medicated and non-medicated patients.

Mentions: A visuomotor power grip force-tracking task previously described [29] was used to assess the accuracy of force control (Fig. 1A). Grip force was recorded at 1 kHz using strain gauge force sensors linked to a CED 1401 running Spike2. The task consisted of a series of visually displayed ramp-hold-and-release target force trajectories to be followed as closely as possible with a cursor (moving vertically as a linear function of grip force), while the target force trajectory scrolled continuously over the screen from right to left. Upcoming force was thus predictable. For both low and high force conditions, the pre-ramp (inter-trial) period lasted 3 s, the ramp period 2 s, and the hold period 4 s, after which the target force dropped instantaneously to baseline (0N). Force-tracking was performed once with the right and once with the left hand (pseudo-randomized across subjects). While tracking with one hand, the other (resting) hand remained passive but still gripped a manipulandum, so that unwanted motor overflow could be quantified. Each task condition consisted of 16 trials. Condition_1: low absolute force level (5N). Condition_2: higher relative force level (10% maximal voluntary grip force, MVC). Trials were grouped by force level in blocks of four trials, and four blocks were performed at each force level (total of 32 trials). Subjects were instructed to minimize the distance (error) between the applied and the target force and to release force immediately at the end of the hold phase. All subjects were familiarized with the task before testing.


Deficient grip force control in schizophrenia: behavioral and modeling evidence for altered motor inhibition and motor noise.

Teremetz M, Amado I, Bendjemaa N, Krebs MO, Lindberg PG, Maier MA - PLoS ONE (2014)

Task and behavioral results.Visuomotor grip force tracking. A. Setup for visual grip force tracking. The subject holds a grip force manipulandum in each hand and performs the task with the tracking hand, while holding the other manipulandum with the resting hand. Inset: Power grip manipulandum (www.sensix.com). B. Single-trial grip force-tracking example for a control subject at the 5N level. C. Corresponding example for a medicated patient. Gray stippled line: target force trajectory; black solid line: actual grip force of the tracking hand. Gray continuous line: force of the resting hand. Note larger deviation from the target in the patient compared to the control subject. D. Relative error (mean ±SD over the ramp and hold period) for the three groups: control subjects, medicated patients, and non-medicated patients (NMP). E. CV of force (mean ±SD over the ramp and hold period) for the three groups. Note that relative error and CV were higher for the low force condition (5N) since both measures are relative to target force level (c.f.28). F. Release duration (mean ±SD) for the three groups. Significant differences were found between controls and both groups of patients (see Results) in all three variables. No difference was found in force tracking variables between medicated and non-medicated patients.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4219790&req=5

pone-0111853-g001: Task and behavioral results.Visuomotor grip force tracking. A. Setup for visual grip force tracking. The subject holds a grip force manipulandum in each hand and performs the task with the tracking hand, while holding the other manipulandum with the resting hand. Inset: Power grip manipulandum (www.sensix.com). B. Single-trial grip force-tracking example for a control subject at the 5N level. C. Corresponding example for a medicated patient. Gray stippled line: target force trajectory; black solid line: actual grip force of the tracking hand. Gray continuous line: force of the resting hand. Note larger deviation from the target in the patient compared to the control subject. D. Relative error (mean ±SD over the ramp and hold period) for the three groups: control subjects, medicated patients, and non-medicated patients (NMP). E. CV of force (mean ±SD over the ramp and hold period) for the three groups. Note that relative error and CV were higher for the low force condition (5N) since both measures are relative to target force level (c.f.28). F. Release duration (mean ±SD) for the three groups. Significant differences were found between controls and both groups of patients (see Results) in all three variables. No difference was found in force tracking variables between medicated and non-medicated patients.
Mentions: A visuomotor power grip force-tracking task previously described [29] was used to assess the accuracy of force control (Fig. 1A). Grip force was recorded at 1 kHz using strain gauge force sensors linked to a CED 1401 running Spike2. The task consisted of a series of visually displayed ramp-hold-and-release target force trajectories to be followed as closely as possible with a cursor (moving vertically as a linear function of grip force), while the target force trajectory scrolled continuously over the screen from right to left. Upcoming force was thus predictable. For both low and high force conditions, the pre-ramp (inter-trial) period lasted 3 s, the ramp period 2 s, and the hold period 4 s, after which the target force dropped instantaneously to baseline (0N). Force-tracking was performed once with the right and once with the left hand (pseudo-randomized across subjects). While tracking with one hand, the other (resting) hand remained passive but still gripped a manipulandum, so that unwanted motor overflow could be quantified. Each task condition consisted of 16 trials. Condition_1: low absolute force level (5N). Condition_2: higher relative force level (10% maximal voluntary grip force, MVC). Trials were grouped by force level in blocks of four trials, and four blocks were performed at each force level (total of 32 trials). Subjects were instructed to minimize the distance (error) between the applied and the target force and to release force immediately at the end of the hold phase. All subjects were familiarized with the task before testing.

Bottom Line: Three behavioral variables were significantly higher in both patient groups: tracking error (by 50%), coefficient of variation of force (by 57%) and duration of force release (up by 37%).Behavioral performance did not differ between patient groups.Computational simulation successfully replicated these findings and predicted that decreased motor inhibition, together with an increased signal-dependent motor noise, are sufficient to explain the observed motor deficits in patients.

View Article: PubMed Central - PubMed

Affiliation: Université Paris Descartes, FR3636 CNRS, Sorbonne Paris Cité, 75006, Paris, France.

ABSTRACT
Whether upper limb sensorimotor control is affected in schizophrenia and how underlying pathological mechanisms may potentially intervene in these deficits is still being debated. We tested voluntary force control in schizophrenia patients and used a computational model in order to elucidate potential cerebral mechanisms underlying sensorimotor deficits in schizophrenia. A visuomotor grip force-tracking task was performed by 17 medicated and 6 non-medicated patients with schizophrenia (DSM-IV) and by 15 healthy controls. Target forces in the ramp-hold-and-release paradigm were set to 5 N and to 10% maximal voluntary grip force. Force trajectory was analyzed by performance measures and Principal Component Analysis (PCA). A computational model incorporating neural control signals was used to replicate the empirically observed motor behavior and to explore underlying neural mechanisms. Grip task performance was significantly lower in medicated and non-medicated schizophrenia patients compared to controls. Three behavioral variables were significantly higher in both patient groups: tracking error (by 50%), coefficient of variation of force (by 57%) and duration of force release (up by 37%). Behavioral performance did not differ between patient groups. Computational simulation successfully replicated these findings and predicted that decreased motor inhibition, together with an increased signal-dependent motor noise, are sufficient to explain the observed motor deficits in patients. PCA also suggested altered motor inhibition as a key factor differentiating patients from control subjects: the principal component representing inhibition correlated with clinical severity. These findings show that schizophrenia affects voluntary sensorimotor control of the hand independent of medication, and suggest that reduced motor inhibition and increased signal-dependent motor noise likely reflect key pathological mechanisms of the sensorimotor deficit.

Show MeSH
Related in: MedlinePlus