Biology subjects

Iturrate, I.

Publications and source records attributed to Iturrate, I..

6 recordsLinked to original sources

bioRxiv2023

Generalization of procedural motor sequence learning after a single practice trial

When humans begin learning new motor skills, they typically display early rapid performance improvements. It is not well understood how knowledge acquired during this early skill learning period generalizes to new, related skills. Here, we addressed this question by investigating factors influencing generalization of early learning from a skill A to a different, but related skill B. Early skill generalization was tested over four experiments (N = 2,095). Subjects successively learned two related motor sequence skills (skills A and B) over different practice schedules. Skill A and B sequences shared ordinal (i.e. - matching keypress locations), transitional (i.e. - ordered keypress pairs), parsing rule (i.e., distinct sequence events like repeated keypresses that can be used as a breakpoint for segmenting the sequence into smaller units) structures, or possessed no structure similarities. Results showed generalization for shared parsing rule structure between skills A and B after only a single 10-second practice trial of skill A. Manipulating the initial practice exposure to skill A (1 to 12 trials) and inter-practice rest interval (0 to 30s) between skills A and B had no impact on parsing rule structure generalization. Furthermore, this generalization was not explained by stronger sensorimotor mapping between individual keypress actions and their symbolic representations. In contrast, learning from skill A did not generalize to skill B during early learning when the sequences shared only ordinal or transitional structure features. These results document sequence structure that can be very rapidly generalized during initial learning to facilitate generalization of skill.

neuroscience↗

bioRxiv2022

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Error-related potentials (ErrP) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. Although there exists an agreement that ErrP signal awareness to errors, it remains poorly understood whether they encode further information. Here we report an experiment with sixteen participants during three recording sessions in which occasional visuomotor rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrP in single trials that provided real-time feedback to participants by changing the color of the cursor upon ErrP detection. The individual ErrP-BCI decoders exhibited good transfer across recording sessions and scalability over the varying magnitude of errors. Our results indicate that ErrPs encode not only the conscious perception of errors, but also their magnitude, in their amplitude and latency. Furthermore, a non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect rotations. The uncovered relationship is consistent with non-human primate studies, which found a similar relationship between the size of errors and simple spike activity of Purkinje cells, and we conjecture a cerebellar contribution to ErrP. Our experimental setup and findings open new avenues to probe and extend current theories of performance monitoring, which are based on response conflict tasks, by incorporating continuous human-interaction tasks as well as analysis of the ErrP complex as a whole rather than individual peaks.

neuroscience↗

bioRxiv2019

Voluntary movement initiation is not coupled to optimal sensorimotor oscillatory phases

Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. M1 activity oscillates between brief excitatory and inhibitory states that correlate with single neuron spiking rates. Here, we asked if the motor commands needed to produce voluntary, self-paced finger movements are preferentially released from M1 during restricted phases of this ongoing sensorimotor oscillatory activity. 21 healthy adults performed a self-paced finger movement task while EEG and EMG signals were recorded. For each finger movement, we identified the individual sensorimotor mu (8-12 Hz) and beta (13-35 Hz) oscillatory phase at the estimated time of motor command release from M1 by subtracting individually-defined MEP latencies from EMG-determined movement onset times. We report that motor commands were preferentially released at ~120{degrees} along the beta cycle but were released uniformly across the mu cycle. These results suggest that motor commands are preferentially released from M1 near optimal peak phases of endogenous beta rhythms.

neuroscience↗

bioRxiv2019

Beta-driven closed-loop deep brain stimulation can compromise human motor behavior in Parkinson’s Disease

Closed-loop or adaptive deep brain stimulation (DBS) for Parkinsons Disease (PD) has shown comparable clinical improvements to continuous stimulation, yet with less stimulation times and side effects. In this form of control, stimulation is driven by pathological beta oscillations recorded from the subthalamic nucleus, which have been shown to correlate with PD motor symptoms. An important consideration is that beta activity is itself modulated during volitional movements, yet it is unknown the impact that these volitional modulations may have on the efficacy of closed-loop systems. Here, three PD patients performed a functional reaching task during closed-loop stimulation while we measured their motor behavior. Our results show that closed-loop stimulation can alter motor performance at distinct movement intervals. Of particular relevance, closed-loop DBS compromised behavior during the returning period by increasing the amount of submovements executed, and in turn delayed movement termination. Following these findings, we hypothesize that the use of machine learning decoding different movement intervals to fully switch off the stimulator may be beneficial, and present here an exemplary approach decoding the initiation of the movement returning interval above chance level. These findings highlight the importance of evaluating these systems during functional tasks, and the need of extracting more robust biomarkers encoding ongoing symptoms or tasks execution intervals.

neuroscience↗

bioRxiv2018

Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

The human capacity to compute the likelihood that a decision is correct - known as metacognition - has proven difficult to study in isolation as it usually co-occurs with decision-making. Here, we isolated post-decisional from decisional contributions to metacognition by combining a novel paradigm with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed. We found better metacognitive performance for committed vs. observed decisions, indicating that committing to a decision informs confidence. Relying on concurrent electroencephalography and hemodynamic recordings, we found a common correlate of confidence following committed and observed decisions in the inferior frontal gyrus, and a dissociation in the anterior prefrontal cortex and anterior insula. We discuss these results in light of decisional and post-decisional accounts of confidence, and propose a generative model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision.

neuroscience↗

bioRxiv2018

Differential contributions of subthalamic beta rhythms and neural noise to Parkinson motor symptoms

BackgroundExcessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinsons disease and associated motor symptoms. However, the relationship between beta activity and motor symptoms has been inconsistent, which may influence the efficacy of closed-loop deep brain stimulation.\n\nHypothesisWe hypothesized that this variability is due to the degree of neural noise in STN recordings. Recent evidence has shown that neural noise is influenced by multiple factors, such as development, aging and disease, and could confound measures of beta activity. In this work, we propose a model that disentangles beta oscillatory activity and neural noise in the STN power spectrum.\n\nMethodsWe investigated the impact of neural noise on estimations of beta activity and motor symptoms from data recorded bilaterally from the subthalamic nuclei of thirteen Parkinsonian patients.\n\nResultsResults showed that the relationship between beta oscillatory amplitude and motor symptoms (bradykinesia and rigidity) significantly improved when neural noise was removed from the estimation of beta activity.\n\nConclusionThese findings emphasize the importance of modeling neural components independently for understanding physiological processes associated with Parkinsons disease, and identifying better biomarkers for characterizing symptom severity. Subsequently, we predict that our findings can have a direct application for closed-loop deep brain stimulation on Parkinsons Disease.

neuroscience↗