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

• Published in: PloS one 2014, Vol.9 (11), p.e111853-e111853
• Main Authors: Teremetz, Maxime, Amado, Isabelle, Bendjemaa, Narjes, Krebs, Marie-Odile, Lindberg, Pavel G, Maier, Marc A
• Format: Article
• Language: English
• Subjects: Adult; Biology and Life Sciences; Coefficient of variation; Computation; Computer applications; Computer simulation; Drugs; Feedback, Sensory; Grasping; Grip force; Hand Strength; Humans; Inhibition; Male; Medicin och hälsovetenskap; Medicine and Health Sciences; Mental disorders; Middle Aged; Neurosciences; Noise; Noise prediction; Patients; Principal components analysis; Psychiatry; Psychomotor Performance; Psychosis; Schizophrenia; Schizophrenia - physiopathology; Schizophrenic Psychology; Sensorimotor integration; Sensorimotor system; Tracking; Trajectory analysis; Transcranial magnetic stimulation; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0111853

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.