151
|
Mari T, Henderson J, Ali SH, Hewitt D, Brown C, Stancak A, Fallon N. Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain. BMC Neurosci 2023; 24:50. [PMID: 37715119 PMCID: PMC10504739 DOI: 10.1186/s12868-023-00819-y] [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] [Received: 07/06/2023] [Accepted: 09/06/2023] [Indexed: 09/17/2023] Open
Abstract
Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.
Collapse
Affiliation(s)
- Tyler Mari
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Jessica Henderson
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK
| | - S Hasan Ali
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK
| | - Danielle Hewitt
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK
| | - Christopher Brown
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK
| | - Andrej Stancak
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK
| | - Nicholas Fallon
- Department of Psychology, Institute of Population Health, University of Liverpool, 2.21 Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK
| |
Collapse
|
152
|
Hewitson CL, Kaplan DM, Crossley MJ. Error-independent effect of sensory uncertainty on motor learning when both feedforward and feedback control processes are engaged. PLoS Comput Biol 2023; 19:e1010526. [PMID: 37683013 PMCID: PMC10522034 DOI: 10.1371/journal.pcbi.1010526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 09/26/2023] [Accepted: 08/15/2023] [Indexed: 09/10/2023] Open
Abstract
Integrating sensory information during movement and adapting motor plans over successive movements are both essential for accurate, flexible motor behaviour. When an ongoing movement is off target, feedback control mechanisms update the descending motor commands to counter the sensed error. Over longer timescales, errors induce adaptation in feedforward planning so that future movements become more accurate and require less online adjustment from feedback control processes. Both the degree to which sensory feedback is integrated into an ongoing movement and the degree to which movement errors drive adaptive changes in feedforward motor plans have been shown to scale inversely with sensory uncertainty. However, since these processes have only been studied in isolation from one another, little is known about how they are influenced by sensory uncertainty in real-world movement contexts where they co-occur. Here, we show that sensory uncertainty may impact feedforward adaptation of reaching movements differently when feedback integration is present versus when it is absent. In particular, participants gradually adjust their movements from trial-to-trial in a manner that is well characterised by a slow and consistent envelope of error reduction. Riding on top of this slow envelope, participants exhibit large and abrupt changes in their initial movement vectors that are strongly correlated with the degree of sensory uncertainty present on the previous trial. However, these abrupt changes are insensitive to the magnitude and direction of the sensed movement error. These results prompt important questions for current models of sensorimotor learning under uncertainty and open up new avenues for future exploration in the field.
Collapse
Affiliation(s)
| | - David M. Kaplan
- School of Psychological Sciences, Macquarie University, Sydney, Australia
- Macquarie University Performance and Expertise Research Centre, Macquarie University, Sydney, Australia
| | - Matthew J. Crossley
- School of Psychological Sciences, Macquarie University, Sydney, Australia
- Macquarie University Performance and Expertise Research Centre, Macquarie University, Sydney, Australia
| |
Collapse
|
153
|
Guo L, Kumar A. Role of interneuron subtypes in controlling trial-by-trial output variability in the neocortex. Commun Biol 2023; 6:874. [PMID: 37620550 PMCID: PMC10449833 DOI: 10.1038/s42003-023-05231-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Trial-by-trial variability is a ubiquitous property of neuronal activity in vivo which shapes the stimulus response. Computational models have revealed how local network structure and feedforward inputs shape the trial-by-trial variability. However, the role of input statistics and different interneuron subtypes in this process is less understood. To address this, we investigate the dynamics of stimulus response in a cortical microcircuit model with one excitatory and three inhibitory interneuron populations (PV, SST, VIP). Our findings demonstrate that the balance of inputs to different neuron populations and input covariances are the primary determinants of output trial-by-trial variability. The effect of input covariances is contingent on the input balances. In general, the network exhibits smaller output trial-by-trial variability in a PV-dominated regime than in an SST-dominated regime. Importantly, our work reveals mechanisms by which output trial-by-trial variability can be controlled in a context, state, and task-dependent manner.
Collapse
Affiliation(s)
- Lihao Guo
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, Stockholm, Sweden.
- Scilife Lab, Stockholm, Sweden.
| | - Arvind Kumar
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, Stockholm, Sweden.
- Scilife Lab, Stockholm, Sweden.
| |
Collapse
|
154
|
Mangalam M, Kelty-Stephen DG, Sommerfeld JH, Stergiou N, Likens AD. Temporal organization of stride-to-stride variations contradicts predictive models for sensorimotor control of footfalls during walking. PLoS One 2023; 18:e0290324. [PMID: 37616227 PMCID: PMC10449478 DOI: 10.1371/journal.pone.0290324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/04/2023] [Indexed: 08/26/2023] Open
Abstract
Walking exhibits stride-to-stride variations. Given ongoing perturbations, these variations critically support continuous adaptations between the goal-directed organism and its surroundings. Here, we report that stride-to-stride variations during self-paced overground walking show cascade-like intermittency-stride intervals become uneven because stride intervals of different sizes interact and do not simply balance each other. Moreover, even when synchronizing footfalls with visual cues with variable timing of presentation, asynchrony in the timings of the cue and footfall shows cascade-like intermittency. This evidence conflicts with theories about the sensorimotor control of walking, according to which internal predictive models correct asynchrony in the timings of the cue and footfall from one stride to the next on crossing thresholds leading to the risk of falling. Hence, models of the sensorimotor control of walking must account for stride-to-stride variations beyond the constraints of threshold-dependent predictive internal models.
Collapse
Affiliation(s)
- Madhur Mangalam
- Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, United States of America
| | - Damian G. Kelty-Stephen
- Department of Psychology, State University of New York at New Paltz, New Paltz, NY, United States of America
| | - Joel H. Sommerfeld
- Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, United States of America
| | - Nick Stergiou
- Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, United States of America
- Department of Department of Physical Education, & Sport Science, Aristotle University, Thessaloniki, Greece
| | - Aaron D. Likens
- Division of Biomechanics and Research Development, Department of Biomechanics, and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE, United States of America
| |
Collapse
|
155
|
Anderson ED, Talukdar T, Goodwin G, Di Pietro V, Yakoub KM, Zwilling CE, Davies D, Belli A, Barbey AK. Assessing blood oxygen level-dependent signal variability as a biomarker of brain injury in sport-related concussion. Brain Commun 2023; 5:fcad215. [PMID: 37649639 PMCID: PMC10465085 DOI: 10.1093/braincomms/fcad215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/02/2023] [Accepted: 08/10/2023] [Indexed: 09/01/2023] Open
Abstract
Mild traumatic brain injury is a complex neurological disorder of significant concern among athletes who play contact sports. Athletes who sustain sport-related concussion typically undergo physical examination and neurocognitive evaluation to determine injury severity and return-to-play status. However, traumatic disruption to neurometabolic processes can occur with minimal detectable anatomic pathology or neurocognitive alteration, increasing the risk that athletes may be cleared for return-to-play during a vulnerable period and receive a repetitive injury. This underscores the need for sensitive functional neuroimaging methods to detect altered cerebral physiology in concussed athletes. The present study compared the efficacy of Immediate Post-concussion Assessment and Cognitive Testing composite scores and whole-brain measures of blood oxygen level-dependent signal variability for classifying concussion status and predicting concussion symptomatology in healthy, concussed and repetitively concussed athletes, assessing blood oxygen level-dependent signal variability as a potential diagnostic tool for characterizing functional alterations to cerebral physiology and assisting in the detection of sport-related concussion. We observed significant differences in regional blood oxygen level-dependent signal variability measures for concussed athletes but did not observe significant differences in Immediate Post-concussion Assessment and Cognitive Testing scores of concussed athletes. We further demonstrate that incorporating measures of functional brain alteration alongside Immediate Post-concussion Assessment and Cognitive Testing scores enhances the sensitivity and specificity of supervised random forest machine learning methods when classifying and predicting concussion status and post-concussion symptoms, suggesting that alterations to cerebrovascular status characterize unique variance that may aid in the detection of sport-related concussion and repetitive mild traumatic brain injury. These results indicate that altered blood oxygen level-dependent variability holds promise as a novel neurobiological marker for detecting alterations in cerebral perfusion and neuronal functioning in sport-related concussion, motivating future research to establish and validate clinical assessment protocols that can incorporate advanced neuroimaging methods to characterize altered cerebral physiology following mild traumatic brain injury.
Collapse
Affiliation(s)
- Evan D Anderson
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA
| | - Tanveer Talukdar
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Grace Goodwin
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Psychology, University of Nevada, Las Vegas, NV 89557, USA
| | - Valentina Di Pietro
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Kamal M Yakoub
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Christopher E Zwilling
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - David Davies
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Antonio Belli
- Neurotrauma and Ophthalmology Research Group, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TH, UK
| | - Aron K Barbey
- Decision Neuroscience Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
- Department of Psychology, University of Illinois, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| |
Collapse
|
156
|
Bredenberg C, Savin C. Desiderata for normative models of synaptic plasticity. ARXIV 2023:arXiv:2308.04988v1. [PMID: 37608931 PMCID: PMC10441445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions. We then discuss how new models have begun to improve on these criteria and suggest avenues for further development. As prototypes, we provide detailed analyses of two specific models - REINFORCE and the Wake-Sleep algorithm. We provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable.
Collapse
Affiliation(s)
- Colin Bredenberg
- Center for Neural Science, New York University, New York, NY 10003, USA
- Mila-Quebec AI Institute, 6666 Rue Saint-Urbain, Montréal, QC H2S 3H1
| | - Cristina Savin
- Center for Neural Science, New York University, New York, NY 10003, USA
- Center for Data Science, New York University, New York, NY 10011, USA
| |
Collapse
|
157
|
Meiser S, Sleeboom JM, Arkhypchuk I, Sandbote K, Kretzberg J. Cell anatomy and network input explain differences within but not between leech touch cells at two different locations. Front Cell Neurosci 2023; 17:1186997. [PMID: 37565030 PMCID: PMC10411907 DOI: 10.3389/fncel.2023.1186997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/06/2023] [Indexed: 08/12/2023] Open
Abstract
Mechanosensory cells in the leech share several common features with mechanoreceptors in the human glabrous skin. Previous studies showed that the six T (touch) cells in each body segment of the leech are highly variable in their responses to somatic current injection and change their excitability over time. Here, we investigate three potential reasons for this variability in excitability by comparing the responses of T cells at two soma locations (T2 and T3): (1) Differential effects of time-dependent changes in excitability, (2) divergent synaptic input from the network, and (3) different anatomical structures. These hypotheses were explored with a combination of electrophysiological double recordings, 3D reconstruction of neurobiotin-filled cells, and compartmental model simulations. Current injection triggered significantly more spikes with shorter latency and larger amplitudes in cells at soma location T2 than at T3. During longer recordings, cells at both locations increased their excitability over time in the same way. T2 and T3 cells received the same amount of synaptic input from the unstimulated network, and the polysynaptic connections between both T cells were mutually symmetric. However, we found a striking anatomical difference: While in our data set all T2 cells innervated two roots connecting the ganglion with the skin, 50% of the T3 cells had only one root process. The sub-sample of T3 cells with one root process was significantly less excitable than the T3 cells with two root processes and the T2 cells. To test if the additional root process causes higher excitability, we simulated the responses of 3D reconstructed cells of both anatomies with detailed multi-compartment models. The anatomical subtypes do not differ in excitability when identical biophysical parameters and a homogeneous channel distribution are assumed. Hence, all three hypotheses may contribute to the highly variable T cell responses, but none of them is the only factor accounting for the observed systematic difference in excitability between cells at T2 vs. T3 soma location. Therefore, future patch clamp and modeling studies are needed to analyze how biophysical properties and spatial distribution of ion channels on the cell surface contribute to the variability and systematic differences of electrophysiological phenotypes.
Collapse
Affiliation(s)
- Sonja Meiser
- Department of Neuroscience, Computational Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
| | - Jana Marie Sleeboom
- Department of Neuroscience, Computational Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
- Institute of Physiology II, Faculty of Medicine, University Clinic Bonn (UKB), University of Bonn, Bonn, Germany
| | - Ihor Arkhypchuk
- Department of Neuroscience, Computational Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
| | - Kevin Sandbote
- Department of Neuroscience, Computational Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
| | - Jutta Kretzberg
- Department of Neuroscience, Computational Neuroscience, Faculty VI, University of Oldenburg, Oldenburg, Germany
- Department of Neuroscience, Cluster of Excellence Hearing4all, Faculty VI, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| |
Collapse
|
158
|
Janik RA. Aesthetics and neural network image representations. Sci Rep 2023; 13:11428. [PMID: 37454170 DOI: 10.1038/s41598-023-38443-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 07/08/2023] [Indexed: 07/18/2023] Open
Abstract
We analyze the spaces of images encoded by generative neural networks of the BigGAN architecture. We find that generic multiplicative perturbations of neural network parameters away from the photo-realistic point often lead to networks generating images which appear as "artistic renditions" of the corresponding objects. This demonstrates an emergence of aesthetic properties directly from the structure of the photo-realistic visual environment as encoded in its neural network parametrization. Moreover, modifying a deep semantic part of the neural network leads to the appearance of symbolic visual representations. None of the considered networks had any access to images of human-made art.
Collapse
Affiliation(s)
- Romuald A Janik
- Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland.
| |
Collapse
|
159
|
Hutt A, Rich S, Valiante TA, Lefebvre J. Intrinsic neural diversity quenches the dynamic volatility of neural networks. Proc Natl Acad Sci U S A 2023; 120:e2218841120. [PMID: 37399421 PMCID: PMC10334753 DOI: 10.1073/pnas.2218841120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/19/2023] [Indexed: 07/05/2023] Open
Abstract
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
Collapse
Affiliation(s)
- Axel Hutt
- Université de Strasbourg, CNRS, Inria, ICube, MLMS, MIMESIS, StrasbourgF-67000, France
| | - Scott Rich
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
| | - Taufik A. Valiante
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ONM5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ONM5S 3G9, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ONM5S 1A8, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ONM5G 2C4, Canada
- Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ONM5G 2A2, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ONM5S 3G8, Canada
| | - Jérémie Lefebvre
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, ONK1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
| |
Collapse
|
160
|
Mulla DM, Keir PJ. Neuromuscular control: from a biomechanist's perspective. Front Sports Act Living 2023; 5:1217009. [PMID: 37476161 PMCID: PMC10355330 DOI: 10.3389/fspor.2023.1217009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/21/2023] [Indexed: 07/22/2023] Open
Abstract
Understanding neural control of movement necessitates a collaborative approach between many disciplines, including biomechanics, neuroscience, and motor control. Biomechanics grounds us to the laws of physics that our musculoskeletal system must obey. Neuroscience reveals the inner workings of our nervous system that functions to control our body. Motor control investigates the coordinated motor behaviours we display when interacting with our environment. The combined efforts across the many disciplines aimed at understanding human movement has resulted in a rich and rapidly growing body of literature overflowing with theories, models, and experimental paradigms. As a result, gathering knowledge and drawing connections between the overlapping but seemingly disparate fields can be an overwhelming endeavour. This review paper evolved as a need for us to learn of the diverse perspectives underlying current understanding of neuromuscular control. The purpose of our review paper is to integrate ideas from biomechanics, neuroscience, and motor control to better understand how we voluntarily control our muscles. As biomechanists, we approach this paper starting from a biomechanical modelling framework. We first define the theoretical solutions (i.e., muscle activity patterns) that an individual could feasibly use to complete a motor task. The theoretical solutions will be compared to experimental findings and reveal that individuals display structured muscle activity patterns that do not span the entire theoretical solution space. Prevalent neuromuscular control theories will be discussed in length, highlighting optimality, probabilistic principles, and neuromechanical constraints, that may guide individuals to families of muscle activity solutions within what is theoretically possible. Our intention is for this paper to serve as a primer for the neuromuscular control scientific community by introducing and integrating many of the ideas common across disciplines today, as well as inspire future work to improve the representation of neural control in biomechanical models.
Collapse
|
161
|
Cravo MI, Bernardes R, Castelo-Branco M. Subtractive adaptation is a more effective and general mechanism in binocular rivalry than divisive adaptation. J Vis 2023; 23:18. [PMID: 37505915 PMCID: PMC10405863 DOI: 10.1167/jov.23.7.18] [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] [Received: 04/07/2023] [Accepted: 06/17/2023] [Indexed: 07/30/2023] Open
Abstract
The activity of neurons is influenced by random fluctuations and can be strongly modulated by firing rate adaptation, particularly in sensory systems. Still, there is ongoing debate about the characteristics of neuronal noise and the mechanisms of adaptation, and even less is known about how exactly they affect perception. Noise and adaptation are critical in binocular rivalry, a visual phenomenon where two images compete for perceptual dominance. Here, we investigated the effects of different noise processes and adaptation mechanisms on visual perception by simulating a model of binocular rivalry with Gaussian white noise, Ornstein-Uhlenbeck noise, and pink noise, in variants with divisive adaptation, subtractive adaptation, and without adaptation. By simulating the nine models in parameter space, we find that white noise only produces rivalry when paired with subtractive adaptation and that subtractive adaptation reduces the influence of noise intensity on rivalry strength and introduces convergence of the mean percept duration, an important metric of binocular rivalry, across all noise processes. In sum, our results show that white noise is an insufficient description of background activity in the brain and that subtractive adaptation is a stronger and more general switching mechanism in binocular rivalry than divisive adaptation, with important noise-filtering properties.
Collapse
Affiliation(s)
- Maria Inês Cravo
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Rui Bernardes
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Brain Imaging Network of Portugal, Portugal
| |
Collapse
|
162
|
Kim KS, Gaines JL, Parrell B, Ramanarayanan V, Nagarajan SS, Houde JF. Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech. PLoS Comput Biol 2023; 19:e1011244. [PMID: 37506120 PMCID: PMC10434967 DOI: 10.1371/journal.pcbi.1011244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 08/17/2023] [Accepted: 06/06/2023] [Indexed: 07/30/2023] Open
Abstract
Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.
Collapse
Affiliation(s)
- Kwang S. Kim
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Jessica L. Gaines
- Graduate Program in Bioengineering, University of California Berkeley-University of California San Francisco, San Francisco, California, United States of America
| | - Benjamin Parrell
- Department of Communication Sciences and Disorders, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Vikram Ramanarayanan
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, California, United States of America
- Modality.AI, San Francisco, California, United States of America
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
| | - John F. Houde
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, California, United States of America
| |
Collapse
|
163
|
Scarciglia A, Catrambone V, Bonanno C, Valenza G. Characterization of Physiological Noise in Complex Cardiovascular Variability Series. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082793 DOI: 10.1109/embc40787.2023.10339997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The cardiovascular system can be analyzed using spectral, nonlinear, and complexity metrics. Nevertheless, dynamical noise may significantly impact these quantifiers. To our knowledge, there has been no attempt to quantify the intrinsic cardiovascular system noise driving heartbeat dynamics. To this end, this study presents a novel, model-free framework to define and quantify physiological noise using nonlinear Approximate Entropy profile. The framework was tested using analytical noisy series and then applied to real Heart Rate Variability (HRV) series gathered from a publicly-available dataset of recordings from 19 young and 19 elderly subjects watching the movie "Fantasia". Results suggest that physiological noise may account for over 15% of cardiovascular dynamics and is influenced by aging, with decreased cardiac noise in the elderly compared to the young subjects. Our findings indicate that physiological noise is a crucial factor in characterizing cardiovascular dynamics, and current spectral, nonlinear, and complexity assessments should take into account underlying dynamical noise estimates.
Collapse
|
164
|
Shine JM, Lewis LD, Garrett DD, Hwang K. The impact of the human thalamus on brain-wide information processing. Nat Rev Neurosci 2023; 24:416-430. [PMID: 37237103 DOI: 10.1038/s41583-023-00701-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2023] [Indexed: 05/28/2023]
Abstract
The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.
Collapse
Affiliation(s)
- James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Douglas D Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Kai Hwang
- Cognitive Control Collaborative, Department of Psychological and Brain Sciences, The University of Iowa, Iowa City, IA, USA.
- Department of Psychiatry, The University of Iowa, Iowa City, IA, USA.
- Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA, USA.
| |
Collapse
|
165
|
Argunsah AÖ, Israely I. The temporal pattern of synaptic activation determines the longevity of structural plasticity at dendritic spines. iScience 2023; 26:106835. [PMID: 37332599 PMCID: PMC10272476 DOI: 10.1016/j.isci.2023.106835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 01/18/2023] [Accepted: 05/04/2023] [Indexed: 06/20/2023] Open
Abstract
Learning is thought to involve physiological and structural changes at individual synapses. Synaptic plasticity has predominantly been studied using regular stimulation patterns, but neuronal activity in the brain normally follows a Poisson distribution. We used two-photon imaging and glutamate uncaging to investigate the structural plasticity of single dendritic spines using naturalistic activation patterns sampled from a Poisson distribution. We showed that naturalistic activation patterns elicit structural plasticity that is both NMDAR and protein synthesis-dependent. Furthermore, we uncovered that the longevity of structural plasticity is dependent on the temporal structure of the naturalistic pattern. Finally, we found that during the delivery of the naturalistic activity, spines underwent rapid structural growth that predicted the longevity of plasticity. This was not observed with regularly spaced activity. These data reveal that different temporal organizations of the same number of synaptic stimulations can produce rather distinct short and long-lasting structural plasticity.
Collapse
Affiliation(s)
- Ali Özgür Argunsah
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
- Laboratory of Neuronal Circuit Assembly, Brain Research Institute (HiFo), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
- Neuroscience Center Zurich (ZNZ), Winterthurerstrasse 190, 8057 Zürich, Switzerland
| | - Inbal Israely
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
- Department of Pathology and Cell Biology, Department of Neuroscience, in the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| |
Collapse
|
166
|
Sihn D, Kwon OS, Kim SP. Robust and efficient representations of dynamic stimuli in hierarchical neural networks via temporal smoothing. Front Comput Neurosci 2023; 17:1164595. [PMID: 37398935 PMCID: PMC10307978 DOI: 10.3389/fncom.2023.1164595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/24/2023] [Indexed: 07/04/2023] Open
Abstract
Introduction Efficient coding that minimizes informational redundancy of neural representations is a widely accepted neural coding principle. Despite the benefit, maximizing efficiency in neural coding can make neural representation vulnerable to random noise. One way to achieve robustness against random noise is smoothening neural responses. However, it is not clear whether the smoothness of neural responses can hold robust neural representations when dynamic stimuli are processed through a hierarchical brain structure, in which not only random noise but also systematic error due to temporal lag can be induced. Methods In the present study, we showed that smoothness via spatio-temporally efficient coding can achieve both efficiency and robustness by effectively dealing with noise and neural delay in the visual hierarchy when processing dynamic visual stimuli. Results The simulation results demonstrated that a hierarchical neural network whose bidirectional synaptic connections were learned through spatio-temporally efficient coding with natural scenes could elicit neural responses to visual moving bars similar to those to static bars with the identical position and orientation, indicating robust neural responses against erroneous neural information. It implies that spatio-temporally efficient coding preserves the structure of visual environments locally in the neural responses of hierarchical structures. Discussion The present results suggest the importance of a balance between efficiency and robustness in neural coding for visual processing of dynamic stimuli across hierarchical brain structures.
Collapse
|
167
|
Ankri N, Debanne D. A fast Markovian method for modeling channel noise in neurons. Heliyon 2023; 9:e16953. [PMID: 37484233 PMCID: PMC10361033 DOI: 10.1016/j.heliyon.2023.e16953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 04/17/2023] [Accepted: 06/01/2023] [Indexed: 07/25/2023] Open
Abstract
Channel noise results from rapid transitions of protein channels from closed to open state and is generally considered as the most dominant source of electrical noise causing membrane-potential fluctuations even in the absence of synaptic inputs. The simulation of a realistic channel noise remains a source of possible error. Although the Markovian method is considered as the golden standard for appropriate description of channel noise, its computation time increasing exponentially with the number of channels, it is poorly suitable to simulate realistic features. We describe here a novel algorithm at discrete time unit for simulating ion channel noise based on Markov chains (MC). Although this new algorithm refers to a Monte-Carlo process, it only needs few random numbers whatever the number of channels involved. Our fast MC (FMC) model does not exhibit the drawbacks due to approximations based on stochastic differential equations and the values of spike jitter are comparable to those obtained with the true Markovian method. In fact, we show here, that these drawbacks can be highlighted in the approximation based on stochastic differential equation methods even for a high number of channels (standard deviation of the 5th spike is about two-fold larger than that of MCF or true Markovian method for 5000 sodium channels). The FMC model appears therefore as the most accurate method to simulate channel noise with a fast execution time that does not depend on the channel number.
Collapse
|
168
|
Whittier TT, Patrick CM, Fling BW. Somatosensory Information in Skilled Motor Performance: A Narrative Review. J Mot Behav 2023; 55:453-474. [PMID: 37245865 DOI: 10.1080/00222895.2023.2213198] [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] [Received: 10/25/2022] [Revised: 04/26/2023] [Accepted: 05/05/2023] [Indexed: 05/30/2023]
Abstract
Historically, research aimed at improving motor performance has largely focused on the neural processes involved in motor execution due to their role in muscle activation. However, accompanying somatosensory and proprioceptive sensory information is also vitally involved in performing motor skills. Here we review research from interdisciplinary fields to provide a description for how somatosensation informs the successful performance of motor skills as well as emphasize the need for careful selection of study methods to isolate the neural processes involved in somatosensory perception. We also discuss upcoming strategies of intervention that have been used to improve performance via somatosensory targets. We believe that a greater appreciation for somatosensation's role in motor learning and control will enable researchers and practitioners to develop and apply methods for the enhancement of human performance that will benefit clinical, healthy, and elite populations alike.
Collapse
Affiliation(s)
- Tyler T Whittier
- Sensorimotor Neuroimaging Laboratory, Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - Christopher M Patrick
- Sensorimotor Neuroimaging Laboratory, Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
- Molecular, Cellular and Integrative Neurosciences Program, Colorado State University, Fort Collins, CO, USA
| | - Brett W Fling
- Sensorimotor Neuroimaging Laboratory, Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
- Molecular, Cellular and Integrative Neurosciences Program, Colorado State University, Fort Collins, CO, USA
| |
Collapse
|
169
|
Chang JC, Perich MG, Miller LE, Gallego JA, Clopath C. De novo motor learning creates structure in neural activity space that shapes adaptation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541925. [PMID: 37293081 PMCID: PMC10245862 DOI: 10.1101/2023.05.23.541925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced. Here, we sought to understand how a neural population's activity repertoire, acquired through long-term learning, affects short-term adaptation by modeling motor cortical neural population dynamics during de novo learning and subsequent adaptation using recurrent neural networks. We trained these networks on different motor repertoires comprising varying numbers of movements. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization created by the neural population activity patterns corresponding to each movement. This structure facilitated adaptation, but only when small changes in motor output were required, and when the structure of the network inputs, the neural activity space, and the perturbation were congruent. These results highlight trade-offs in skill acquisition and demonstrate how prior experience and external cues during learning can shape the geometrical properties of neural population activity as well as subsequent adaptation.
Collapse
Affiliation(s)
- Joanna C. Chang
- Department of Bioengineering, Imperial College London, London, UK
| | - Matthew G. Perich
- Département de neurosciences, Université de Montréal, Montréal, Canada
| | - Lee E. Miller
- Department of Neuroscience, Northwestern University, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London, UK
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
| |
Collapse
|
170
|
Hasson CJ, Manczurowsky J, Collins EC, Yarossi M. Neurorehabilitation robotics: how much control should therapists have? Front Hum Neurosci 2023; 17:1179418. [PMID: 37250692 PMCID: PMC10213717 DOI: 10.3389/fnhum.2023.1179418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 04/18/2023] [Indexed: 05/31/2023] Open
Abstract
Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.
Collapse
Affiliation(s)
- Christopher J. Hasson
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
- Department of Bioengineering, Northeastern University, Boston, MA, United States
- Institute for Experiential Robotics, Northeastern University, Boston, MA, United States
| | - Julia Manczurowsky
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
| | - Emily C. Collins
- Institute for Experiential Robotics, Northeastern University, Boston, MA, United States
| | - Mathew Yarossi
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
- Institute for Experiential Robotics, Northeastern University, Boston, MA, United States
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| |
Collapse
|
171
|
Mosberger AC, Sibener LJ, Chen TX, Rodrigues H, Hormigo R, Ingram JN, Athalye VR, Tabachnik T, Wolpert DM, Murray JM, Costa RM. Exploration biases how forelimb reaches to a spatial target are learned. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539291. [PMID: 37214823 PMCID: PMC10197595 DOI: 10.1101/2023.05.08.539291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The brain can learn to generate actions, such as reaching to a target, using different movement strategies. Understanding how different variables bias which strategies are learned to produce such a reach is important for our understanding of the neural bases of movement. Here we introduce a novel spatial forelimb target task in which perched head-fixed mice learn to reach to a circular target area from a set start position using a joystick. These reaches can be achieved by learning to move into a specific direction or to a specific endpoint location. We find that mice gradually learn to successfully reach the covert target. With time, they refine their initially exploratory complex joystick trajectories into controlled targeted reaches. The execution of these controlled reaches depends on the sensorimotor cortex. Using a probe test with shifting start positions, we show that individual mice learned to use strategies biased to either direction or endpoint-based movements. The degree of endpoint learning bias was correlated with the spatial directional variability with which the workspace was explored early in training. Furthermore, we demonstrate that reinforcement learning model agents exhibit a similar correlation between directional variability during training and learned strategy. These results provide evidence that individual exploratory behavior during training biases the control strategies that mice use to perform forelimb covert target reaches.
Collapse
|
172
|
Howard CK, Van Gemmert AWA, Kuznetsov NA. Attentional focus effects on joint covariation in a reaching task. Hum Mov Sci 2023; 89:103089. [PMID: 37150111 DOI: 10.1016/j.humov.2023.103089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/19/2023] [Accepted: 04/09/2023] [Indexed: 05/09/2023]
Abstract
Adopting an external focus of attention (EF) has been found beneficial over internal focus (IF) for performing motor skills. Previous studies primarily examined focus of attention (FOA) effects on performance outcomes (such as error and accuracy), with relatively less emphasis on movement coordination. Given that human movements are kinematically and kinetically abundant (Gefland & Latash, 1998), FOA instructions may change how motor abundance is utilized by the CNS. This study applied the uncontrolled manifold analysis (UCM) to address this question in a reaching task. Healthy young adults (N = 38; 22 ± 1 yr; 7 men, 31 women) performed planar reaching movements to a target using either the dominant or nondominant arm under two different FOA instructions: EF and IF. Reaching was performed without online visual feedback and at a preferred pace. Joint angles of the clavicle-scapula, shoulder, elbow, and wrist were recorded, and their covariation for controlling dowel endpoint position was analyzed via UCM. As expected, IF led to a higher mean radial error than EF, driven by increases in aiming bias and variability. Consistent with this result, the UCM analysis showed that IF led to higher goal-relevant variance among the joints (VORT) compared to EF starting from the first 20% of the reach to the end. However, the goal-irrelevant variance (VUCM)-index of joint variance that does not affect the end-effector position-did not show FOA effects. The index of stability of joint coordination with respect to endpoint position (ΔV) was also not different between the EF and IF. Consistent with the constrained action hypothesis, these results provide evidence that IF disrupted goal-relevant joint covariation starting in the early phases of the reach without affecting goal-irrelevant coordination.
Collapse
Affiliation(s)
- Charlend K Howard
- School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA.
| | | | - Nikita A Kuznetsov
- Department of Rehabilitation, Exercise and Nutrition Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, Ohio, USA.
| |
Collapse
|
173
|
Bouhadjar Y, Wouters DJ, Diesmann M, Tetzlaff T. Coherent noise enables probabilistic sequence replay in spiking neuronal networks. PLoS Comput Biol 2023; 19:e1010989. [PMID: 37130121 PMCID: PMC10153753 DOI: 10.1371/journal.pcbi.1010989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 03/02/2023] [Indexed: 05/03/2023] Open
Abstract
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type of decision making central to cognition is sequential memory recall in response to ambiguous cues. A previously developed spiking neuronal network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. In response to an ambiguous cue, the model deterministically recalls the sequence shown most frequently during training. Here, we present an extension of the model enabling a range of different decision strategies. In this model, explorative behavior is generated by supplying neurons with noise. As the model relies on population encoding, uncorrelated noise averages out, and the recall dynamics remain effectively deterministic. In the presence of locally correlated noise, the averaging effect is avoided without impairing the model performance, and without the need for large noise amplitudes. We investigate two forms of correlated noise occurring in nature: shared synaptic background inputs, and random locking of the stimulus to spatiotemporal oscillations in the network activity. Depending on the noise characteristics, the network adopts various recall strategies. This study thereby provides potential mechanisms explaining how the statistics of learned sequences affect decision making, and how decision strategies can be adjusted after learning.
Collapse
Affiliation(s)
- Younes Bouhadjar
- Institute of Neuroscience and Medicine (INM-6), & Institute for Advanced Simulation (IAS-6), & JARA BRAIN Institute Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Peter Grünberg Institute (PGI-7,10), Jülich Research Centre and JARA, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Dirk J Wouters
- Institute of Electronic Materials (IWE 2) & JARA-FIT, RWTH Aachen University, Aachen, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), & Institute for Advanced Simulation (IAS-6), & JARA BRAIN Institute Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, & Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Tom Tetzlaff
- Institute of Neuroscience and Medicine (INM-6), & Institute for Advanced Simulation (IAS-6), & JARA BRAIN Institute Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| |
Collapse
|
174
|
Zheng Y, Tang S, Zheng H, Wang X, Liu L, Yang Y, Zhen Y, Zheng Z. Noise improves the association between effects of local stimulation and structural degree of brain networks. PLoS Comput Biol 2023; 19:e1010866. [PMID: 37167331 PMCID: PMC10205011 DOI: 10.1371/journal.pcbi.1010866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/23/2023] [Accepted: 04/20/2023] [Indexed: 05/13/2023] Open
Abstract
Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.
Collapse
Affiliation(s)
- Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing (BABEC), Beijing, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, P.R. China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| |
Collapse
|
175
|
Martínez N, Deza RR, Montani F. Characterizing the information transmission of inverse stochastic resonance and noise-induced activity amplification in neuronal systems. Phys Rev E 2023; 107:054402. [PMID: 37329070 DOI: 10.1103/physreve.107.054402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 04/13/2023] [Indexed: 06/18/2023]
Abstract
Purkinje cells exhibit a reduction of the mean firing rate at intermediate-noise intensities, which is somewhat reminiscent of the response enhancement known as "stochastic resonance" (SR). Although the comparison with the stochastic resonance ends here, the current phenomenon has been given the name "inverse stochastic resonance" (ISR). Recent research has demonstrated that the ISR effect, like its close relative "nonstandard SR" [or, more correctly, noise-induced activity amplification (NIAA)], has been shown to stem from the weak-noise quenching of the initial distribution, in bistable regimes where the metastable state has a larger attraction basin than the global minimum. To understand the underlying mechanism of the ISR and NIAA phenomena, we study the probability distribution function of a one-dimensional system subjected to a bistable potential that has the property of symmetry, i.e., if we change the sign of one of its parameters, we can obtain both phenomena with the same properties in the depth of the wells and the width of their basins of attraction subjected to Gaussian white noise with variable intensity. Previous work has shown that one can theoretically determine the probability distribution function using the convex sum between the behavior at small and high noise intensities. To determine the probability distribution function more precisely, we resort to the "weighted ensemble Brownian dynamics simulation" model, which provides an accurate estimate of the probability distribution function for both low and high noise intensities and, most importantly, for the transition of both behaviors. In this way, on the one hand, we show that both phenomena emerge from a metastable system where, in the case of ISR, the global minimum of the system is in a state of lower activity, while in the case of NIAA, the global minimum is in a state of increased activity, the importance of which does not depend on the width of the basins of attraction. On the other hand, we see that quantifiers such as Fisher information, statistical complexity, and especially Shannon entropy fail to distinguish them, but they show the existence of the mentioned phenomena. Thus, noise management may well be a mechanism by which Purkinje cells find an efficient way to transmit information in the cerebral cortex.
Collapse
Affiliation(s)
- Nataniel Martínez
- IFIMAR (CONICET), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, B7602AYL Mar del Plata, Argentina
| | - Roberto R Deza
- IFIMAR (CONICET), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, B7602AYL Mar del Plata, Argentina
| | - Fernando Montani
- IFLP (CONICET), Facultad de Ciencias Exactas, Universidad Nacional de La Plata, B1900 La Plata, Argentina
| |
Collapse
|
176
|
Negrón A, Getz MP, Handy G, Doiron B. The mechanics of correlated variability in segregated cortical excitatory subnetworks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538323. [PMID: 37162867 PMCID: PMC10168290 DOI: 10.1101/2023.04.25.538323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Understanding the genesis of shared trial-to-trial variability in neural activity within sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since this variability likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells.
Collapse
Affiliation(s)
- Alex Negrón
- Department of Applied Mathematics, Illinois Institute of Technology
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago
| | - Matthew P. Getz
- Departments of Neurobiology and Statistics, University of Chicago
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago
| | - Gregory Handy
- Departments of Neurobiology and Statistics, University of Chicago
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago
| | - Brent Doiron
- Departments of Neurobiology and Statistics, University of Chicago
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago
| |
Collapse
|
177
|
Lacquaniti F, La Scaleia B, Zago M. Noise and vestibular perception of passive self-motion. Front Neurol 2023; 14:1159242. [PMID: 37181550 PMCID: PMC10169592 DOI: 10.3389/fneur.2023.1159242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 03/29/2023] [Indexed: 05/16/2023] Open
Abstract
Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber's law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation.
Collapse
Affiliation(s)
- Francesco Lacquaniti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Systems Medicine, Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy
| | - Barbara La Scaleia
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Myrka Zago
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Civil Engineering and Computer Science Engineering, Centre of Space Bio-medicine, University of Rome Tor Vergata, Rome, Italy
| |
Collapse
|
178
|
Buchholz MO, Gastone Guilabert A, Ehret B, Schuhknecht GFP. How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output. PLoS Comput Biol 2023; 19:e1011046. [PMID: 37068099 PMCID: PMC10153727 DOI: 10.1371/journal.pcbi.1011046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/02/2023] [Accepted: 03/24/2023] [Indexed: 04/18/2023] Open
Abstract
Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.
Collapse
Affiliation(s)
- Moritz O Buchholz
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
| | | | - Benjamin Ehret
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
| | - Gregor F P Schuhknecht
- Institute of Neuroinformatics, University of Zürich and ETH Zürich Zürich, Switzerland
- Department of Molecular and Cellular Biology, Harvard University Cambridge, Massachusetts, United States of America
| |
Collapse
|
179
|
Ilan Y. Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 180-181:37-48. [PMID: 37068713 DOI: 10.1016/j.pbiomolbio.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/26/2023] [Accepted: 04/14/2023] [Indexed: 04/19/2023]
Abstract
The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.
Collapse
Affiliation(s)
- Yaron Ilan
- Faculty of Medicine, Hebrew University, Department of Medicine, Hadassah Medical Center, Jerusalem, Israel.
| |
Collapse
|
180
|
Selen LPJ, Corneil BD, Medendorp WP. Single-Trial Dynamics of Competing Reach Plans in the Human Motor Periphery. J Neurosci 2023; 43:2782-2793. [PMID: 36898839 PMCID: PMC10089241 DOI: 10.1523/jneurosci.1640-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 03/12/2023] Open
Abstract
Contemporary motor control theories propose competition between multiple motor plans before the winning command is executed. While most competitions are completed before movement onset, movements are often initiated before the competition has been resolved. An example of this is saccadic averaging, wherein the eyes land at an intermediate location between two visual targets. Behavioral and neurophysiological signatures of competing motor commands have also been reported for reaching movements, but debate remains about whether such signatures attest to an unresolved competition, arise from averaging across many trials, or reflect a strategy to optimize behavior given task constraints. Here, we recorded EMG activity from an upper limb muscle (m. pectoralis) while 12 (8 female) participants performed an immediate response reach task, freely choosing between one of two identical and suddenly presented visual targets. On each trial, muscle recruitment showed two distinct phases of directionally tuned activity. In the first wave, time-locked ∼100 ms of target presentation, muscle activity was clearly influenced by the nonchosen target, reflecting a competition between reach commands that was biased in favor of the ultimately chosen target. This resulted in an initial movement intermediate between the two targets. In contrast, the second wave, time-locked to voluntary reach onset, was not biased toward the nonchosen target, showing that the competition between targets was resolved. Instead, this wave of activity compensated for the averaging induced by the first wave. Thus, single-trial analysis reveals an evolution in how the nonchosen target differentially influences the first and second wave of muscle activity.SIGNIFICANCE STATEMENT Contemporary theories of motor control suggest that multiple motor plans compete for selection before the winning command is executed. Evidence for this is found in intermediate reach movements toward two potential target locations, but recent findings have challenged this notion by arguing that intermediate reaching movements reflect an optimal response strategy. By examining upper limb muscle recruitment during a free-choice reach task, we show early recruitment of a suboptimal averaged motor command to the two targets that subsequently transitions to a single motor command that compensates for the initially averaged motor command. Recording limb muscle activity permits single-trial resolution of the dynamic influence of the nonchosen target through time.
Collapse
Affiliation(s)
- Luc P J Selen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6500 HB, The Netherlands
| | - Brian D Corneil
- Department of Physiology and Pharmacology
- Department of Psychology, Western University, London, Ontario N6A 5B7, Canada
- Robarts Research Institute, London, Ontario, Canada, N6A 5B7
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6500 HB, The Netherlands
| |
Collapse
|
181
|
Jacob M, Ford J, Deacon T. Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI. Front Hum Neurosci 2023; 17:976036. [PMID: 37113322 PMCID: PMC10126302 DOI: 10.3389/fnhum.2023.976036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/02/2023] [Indexed: 04/29/2023] Open
Abstract
The brain is a living organ with distinct metabolic constraints. However, these constraints are typically considered as secondary or supportive of information processing which is primarily performed by neurons. The default operational definition of neural information processing is that (1) it is ultimately encoded as a change in individual neuronal firing rate as this correlates with the presentation of a peripheral stimulus, motor action or cognitive task. Two additional assumptions are associated with this default interpretation: (2) that the incessant background firing activity against which changes in activity are measured plays no role in assigning significance to the extrinsically evoked change in neural firing, and (3) that the metabolic energy that sustains this background activity and which correlates with differences in neuronal firing rate is merely a response to an evoked change in neuronal activity. These assumptions underlie the design, implementation, and interpretation of neuroimaging studies, particularly fMRI, which relies on changes in blood oxygen as an indirect measure of neural activity. In this article we reconsider all three of these assumptions in light of recent evidence. We suggest that by combining EEG with fMRI, new experimental work can reconcile emerging controversies in neurovascular coupling and the significance of ongoing, background activity during resting-state paradigms. A new conceptual framework for neuroimaging paradigms is developed to investigate how ongoing neural activity is "entangled" with metabolism. That is, in addition to being recruited to support locally evoked neuronal activity (the traditional hemodynamic response), changes in metabolic support may be independently "invoked" by non-local brain regions, yielding flexible neurovascular coupling dynamics that inform the cognitive context. This framework demonstrates how multimodal neuroimaging is necessary to probe the neurometabolic foundations of cognition, with implications for the study of neuropsychiatric disorders.
Collapse
Affiliation(s)
- Michael Jacob
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Judith Ford
- Mental Health Service, San Francisco VA Healthcare System, San Francisco, CA, United States
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Terrence Deacon
- Department of Anthropology, University of California, Berkeley, Berkeley, CA, United States
| |
Collapse
|
182
|
Ferguson B, Glick C, Huguenard JR. Prefrontal PV interneurons facilitate attention and are linked to attentional dysfunction in a mouse model of absence epilepsy. eLife 2023; 12:e78349. [PMID: 37014118 PMCID: PMC10072875 DOI: 10.7554/elife.78349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 02/07/2023] [Indexed: 04/05/2023] Open
Abstract
Absence seizures are characterized by brief periods of unconsciousness accompanied by lapses in motor function that can occur hundreds of times throughout the day. Outside of these frequent moments of unconsciousness, approximately a third of people living with the disorder experience treatment-resistant attention impairments. Convergent evidence suggests prefrontal cortex (PFC) dysfunction may underlie attention impairments in affected patients. To examine this, we use a combination of slice physiology, fiber photometry, electrocorticography (ECoG), optogenetics, and behavior in the Scn8a+/-mouse model of absence epilepsy. Attention function was measured using a novel visual attention task where a light cue that varied in duration predicted the location of a food reward. In Scn8a+/-mice, we find altered parvalbumin interneuron (PVIN) output in the medial PFC (mPFC) in vitro and PVIN hypoactivity along with reductions in gamma power during cue presentation in vivo. This was associated with poorer attention performance in Scn8a+/-mice that could be rescued by gamma-frequency optogenetic stimulation of PVINs. This highlights cue-related PVIN activity as an important mechanism for attention and suggests PVINs may represent a therapeutic target for cognitive comorbidities in absence epilepsy.
Collapse
Affiliation(s)
- Brielle Ferguson
- Department of Neurology and Neurological Sciences, Stanford UniversityStanfordUnited States
- Department of Genetics, Harvard Medical SchoolBostonUnited States
- Program in Neurobiology and Department of Neurology, Boston Children's HospitalBostonUnited States
| | - Cameron Glick
- Department of Neurology and Neurological Sciences, Stanford UniversityStanfordUnited States
| | - John R Huguenard
- Department of Neurology and Neurological Sciences, Stanford UniversityStanfordUnited States
| |
Collapse
|
183
|
Ren Z, Sun J, Liu C, Li X, Li X, Li X, Liu Z, Bi T, Qiu J. Individualized prediction of trait self-control from whole-brain functional connectivity. Psychophysiology 2023; 60:e14209. [PMID: 36325626 DOI: 10.1111/psyp.14209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 09/24/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
Self-control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self-control, its neural mechanisms remain unclear. In this study, we employed a machine-learning approach-relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self-control in a large sample (N = 390). We used resting-state functional MRI (fMRI) to explore whole-brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self-control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto-parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self-control. Our findings further assert that self-control is a multidimensional construct rooted in the interactions between multiple neural networks.
Collapse
Affiliation(s)
- Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
- College of International Studies, Southwest University, Chongqing, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyue Li
- Department of Psychology, University of Washington, Seattle, Washington, USA
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyi Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Zeqing Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Taiyong Bi
- Centre for Mental Health Research in School of Management, Zunyi Medical University, Zunyi, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| |
Collapse
|
184
|
Nakuci J, Covey TJ, Shucard JL, Shucard DW, Muldoon SF. Single trial variability in neural activity during a working memory task reveals multiple distinct information processing sequences. Neuroimage 2023; 269:119895. [PMID: 36717041 DOI: 10.1016/j.neuroimage.2023.119895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/29/2022] [Accepted: 01/20/2023] [Indexed: 01/29/2023] Open
Abstract
Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task.
Collapse
Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, Buffalo, NY, United States; School of Psychology, Georgia Institute of Technology, Atlanta, Georgia.
| | - Thomas J Covey
- Neuroscience Program, University at Buffalo, Buffalo, NY, United States; Department of Neurology, Division of Cognitive and Behavioral Neurosciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
| | - Janet L Shucard
- Neuroscience Program, University at Buffalo, Buffalo, NY, United States; Department of Neurology, Division of Cognitive and Behavioral Neurosciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
| | - David W Shucard
- Neuroscience Program, University at Buffalo, Buffalo, NY, United States; Department of Neurology, Division of Cognitive and Behavioral Neurosciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States; Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States; Department of Psychology, University at Buffalo, Buffalo, NY, United States
| | - Sarah F Muldoon
- Neuroscience Program, University at Buffalo, Buffalo, NY, United States; Department of Mathematics and CDSE Program, University at Buffalo, Buffalo, NY 14260-2900, United States.
| |
Collapse
|
185
|
Calalo JA, Roth AM, Lokesh R, Sullivan SR, Wong JD, Semrau JA, Cashaback JGA. The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability. J Neurophysiol 2023; 129:751-766. [PMID: 36883741 PMCID: PMC10069957 DOI: 10.1152/jn.00472.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/13/2023] [Accepted: 03/01/2023] [Indexed: 03/09/2023] Open
Abstract
The naturally occurring variability in our movements often poses a significant challenge when attempting to produce precise and accurate actions, which is readily evident when playing a game of darts. Two differing, yet potentially complementary, control strategies that the sensorimotor system may use to regulate movement variability are impedance control and feedback control. Greater muscular co-contraction leads to greater impedance that acts to stabilize the hand, while visuomotor feedback responses can be used to rapidly correct for unexpected deviations when reaching toward a target. Here, we examined the independent roles and potential interplay of impedance control and visuomotor feedback control when regulating movement variability. Participants were instructed to perform a precise reaching task by moving a cursor through a narrow visual channel. We manipulated cursor feedback by visually amplifying movement variability and/or delaying the visual feedback of the cursor. We found that participants decreased movement variability by increasing muscular co-contraction, aligned with an impedance control strategy. Participants displayed visuomotor feedback responses during the task but, unexpectedly, there was no modulation between conditions. However, we did find a relationship between muscular co-contraction and visuomotor feedback responses, suggesting that participants modulated impedance control relative to feedback control. Taken together, our results highlight that the sensorimotor system modulates muscular co-contraction, relative to visuomotor feedback responses, to regulate movement variability and produce accurate actions.NEW & NOTEWORTHY The sensorimotor system has the constant challenge of dealing with the naturally occurring variability in our movements. Here, we investigated the potential roles of muscular co-contraction and visuomotor feedback responses to regulate movement variability. When we visually amplified movements, we found that the sensorimotor system primarily uses muscular co-contraction to regulate movement variability. Interestingly, we found that muscular co-contraction was modulated relative to inherent visuomotor feedback responses, suggesting an interplay between impedance and feedback control.
Collapse
Affiliation(s)
- Jan A Calalo
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, United States
| | - Adam M Roth
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, United States
| | - Rakshith Lokesh
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
| | - Seth R Sullivan
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
| | - Jeremy D Wong
- Department of Kinesiology, University of Calgary, Calgary, Alberta, Canada
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Jennifer A Semrau
- Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, United States
- Biomechanics and Movement Science Program, University of Delaware, Newark, Delaware, United States
- Interdisciplinary Neuroscience Graduate Program, University of Delaware, Newark, Delaware, United States
| | - Joshua G A Cashaback
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States
- Department of Mechanical Engineering, University of Delaware, Newark, Delaware, United States
- Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, United States
- Biomechanics and Movement Science Program, University of Delaware, Newark, Delaware, United States
- Interdisciplinary Neuroscience Graduate Program, University of Delaware, Newark, Delaware, United States
| |
Collapse
|
186
|
Gogulski J, Ross JM, Talbot A, Cline CC, Donati FL, Munot S, Kim N, Gibbs C, Bastin N, Yang J, Minasi C, Sarkar M, Truong J, Keller CJ. Personalized Repetitive Transcranial Magnetic Stimulation for Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:351-360. [PMID: 36792455 DOI: 10.1016/j.bpsc.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, in which focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) modulate brain circuits and alleviate clinical symptoms. rTMS is well tolerated and clinically effective for treatment-resistant depression and other neuropsychiatric disorders. Despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (∼50%). In this review, we examine components of rTMS that can be optimized to account for interindividual variability in neural function and anatomy. We discuss current treatment options for treatment-resistant depression, the neural mechanisms thought to underlie treatment, targeting strategies, stimulation parameter selection, and adaptive closed-loop treatment. We conclude that a better understanding of the wide and modifiable parameter space of rTMS will greatly improve the clinical outcome.
Collapse
Affiliation(s)
- Juha Gogulski
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Jessica M Ross
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Austin Talbot
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Christopher C Cline
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Francesco L Donati
- Department of Health Sciences, San Paolo Hospital, University of Milan, Milan, Italy
| | - Saachi Munot
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Naryeong Kim
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Ciara Gibbs
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Nikita Bastin
- Department of Radiology and Orthopedics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jessica Yang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Christopher Minasi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Manjima Sarkar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Jade Truong
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California.
| |
Collapse
|
187
|
Bettmann LP, Kewming MJ, Goold J. Thermodynamics of a continuously monitored double-quantum-dot heat engine in the repeated interactions framework. Phys Rev E 2023; 107:044102. [PMID: 37198837 DOI: 10.1103/physreve.107.044102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/14/2023] [Indexed: 05/19/2023]
Abstract
Understanding the thermodynamic role of measurement in quantum mechanical systems is a burgeoning field of study. In this article, we study a double quantum dot (DQD) connected to two macroscopic fermionic thermal reservoirs. We assume that the DQD is continuously monitored by a quantum point contact (QPC), which serves as a charge detector. Starting from a minimalist microscopic model for the QPC and reservoirs, we show that the local master equation of the DQD can alternatively be derived in the framework of repeated interactions and that this framework guarantees a thermodynamically consistent description of the DQD and its environment (including the QPC). We analyze the effect of the measurement strength and identify a regime in which particle transport through the DQD is both assisted and stabilized by dephasing. We also find that in this regime the entropic cost of driving the particle current with fixed relative fluctuations through the DQD is reduced. We thus conclude that under continuous measurement a more constant particle current may be achieved at a fixed entropic cost.
Collapse
Affiliation(s)
| | - Michael J Kewming
- School of Physics, Trinity College Dublin, College Green, Dublin 2, Ireland
| | - John Goold
- School of Physics, Trinity College Dublin, College Green, Dublin 2, Ireland
| |
Collapse
|
188
|
Lee JK, Rouault M, Wyart V. Adaptive tuning of human learning and choice variability to unexpected uncertainty. SCIENCE ADVANCES 2023; 9:eadd0501. [PMID: 36989365 PMCID: PMC10058239 DOI: 10.1126/sciadv.add0501] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Human value-based decisions are notably variable under uncertainty. This variability is known to arise from two distinct sources: variable choices aimed at exploring available options and imprecise learning of option values due to limited cognitive resources. However, whether these two sources of decision variability are tuned to their specific costs and benefits remains unclear. To address this question, we compared the effects of expected and unexpected uncertainty on decision-making in the same reinforcement learning task. Across two large behavioral datasets, we found that humans choose more variably between options but simultaneously learn less imprecisely their values in response to unexpected uncertainty. Using simulations of learning agents, we demonstrate that these opposite adjustments reflect adaptive tuning of exploration and learning precision to the structure of uncertainty. Together, these findings indicate that humans regulate not only how much they explore uncertain options but also how precisely they learn the values of these options.
Collapse
Affiliation(s)
- Junseok K. Lee
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France
- Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France
| | - Marion Rouault
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France
- Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France
| | - Valentin Wyart
- Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France
- Département d’Études Cognitives, École Normale Supérieure, Université PSL, Paris, France
- Institut du Psychotraumatisme de l’Enfant et de l’Adolescent, Conseil Départemental Yvelines et Hauts-de-Seine, Versailles, France
| |
Collapse
|
189
|
Madhani AS, King S, Zhu J, Karmali F, Welling DB, Cai W, Jordan JT, Lewis RF. Vestibular dysfunction in neurofibromatosis type 2-related schwannomatosis. Brain Commun 2023; 5:fcad089. [PMID: 37025569 PMCID: PMC10072238 DOI: 10.1093/braincomms/fcad089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 12/09/2022] [Accepted: 03/21/2023] [Indexed: 04/08/2023] Open
Abstract
Neurofibromatosis type 2-related schwannomatosis is a genetic disorder characterized by neurologic tumours, most typically vestibular schwannomas that originate on the vestibulo-cochlear nerve(s). Although vestibular symptoms can be disabling, vestibular function has never been carefully analysed in neurofibromatosis type 2-related schwannomatosis. Furthermore, chemotherapy (e.g. bevacizumab) can reduce tumour volume and improve hearing in neurofibromatosis type 2-related schwannomatosis, but nothing is known about its vestibular effects. In this report, we studied the three primary vestibular-mediated behaviours (eye movements, motion perception and balance), clinical vestibular disability (dizziness and ataxia), and imaging and hearing in eight untreated patients with neurofibromatosis type 2-related schwannomatosis and compared their results with normal subjects and patients with sporadic, unilateral vestibular schwannoma tumours. We also examined how bevacizumab affected two patients with neurofibromatosis type 2-related schwannomatosis. Vestibular schwannomas in neurofibromatosis type 2-related schwannomatosis degraded vestibular precision (inverse of variability, reflecting a reduced central signal-to-noise ratio) but not vestibular accuracy (amplitude relative to ideal amplitude, reflecting the central signal magnitude) and caused clinical disability. Bevacizumab improved vestibular precision and clinical disability in both patients with neurofibromatosis type 2-related schwannomatosis but did not affect vestibular accuracy. These results demonstrate that vestibular schwannoma tumours in our neurofibromatosis type 2-related schwannomatosis population degrade the central vestibular signal-to-noise ratio, while bevacizumab improves the signal-to-noise ratio, changes that can be explained mechanistically by the addition (schwannoma) and suppression (bevacizumab) of afferent neural noise.
Collapse
Affiliation(s)
- Amsal S Madhani
- Department of Otolargynology, Massachusetts Eye and Ear,
Boston, MA, USA
| | - Susan King
- Department of Otolargynology, Massachusetts Eye and Ear,
Boston, MA, USA
| | - Jennifer Zhu
- Department of Otolargynology, Massachusetts Eye and Ear,
Boston, MA, USA
| | - Faisal Karmali
- Department of Otolargynology, Massachusetts Eye and Ear,
Boston, MA, USA
- Department of Otolaryngology Head and Neck Surgery, Harvard Medical
School, Boston, MA, USA
| | - D Bradley Welling
- Department of Otolargynology, Massachusetts Eye and Ear,
Boston, MA, USA
- Department of Otolaryngology Head and Neck Surgery, Harvard Medical
School, Boston, MA, USA
| | - Wenli Cai
- Department of Neurology, Massachusetts General Hospital,
Boston, MA, USA
- Department of Radiology, Harvard Medical School,
Boston, MA, USA
| | - Justin T Jordan
- Department of Neurology, Massachusetts General Hospital,
Boston, MA, USA
- Department of Neurology, Harvard Medical School,
Boston, MA, USA
| | - Richard F Lewis
- Department of Otolargynology, Massachusetts Eye and Ear,
Boston, MA, USA
- Department of Otolaryngology Head and Neck Surgery, Harvard Medical
School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital,
Boston, MA, USA
- Department of Neurology, Harvard Medical School,
Boston, MA, USA
| |
Collapse
|
190
|
Teichner R, Shomar A, Barak O, Brenner N, Marom S, Meir R, Eytan D. Identifying regulation with adversarial surrogates. Proc Natl Acad Sci U S A 2023; 120:e2216805120. [PMID: 36920920 PMCID: PMC10041131 DOI: 10.1073/pnas.2216805120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/15/2023] [Indexed: 03/16/2023] Open
Abstract
Homeostasis, the ability to maintain a relatively constant internal environment in the face of perturbations, is a hallmark of biological systems. It is believed that this constancy is achieved through multiple internal regulation and control processes. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. In this work, we develop a robust data-driven method to identify these objectives, namely to understand: "what does the system care about?". We propose an algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), that receives an array of temporal measurements of the system and outputs a candidate for the control objective, expressed as a combination of observed variables. IRAS is an iterative algorithm consisting of two competing players. The first player, realized by an artificial deep neural network, aims to minimize a measure of invariance we refer to as the coefficient of regulation. The second player aims to render the task of the first player more difficult by forcing it to extract information about the temporal structure of the data, which is absent from similar "surrogate" data. We test the algorithm on four synthetic and one natural data set, demonstrating excellent empirical results. Interestingly, our approach can also be used to extract conserved quantities, e.g., energy and momentum, in purely physical systems, as we demonstrate empirically.
Collapse
Affiliation(s)
- Ron Teichner
- Viterbi Department of Electrical & Computer Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Aseel Shomar
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Department of Chemical Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Omri Barak
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Naama Brenner
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Department of Chemical Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Shimon Marom
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Ron Meir
- Viterbi Department of Electrical & Computer Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Danny Eytan
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| |
Collapse
|
191
|
Chen S, Yang Q, Lim S. Efficient inference of synaptic plasticity rule with Gaussian process regression. iScience 2023; 26:106182. [PMID: 36879810 PMCID: PMC9985048 DOI: 10.1016/j.isci.2023.106182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.
Collapse
Affiliation(s)
- Shirui Chen
- Department of Applied Mathematics, University of Washington, Lewis Hall 201, Box 353925, Seattle, WA 98195-3925, USA
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Qixin Yang
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, The Suzanne and Charles Goodman Brain Sciences Building, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Sukbin Lim
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, 3663 Zhongshan Road North, Shanghai, 200062, China
| |
Collapse
|
192
|
Albanese GA, Marini F, Morasso P, Campus C, Zenzeri J. μ-band desynchronization in the contralateral central and central-parietal areas predicts proprioceptive acuity. Front Hum Neurosci 2023; 17:1000832. [PMID: 37007684 PMCID: PMC10050694 DOI: 10.3389/fnhum.2023.1000832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionPosition sense, which belongs to the sensory stream called proprioception, is pivotal for proper movement execution. Its comprehensive understanding is needed to fill existing knowledge gaps in human physiology, motor control, neurorehabilitation, and prosthetics. Although numerous studies have focused on different aspects of proprioception in humans, what has not been fully investigated so far are the neural correlates of proprioceptive acuity at the joints.MethodsHere, we implemented a robot-based position sense test to elucidate the correlation between patterns of neural activity and the degree of accuracy and precision exhibited by the subjects. Eighteen healthy participants performed the test, and their electroencephalographic (EEG) activity was analyzed in its μ band (8–12 Hz), as the frequency band related to voluntary movement and somatosensory stimulation.ResultsWe observed a significant positive correlation between the matching error, representing proprioceptive acuity, and the strength of the activation in contralateral hand motor and sensorimotor areas (left central and central-parietal areas). In absence of visual feedback, these same regions of interest (ROIs) presented a higher activation level compared to the association and visual areas. Remarkably, central and central-parietal activation was still observed when visual feedback was added, although a consistent activation in association and visual areas came up.ConclusionSumming up, this study supports the existence of a specific link between the magnitude of activation of motor and sensorimotor areas related to upper limb proprioceptive processing and the proprioceptive acuity at the joints.
Collapse
Affiliation(s)
- Giulia Aurora Albanese
- Department of Robotics, Brain and Cognitive Sciences, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy
- *Correspondence: Giulia Aurora Albanese,
| | | | - Pietro Morasso
- Department of Robotics, Brain and Cognitive Sciences, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Claudio Campus
- U-VIP Unit for Visually Impaired People, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Jacopo Zenzeri
- Department of Robotics, Brain and Cognitive Sciences, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
- ReWing S.r.l., Milan, Italy
| |
Collapse
|
193
|
Naudin L. Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate. J Comput Neurosci 2023; 51:201-206. [PMID: 36905484 DOI: 10.1007/s10827-023-00848-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 03/12/2023]
Affiliation(s)
- Loïs Naudin
- Laboratoire Lorrain de Recherche en Informatique et ses Applications, CNRS, Université de Lorraine, Nancy, France. .,Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, F-75012, France.
| |
Collapse
|
194
|
Gonzalez J, Follmann R, Rosa E, Stein W. Computational and experimental modulation of a noisy chaotic neuronal system. CHAOS (WOODBURY, N.Y.) 2023; 33:033109. [PMID: 37003818 DOI: 10.1063/5.0130874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/13/2023] [Indexed: 06/19/2023]
Abstract
In this work, we study the interplay between chaos and noise in neuronal state transitions involving period doubling cascades. Our approach involves the implementation of a neuronal mathematical model under the action of neuromodulatory input, with and without noise, as well as equivalent experimental work on a biological neuron in the stomatogastric ganglion of the crab Cancer borealis. Our simulations show typical transitions between tonic and bursting regimes that are mediated by chaos and period doubling cascades. While this transition is less evident when intrinsic noise is present in the model, the noisy computational output displays features akin to our experimental results. The differences and similarities observed in the computational and experimental approaches are discussed.
Collapse
Affiliation(s)
- Josselyn Gonzalez
- School of Biological Sciences, Illinois State University, Normal, Illinois 61790, USA
| | - Rosangela Follmann
- School of Information Technology, Illinois State University, Normal, Illinois 61790, USA
| | - Epaminondas Rosa
- School of Biological Sciences, Illinois State University, Normal, Illinois 61790, USA
| | - Wolfgang Stein
- School of Biological Sciences, Illinois State University, Normal, Illinois 61790, USA
| |
Collapse
|
195
|
Kutschireiter A, Basnak MA, Wilson RI, Drugowitsch J. Bayesian inference in ring attractor networks. Proc Natl Acad Sci U S A 2023; 120:e2210622120. [PMID: 36812206 PMCID: PMC9992764 DOI: 10.1073/pnas.2210622120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/12/2023] [Indexed: 02/24/2023] Open
Abstract
Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This "Bayesian ring attractor" performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms.
Collapse
Affiliation(s)
| | | | - Rachel I. Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| | - Jan Drugowitsch
- Department of Neurobiology, Harvard Medical School, Boston, MA02115
| |
Collapse
|
196
|
Recurrent networks endowed with structural priors explain suboptimal animal behavior. Curr Biol 2023; 33:622-638.e7. [PMID: 36657448 DOI: 10.1016/j.cub.2022.12.044] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/03/2022] [Accepted: 12/16/2022] [Indexed: 01/19/2023]
Abstract
The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials. We hypothesize that, although RNNs can optimize their behavior in the 2AFC task without any a priori restrictions, rats' strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats' behavior. Population analyses show that the pre-trained networks form an accurate representation of the sequence statistics independently of the outcome in the previous trial. After error trials, gating is implemented by a change in the network dynamics that temporarily decouple the categorization of the stimulus from the across-trial accumulated evidence. Our results suggest that the rats' suboptimal behavior reflects the influence of a structural prior that reacts to errors by isolating the network decision dynamics from the context, ultimately constraining the performance in a 2AFC laboratory task.
Collapse
|
197
|
Mo A, Izzi F, Gönen EC, Haeufle D, Badri-Spröwitz A. Slack-based tunable damping leads to a trade-off between robustness and efficiency in legged locomotion. Sci Rep 2023; 13:3290. [PMID: 36841875 PMCID: PMC9968281 DOI: 10.1038/s41598-023-30318-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 02/20/2023] [Indexed: 02/27/2023] Open
Abstract
Animals run robustly in diverse terrain. This locomotion robustness is puzzling because axon conduction velocity is limited to a few tens of meters per second. If reflex loops deliver sensory information with significant delays, one would expect a destabilizing effect on sensorimotor control. Hence, an alternative explanation describes a hierarchical structure of low-level adaptive mechanics and high-level sensorimotor control to help mitigate the effects of transmission delays. Motivated by the concept of an adaptive mechanism triggering an immediate response, we developed a tunable physical damper system. Our mechanism combines a tendon with adjustable slackness connected to a physical damper. The slack damper allows adjustment of damping force, onset timing, effective stroke, and energy dissipation. We characterize the slack damper mechanism mounted to a legged robot controlled in open-loop mode. The robot hops vertically and planarly over varying terrains and perturbations. During forward hopping, slack-based damping improves faster perturbation recovery (up to 170%) at higher energetic cost (27%). The tunable slack mechanism auto-engages the damper during perturbations, leading to a perturbation-trigger damping, improving robustness at a minimum energetic cost. With the results from the slack damper mechanism, we propose a new functional interpretation of animals' redundant muscle tendons as tunable dampers.
Collapse
Affiliation(s)
- An Mo
- Dynamic Locomotion Group, Max Planck Institute for Intelligent Systems, 70569, Stuttgart, Germany.
| | - Fabio Izzi
- grid.419534.e0000 0001 1015 6533Dynamic Locomotion Group, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany ,grid.10392.390000 0001 2190 1447Hertie Institute for Clinical Brain Research and Center for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany
| | - Emre Cemal Gönen
- grid.419534.e0000 0001 1015 6533Dynamic Locomotion Group, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
| | - Daniel Haeufle
- grid.10392.390000 0001 2190 1447Hertie Institute for Clinical Brain Research and Center for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany ,grid.5719.a0000 0004 1936 9713Institute for Modelling and Simulation of Biomechanical Systems, Computational Biophysics and Biorobotics, University of Stuttgart, 70569 Stuttgart, Germany
| | - Alexander Badri-Spröwitz
- grid.419534.e0000 0001 1015 6533Dynamic Locomotion Group, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany ,grid.5596.f0000 0001 0668 7884Department of Mechanical Engineering, KU Leuven, 3001 Leuven, Belgium
| |
Collapse
|
198
|
Oh M, Weaver DF. Alzheimer's disease as a fundamental disease of information processing systems: An information theory perspective. Front Neurosci 2023; 17:1106623. [PMID: 36845437 PMCID: PMC9950401 DOI: 10.3389/fnins.2023.1106623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
The human brain is a dynamic multiplex of information, both neural (neurotransmitter-to-neuron, involving 1.5×1015 action potentials per minute) and immunological (cytokine-to-microglia, providing continuous immune surveillance via 1.5×1010 immunocompetent cells). This conceptualization highlights the opportunity of exploiting "information" not only in the mechanistic understanding of brain pathology, but also as a potential therapeutic modality. Arising from its parallel yet interconnected proteopathic-immunopathic pathogeneses, Alzheimer's disease (AD) enables an exploration of the mechanistic and therapeutic contributions of information as a physical process central to brain disease progression. This review first considers the definition of information and its relevance to neurobiology and thermodynamics. Then we focus on the roles of information in AD using its two classical hallmarks. We assess the pathological contributions of β-amyloid peptides to synaptic dysfunction and reconsider this as a source of noise that disrupts information transfer between presynaptic and postsynaptic neurons. Also, we treat the triggers that activate cytokine-microglial brain processes as information-rich three-dimensional patterns, including pathogen-associated molecular patterns and damage-associated molecular patterns. There are structural and functional similarities between neural and immunological information with both fundamentally contributing to brain anatomy and pathology in health and disease. Finally, the role of information as a therapeutic for AD is introduced, particularly cognitive reserve as a prophylactic protective factor and cognitive therapy as a therapeutic contributor to the comprehensive management of ongoing dementia.
Collapse
Affiliation(s)
- Myongin Oh
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Donald F. Weaver
- Krembil Research Institute, University Health Network, Toronto, ON, Canada,Department of Chemistry, University of Toronto, Toronto, ON, Canada,Department of Pharmaceutical Sciences, University of Toronto, Toronto, ON, Canada,Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada,*Correspondence: Donald F. Weaver,
| |
Collapse
|
199
|
On the Homology of the Dominant and Non-Dominant Corticospinal Tracts: A Novel Neurophysiological Assessment. Brain Sci 2023; 13:brainsci13020278. [PMID: 36831821 PMCID: PMC9954672 DOI: 10.3390/brainsci13020278] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
OBJECTIVES The homology of hemispheric cortical areas plays a crucial role in brain functionality. Here, we extend this concept to the homology of the dominant and non-dominant hemi-bodies, investigating the relationship of the two corticospinal tracts (CSTs). The evoked responses provide an estimate of the number of in-phase recruitments via their amplitude as a suitable indicator of the neuronal projections' integrity. An innovative concept derived from experience in the somatosensory system is that their morphology reflects the recruitment pattern of the whole circuit. METHODS CST homology was assessed via the Fréchet distance between the morphologies of motor-evoked potentials (MEPs) using a transcranial magnetic stimulation (TMS) in the homologous left- and right-hand first dorsal interosseous muscles of 40 healthy volunteers (HVs). We tested the working hypothesis that the inter-side Fréchet distance was higher than the two intra-side distances. RESULTS In addition to a clear confirmation of the working hypothesis (p < 0.0001 for both hemi-bodies) verified in all single subjects, we observed that the intra-side Fréchet distance was higher for the dominant than the non-dominant one. Interhemispheric morphology similarity increased with right-handedness prevalence (p = 0.004). CONCLUSIONS The newly introduced measure of circuit recruitment patterning represents a potential benchmark for the evaluation of inter-lateral mechanisms expressing the relationship between homologous hemilateral structures subtending learning and suggests that variability in recruitment patterning physiologically increases in circuits expressing greater functionality.
Collapse
|
200
|
Zhang J, Liu DQ, Qian S, Qu X, Zhang P, Ding N, Zang YF. The neural correlates of amplitude of low-frequency fluctuation: a multimodal resting-state MEG and fMRI-EEG study. Cereb Cortex 2023; 33:1119-1129. [PMID: 35332917 DOI: 10.1093/cercor/bhac124] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 11/13/2022] Open
Abstract
The amplitude of low-frequency fluctuation (ALFF) describes the regional intensity of spontaneous blood-oxygen-level-dependent signal in resting-state functional magnetic resonance imaging (fMRI). How the fMRI-ALFF relates to the amplitude in electrophysiological signals remains unclear. We here aimed to investigate the neural correlates of fMRI-ALFF by comparing the spatial difference of amplitude between the eyes-closed (EC) and eyes-open (EO) states from fMRI and magnetoencephalography (MEG), respectively. By synthesizing MEG signal into amplitude-based envelope time course, we first investigated 2 types of amplitude in MEG, meaning the amplitude of neural activities from delta to gamma (i.e. MEG-amplitude) and the amplitude of their low-frequency modulation at the fMRI range (i.e. MEG-ALFF). We observed that the MEG-ALFF in EC was increased at parietal sensors, ranging from alpha to beta; whereas the MEG-amplitude in EC was increased at the occipital sensors in alpha. Source-level analysis revealed that the increased MEG-ALFF in the sensorimotor cortex overlapped with the most reliable EC-EO differences observed in fMRI at slow-3 (0.073-0.198 Hz), and these differences were more significant after global mean standardization. Taken together, our results support that (i) the amplitude at 2 timescales in MEG reflect distinct physiological information and that (ii) the fMRI-ALFF may relate to the ALFF in neural activity.
Collapse
Affiliation(s)
- Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, Guangdong Province 518055, China.,College of Psychology, Shenzhen University, Shenzhen 518055, China
| | - Dong-Qiang Liu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
| | - Shufang Qian
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
| | - Xiujuan Qu
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
| | - Peiwen Zhang
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China
| | - Nai Ding
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou 310027, China.,Zhejiang Lab, Hangzhou 311121, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310015, China.,TMS center, Deqing Hospital of Hangzhou Normal University, Deqing, Zhejiang 313200, China.,Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou 311121, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
| |
Collapse
|