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Ryu J, Torres E. Toward interpretable digital biomarkers of walking and reaching in Parkinson's disease. WEARABLE TECHNOLOGIES 2022; 3:e21. [PMID: 38486899 PMCID: PMC10936352 DOI: 10.1017/wtc.2022.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 03/17/2024]
Abstract
Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson's disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.
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Affiliation(s)
- Jihye Ryu
- Neurosurgery Department, University of California Los Angeles, Los Angeles, California90095, USA
- Psychology Department, Rutgers University, Piscataway, New Jersey, USA
| | - Elizabeth Torres
- Psychology Department, Rutgers University, Piscataway, New Jersey, USA
- Rutgers University Center for Cognitive Science, Piscataway, New Jersey, USA
- Computer Science Department, Computational Biomedicine Imaging and Modeling Center, Rutgers University, Piscataway, New Jersey, USA
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2
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Nataraj R, Sanford S, Liu M, Harel NY. Hand dominance in the performance and perceptions of virtual reach control. Acta Psychol (Amst) 2022; 223:103494. [PMID: 35045355 PMCID: PMC11056909 DOI: 10.1016/j.actpsy.2022.103494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/24/2021] [Accepted: 01/03/2022] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Efforts to optimize human-computer interactions are becoming increasingly prevalent, especially with virtual reality (VR) rehabilitation paradigms that utilize engaging interfaces. We hypothesized that motor and perceptional behaviors within a virtual environment are modulated uniquely through different modes of control of a hand avatar depending on limb dominance. This study investigated the effects of limb dominance on performance and concurrent changes in perceptions, such as time-based measures for intentional binding, during virtual reach-to-grasp. METHODS Participants (n = 16, healthy) controlled a virtual hand through their own hand motions with control adaptations in speed, noise, and automation. RESULTS A significant (p < 0.01) positive relationship between performance (reaching pathlength) and binding (time-interval estimation of beep-sound after grasp contact) was observed for the dominant hand. Unique changes in performance (p < 0.0001) and binding (p < 0.0001) were observed depending on handedness and which control mode was applied. CONCLUSIONS Developers of VR paradigms should consider limb dominance to optimize settings that facilitate better performance and perceptional engagement. Adapting VR rehabilitation for handedness may particularly benefit unilateral impairments, like hemiparesis or single-limb amputation.
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Affiliation(s)
- Raviraj Nataraj
- Movement Control Rehabilitation (MOCORE) Laboratory, Altorfer Complex, Room 201, Stevens Institute of Technology, Hoboken, NJ, USA; Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.
| | - Sean Sanford
- Movement Control Rehabilitation (MOCORE) Laboratory, Altorfer Complex, Room 201, Stevens Institute of Technology, Hoboken, NJ, USA; Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Mingxiao Liu
- Movement Control Rehabilitation (MOCORE) Laboratory, Altorfer Complex, Room 201, Stevens Institute of Technology, Hoboken, NJ, USA; Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Noam Y Harel
- Spinal Cord Damage Research Center, James J. Peters VA Medical Center, Bronx, NY, USA; Departments of Neurology and Rehabilitation & Human Performance, Icahn School of Medicine at Mount Sinai, USA
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Bermperidis T, Rai R, Ryu J, Zanotto D, Agrawal SK, Lalwani AK, Torres EB. Optimal time lags from causal prediction model help stratify and forecast nervous system pathology. Sci Rep 2021; 11:20904. [PMID: 34686679 PMCID: PMC8536772 DOI: 10.1038/s41598-021-00156-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 09/27/2021] [Indexed: 12/26/2022] Open
Abstract
Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster's symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.
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Affiliation(s)
| | - Richa Rai
- Psychology Department, Rutgers University, Piscataway, USA
| | - Jihye Ryu
- Neurosurgery Department, University of California Los Angeles, Los Angeles, USA
| | - Damiano Zanotto
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, USA
| | - Sunil K Agrawal
- Department of Mechanical Engineering, School of Engineering, Columbia University, New York, USA.,Department of Rehabilitative and Regenerative Medicine, Columbia University Medical Center, New York, USA
| | - Anil K Lalwani
- New York Presbyterian-Columbia University Irving Medical Center, New York, USA.,Division of Otology, Neurotology, and Skull Base Surgery, Department of Otolaryngology-Head and Neck Surgery, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Elizabeth B Torres
- Psychology Department, Rutgers University, Piscataway, USA. .,Rutgers University Center for Cognitive Science (RUCCS), Piscataway, USA. .,Computational Biomedicine Imaging and Modelling, Piscataway, USA.
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Personalized Biometrics of Physical Pain Agree with Psychophysics by Participants with Sensory over Responsivity. J Pers Med 2021; 11:jpm11020093. [PMID: 33540769 PMCID: PMC7913075 DOI: 10.3390/jpm11020093] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 02/06/2023] Open
Abstract
The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.
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Nataraj R, Sanford S. Control Modification of Grasp Force Covaries Agency and Performance on Rigid and Compliant Surfaces. Front Bioeng Biotechnol 2021; 8:574006. [PMID: 33520950 PMCID: PMC7838614 DOI: 10.3389/fbioe.2020.574006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 12/16/2020] [Indexed: 11/30/2022] Open
Abstract
This study investigated how modifications in the display of a computer trace under user control of grasp forces can co-modulate agency (perception of control) and performance of grasp on rigid and compliant surfaces. We observed positive correlation (p < 0.01) between implicit agency, measured from time-interval estimation for intentional binding, and grasp performance, measured by force-tracking error, across varying control modes for each surface type. The implications of this work are design directives for cognition-centered device interfaces for rehabilitation of grasp after neurotraumas such as spinal cord and brain injuries while considering if grasp interaction is rigid or compliant. These device interfaces should increase user integration to virtual reality training and powered assistive devices such as exoskeletons and prostheses. The modifications in control modes for this study included changes in force magnitude, addition of mild noise, and a measure of automation. Significant differences (p < 0.001) were observed for each surface type across control modes with metrics for implicit agency, performance, and grasp control efficiency. Explicit agency, measured from user survey responses, did not exhibit significant variations in this study, suggesting implicit measures of agency are needed for identifying co-modulation with grasp performance. Grasp on the compliant surface resulted in greater dependence of performance on agency and increases in agency and performance with the addition of mild noise. Noise in conjunction with perceived freedom at a flexible surface may have amplified visual feedback responses. Introducing automation in control decreased agency and performance for both surfaces, suggesting the value in continuous user control of grasp. In conclusion, agency and performance of grasp can be co-modulated across varying modes of control, especially for compliant grasp actions. Future studies should consider reliable measures of implicit agency, including physiological recordings, to automatically adapt rehabilitation interfaces for better cognitive engagement and to accelerate functional outcomes.
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Affiliation(s)
- Raviraj Nataraj
- Movement Control Rehabilitation Laboratory, Stevens Institute of Technology, Hoboken, NJ, United States.,Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Sean Sanford
- Movement Control Rehabilitation Laboratory, Stevens Institute of Technology, Hoboken, NJ, United States.,Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
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6
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The Autonomic Nervous System Differentiates between Levels of Motor Intent and End Effector. J Pers Med 2020; 10:jpm10030076. [PMID: 32751933 PMCID: PMC7563544 DOI: 10.3390/jpm10030076] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 07/28/2020] [Accepted: 07/28/2020] [Indexed: 12/22/2022] Open
Abstract
While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored, however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of redundant degrees of freedom across the body in motion. We posit that the consequences of such unintended actions implicitly contribute to our autonomous sense of action ownership and agency. We question whether biorhythmic activities from these motions are separable from those which intentionally occur. Here we find that fluctuations in the biorhythmic activities of the nervous systems can unambiguously differentiate across levels of intent. More important yet, this differentiation is remarkable when we examine the fluctuations in biorhythmic activity from the autonomic nervous systems. We find that when the action is intended, the heart signal leads the body kinematics signals; but when the action segment spontaneously occurs without instructions, the heart signal lags the bodily kinematics signals. We conclude that the autonomic nervous system can differentiate levels of intent. Our results are discussed while considering their potential translational value.
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Torres EB, Caballero C, Mistry S. Aging with Autism Departs Greatly from Typical Aging. SENSORS 2020; 20:s20020572. [PMID: 31968701 PMCID: PMC7014496 DOI: 10.3390/s20020572] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 01/13/2020] [Accepted: 01/14/2020] [Indexed: 01/01/2023]
Abstract
Autism has been largely portrayed as a psychiatric and childhood disorder. However, autism is a lifelong neurological condition that evolves over time through highly heterogeneous trajectories. These trends have not been studied in relation to normative aging trajectories, so we know very little about aging with autism. One aspect that seems to develop differently is the sense of movement, inclusive of sensory kinesthetic-reafference emerging from continuously sensed self-generated motions. These include involuntary micro-motions eluding observation, yet routinely obtainable in fMRI studies to rid images of motor artifacts. Open-access repositories offer thousands of imaging records, covering 5-65 years of age for both neurotypical and autistic individuals to ascertain the trajectories of involuntary motions. Here we introduce new computational techniques that automatically stratify different age groups in autism according to probability distance in different representational spaces. Further, we show that autistic cross-sectional population trajectories in probability space fundamentally differ from those of neurotypical controls and that after 40 years of age, there is an inflection point in autism, signaling a monotonically increasing difference away from age-matched normative involuntary motion signatures. Our work offers new age-appropriate stochastic analyses amenable to redefine basic research and provide dynamic diagnoses as the person's nervous systems age.
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Affiliation(s)
- Elizabeth B. Torres
- Psychology Department Center for Biomedicine Imaging and Modelling, CS Department and Rutgers Center for Cognitive Science, Rutgers University, Camden, NJ 08854, USA
- Correspondence: ; Tel.: +1-732-208-3158
| | - Carla Caballero
- Sports Science Department, Miguel Hernandez University of Elche, 03202 Alicante, Spain;
| | - Sejal Mistry
- Biomathematics Department, Rutgers University, Camden, NJ 08854, USA;
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Zapata-Fonseca L, Dotov D, Fossion R, Froese T, Schilbach L, Vogeley K, Timmermans B. Multi-Scale Coordination of Distinctive Movement Patterns During Embodied Interaction Between Adults With High-Functioning Autism and Neurotypicals. Front Psychol 2019; 9:2760. [PMID: 30687197 PMCID: PMC6336705 DOI: 10.3389/fpsyg.2018.02760] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 12/21/2018] [Indexed: 12/02/2022] Open
Abstract
Autism Spectrum Disorder (ASD) can be understood as a social interaction disorder. This requires researchers to take a “second-person” stance and to use experimental setups based on bidirectional interactions. The present work offers a quantitative description of movement patterns exhibited during computer-mediated real-time sensorimotor interaction in 10 dyads of adult participants, each consisting of one control individual (CTRL) and one individual with high-functioning autism (HFA). We applied time-series analyses to their movements and found two main results. First, multi-scale coordination between participants was present. Second, despite this dyadic alignment and our previous finding that individuals with HFA can be equally sensitive to the other’s presence, individuals’ movements differed in style: in contrast to CTRLs, HFA participants appeared less inclined to sustain mutual interaction and instead explored the virtual environment more generally. This finding is consistent with social motivation deficit accounts of ASD, as well as with hypersensitivity-motivated avoidance of overstimulation. Our research demonstrates the utility of time series analyses for the second-person stance and complements previous work focused on non-dynamical and performance-based variables.
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Affiliation(s)
- Leonardo Zapata-Fonseca
- Plan of Combined Studies in Medicine (PECEM), Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico.,Center for the Sciences of Complexity (C3), National Autonomous University of Mexico, Mexico City, Mexico
| | - Dobromir Dotov
- Research and High Performance Computing, LIVELab, McMaster University, Hamilton, ON, Canada
| | - Ruben Fossion
- Center for the Sciences of Complexity (C3), National Autonomous University of Mexico, Mexico City, Mexico.,Department of Matter Structure, Nuclear Sciences Institute, National Autonomous University of Mexico, Mexico City, Mexico
| | - Tom Froese
- Center for the Sciences of Complexity (C3), National Autonomous University of Mexico, Mexico City, Mexico.,Department of Computer Science, Institute of Applied Mathematics and Systems Research, National Autonomous University of Mexico, Mexico City, Mexico
| | - Leonhard Schilbach
- Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany.,Department of Psychiatry, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kai Vogeley
- Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany.,Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany
| | - Bert Timmermans
- The School of Psychology, University of Aberdeen, Aberdeen, United Kingdom
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9
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Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion. SENSORS 2018; 18:s18093117. [PMID: 30223588 PMCID: PMC6164645 DOI: 10.3390/s18093117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 09/06/2018] [Accepted: 09/14/2018] [Indexed: 11/23/2022]
Abstract
Dyadic interactions are ubiquitous in our lives, yet they are highly challenging to study. Many subtle aspects of coupled bodily dynamics continuously unfolding during such exchanges have not been empirically parameterized. As such, we have no formal statistical methods to describe the spontaneously self-emerging coordinating synergies within each actor’s body and across the dyad. Such cohesive motion patterns self-emerge and dissolve largely beneath the awareness of the actors and the observers. Consequently, hand coding methods may miss latent aspects of the phenomena. The present paper addresses this gap and provides new methods to quantify the moment-by-moment evolution of self-emerging cohesiveness during highly complex ballet routines. We use weighted directed graphs to represent the dyads as dynamically coupled networks unfolding in real-time, with activities captured by a grid of wearable sensors distributed across the dancers’ bodies. We introduce new visualization tools, signal parameterizations, and a statistical platform that integrates connectivity metrics with stochastic analyses to automatically detect coordination patterns and self-emerging cohesive coupling as they unfold in real-time. Potential applications of these new techniques are discussed in the context of personalized medicine, basic research, and the performing arts.
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Connors BL, Rende R. Embodied Decision-Making Style: Below and Beyond Cognition. Front Psychol 2018; 9:1123. [PMID: 30072930 PMCID: PMC6058971 DOI: 10.3389/fpsyg.2018.01123] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 06/12/2018] [Indexed: 12/11/2022] Open
Abstract
There is growing recognition of the essential role of sensorimotor processes as not just a supporter of the cognitive aspects of decision making, but rather as a foundation for all the coordinated physical and mental activities that go into how we make decisions. We illuminate concepts and methods for examining embodied decision making through the lens of Movement Pattern Analysis (MPA). MPA is as a prime example of a conceptually rooted observational methodology for deciphering embodied decision making and for decoding how people differ as decision makers with respect to cognitive motivational priorities. The historical origins of MPA that predated the formalized recognition of embodied cognition are presented, along with an overview of both the theoretical model and methodology. Advances in research on two psychometric benchmarks of observational research-inter-rater reliability and predictive validity-are highlighted as an empirical platform for the strong promise of MPA as a tool for understanding individual differences in embodied decision-making style. Future directions for research are considered-specifically with respect to the potential for utilizing automated coding, and the need for collaborative neuroscience research efforts-which would support further understanding of how decoding movement patterning captures human motivation at the level of sensory, motoric, cognitive and action integration which drives how people function as decision makers.
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Affiliation(s)
- Brenda L Connors
- Office of the President, Naval War College, Newport, RI, United States
| | - Richard Rende
- Social Behavioral Research Applications, Phoenix, AZ, United States
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