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Valente, M.

Publications and source records attributed to Valente, M..

2 recordsLinked to original sources
bioRxiv2018

\"DoC-feeling\": a new behavioural tool to help diagnose the Minimally Conscious State.

ObjectivesThe clinical distinction between vegetative state/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) is a key step to elaborate a prognosis and formulate an appropriate medical plan for any patient suffering from disorders of consciousness (DoC). However, this assessment is often challenging and may require specialized expertise. In this study, we hypothesized that pooling subjective reports of the level of consciousness of a given patient across several nursing staff members can be used to clinically detect MCS.\n\nSetting and ParticipantsPatients referred for consciousness assessment were prospectively screened. MCS (target condition) was defined according to the best Coma Recovery Scale-Revised score (CRS-R) obtained from expert physicians (reference standard). \"DoC-feeling\" score consisted in the median value of multiple ratings of patients behavior observation pooled from multiple staff members during a week of hospitalisation (index test). Individual ratings were collected at the end of each shift using a 100mm visual analog scale, blinded from the reference standard. Diagnostic accuracy was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and specificity metrics.\n\nResults692 ratings performed by 83 nursing staff members were collected from 47 patients. Twenty patients were in a UWS and 27 in a MCS. DoC-feeling scores obtained by pooling all individual ratings obtained for a given patient were significantly greater in MCS than in UWS patients (59.2 mm [IQR: 27.3-77.3] vs. 7.2 mm [IQR: 2.4-11.4]; p<0.001) yielding an AUC of 0.92 (95%CI: 0.84-0.99).\n\nConclusionsDoC-feeling capitalizes on the expertise of nursing staff to evaluate patients consciousness. Together with the CRS-R as well as with brain imaging, DoC-feeling might improve diagnostic and prognostic accuracy of DoC patients.\n\nStrengths and limitations of this studyO_LIWe designed a new behavioural tool called \"DoC-feeling\" to help face the clinical challenge of the detection of Minimally Conscious State in patients suffering from disorders of consciousness (DoC)\nC_LIO_LI\"DoC-feeling score\" quantifies nursing staffs subjective perception of patients consciousness by pooling multiple assessments obtained from multiple caregivers (\"wisdom of the crowds\")\nC_LIO_LIThis score which requires no particular training showed a very good accuracy when compared to the gold standard (repeated expert clinical assessment using the Coma Recovery Scale - Revised (CRS-R))\nC_LIO_LIA validation in a separate cohort would help to determine its place in consciousness assessment\nC_LIO_LIThis score should be tested not only against the CRS-R but also against brain-imaging techniques to test for its capacity to detect covert signs of consciousness\nC_LI

neuroscience↗
bioRxiv2018

Weber’s law is the result of exact temporal accumulation of evidence

Webers law states that the discriminability between two stimulus intensities depends only on their ratio. Despite its status as the cornerstone of psychophysics, the mecha-nisms underlying Webers law are still debated, as no principled way exists to choose between its many proposed alternative explanations. We studied this problem training rats to discriminate the lateralization of sounds of different overall level. We found that the rats discrimination accuracy in this task is level-invariant, consistent with Webers law. Surprisingly, the shape of the reaction time distributions is also level-invariant, implying that the only behavioral effect of changes in the overall level of the sounds is a uniform scaling of time. Furthermore, we demonstrate that Webers law breaks down if the stimulus duration is capped at values shorter than the typical reaction time. Together, these facts suggest that Webers law is associated to a process of bounded evidence accumulation. Consistent with this hypothesis, we show that, among a broad class of sequential sampling models, the only robust mechanism consistent with reaction time scale-invariance is based on perfect accumulation of evidence up to a constant bound, Poisson-like statistics, and a power-law encoding of stimulus intensity. Fits of a minimal diffusion model with these characteristics describe the rats performance and reaction time distributions with virtually no error. Various manipulations of motivation were unable to alter the rats psychometric function, demonstrating the stability of the just-noticeable-difference and suggesting that, at least under some conditions, the bound for evidence accumulation can set a hard limit on discrimination accuracy. Our results establish the mechanistic foundation of the process of intensity discrimination and clarify the factors that limit the precision of sensory systems.

neuroscience↗