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Pimentel JM, Moioli RC, De Araujo MFP, Vargas PA. An Integrated Neurorobotics Model of the Cerebellar-Basal Ganglia Circuitry. Int J Neural Syst 2023; 33:2350059. [PMID: 37791495 DOI: 10.1142/s0129065723500594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
This work presents a neurorobotics model of the brain that integrates the cerebellum and the basal ganglia regions to coordinate movements in a humanoid robot. This cerebellar-basal ganglia circuitry is well known for its relevance to the motor control used by most mammals. Other computational models have been designed for similar applications in the robotics field. However, most of them completely ignore the interplay between neurons from the basal ganglia and cerebellum. Recently, neuroscientists indicated that neurons from both regions communicate not only at the level of the cerebral cortex but also at the subcortical level. In this work, we built an integrated neurorobotics model to assess the capacity of the network to predict and adjust the motion of the hands of a robot in real time. Our model was capable of performing different movements in a humanoid robot by respecting the sensorimotor loop of the robot and the biophysical features of the neuronal circuitry. The experiments were executed in simulation and the real world. We believe that our proposed neurorobotics model can be an important tool for new studies on the brain and a reference toward new robot motor controllers.
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Affiliation(s)
- Jhielson M Pimentel
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh EH14 4AS, UK
| | - Renan C Moioli
- Bioinformatics Multidisciplinary Environment, Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | | | - Patricia A Vargas
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh EH14 4AS, UK
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Thorsson M, Galazka MA, Hajjari P, Fernell E, Delafield-Butt J, Gillberg C, Johnson M, Åsberg Johnels J, Hadjikhani N. A novel tablet-based motor coordination test performs on par with the Beery VMI subtest and offers superior temporal metrics: findings from children with pediatric acute-onset neuropsychiatric syndrome. Exp Brain Res 2023; 241:1421-1436. [PMID: 37052647 PMCID: PMC10130113 DOI: 10.1007/s00221-023-06612-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023]
Abstract
Neuropsychiatric and neurodevelopmental disorders are often associated with coordination problems. Pediatric Acute-onset Neuropsychiatric Syndrome (PANS) constitutes a specific example of acute and complex symptomatology that includes difficulties with motor control. The present proof-of-concept study aimed at testing a new, bespoke tablet-based motor coordination test named SpaceSwipe, providing fine-grained measures that could be used to follow-up on symptoms evolution in PANS. This test enables computationally precise and objective metrics of motor coordination, taking into account both directional and spatial features continuously. We used SpaceSwipe to assess motor coordination in a group of children with PANS (n = 12, assessed on in total of 40 occasions) and compared it against the motor coordination subtest from the Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery VMI) 6th edition, traditionally used to follow-up symptomatology. Using a bivariate linear regression, we found that 33 s of the directional offset from tracking a moving target in SpaceSwipe could predict the Beery VMI motor coordination (VMI MC) raw scores (mean absolute error: 1.75 points). Positive correlations between the predicted scores and the VMI MC scores were found for initial testing (radj = 0.87) and for repeated testing (radj = 0.79). With its short administration time and its close prediction to Beery VMI scores, this proof-of-concept study demonstrates the potential for SpaceSwipe as a patient-friendly tool for precise, objective assessment of motor coordination in children with neurodevelopmental or neuropsychiatric disorders.
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Affiliation(s)
- Max Thorsson
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden.
- , Gothenburg, Sweden.
| | - Martyna A Galazka
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
| | - Parisa Hajjari
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
| | - Elisabeth Fernell
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
| | | | - Christopher Gillberg
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
| | - Mats Johnson
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
| | - Jakob Åsberg Johnels
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
- Section of Speech and Language Pathology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
| | - Nouchine Hadjikhani
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, Sahlgrenska Academy, Gothenburg, Sweden
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
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Sengupta S, Medendorp WP, Selen LPJ, Praamstra P. Exploration of sensory-motor tradeoff behavior in Parkinson’s disease. Front Hum Neurosci 2022; 16:951313. [DOI: 10.3389/fnhum.2022.951313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
While slowness of movement is an obligatory characteristic of Parkinson’s disease (PD), there are conditions in which patients move uncharacteristically fast, attributed to deficient motor inhibition. Here we investigate deficient inhibition in an optimal sensory-motor integration framework, using a game in which subjects used a paddle to catch a virtual ball. Display of the ball was extinguished as soon as the catching movement started, segregating the task into a sensing and acting phase. We analyzed the behavior of 9 PD patients (ON medication) and 10 age-matched controls (HC). The switching times (between sensing and acting phase) were compared to the predicted optimal switching time, based on the individual estimates of sensory and motor uncertainties. The comparison showed that deviation from predicted optimal switching times were similar between groups. However, PD patients showed a weaker correlation between variability in switching time and sensory-motor uncertainty, indicating a reduced propensity to generate exploratory behavior for optimizing goal-directed movements. Analysis of the movement kinematics revealed that PD patients, compared to controls, used a lower peak velocity of the paddle and intercepted the ball with greater velocity. Adjusting the trial duration to the time for the paddle to stop moving, we found that PD patients spent a smaller proportion of the trial duration for observing the ball. Altogether, the results do not show the premature movement initiation and truncated sensory processing that we predicted to ensue from deficient inhibition in PD.
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Kamat A, Makled B, Norfleet J, Schwaitzberg SD, Intes X, De S, Dutta A. Directed information flow during laparoscopic surgical skill acquisition dissociated skill level and medical simulation technology. NPJ SCIENCE OF LEARNING 2022; 7:19. [PMID: 36008451 PMCID: PMC9411170 DOI: 10.1038/s41539-022-00138-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 08/04/2022] [Indexed: 05/11/2023]
Abstract
Virtual reality (VR) simulator has emerged as a laparoscopic surgical skill training tool that needs validation using brain-behavior analysis. Therefore, brain network and skilled behavior relationship were evaluated using functional near-infrared spectroscopy (fNIRS) from seven experienced right-handed surgeons and six right-handed medical students during the performance of Fundamentals of Laparoscopic Surgery (FLS) pattern of cutting tasks in a physical and a VR simulator. Multiple regression and path analysis (MRPA) found that the FLS performance score was statistically significantly related to the interregional directed functional connectivity from the right prefrontal cortex to the supplementary motor area with F (2, 114) = 9, p < 0.001, and R2 = 0.136. Additionally, a two-way multivariate analysis of variance (MANOVA) found a statistically significant effect of the simulator technology on the interregional directed functional connectivity from the right prefrontal cortex to the left primary motor cortex (F (1, 15) = 6.002, p = 0.027; partial η2 = 0.286) that can be related to differential right-lateralized executive control of attention. Then, MRPA found that the coefficient of variation (CoV) of the FLS performance score was statistically significantly associated with the CoV of the interregionally directed functional connectivity from the right primary motor cortex to the left primary motor cortex and the left primary motor cortex to the left prefrontal cortex with F (2, 22) = 3.912, p = 0.035, and R2 = 0.262. This highlighted the importance of the efference copy information from the motor cortices to the prefrontal cortex for postulated left-lateralized perceptual decision-making to reduce behavioral variability.
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Affiliation(s)
- Anil Kamat
- Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Basiel Makled
- US Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, USA
| | - Jack Norfleet
- US Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, USA
| | | | - Xavier Intes
- Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Suvranu De
- Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Anirban Dutta
- Neuroengineering and Informatics for Rehabilitation Laboratory, Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA.
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Anwar H, Caby S, Dura-Bernal S, D’Onofrio D, Hasegan D, Deible M, Grunblatt S, Chadderdon GL, Kerr CC, Lakatos P, Lytton WW, Hazan H, Neymotin SA. Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning. PLoS One 2022; 17:e0265808. [PMID: 35544518 PMCID: PMC9094569 DOI: 10.1371/journal.pone.0265808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.
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Affiliation(s)
- Haroon Anwar
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Simon Caby
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Salvador Dura-Bernal
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
| | - David D’Onofrio
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Daniel Hasegan
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Matt Deible
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Sara Grunblatt
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - George L. Chadderdon
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
| | - Cliff C. Kerr
- Dept Physics, University of Sydney, Sydney, Australia
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Psychiatry, NYU Grossman School of Medicine, New York, New York, United States of America
| | - William W. Lytton
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
- Dept Neurology, Kings County Hospital Center, Brooklyn, New York, United States of America
| | - Hananel Hazan
- Dept of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Samuel A. Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Psychiatry, NYU Grossman School of Medicine, New York, New York, United States of America
- * E-mail:
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