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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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Model data: functional consequences of gain changes.A. Single trial runs with identical seed for a simulated average control subject (left) and an average schizophrenia patient (right) at the low force level. C–F. Performance measures as a function of gains. Twenty runs with pseudo-randomized initial seeds were computed for each condition. Performance measures (mean ± SD) were calculated similar to the empirical data. Black: low force condition (FL), gray: high force condition (FH). C, E. Influence of SDN-gain on relative error (C) and on release duration (E). Increasing SDN-gains provides higher relative error (and higher CV, not shown), but has no effect on release duration. In C, stippled vertical lines indicate the average SDN_gain for controls (0.016) and patients (0.028). D, F. Impact of inhibition-gain on relative error (D), and on release duration (F). Increasing inhibitory gain has little effect on relative error (and CV, not shown), but decreases the release duration. In F, stippled vertical lines indicate the average I_Gain for controls (0.2) and patients (0.12). Note that, for a given gain, error and CV are always higher for the low force compared to the high force condition (c.f. Fig. 1C, D). B. Relation between I_Gain and SDN_Gain after fitting the gains to each subject's performance. There is a significant negative correlation (regression line stippled), across the whole population [controls, medicated patients, and non-medicated patients (NMP)], with patients tending to have lower I_Gains and higher SDN_Gains. Note: this resembles the correlation found empirically between mean error and release duration (see Results).
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pone-0111853-g005: Model data: functional consequences of gain changes.A. Single trial runs with identical seed for a simulated average control subject (left) and an average schizophrenia patient (right) at the low force level. C–F. Performance measures as a function of gains. Twenty runs with pseudo-randomized initial seeds were computed for each condition. Performance measures (mean ± SD) were calculated similar to the empirical data. Black: low force condition (FL), gray: high force condition (FH). C, E. Influence of SDN-gain on relative error (C) and on release duration (E). Increasing SDN-gains provides higher relative error (and higher CV, not shown), but has no effect on release duration. In C, stippled vertical lines indicate the average SDN_gain for controls (0.016) and patients (0.028). D, F. Impact of inhibition-gain on relative error (D), and on release duration (F). Increasing inhibitory gain has little effect on relative error (and CV, not shown), but decreases the release duration. In F, stippled vertical lines indicate the average I_Gain for controls (0.2) and patients (0.12). Note that, for a given gain, error and CV are always higher for the low force compared to the high force condition (c.f. Fig. 1C, D). B. Relation between I_Gain and SDN_Gain after fitting the gains to each subject's performance. There is a significant negative correlation (regression line stippled), across the whole population [controls, medicated patients, and non-medicated patients (NMP)], with patients tending to have lower I_Gains and higher SDN_Gains. Note: this resembles the correlation found empirically between mean error and release duration (see Results).

Mentions: At the group level (Table 2), SDN_Gain was first set such that the ratio of relative error (and of CV) between low and high force conditions was as close as possible to the empirical observed ratios. This was done separately for the grand average of controls and of patients. Second, I_Gain (and therefore TP_Gain) was set such that release duration was as close as possible to empirical values, again done separately for the grand average of controls and of patients (Table 2). In addition, for comparison at the individual level (Fig. 5B) this fitting procedure was applied subject-by-subject.


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)

Model data: functional consequences of gain changes.A. Single trial runs with identical seed for a simulated average control subject (left) and an average schizophrenia patient (right) at the low force level. C–F. Performance measures as a function of gains. Twenty runs with pseudo-randomized initial seeds were computed for each condition. Performance measures (mean ± SD) were calculated similar to the empirical data. Black: low force condition (FL), gray: high force condition (FH). C, E. Influence of SDN-gain on relative error (C) and on release duration (E). Increasing SDN-gains provides higher relative error (and higher CV, not shown), but has no effect on release duration. In C, stippled vertical lines indicate the average SDN_gain for controls (0.016) and patients (0.028). D, F. Impact of inhibition-gain on relative error (D), and on release duration (F). Increasing inhibitory gain has little effect on relative error (and CV, not shown), but decreases the release duration. In F, stippled vertical lines indicate the average I_Gain for controls (0.2) and patients (0.12). Note that, for a given gain, error and CV are always higher for the low force compared to the high force condition (c.f. Fig. 1C, D). B. Relation between I_Gain and SDN_Gain after fitting the gains to each subject's performance. There is a significant negative correlation (regression line stippled), across the whole population [controls, medicated patients, and non-medicated patients (NMP)], with patients tending to have lower I_Gains and higher SDN_Gains. Note: this resembles the correlation found empirically between mean error and release duration (see Results).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219790&req=5

pone-0111853-g005: Model data: functional consequences of gain changes.A. Single trial runs with identical seed for a simulated average control subject (left) and an average schizophrenia patient (right) at the low force level. C–F. Performance measures as a function of gains. Twenty runs with pseudo-randomized initial seeds were computed for each condition. Performance measures (mean ± SD) were calculated similar to the empirical data. Black: low force condition (FL), gray: high force condition (FH). C, E. Influence of SDN-gain on relative error (C) and on release duration (E). Increasing SDN-gains provides higher relative error (and higher CV, not shown), but has no effect on release duration. In C, stippled vertical lines indicate the average SDN_gain for controls (0.016) and patients (0.028). D, F. Impact of inhibition-gain on relative error (D), and on release duration (F). Increasing inhibitory gain has little effect on relative error (and CV, not shown), but decreases the release duration. In F, stippled vertical lines indicate the average I_Gain for controls (0.2) and patients (0.12). Note that, for a given gain, error and CV are always higher for the low force compared to the high force condition (c.f. Fig. 1C, D). B. Relation between I_Gain and SDN_Gain after fitting the gains to each subject's performance. There is a significant negative correlation (regression line stippled), across the whole population [controls, medicated patients, and non-medicated patients (NMP)], with patients tending to have lower I_Gains and higher SDN_Gains. Note: this resembles the correlation found empirically between mean error and release duration (see Results).
Mentions: At the group level (Table 2), SDN_Gain was first set such that the ratio of relative error (and of CV) between low and high force conditions was as close as possible to the empirical observed ratios. This was done separately for the grand average of controls and of patients. Second, I_Gain (and therefore TP_Gain) was set such that release duration was as close as possible to empirical values, again done separately for the grand average of controls and of patients (Table 2). In addition, for comparison at the individual level (Fig. 5B) this fitting procedure was applied subject-by-subject.

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