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Movement Parameters and Neural Activity in Motor Cortex and Area 5
Abstract
The relations of ongoing single-cell activity in the arm area of the motor cortex and area 5 to parameters of evolving arm
movements in two-dimensional (2D) space were investigated. A multiple linear regression model was used in which the ongoing
impulse activity of cells at time t + τ was expressed as a function of the (X, Y) components of the target direction and of position, velocity, and acceleration of the hand at time t, where τ was a time
shift (−200 to +200 msec). Analysis was done on 290 cells in the motor cortex and 207 cells in area 5. The time shift at which
the highest coefficient of determination (R2) was observed was determined and the statistical significance of the model tested. The median R2 was 0.581 and 0.530 for motor cortex and area 5, respectively. The median shift at which the highest R2 was observed was −90 and +90 msec for motor cortex and area 5, respectively. For most cells statistically significant relations
were observed to all four parameters tested; most prominent were the relations to target direction and least prominent those
to acceleration.
... In the 1980s, Georgopoulos and colleagues found a correlation between the movement direction of the hand and the motor cortical activity [6,7]. Moreover, speed and, with a less prominent effect, acceleration and position are continuously represented in motor cortical activity during reaching [8,9]. There is some controversy about whether the motor cortex represents so-called high-level features of the hand as described above (direction, speed, and acceleration) or low-level features for muscle groups, such as muscle activity and force [10][11][12]. ...
... Building on this thesis, we demonstrate that muscle activity can be generated artificially for known and unknown motion based on high-level motion features for each joint (or for the hand instead), which is similarly represented in our brain [6][7][8][9]. For this, we develop a recurrent neural network with long-short term dependencies in a supervised learning session with motion parameters such as angular position, velocity, and acceleration of the arm. ...
... The objective of this study is to showcase the feasibility of inducing muscle activity via a kinematic representation. Previous research provides evidence that the brain's neural activity encodes kinematic representation to some extent [6][7][8][9]. In this study, we substituted the neural representation of kinematics with actual measured kinematic parameters to generate a movement command. ...
Background
The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.
Methods
We develop a novel approach utilizing recurrent neural networks that are able to predict muscle activity of the upper limbs associated with complex 3D human arm motions. Therefore, motion parameters such as joint angle, velocity, acceleration, hand position, and orientation, serve as input for the models. In addition, these models are trained on multiple subjects (n=5 including , 3 male in the age of 26±2 years) and thus can generalize across individuals. In particular, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific fine-tuned model using a transfer learning approach to adapt the model to a new subject. Estimators such as mean square error MSE, correlation coefficient r, and coefficient of determination R² are used to evaluate the goodness of fit. We additionally assess performance by developing a new score called the zero-line score. The present approach was compared with multiple other architectures.
Results
The presented approach predicts the muscle activity for previously through different subjects with remarkable high precision and generalizing nicely for new motions that have not been trained before. In an exhausting comparison, our recurrent network outperformed all other architectures. In addition, the high inter-subject variation of the recorded muscle activity was successfully handled using a transfer learning approach, resulting in a good fit for the muscle activity for a new subject.
Conclusions
The ability of this approach to efficiently predict muscle activity contributes to the fundamental understanding of motion control. Furthermore, this approach has great potential for use in rehabilitation contexts, both as a therapeutic approach and as an assistive device. The predicted muscle activity can be utilized to guide functional electrical stimulation, allowing specific muscles to be targeted and potentially improving overall rehabilitation outcomes.
... After the work of Georgopoulos, other experiments proved that activity of neurons in the primary motor cortex correlates with a broader variety of movement-related variables, including endpoint position, velocity, acceleration (see for example Kettner et al., 1988;Moran & Schwartz, 1999;Schwartz, 2007), as well as joint angles (see Ajemian et al., 2001;Teka et al., 2017), endpoint force (Georgopoulos, 1992), muscle tensions (Evarts, 1967;Todorov, 2000;Holdefer & Miller, 2002). It is also proved that the tuning for movement parameters is not static, but varies with time (Ashe & Georgopoulos, 1994;Moran & Schwartz, 1999;Churchland & Shenoy, 2007;Paninski et al., 2004) and for this reason Hatsopoulos (Hatsopoulos et al., 2007;Reimer & Hatsopoulos, 2009) argues that individual motor cortical cells rather encode "movement fragments", i.e. movement trajectories. This feature can be considered in a more general perspective developed by M.S.A. Graziano who proposed that the motor cortex is organized into action maps (see Graziano et al., 2002;Graziano & Aflalo, 2007;Aflalo & Graziano 2006b). ...
... A fundamental class is formed by "extrinsic" or "hand-centered" parameters which typically describe cortical activity with respect to hand's movement. These variables mainly refer to endpoint position, velocity and acceleration of the hand both in two-dimensional and three-dimensional space (see Ashe & Georgopoulos, 1994;Georgopoulos et al., 1984;Kettner et al., 1988;Moran & Schwartz, 1999;Stark et al., 2009), in addition to the movement direction variable. This class of parameters has been broadly used for the characterization of the spatio-temporal form of movement (see for example the work of Flash & Hogan, 1985;Flash et al., 1992;Hogan, 1984). ...
... This class of parameters has been broadly used for the characterization of the spatio-temporal form of movement (see for example the work of Flash & Hogan, 1985;Flash et al., 1992;Hogan, 1984). It is important to note that the sensibility of each neuron to these variables can depend on time (Ashe & Georgopoulos, 1994;Moran & Schwartz, 1999;Paninski et al., 2004;Churchland & Shenoy, 2007;Reimer & Hatsopoulos, 2009;Hatsopoulos et al., 2007). In particular, Hatsopoulos et al. (2007) (see also Reimer & Hatsopoulos, 2009) highlighted that tuning to movement parameters varies with time and proposed to describe the activity of neurons through a trajectory encoding model. ...
In this paper we propose a neurogeometrical model of the behaviour of cells of the arm area of the primary motor cortex (M1). We will mathematically express as a fiber bundle the hypercolumnar organization of this cortical area, first modelled by Georgopoulos (Georgopoulos et al., 1982; Georgopoulos, 2015). On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the notion of fragments introduced by Hatsopoulos et al. (2007) which describes the selectivity of neurons to movement direction varying in time. This leads to consider a higher dimensional geometrical structure where fragments are represented as integral curves. A comparison with the curves obtained through numerical simulations and experimental data will be presented. Moreover, neural activity shows coherent behaviours represented in terms of movement trajectories pointing to a specific pattern of movement decomposition Kadmon Harpaz et al. (2019). Here, we will recover this pattern through a spectral clustering algorithm in the subriemannian structure we introduced, and compare our results with the neurophysiological one of Kadmon Harpaz et al. (2019).
... After the work of Georgopoulos, other experiments proved that activity of neurons in the primary motor cortex correlates with a broader variety of movement-related variables, including endpoint position, velocity, acceleration (see for example [48], [51], [64]), as well as joint angles (see [5], [68]), endpoint force [30], muscle tensions ( [21], [69], [40]). It is also proved that the tuning for movement parameters is not static, but varies with time ( [7], [51], [14], [58]) and for this reason Hatsopoulos ( [37,62]) argues that individual motor cortical cells rather encode "movement fragments", i.e. movement trajectories. This feature can be considered in a more general perspective developed by M.S.A. Graziano who proposed that the motor cortex is organized into action maps (see [36], [35], [3]). ...
... A fundamental class is formed by "extrinsic" or "hand-centered" parameters which typically describe cortical activity with respect to hand's movement. These variables mainly refer to endpoint position, velocity and acceleration of the hand both in two-dimensional and three-dimensional space (see [7], [26], [48], [51], [67]), in addition to the movement direction variable. This class of parameters has been broadly used for the characterization of the spatio-temporal form of movement (see for example the work of Flash and Hogan [24] and [22], [39]). ...
... This class of parameters has been broadly used for the characterization of the spatio-temporal form of movement (see for example the work of Flash and Hogan [24] and [22], [39]). It is important to note that the sensibility of each neuron to these variables can depend on time ( [7], [51], [58], [14], [62,37]). In particular, Hatsopolous [37] (see also [62]) highlighted that tuning to movement parameters varies with time and proposed to describe the activity of neurons through a trajectory encoding model. ...
In this paper we propose a neurogeometrical model for the behaviour of cells of the arm area of the primary motor cortex (M1). From Georgopoulos neural models [31, 29], we will provide a fiber bundle structure which is able to describe the hypercolumnar organization of the cortical area. On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the direction of arm movement which varies in time, as experimentally measured by Hatsopoulos [37] by introducing the notion of movement fragments. This leads to consider a higher dimensional geometrical structure where fragments will be represented as integral curves. A fitting of parameters with neurophysiological data will be described, and a comparison with the curves obtained through numerical simulations and experimental data will be presented. Finally, we will compare our model with the area of V1 responsible for movement coding, which exhibits analogous time-dependent receptive profiles.
... After the work of Georgopoulos, other experiments proved that activity of neurons in the primary motor cortex correlates with a broader variety of movement-related variables, including endpoint position, velocity, acceleration (see for example [48], [51], [64]), as well as joint angles (see [5], [68]), endpoint force [30], muscle tensions ( [21], [69], [40]). It is also proved that the tuning for movement parameters is not static, but varies with time ( [7], [51], [14], [58]) and for this reason Hatsopoulos ( [37,62]) argues that individual motor cortical cells rather encode "movement fragments", i.e. movement trajectories. This feature can be considered in a more general perspective developed by M.S.A. Graziano who proposed that the motor cortex is organized into action maps (see [36], [35], [3]). ...
... A fundamental class is formed by "extrinsic" or "hand-centered" parameters which typically describe cortical activity with respect to hand's movement. These variables mainly refer to endpoint position, velocity and acceleration of the hand both in two-dimensional and three-dimensional space (see [7], [26], [48], [51], [67]), in addition to the movement direction variable. This class of parameters has been broadly used for the characterization of the spatio-temporal form of movement (see for example the work of Flash and Hogan [24] and [22], [39]). ...
... This class of parameters has been broadly used for the characterization of the spatio-temporal form of movement (see for example the work of Flash and Hogan [24] and [22], [39]). It is important to note that the sensibility of each neuron to these variables can depend on time ( [7], [51], [58], [14], [62,37]). In particular, Hatsopolous [37] (see also [62]) highlighted that tuning to movement parameters varies with time and proposed to describe the activity of neurons through a trajectory encoding model. ...
In this paper we propose a neurogeometrical model for the behaviour of cells of the arm area of the primary motor cortex (M1). From Georgopoulos neural models \cite{georgopoulos1982relations, georgopoulos2015columnar}, we will provide a fiber bundle structure which is able to describe the hypercolumnar organization of the cortical area. On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the direction of arm movement which varies in time, as experimentally measured by Hatsopoulos \cite{Encoding} by introducing the notion of movement fragments. This leads to consider a higher dimensional geometrical structure where fragments will be represented as integral curves. A fitting of parameters with neurophysiological data will be described, and a comparison with the curves obtained through numerical simulations and experimental data will be presented. Finally, we will compare our model with the area of V1 responsible for movement coding, which exhibits analogous time-dependent receptive profiles.
... 83 Positiondependent neuronal discharges (i.e., place field-like representations) relating to hand movement through space are wellestablished in the primate motor cortex. 84 In fact, considering movement of the hand or forelimb in general: speed, 84,85 (hand) direction 84 and conjunctive 84,85 coding in the motor cortices can be seen as homologous to their spatial navigation counterparts in the limbic system. Essentially, it appears the motor cortex contains spatially tuned activities for specific body parts that are analogous to limbic activities that appear to represent whole body/head movement. ...
... 83 Positiondependent neuronal discharges (i.e., place field-like representations) relating to hand movement through space are wellestablished in the primate motor cortex. 84 In fact, considering movement of the hand or forelimb in general: speed, 84,85 (hand) direction 84 and conjunctive 84,85 coding in the motor cortices can be seen as homologous to their spatial navigation counterparts in the limbic system. Essentially, it appears the motor cortex contains spatially tuned activities for specific body parts that are analogous to limbic activities that appear to represent whole body/head movement. ...
... 83 Positiondependent neuronal discharges (i.e., place field-like representations) relating to hand movement through space are wellestablished in the primate motor cortex. 84 In fact, considering movement of the hand or forelimb in general: speed, 84,85 (hand) direction 84 and conjunctive 84,85 coding in the motor cortices can be seen as homologous to their spatial navigation counterparts in the limbic system. Essentially, it appears the motor cortex contains spatially tuned activities for specific body parts that are analogous to limbic activities that appear to represent whole body/head movement. ...
Spatially selective firing of place cells, grid cells, boundary vector/border cells and head direction cells constitutes the basic building blocks of a canonical spatial navigation system centered on the hippocampal-entorhinal complex. While head direction cells can be found throughout the brain, spatial tuning outside the hippocampal formation is often non-specific or conjunctive to other representations such as a reward. Although the precise mechanism of spatially selective firing activity is not understood, various studies show sensory inputs, particularly vision, heavily modulate spatial representation in the hippocampal-entorhinal circuit. To better understand the contribution of other sensory inputs in shaping spatial representation in the brain, we performed recording from the primary somatosensory cortex in foraging rats. To our surprise, we were able to detect the full complement of spatially selective firing patterns similar to that reported in the hippocampal-entorhinal network, namely, place cells, head direction cells, boundary vector/border cells, grid cells and conjunctive cells, in the somatosensory cortex. These newly identified somatosensory spatial cells form a spatial map outside the hippocampal formation and support the hypothesis that location information modulates body representation in the somatosensory cortex. Our findings provide transformative insights into our understanding of how spatial information is processed and integrated in the brain, as well as functional operations of the somatosensory cortex in the context of rehabilitation with brain-machine interfaces.
... Findings show that some neurons in the primary motor cortex encode the kinematics of movement and some kinetics. Of course, this relation is not linear, and some neurons are found to discharge firmly during weak activities and be relatively quiet during a strong movement of a finger [114][115][116][117][118]. ...
... Neuronal activities governing desired kinematics and required movement kinetics are generated simultaneously in different yet overlapping neural populations. Thus, the role of the motor cortex may be the transformation between what movement to make (kinematics) and how to make it (kinetics) [114,117,120]. ...
Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors’ cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.
... Despite its name, M1 at these ages does not produce movement, but instead functions exclusively as a somatosensory structure several weeks before the emergence of motor outflow [20][21][22]. Recently, we showed that neurons within M1's nascent somatosensory map are tuned to limb kinematics, as is the case for M1's adult motor map [23,24]. Specifically, we found precise sensory tuning to movement amplitude at P8-especially for the limb twitches that occur during active (REM) sleep [6]. ...
... . CC-BY-NC 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Although we observed a loss of decoding accuracy at P12, M1 activity is nonetheless highly correlated with limb kinematics in adults [23,24]. Accordingly, we hypothesized that M1 activity continues to represent movement kinematics after the emergence of continuous activity, but that this representation is too complex to be revealed using a linear decoder. ...
Primary motor cortex (M1) exhibits a protracted period of development that includes the establishment of a somatosensory map long before motor outflow emerges. In rats, the sensory representation is established by postnatal day (P) 8 when cortical activity is still "discontinuous." Here, we ask how the representation survives the sudden transition to noisy "continuous" activity at P12. Using neural decoding to predict forelimb movements based solely on M1 activity, we show that a linear decoder is sufficient to predict limb movements at P8, but not at P12; in contrast, a nonlinear decoder effectively predicts limb movements at P12. The change in decoder performance at P12 reflects an increase in both the complexity and uniqueness of kinematic information available in M1. We next show that the representation at P12 is more susceptible to the deleterious effects of "lesioning" inputs and to "transplanting" M1's encoding scheme from one pup to another. We conclude that the emergence of continuous cortical activity signals the developmental onset in M1 of more complex, informationally sparse, and individualized sensory representations.
... As a starting point, we will consider the geometry arising from the first monodimensional kinematic model of cells selective behaviour. Motor cortical cells are selective of time, position, velocity and acceleration of the hand ( [7], [61]), which will be denoted by pt, x, v, aq P R 4 . The differential constraints relating the kinematic variables endow very naturally the cortical features space with an Engel structure (see [52] and [53], section 6.2.2). ...
... The parameters to which M1 cells are sensible during reaching movements comprise a temporal variable ( [33,45]) together with the speed, acceleration and position of the hand ( [7,45,55,67,65]). In [48] we started by introducing a simple, mono-dimensional model, where the set of kinematic variables can be as ...
In this paper, we propose a model of arm reaching movements expressed in terms of geodesics in a sub-Riemannian space. We will choose a set of kinematic variables to which motor cortical cells are selective with the purpose of modelling the specific task of reaching. Minimizing trajectories will be recovered as suitable geodesics of the geometric spaces arising from the selective behaviour of M1 neurons. We will then extend this model by including the direction of arm movement. On this set, we will define a suitable sub-Riemannian metric able to provide a geometric interpretation of two-dimensional task-dependent arm reaching movements.
... In the 1980s, Georgopoulos and colleagues found a correlation between the movement direction of the hand and the motor cortical activity [1,2]. Moreover, speed and, with a less prominent effect, acceleration and position are continuously represented in motor cortical activity during reaching [3,4]. There is some controversy about whether the motor cortex represents so-called high-level features of the hand as described above (direction, speed, and acceleration) or low-level features for muscle groups such as muscle activity and force motivated by [5,6,7]. ...
... This is consistent with findings in motor cortical activity during reaching where besides the movement direction also less dominant correlations velocity and acceleration are represented [3,4]. From an analytical point of view the redundancy of input parameters, for example velocity, should not increase the accuracy of the overall model. ...
Voluntary human motion is the product of muscle activity that results from upstream motion planning of the motor cortical areas. We show that muscle activity can be artificially generated based on motion features such as position, velocity, and acceleration. For this purpose, we specifically develop an approach based on recurrent neural network that is trained in a supervised learning session; additional neural network architectures are considered and evaluated. The performance is evaluated by a new score called the zero-line score. The latter adaptively rescales the loss function of the generated signal for all channels comparing the overall range of muscle activity and thus dynamically evaluates similarities between both signals. The model achieves remarkable precision for previously trained movements and maintains significantly high precision for new movements that have not been previously trained. Further, these models are trained on multiple subjects and thus are able to generalize across individuals. In addition, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific pre-trained model that uses the general model as a basis and is adapted to a specific subject afterward. The subject-specific generation of muscle activity can be further used to improve the rehabilitation of neuromuscular diseases with myoelectric prostheses and functional electric stimulation.
... Many studies have attempted to determine how motor commands are encoded at the level of individual neurons or neuronal populations. Evidence abounds that single neurons in primate M1 may encode a variety of kinematic parameters related to movement, such as position (Georgopoulos et al., 1984;Paninski et al., 2004), direction of movement (Georgopoulos et al., 1982), amplitude (Messier and Kalaska, 2000), and acceleration (Ashe and Georgopoulos, 1994). Indeed, a large variable set of finger movements may be decoded from the activity patterns of a relatively small number of neurons in monkey M1 (Ben Hamed et al., 2007). ...
Motor learning is crucial for the survival of many animals. Acquiring a new motor skill involves complex alterations in both local neural circuits in many brain regions and long-range connections between them. Such changes can be observed anatomically and functionally. The primary motor cortex (M1) integrates information from diverse brain regions and plays a pivotal role in the acquisition and refinement of new motor skills. In this review, we discuss how motor learning affects the M1 at synaptic, cellular, and circuit levels. Wherever applicable, we attempt to relate and compare findings in humans, non-human primates, and rodents. Understanding the underlying principles shared by different species will deepen our understanding of the neurobiological and computational basis of motor learning.
... The cerebellar messages originating mainly in the interpositus and the dentatus nuclei (Rinvik & Grofova, 1974;Sasaki, Kawaguchi, Matsuda, & Mitzuno, 1972a;Sasaki, Matsuda, Kawaguchi, & Mitzuno, 1972b;Wannier, Kakei, & Shinoda, 1992;Yamamoto et al., 1984) would still gain access to the motor cortex via the VA and the parietal areas. Since the seventies, the importance of parietal cortex for perception and motor exploration of extrapersonal space has been documented in both primates (Andersen, 1987;Ashe & Georgopoulos, 1994;Faugier-Grimaud, Frenois, & Stein, 1978;Hyvarinen & Poranen, 1974;Kalaska, Caminiti, & Georgopoulos, 1983;Lynch, 1980;Milner, Ockleford, & Dewar, 1977;Mountcastle, Lynch, Georgopoulos, Sakata, & Acuna, 1975) and in cats (Fabre & Buser, 1981;Il'icheva, Khitrova-Orlova, Korenyuk, & Pavlenko, 1992;Joseph & Giroud, 1986). It could play an important role in the recovery observed in the present study. ...
The small effects of bilateral lesions of motor thalamus on motor control and the transient deficits induced by bilateral kainic red nucleus (RN) lesions have been explained by a parallel competitive role of the cortico- and rubro-spinal pathways: Either pathway can take over motor control if the other is damaged. In this study the effect of bilateral and simultaneous lesions of both RN and motor thalamus was analyzed on cats overtrained to reach toward a moving target. After lesion, accuracy was impaired, movement onset was delayed, and movement execution was perturbed. However, postoperative retraining led to full recovery of the preoperative accuracy level although movement latency remained higher. The relative mildness of the long-lasting deficit after lesioning 2 main motor brain structures underlines the robustness of overlearned movements and widens the idea of parallelism in the motor system to other (subcortical?) pathways.
... It is commonly assumed that the neural activity can be related to the kinematics of arm movements, including position, velocity and speed, via a linear model [25,26,27,28,29] or generalized linear model [30,31]. It has recently been demonstrated that SBP is related to the kinematics of the finger groups, including the position of each fingergroup (i.e., distance along the corresponding arc), P , and its rate of change, V [22]. ...
Objective:
While brain machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.
Approach:
Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e., consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.
Results:
First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below 5%, it was possible to achieve mean true positive rate of 28.1% online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.
Significance:
Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements in detection and correction can come from improving classification and correction strategies.
... Single cell recordings caudally from the anterior bank of monkey central gyrus (caudal M1) revealed coding of hand force dynamics (Sergio & Kalaska, 1998) and systematic shifts in the preferred direction of these neurons occurred as a consequence of variation in dynamics of hand force (Sergio et al., 2005). On the contrary, neurons recorded rostrally on the precentral gyrus of monkey M1 (rostral M1) did code movement direction but also its velocity (Ashe & Georgopoulos, 1994;Crammond & Kalaska, 1996). Movement direction and its velocity are coded in an additive fashion, and the relative weights of these signals turn out to change dynamically during reaching (Wang et al., 2007). ...
Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.
... Central features of fast limb movement control Regional distribution of brain activity associated with movement speed In order to better understand the mechanisms behind the programming and execution of these movements, it is useful to start at the brain regions showing speeddependent activity, since they constitute the starting point of the descending motor command. Regarding this aspect, most studies have found an increase in brain activity with greater movement rate or speed at different areas such as the primary motor cortex (M1) (Agnew et al. 2004; Ashe and Georgopoulos 1994;Blinkenberg et al. 1996;Jäncke et al. 1998b;Kawashima et al. 1999;Lutz et al. 2004;Park et al. 2008;Rao et al. 1996;Sadato et al. 1997;Sauvage et al. 2013;Schlaug et al. 1996;Toma et al. 2002), medial frontal area (Toma et al. 2002), premotor cortex (PMC) (Kawashima et al. 1999;Mayville et al. 1999), supplementary motor area (SMA) (Agnew et al. 2004;Jäncke et al. 1998aJäncke et al. , 1998bMayville et al. 1999;Schlaug et al. 1996), cingulate motor area (Lutz et al. 2004), and cerebellum (Agnew et al. 2004;Lutz et al. 2004;Park et al. 2008;Sadato et al. 1997), and cerebellar nuclei (Lutz et al. 2004). These activity patterns were mostly seen in hand/ finger and foot movement rate tasks, since the imaging methods used to assess brain activity do not usually allow large joint movement to be assessed. ...
The ability to produce high movement speeds is a crucial factor in human motor performance, from the skilled athlete to someone avoiding a fall. Despite this relevance, there remains a lack of both an integrative brain-to-behavior analysis of these movements and applied studies linking the known dependence on open-loop, central control mechanisms of these movements to their real-world implications, whether in the sports, performance arts, or occupational setting. In this review, we cover factors associated with the planning and performance of fast limb movements, from the generation of the motor command in the brain to the observed motor output. At each level (supraspinal, peripheral, and motor output), the influencing factors are presented and the changes brought by training and fatigue are discussed. The existing evidence of more applied studies relevant to practical aspects of human performance is also discussed. Inconsistencies in the existing literature both in the definitions and findings are highlighted, along with suggestions for further studies on the topic of fast limb movement control. The current heterogeneity in what is considered a fast movement and in experimental protocols makes it difficult to compare findings in the existing literature. We identified the role of the cerebellum in movement prediction and of surround inhibition in motor slowing, as well as the effects of fatigue and training on central motor control, as possible avenues for further research, especially in performance-driven populations.
... For example, Bullock and Grossberg (1988) demonstrated that positional information regarding a target's position (i.e., within Broadmann's area 5) was transformed into the velocity domain (i.e., within Broadmann's area 4) before the initiation of a voluntary arm movement. Additionally, Ashe and Georgopoulos (1994) showed that within these same cortical regions, target direction and hand velocity were strongly associated with neural activation patterns. Lastly, Churchland et al. (2006) provided evidence that variability in preparatory motor activity occurring within the dorsal pre-motor cortex was highly correlated with peak limb velocity variability during upper-limb reaching. ...
The present study examined spatial accuracy of mallet endpoints in a marimba performance context. Trained percussionists performed two- (i.e., Experiment 1) and four-mallet (i.e., Experiment 2) excerpts in three tempo conditions including slow, intermediate, and fast. Motion capture was utilized to gather data of upper-limb and mallet movements, as well as to compute velocities of the upper-limb joints. Mallet spatial accuracy was assessed by comparing mallet endpoints to a visual target positioned on the marimba. It was hypothesized that mallet spatial accuracy would be reduced as tempo condition increased, with effects on joint kinematics potentially revealing sensorimotor mechanisms underlying optimal sound production in marimba. Across both experiments, mallet accuracy was reduced as tempo condition increased. Interestingly, velocity variability in the elbows, wrists, and hands increased as mallet accuracy decreased. Such a pattern of effects suggested that sound production in marimba is suboptimal at fast relative to slow tempi. In addition, the velocity variability effects highlight the impact of motor planning mechanisms on sound production. Overall, the results shed new light on sensorimotor control in percussion which can be leveraged to enhance the training of percussionists.
... Note, this utilization of the jPCA algorithm on only the CI activity is different from the typical application of jPCA to data containing the condition-dependent activity. Additionally, we find the results of the dynamical system are more stable when the firing rates are square-root transformed to equalize variance between high and low firing rates (Kihlberg et al., 1972;Snedecor and Cochran, 1980;Ashe and Georgopoulos, 1994) and thus performed this transform before submitting firing rates to jPCA. ...
Reaching movements are known to have large condition-independent neural activity and cyclic neural dynamics. A new precision center-out task was performed by rhesus macaques to test the hypothesis that cyclic, condition-independent neural activity in the primary motor cortex (M1) occurs not only during initial reaching movements but also during subsequent corrective movements. Corrective movements were observed to be discrete with time courses and bell-shaped speed profiles similar to the initial movements. Condition-independent cyclic neural trajectories were similar and repeated for initial and each additional corrective submovement. The phase of the cyclic condition-independent neural activity predicted the time of peak movement speed more accurately than regression of instantaneous firing rate, even when the subject made multiple corrective movements. Rather than being controlled as continuations of the initial reach, a discrete cycle of motor cortex activity encodes each corrective submovement.Significance StatementDuring a precision center-out task, initial and subsequent corrective movements occur as discrete submovements with bell-shaped speed profiles. A cycle of condition-independent activity in primary motor cortex neuron populations corresponds to each submovement, such that the phase of this cyclic activity predicts the time of peak speeds-both initial and corrective. These submovements accompanied by cyclic neural activity offer important clues into how we successfully execute precise, corrective reaching movements and may have implications for optimizing control of brain-computer interfaces.
... Moreover, because the finger SM1 region is similar for conventional ACC-based CKC and CKC with deep learningassisted motion capture ( Supplementary Fig. 1), the determination of CKC with deep learning-assisted motion capture has been proven to be reliable. Previous studies involving non-human primates have revealed that several movement parameters, such as position, rotation, direction, and movement velocity, are encoded in the SM1, as determined using the recordings of a single neuron, local field potential, and multi-unit activity [32][33][34][35][36][37] . MEG studies involving humans have also revealed the significance of the SM1 cortex oscillations for encoding the parameters of voluntary movements, such as velocity 38 and acceleration 6,31 . ...
Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.
... We found that that direction of cursor's motion and task identity can be decoded prior to cursor's motion onset in the highest-ranked components and intertwined with phase dynamics (Figs. 3 and 4), rather than being only expressed in lower-ranked components. Our PCA-based analysis hence recovers the results of numerous previous studies that showed that spatial and motor parameters such as movement direction (Georgopoulos et al., 1986(Georgopoulos et al., , 1982 and force can be accurately decoded from cortical populations (Ashe and Georgopoulos, 1994;Evarts, 1968;Georgopoulos et al., 1983). ...
Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor’s motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been suggested to reflect a transformation from planning to movement execution, may rather reflect higher cognitive-motor processes, such as the covert representation of actions and goals shared across tasks that require movement and those that do not.
... For example, Bullock and Grossberg (1988) demonstrated that positional information regarding a target's position (i.e., within Broadmann's area 5) was transformed into the velocity domain (i.e., within Broadmann's area 4) before the initiation of a voluntary arm movement. Additionally, Ashe and Georgopoulos (1994) showed that within these same cortical regions, target direction and hand velocity were strongly associated with neural activation patterns. Lastly, Churchland et al. (2006) provided evidence that variability in preparatory motor activity occurring within the dorsal pre-motor cortex was highly correlated with peak limb velocity variability during upper-limb reaching. ...
Musicians execute thousands of complex movements during a performance. This study examined the accuracy of such movements in a marimba performance context. Marimba performance is associated with an idealistic performance accuracy component wherein the terminal position of the mallets should strike the bar over the resonator to produce optimal sound. The aims were to investigate how performance tempo impacts mallet accuracy as well as to assess potential kinematic mechanisms underlying such performance. Thirteen percussion majors performed a two-mallet excerpt in slow (110 bpm), intermediate (120 bpm), and fast tempo conditions (130 bpm). Motion tracking was used to monitor the positions and compute velocities of the mallets, hands, wrists, and elbows. Endpoint errors were obtained by comparing the mallet's terminal position to that of the visual target located over the resonator on the marimba's bars. It was hypothesized that lower mallet accuracy would be observed in the fast vs. the slow condition and that mallet accuracy would be driven by altered limb segment velocity relating to tempo condition. Indeed, the mallet accuracy analysis revealed lower accuracy in the fast vs. the slow tempo conditions. Interestingly, velocity variability was greater in the intermediate and fast conditions compared to the slow condition in the left elbow, left wrist, and right hand. This pattern may suggest that limb velocity variability can negatively impact mallet accuracy and thus reduce optimal sound production. Therefore, identifying strategies to target limb velocity during the skill acquisition phase may enhance motor learning in marimba performance.
Acknowledgments: * Indicates joint first-authorship; The Canada Foundation For Innovation and the Percussion Department in the Faculty of Music at U of T
... However, even in these previous experiments, effects on action tended to be small, suggesting that the motor system might not be as susceptible to body ownership illusions as conscious perception (see also Matsumiya, 2021). In addition, our results are at odds with the reduction in corticospinal excitability reported in illusion-susceptible individuals by della Gatta et al. (2016), particularly given that movement force or velocity is related to the discharge of neurons in M1 (Ashe & Georgopoulos, 1994;Cheney, 1985;Evarts, 1981;Graziano et al., 2002;Jäncke et al., 2004). It is possible that the change in corticospinal excitability reported by della Gatta et al. (2016) has little import to motor behaviour. ...
Body ownership refers to the distinct sensation that our observed body belongs to us, which is believed to stem from multisensory integration. This is commonly shown through the rubber hand illusion (RHI), which induces a sense of ownership over a false limb. Whilst the RHI may interfere with object-directed action and alter motor cortical activity, it is not yet clear whether a sense of ownership over an artificial hand has functional consequences for movement production per se. As such, we performed two motion-tracking experiments (n=117) to examine the effects of the RHI on the reaction time, acceleration, and velocity of rapid index finger abduction. We observed little convincing evidence that the induction of the RHI altered these kinematic variables. Moreover, the subjective sensations of rubber hand ownership, referral of touch, and agency did not convincingly correlate with kinematic variables, and nor did proprioceptive drift, suggesting that changes in body representation elicited by the RHI may not influence basic movement. Whilst experiment 1 suggested that individuals reporting a greater sensation of the real hand disappearing performed movements with smaller acceleration and velocity following illusion induction, we did not replicate this effect in a second experiment, suggesting that these effects may be small or not particularly robust. Overall, these results indicate that manipulating the conscious experience of body ownership has little impact on basic motor control, at least in the RHI with healthy participants.
... In adult monkeys, motor activity in M1 is correlated with movement amplitude (Ashe and Georgopoulos, 1994). In P5 rats, we recently found that neural activity in primary somatosensory cortex (S1) is similarly correlated with the amplitude of whisker movements . ...
Primary motor cortex (M1) undergoes protracted development in mammals, functioning initially as a sensory structure. Throughout the first postnatal week in rats, M1 is strongly activated by self-generated forelimb movements-especially by the twitches that occur during active sleep. Here, we quantify the kinematic features of forelimb movements to reveal receptive-field properties of individual units within the forelimb region of M1. At postnatal day (P) 8, nearly all units were strongly modulated by movement amplitude, especially during active sleep. By P12, only a minority of units continued to exhibit amplitude-tuning, regardless of behavioral state. At both ages, movement direction also modulated M1 activity, though to a lesser extent. Finally, at P12, M1 population-level activity became more sparse and decorrelated, along with a substantial alteration in the statistical distribution of M1 responses to limb movements. These findings reveal a transition toward a more complex and informationally rich representation of movement long before M1 develops its motor functionality.SIGNIFICANCE STATEMENT:Primary motor cortex (M1) plays a fundamental role in the generation of voluntary movements and motor learning in adults. In early development, however, M1 functions as a prototypical sensory structure. Here, we demonstrate in infant rats that M1 codes for the kinematics of self-generated limb movements long before M1 develops its capacity to drive movements themselves. Moreover, we identify a key transition during the second postnatal week in which M1 activity becomes more informationally complex. Together, these findings further delineate the complex developmental path by which M1 develops its sensory functions in support of its later-emerging motor capacities.
... Two populations of 25 units each were simulated: (1) units that encode just the estimated state (2-dimensional vectors of estimated position and estimated velocity, and speed), and (2) units that also encode the 2-dimensional optimal control vector and its magnitude. Based on the evidence in the literature (Georgopoulos et al., 1982;Ashe and Georgopoulos, 1994;Ashe, 1997;Messier and Kalaska, 2000;Hendrix et al., 2009), we expect the behavior of simulated neurons in those two populations to be similar to the behavior of recorded PMd and M1 units, respectively, and hence refer to them as PMd-like and M1-like neurons. The 1:1 ratio between the number of M1-like and PMd-like units was based on a similar ratio (56:55) between the number of recorded M1 and PMd units in Carmena et al. (2003). ...
Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis. Simulations are based on the assumption that the brain implements optimal state estimation and feedback control and that cortical motor neurons encode the estimated state and control vector. Our simulations successfully reproduce apparent shifts in EPDs observed in BMI experiments with different BMI filters, including linear, Kalman and re-calibrated Kalman filters, even with no neural adaptation. Theoretical analysis identifies the conditions for reducing those shifts. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. We conclude that the observed shifts in EPDs may result from experimental conditions, and in particular correlated velocities or tuning weights, even with no adaptation. Under the above assumptions, we show that if neurons are tuned differently to the estimated velocity, estimated position and control signal, the EPD with respect to actual velocity may not capture the real PD in which the neuron encodes the estimated velocity. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments.
... We found that that direction of cursor's motion and task identity can be decoded prior to movement onset in the highest-ranked components and intertwined with phase dynamics (Fig. 3-4), rather than being only expressed in lower-ranked components. Our PCA-based analysis hence recovers the results of numerous previous studies that showed that spatial and motor parameters such as movement direction 36,37 and force can be accurately decoded from cortical populations [38][39][40] . ...
Studies of neural population dynamics of cell activity from monkey motor areas during reaching show that it mostly represents the generation and timing of motor behavior. We compared neural dynamics in dorsal premotor cortex (PMd) during the performance of a visuomotor task executed individually or cooperatively and during an observation task. In the visuomotor conditions, monkeys applied isometric forces on a joystick to guide a visual cursor in different directions, either alone or jointly with a conspecific. In the observation condition, they observed the cursor’s motion guided by the partner. We found that in PMd neural dynamics were widely shared across action execution and observation, with cursor motion directions more accurately discriminated than task types. This suggests that PMd encodes spatial aspects irrespective of specific behavioral demands. Furthermore, our results suggest that largest components of premotor population dynamics, which have previously been suggested to reflect a transformation from planning to movement execution, may rather reflect higher cognitive-motor processes, such as the covert representation of actions and goals shared across tasks that require movement and those that do not.
HIGHLIGHTS
In PMd neural dynamics is shared across action execution and mere observation
Task directional features are more accurately discriminated than action types
Spatial aspects are encoded in PMd independently from specific behavioral demands
PMd dynamics largely reflect higher cognitive-motor processes rather than strictly motor-related functions
... where FR t ð Þ is the instantaneous firing rate of the BCI unit estimated using spike counts in 10 ms bins convolved with a 500 ms Gaussian filter and A is an empirical value set to 6 screen units per 10 ms. Square-root transformation of the unit's firing rate was used to reduce variance (Kihlberg et al., 1972;Ashe and Georgopoulos, 1994;Rouse and Schieber, 2016). The 80th and 20th percentiles of the BCI unit's firing rate distribution, FR 80% and FR 20% , were estimated initially from the cumulative distribution of firing rates recorded during joystick-controlled trials before beginning the BCI task each day. ...
Voluntary control of visually-guided upper extremity movements involves neuronal activity in multiple areas of the cerebral cortex. Studies of brain-computer interfaces (BCIs) that use spike recordings for input, however, have focused largely on activity in the region from which those neurons that directly control the BCI, which we call BCI units, are recorded. We hypothesized that just as voluntary control of the arm and hand involves activity in multiple cortical areas, so does voluntary control of a BCI. In two subjects (Macaca mulatta) performing a center-out task both with a hand-held joystick and with a BCI directly controlled by four primary motor cortex (M1) BCI units, we recorded the activity of other, non-BCI units in M1, dorsal premotor cortex (PMd) and ventral premotor cortex (PMv), primary somatosensory cortex (S1), dorsal posterior parietal cortex (dPPC), and the anterior intraparietal area (AIP). In most of these areas, non-BCI units were active in similar percentages and at similar modulation depths during both joystick and BCI trials. Both BCI and non-BCI units showed changes in preferred direction (PD). Additionally, the prevalence of effective connectivity between BCI and non-BCI units was similar during both tasks. The subject with better BCI performance showed increased percentages of modulated non-BCI units with increased modulation depth and increased effective connectivity during BCI as compared with joystick trials; such increases were not found in the subject with poorer BCI performance. During voluntary, closed-loop control, non-BCI units in a given cortical area may function similarly whether the effector is the native upper extremity or a BCI-controlled device.
... A large body of work has demonstrated that the relationship between the instantaneous velocity vector v i and the neural activity signal y i in the motor cortex area M1 is approximately linear [29][30][31] . More recently, studies have shown that neurons are also tuned to to the magnitude of the 2D velocities 13 . ...
Brain decoders use neural recordings to infer a user’s activity or intent. To train a decoder, we generally need infer the variables of interest (covariates) using simultaneously measured neural activity. However, there are many cases where this approach is not possible. Here we overcome this problem by introducing a fundamentally new approach for decoding called distribution alignment decoding (DAD). We use the statistics of movement, much like cryptographers use the statistics of language, to find a mapping between neural activity and motor variables. DAD learns a linear decoder which aligns the distribution of its output with the typical distribution of motor outputs by minimizing their KL-divergence. We apply our approach to a two datasets collected from the motor cortex of non-human primates (NHPs): a reaching task and an isometric force production task. We study the performance of DAD and find regimes where DAD provides comparable and in some cases, better performance than a typical supervised decoder. As DAD does not rely on the ability to record motor-related outputs, it promises to broaden the set of potential applications of brain decoding.
... Further studies probing the relationship between movement variables such as limb joints and neuronal activity in the motor cortex were performed in monkeys. In restricted, simplified movement sets, such as a set of reach directions, neuronal activity has been proposed to relate to direction, force, speed, joint angle, movement trajectories and muscle activity, among other variables 17,[20][21][22]25,26,60,61 . ...
Neuronal networks of the mammalian motor cortex (M1) are important for dexterous control of limb joints. Yet it remains unclear how encoding of joint movement in M1 networks depends on varying environmental contexts. Using calcium imaging we measured neuronal activity in layer 2/3 of the mouse M1 forelimb region while mice grasped either regularly or irregularly spaced ladder rungs during locomotion. We found that population coding of forelimb joint movements is sparse and varies according to the flexibility demanded from them in the regular and irregular context, even for equivalent grasping actions across conditions. This context-dependence of M1 network encoding emerged during learning of the locomotion task, fostered more precise grasping actions, but broke apart upon silencing of projections from secondary motor cortex (M2). These findings suggest that M2 reconfigures M1 neuronal circuits to adapt joint processing to the flexibility demands in specific familiar contexts, thereby increasing the accuracy of motor output.
... Since the classical studies by Georgopoulos and colleagues, it is known that many neurons of the primary motor cortex (M1) are maximally sensitive to specific, "preferred" directions of arm movement and generate progressively less activity during movements in other directions (Amirikian and Georgopoulos 2000;Ashe and Georgopoulos 1994;Caminiti et al. 1990Caminiti et al. , 1991Fu et al. 1995;Georgopoulos and Carpenter 2015;Georgopoulos et al. 1982Georgopoulos et al. , 1983Georgopoulos et al. , 1988Kalaska et al. 1989; Schwartz 1999a, 1999b;Schwartz 2016;Schwartz and Moran 1999;Sergio et al. 2005). This property of neurons is described by a directional tuning curve usually approximated by a cosine function of the angular deflection of the preferred from the actual direction of arm movement. ...
Feldman AG. Indirect, referent control of motor actions underlies directional tuning of neurons.
... Work in non-human primates suggests that the activity of cells in Ml code for the direction of limb movement through the construction of a population vector, such that the activity of directionally tuned cells in Ml is proportional to the angle between the actual direction of movement and the preferred direction of the cell (Georgopoulos et al., 1982;Georgopoulos et al., 1986). Subsequent experiments suggest that the motor cortex represents movements in terms of the direction and velocity of the required movement, not on the basis of individual muscles (Alexander and Crutcher, 1990;Ashe and Georgopoulos, 1994;Kakei et al., 1999). Experiments in human subjects suggests that this internal model or mapping between the intended movement and muscle activation is plastic and can be re learnt, for example if subjects are required to make movements in an artificial forcefield (Shadmehr and Mussa-lvaldi, 1994). ...
Repetitive Transcranial Magnetic Stimulation (rTMS) can be used to induce temporary alterations in the excitability of the brain in healthy subjects. For some motor behaviour it has been possible to impair or improve performance following rTMS, but for most simple tasks performance is unaltered. This suggests that the motor system is able to compensate, to some extent, for the changes in excitability induced by rTMS. Potentially this makes rTMS a useful tool for studying reorganisation in the healthy motor system, and may provide insights into adaptive mechanisms after injury such as ischaemic stroke. The work presented in this thesis examines rTMS-induced changes in regional excitability following 1Hz rTMS to the primary motor cortex, and potential compensatory mechanisms during various motor tasks. The results of three functional neuroimaging experiments reveal significant changes in movement-related responses and coupling with the motor system following rTMS. The results of a behavioural experiment suggest that the increases in movement-related responses in the right premotor cortex have a functional role in maintaining motor performance following 1Hz rTMS to left primary motor cortex. Analyses of effective connectivity suggest that the influence of the right premotor cortex in maintaining motor performance after rTMS is mediated via increased transcallosal connections from right to left premotor cortex, as opposed to non-homologous connections from right premotor to left motor cortex. Increased activity in motor areas not normally engaged in task performance may contribute to compensatory mechanisms during altered cortical excitability. Analyses of effective connectivity suggest that operational remapping of motor networks may also occur, and this may also contribute to compensatory mechanisms for rTMS-induced reductions in cortical excitability. Mapping these patterns of reorganisation in the motor system may provide a useful method to study acute compensatory plasticity of the human brain and may help to understand how the brain reacts to more permanent lesions. Establishing the functional relevance of increased activity in areas not normally engaged in task performance using TMS may play a key role in rehabilitation and provide a mechanistic understanding of compensatory mechanisms in stroke patients.
... Combined Timing Tasks: Timed Spatial Reproduction and Timed DM Tasks. It is a ubiquitous phenomenon that neural networks encode more than one quantity simultaneously (35)(36)(37). In this subsection, we will discuss how neural networks encode temporal and spatial information (or decision choice) simultaneously, which enables the brain to take the right action at the right time. ...
To maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then anticipate or act correctly at the right time. How do animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multiseconds in working memory? How is temporal information processed concurrently with spatial information and decision making? Why are there strong neuronal temporal signals in tasks in which temporal information is not required? A systematic understanding of the underlying neural mechanisms is still lacking. Here, we addressed these problems using supervised training of recurrent neural network models. We revealed that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling state evolution speed. Temporal and nontemporal information is coded in subspaces orthogonal with each other, and the state trajectories with time at different nontemporal information are quasiparallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and nontemporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of nontemporal information are similar or not. We identified four factors that facilitate strong temporal signals in nontiming tasks, including the anticipation of coming events. Our work discloses fundamental computational principles of temporal processing, and it is supported by and gives predictions to a number of experimental phenomena.
... Increasing evidence indicates that multiple parameters can be reflected in the activity of single neurons, suggesting that highly integrated multi-modal tuning may be a fundamental feature of motor cortical activity. For instance, recordings from macaques during unrestrained arm movements showed that parameters such as movement direction or end position of the limb could account for only a portion of spiking patterns in single motor cortical neurons, indicating that individual neurons may be tuned in a multidimensional space and that testing neural activity relative to any single parameter may account only partially for multidimensional tuning profiles (Ashe and Georgopoulos, 1994;Fu et al., 1995;Moran and Schwartz, 1999;Graziano, 2006, 2007). Likewise, we found that single AId neurons could be modulated both during individual movements and during behavioral state periods that did not include those movements, indicating that single neurons were modulated by multiple factors. ...
A region within songbird cortex, AId (dorsal intermediate arcopallium), is functionally analogous to motor cortex in mammals and has been implicated in vocal learning during development. AId thus serves as a powerful model for investigating motor cortical contributions to developmental skill learning. We made extracellular recordings in AId of freely behaving juvenile zebra finches and evaluated neural activity during diverse motor behaviors throughout entire recording sessions, including song production as well as hopping, pecking, preening, fluff-ups, beak interactions with cage objects, scratching, and stretching. A large population of single neurons showed significant modulation of activity during singing relative to quiescence. In addition, AId neurons demonstrated heterogeneous response patterns that were evoked during multiple movements, with single neurons often demonstrating excitation during one movement type and suppression during another. Lesions of AId do not disrupt vocal motor output or impair generic movements, suggesting that the responses observed during active behavior do not reflect direct motor drive. Consistent with this idea, we found that some AId neurons showed differential activity during pecking movements depending on the context in which pecks occurred, suggesting that AId circuitry encodes diverse inputs beyond generic motor parameters. Moreover, we found evidence of neurons that did not respond during discrete movements but were nonetheless modulated during active behavioral states compared to quiescence. Taken together, our results support the idea that AId neurons are involved in sensorimotor integration of external sensory inputs and/or internal feedback cues to help modulate goal-directed behaviors.
SIGNIFICANCE STATEMENT
Motor cortex across taxa receives highly integrated, multi-modal information and has been implicated in both execution and acquisition of complex motor skills, yet studies of motor cortex typically employ restricted behavioral paradigms that target select movement parameters, preventing wider assessment of the diverse sensorimotor factors that can affect motor cortical activity. Recording in AId of freely behaving juvenile songbirds that are actively engaged in sensorimotor learning offers unique advantages for elucidating the functional role of motor cortical neurons. The results demonstrate that a diverse array of factors modulate motor cortical activity and lay important groundwork for future investigations of how multi-modal information is integrated in motor cortical regions to contribute to learning and execution of complex motor skills.
... Regarding the sense of ownership over the rubber hand, we observed mixed evidence in favour of and Georgopoulos 1994;Cheney 1985;Evarts 1981;Graziano et al. 2002; 739 Jäncke et al. 2004). It is possible that the change in corticospinal excitability reported by della Gatta et al. (2016) 740 has little import to motor behaviour. ...
Body ownership refers to the distinct sensation that our observed body belongs to us, which is believed to stem from multisensory integration. The rubber hand illusion (RHI) provides the most well-known evidence for this proposal: synchronous (but not asynchronous) stroking of a fake hand and a participant’s real hidden hand can induce a sense of ownership over the false limb. Whilst the RHI may interfere with object-directed action and alter motor cortical activity, it is not yet clear whether a sense of ownership over an artificial hand has functional consequences for movement production per se. As such, we performed two motion-tracking experiments (n=117) to examine the effects of the RHI on the reaction time, acceleration, and velocity of rapid index finger abduction. We also examined whether subjective components of the illusion and the associated changes in hand position sense (proprioceptive drift) were correlated with changes in the kinematic variables. We observed convincing evidence that the induction of the RHI did not alter any kinematic variables. Moreover, the subjective sensations of rubber hand ownership, referral of touch, and agency did not convincingly correlate with kinematic variables, and nor did proprioceptive drift, suggesting that changes in body representation elicited by the RHI may not influence basic movement. Whilst experiment 1 suggested that individuals reporting a greater sensation of the real hand disappearing performed movements with smaller acceleration and velocity following illusion induction, we did not replicate this effect in a second experiment, suggesting that these effects may be small or not particularly robust.
Although the motor cortex has been found to be modulated by sensory or cognitive sequences, the linkage between multiple movement elements and sequence-related responses is not yet understood. Here, we recorded neuronal activity from the motor cortex with implanted micro-electrode arrays and single electrodes while monkeys performed a double-reach task that was instructed by simultaneously presented memorized cues. We found that there existed a substantial multiplicative component jointly tuned to impending and subsequent reaches during preparation, then the coding mechanism transferred to an additive manner during execution. This multiplicative joint coding, which also spontaneously emerged in recurrent neural networks trained for double reach, enriches neural patterns for sequential movement, and might explain the linear readout of elemental movements.
The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.
The primary motor cortex (M1) exhibits a protracted period of development, including the development of a sensory representation long before motor outflow emerges. In rats, this representation is present by postnatal day (P) 8, when M1 activity is "discontinuous." Here, we ask how the representation changes upon the transition to "continuous" activity at P12. We use neural decoding to predict forelimb movements from M1 activity and show that a linear decoder effectively predicts limb movements at P8 but not at P12; instead, a nonlinear decoder better predicts limb movements at P12. The altered decoder performance reflects increased complexity and uniqueness of kinematic information in M1. We next show that M1's representation at P12 is more susceptible to "lesioning" of inputs and "transplanting" of M1's encoding scheme from one pup to another. Thus, the emergence of continuous M1 activity signals the developmental onset of more complex, informationally sparse, and individualized sensory representations.
The fluid movement of an arm requires multiple spatiotemporal parameters to be set independently. Recent studies have argued that arm movements are generated by the collective dynamics of neurons in motor cortex. An untested prediction of this hypothesis is that independent parameters of movement must map to independent components of the neural dynamics. Using a task where monkeys made a sequence of reaching movements to randomly placed targets, we show that the spatial and temporal parameters of arm movements are independently encoded in the low-dimensional trajectories of population activity in motor cortex: Each movement’s direction corre-sponds to a fixed neural trajectory through neural state space and its speed to how quickly that trajectory is traversed. Recurrent neural network models show this coding allows independent control over the spatial and temporal parameters of movement by separate network parameters. Our results support a key prediction of the dynamical systems view of motor cortex, but also argue that not all parameters of movement are defined by different trajectories of population activity.
Significance Statement
From delicate strokes while drawing to ballistic swings while playing tennis, a skilled arm movement requires precise control of both its direction and speed. Motor cortex is thought to play a key role in controlling both, but it is unclear how they are jointly controlled. We show here that the population activity in motor cortex represents both the spatial and temporal properties of arm movements in the same low-dimensional signal. This representation was remarkably simple: the movement’s direction is represented by the trajectory that signal takes; the movement’s speed by how quickly the signal moves along its trajectory. Our network modelling shows this encoding allows an arm movement’s direction and speed to be simultaneously and independently controlled.
Although motor cortex has been found to be modulated by sensory or cognitive sequences, the linkage between multiple movement elements and sequence-related responses is not yet understood. Here, we recorded neuronal activity from the motor cortex with implanted micro-electrode arrays and single electrodes while monkeys performed a double-reach task that was instructed by simultaneously presented memorized cues. We found that there existed a substantial multiplicative component jointly tuned to impending and subsequent reaches during preparation, then the coding mechanism transferred to an additive manner during execution. Multiplicative joint coding, which also spontaneously emerged in a recurrent neural network trained for double-reach, enriches neural patterns for sequential movement, and might explain the linear readout of elemental movements.
Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.
In two freestanding volumes, the Textbook of Neural Repair and Rehabilitation provides comprehensive coverage of the science and practice of neurological rehabilitation. Revised throughout, bringing the book fully up to date, this volume, Neural Repair and Plasticity, covers the basic sciences relevant to recovery of function following injury to the nervous system, reviewing anatomical and physiological plasticity in the normal central nervous system, mechanisms of neuronal death, axonal regeneration, stem cell biology, and research strategies targeted at axon regeneration and neuron replacement. New chapters have been added covering pathophysiology and plasticity in cerebral palsy, stem cell therapies for brain disorders and neurotrophin repair of spinal cord damage, along with numerous others. Edited and written by leading international authorities, it is an essential resource for neuroscientists and provides a foundation for the work of clinical rehabilitation professionals.
Significance
Most studies in sensorimotor neurophysiology have utilized reactive movements to stationary goals pre-defined by sensory cues, but this approach is fundamentally incapable of determining whether the observed neural activity reflects current sensory stimuli or predicts future movements. In the present study, we recorded single-neuron activity from behaving monkeys engaged in a dynamic, flexible, stimulus-response contingency task that enabled us to distinguish activity co-varying with sensory inflow from that co-varying with motor outflow in the posterior parietal cortex.
The connectivity among architectonically defined areas of the frontal, parietal, and temporal cortex of the macaque has been extensively mapped through tract tracing methods. To investigate the statistical organization underlying this connectivity, and identify its underlying architecture, we performed a hierarchical cluster analysis on 69 cortical areas based on their anatomically defined inputs. We identified 10 frontal, 4 parietal, and 5 temporal hierarchically related sets of areas (clusters), defined by unique sets of inputs and typically composed of anatomically contiguous areas. Across cortex, clusters that share functional properties were linked by dominant information processing circuits in a topographically organized manner that reflects the organization of the main fiber bundles in the cortex. This led to a dorsal-ventral subdivision of the frontal cortex, where dorsal and ventral clusters showed privileged connectivity with parietal and temporal areas, respectively. Ventrally, temporo-frontal circuits encode information to discriminate objects in the environment, their value, emotional properties, and functions such as memory and spatial navigation. Dorsal parieto-frontal circuits encode information for selecting, generating, and monitoring appropriate actions based on visual-spatial and somatosensory information. This organization may reflect evolutionary antecedents, in which the vertebrate pallium, which is the ancestral cortex, was defined by a ventral and lateral olfactory region and a medial hippocampal region.
Supraspinal signals play a significant role in compensatory responses to postural perturbations. While the cortex is not necessary for basic postural tasks in intact animals, its role in responding to unexpected postural perturbations after spinal cord injury (SCI) has not been studied. To better understand how SCI impacts cortical encoding of postural perturbations, the activity of single neurons in the hindlimb sensorimotor cortex (HLSMC) was recorded in the rat during unexpected tilts before and after a complete midthoracic spinal transection. In a subset of animals, limb ground reaction forces were also collected. HLSMC activity was strongly modulated in response to different tilt profiles. As the velocity of the tilt increased, more information was conveyed by the HLSMC neurons about the perturbation due to increases in both the number of recruited neurons and the magnitude of their responses. SCI led to attenuated and delayed hindlimb ground reaction forces. However, HLSMC neurons remained responsive to tilts after injury but with increased latencies and decreased tuning to slower tilts. Information conveyed by cortical neurons about the tilts was therefore reduced after SCI, requiring more cells to convey the same amount of information as before the transection. Given that reorganization of the hindlimb sensorimotor cortex in response to therapy after complete mid-thoracic SCI is necessary for behavioral recovery, this sustained encoding of information after SCI could be a substrate for the reorganization that uses sensory information from above the lesion to control trunk muscles that permit weight-supported stepping and postural control.
A bstract
Primary motor cortex (M1) undergoes protracted development in rodents, functioning initially as a sensory structure. As we reported previously in neonatal rats (Dooley and Blumberg, 2018), self-generated forelimb movements—especially the twitch movements that occur during active sleep—trigger sensory feedback (reafference) that strongly activates M1. Here, we expand our investigation by using a video-based approach to quantify the kinematic features of forelimb movements with sufficient precision to reveal receptive-field properties of individual M1 units. At postnatal day (P) 8, nearly all M1 units were strongly modulated by movement amplitude, but only during active sleep. By P12, the majority of M1 units no longer exhibited amplitude-dependence, regardless of sleepwake state. At both ages, movement direction produced changes in M1 activity, but to a much lesser extent than did movement amplitude. Finally, we observed that population spiking activity in M1 becomes more continuous and decorrelated between P8 and P12. Altogether, these findings reveal that M1 undergoes a sudden transition in its receptive field properties and population-level activity during the second postnatal week. This transition marks the onset of the next stage in M1 development before the emergence of its later-emerging capacity to influence motor outflow.
The ability to interact with our environment requires the brain to transform spatially-represented sensory signals into temporally-encoded motor commands for appropriate control of the relevant effectors. For visually-guided eye movements, or saccades, the superior colliculus (SC) is assumed to be the final stage of spatial representation, and instantaneous control of the movement is achieved through a rate code representation in the lower brain stem. We questioned this dogma and investigated whether SC activity also employs a dynamic rate code, in addition to the spatial representation. Noting that the kinematics of repeated movements exhibits trial-to-trial variability, we regressed instantaneous SC activity with instantaneous eye velocity and found a robust correlation throughout saccade duration. Peak correlation was tightly linked to time of peak velocity, and SC neurons with higher firing rates exhibited stronger correlations. Moreover, the strong correlative relationship was preserved when eye movement profiles were substantially altered by a blink-induced perturbation. These results indicate that the rate code of individual SC neurons can control instantaneous eye velocity, similar to how primary motor cortex controls hand movements, and argue against a serial process for transforming spatially encoded information into a rate code.
A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and pre-motor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission, and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.
A region within songbird cortex, dorsal intermediate arcopallium (AId), is functionally analogous to motor cortex in mammals and has been implicated in song learning during development. Non-vocal factors such as visual and social cues are known to mediate song learning and performance, yet previous chronic-recording studies of regions important for song behavior have focused exclusively on neural activity in relation to song production. Thus, we have little understanding of the range of non-vocal information that single neurons may encode. We made chronic recordings in AId of freely behaving juvenile zebra finches and evaluated neural activity during diverse motor behaviors throughout entire recording sessions, including song production as well as hopping, pecking, preening, fluff-ups, beak interactions, scratching, and stretching. These movements are part of natural behavioral repertoires and are important components of both song learning and courtship behavior. A large population of AId neurons showed significant modulation of activity during singing. In addition, single neurons demonstrated heterogeneous response patterns during multiple movements (including excitation during one movement type and suppression during another), and some neurons showed differential activity depending on the context in which movements occurred. Moreover, we found evidence of neurons that did not respond during discrete movements but were nonetheless modulated during active behavioral states compared with quiescence. Our results suggest that AId neurons process both vocal and non-vocal information, highlighting the importance of considering the variety of multimodal factors that can contribute to vocal motor learning during development.
First paragraph: Coordinate Frameworks, Serial Hierarchies, and Parallel Processing Reaching movements of the arm to visual targets in different spatial locations can be described in terms of many different spatiotemporal movement attributes, that is to say, parameters of movement kinematics. These include the spatial location of the target, the path and instantaneous velocity of the hand through external space toward the target and the sequence of joint angle changes. The same movement can also be described in terms of movement dynamics, such as the causative forces, joint torques and muscle activity patterns (Kalaska 1991b; note that this use of kinematics and dynamics does not conform to their formal definitions in mechanics, but is intended to distinguish parameters that are purely descriptive of the movement itself from those which reflect its underlying causal forces). Each set of parameters defines a coordinate framework for the description of motor behavior (Hildreth and Hollerbach 1987; Soechting and Flanders 1991; Kalaska 1991a, 1991b). There is, however, no fixed or predetermined relation among the different reference frames. For instance, one can approach a given target location via many different handpaths. One can follow a particular handpath while positioning the arm in different geometrical configurations, i.e., while using different joint angle sequences, and one can use many different patterns of muscle activity to produce any specific sequence of joint angle changes. This is one manifestation of the problems of degrees of freedom and redundancy in the motor system (Bernstein 1967; Hildreth and Hollerbach 1987). Within limits set by biomechanics and the laws of mechanics, the motor system possesses a considerable degree of independent control of movement in each of the reference frames. Intuition suggests, therefore, that the central nervous system must generate multiple neuronal representations of movement, one for each coordinate system, in order to control each attribute of movement.
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Experiments were made on the posterior parietal association cortical areas 5 and in 17 hemispheres of 11 monkeys, 6 M. mulatta and 5 M. arctoides. The electrical signs of the activity of single cortical cells were recorded with microelectrodes in waking animals as they carried out certain behavioral acts in response to a series of sensory cues. The behavioral paradigms were one for detection alone, and a second for detection plus projection of the arm to contact a stationary or moving target placed at arm's length. Of the 125 microelectrode penetrations made, 1,451 neurons were identified in terms of the correlation of their activity with the behavioral acts and their sensitivity or lack of it to sensory stimuli delivered passively; 180 were studied quantitatively. The locations of cortical neurons were identified in serial sections; 94 penetrations and 1,058 neurons were located with certainty. About two-thirds of the neurons of area 5 were activated by passive rotation of the limbs at their joints; of these, 82% were related to single, contralateral joints, 10% to two or more contralateral joints, 6% to ipsilateral, and 2% to joints on both sides of the body. A few of the latter were active during complex bodily postures. A large proportion of area 5 neurons were relatively insensitive to passive joint rotations, as compared with similar neurons of the postcentral gyrus, but were driven to high rates of discharge when the same joint was rotated during an active movement of the animal...
neurophysiological studies of reaching are reviewed with emphasis on recordings from behaving monkeys
the relations between reaching parameters (direction and amplitude) and single-cell activity in the motor and parietal cortices are described
the role of motor cortex in the specifications of reaching and recent advances in spinal circuits that may subserve reaching movements are discussed (PsycINFO Database Record (c) 2012 APA, all rights reserved)
The connections of areas 3, 1 and 2 in the postcentral gyrus of the rhesus monkey are investigated using ablation-degeneration techniques following both full depth lesions and lesions which involved fewer than six cortical layers. Analysis of the topographic and laminar organization of these connections reveal that each of these areas has a differential connection pattern both within the parietal lobe and with respect to motor cortex. Area 3 projects predominantly to area 1 via a horizontal, intracortical fiber system which courses through layers III and V without entering the white matter while other efferents of area 3 to areas 2, 3a and second somatosensory cortex (SII) are less dense and course through the white matter. There is no indication that area 3 efferents terminate in areas 4 or 5. In comparison to area 3, area 1 has a wider projection field. Its primary outflow reaches area 2 via a white matter course while moderately strong connections are directed to areas 3a, S II, 4 and supplementary motor cortex (M II) and a minor projection to area 5. Lesions involving the supragranular layers of area 1 demonstrate that efferents from these layers (II-III) travel directly through the cortex to terminate in layer I of area 3 as well as entering the white matter before terminating in area 2 ventral to the tip of the intraparietal sulcus. Finally, area 2 projects primarily to area 5 via both an intracortical fiber system in layers III-V as well as through the white matter. While area 2 also has connections with areas 1, 3a, S II, 6 and M II, it was not observed to project to area 3. In addition, layers I-IV of area 2 ventral to the intraparietal sulcus send a number of horizontally oriented fibers mainly through layer IIIb to terminate in rostral area 7 (area PF of Bonin and Bailey, '47).
The laminar and tangential distributions of association neurons projecting from areas 4 and 6 of the frontal lobe to area 5 of the superior parietal lobule were studied in macaque monkeys by using horseradish peroxidase histochemistry. In both areas 4 and 6 association neurons were medium-large pyramidal cells of layers II and III, and pyramidal and fusiform cells of layers V-VI. Tangentially, they were distributed unevenly over the cortical surface occupying only certain parts of areas 4 and 6, including the dorsomedial part of area 6, the proximal arm region of Woolsey's M1 map, parts of the postarcuate cortex, and the supplementary motor area. Within these projection zones, the number of projection cells waxed and waned in a periodic fashion.
Quantitative methods, including spectral analysis techniques, were used to characterize precisely spatial periodicities along the rostrocaudal dimension. The same quantitative analyses were used to determine the nature of the tangential distribution of corticocallosal cells of area 5 projecting to contralateral area 5. Both association and callosal spectra contained a strong component in the range of low spatial frequencies, corresponding to periods greater than 2 mm. Moreover, a consistent peak was observed in both spectra at spatial frequencies corresponding to periods ranging from 0.85 to 1.28 mm. This peak is in accord with the hypothesis of a modular organization of the cells of origin of these projection systems.
During a single-step visual tracking task of monkeys, parametric changes of the wrist extension-flexion movement and related discharge rate changes of pyramidal tract neurons (PTNs) of hand-arm motor area were studied. The task consisted of preparatory, precontraction, contraction and target periods. If the displacement amplitude was changed from narrow (10–20°) to moderate (40°) range, peak velocity, peak acceleration and contraction period increased linearly but precontraction period decreased slightly. In 61 movement-related PTNs, no linear relationships were found between PTN discharge rate during precontraction or contraction period and displacement amplitude, velocity, acceleration, precontraction period or contraction period. In less than 20% of PTNs, however, correlations between PTN discharge rate during precontraction period and velocity or acceleration were found in the moderate range task. It occurred less frequently in narrow range task. It is said in a visual tracking task that PTN activity is not dependent upon factors related to the task parameters, such as velocity, acceleration. Possible related factors were discussed.
In awake monkeys performing a wrist flexion-extension task, neurons which responded both dynamically and statically to joint rotation were found in contralateral area 5 of parietal cortex. Most received prominent input arising from deep receptors of the arm. The responses were directionally reciprocal and identical for passive and active joint rotations. In active movements, however, some firing patterns could not be fully explained in terms of sensory input alone. Thus, both peripheral and the postulated central inputs to area 5 may participate in the analysis of relative position of limb parts in space by this cortical region.
Anterograde and retrograde transport methods were used to study the corticocortical connectivity of areas 3a, 3b, 1, 2, 5, 4 and 6 of the monkey cerebral cortex. Fields were identified by cytoarchitectonic features and by thalamic connectivity in the same brains. Area 3a was identified by first recording a short latency group I afferent evoked potential. Attempts were made to analyze the data in terms of: (1) routes whereby somatic sensory input might influence the performance of motor cortex neurons; (2) possible multiple representations of the body surface in the component fields of the first somatic sensory area (SI).
Apart from vertical interlaminar connections, two types of intracortical connectivity are recognized. The first, regarded as “non‐specific,” consists of axons spreading out in layers I, III and V‐VI from all sides of an injection of isotope; these cross architectonic borders indiscrimininately. They are not unique to the regions studied. The second is formed by axons entering the white matter and re‐entering other fields. In these, they terminate in layers I‐IV in one or more mediolaterally oriented strips of fairly constant width (0.5–;1 mm) and separated by gaps of comparable size. Though there is a broadly systematic topography in these projections, the strips are probably best regarded as representing some feature other than receptive field position. Separate representations are nevertheless implied in area 3b, in areas 1 and 2 (together), in areas 3a and 4 (together) and in area 5; with, in each case, the representations of the digits pointed at the central sulcus.
Area 3b is not connected with areas 3a or 4, but projects to a combined areas 1 and 2. Area 1 is reciprocally connected with area 3a and area 2 reciprocally with area 4. The connectivity of area 3a, as conventionally identified, is such that it is probably best regarded not as an entity, but as a part of area 4. Areas identified by others as area 3a should probably be regraded as parts of area 3b. Parts of area 5 that should be more properly considered as area 2, and other parts that receive thalamic input not from the ventrobasal complex but from the lateral nuclear complex and anterior pulvinar, are also interconnected with area 4. More posterior parts of area 5 are connected with laterally placed parts of area 6. A more medial part of area 6, the supplementary motor area, occupies a pivotal position in the sensory‐motor cortex, for it receives fibers from areas 3a, 4, 1, 2 and 5 (all parts), and projects back to areas 3a, 4 and 5.
Unit recordings were performed in the sensorimotor cortex of awake monkeys during performance of learned movements of the contralateral arm. The movements were triggered by a sound stimulus. The neuronal discharge in association with movement was observed before and after deafferentation (C2--T5) of the operant limb. Whereas neurons recorded in the motor cortex (area 4) and in the parietal association cortex (area 5) still modified their activity in relation to movement performed by the deafferented arm, the neurons recorded in the primary sensory cortex (areas3, 1 and 2) showed no activity change in relation to movement after deafferentation. This finding strongly suggests that modification of discharge of postcentral neurons seen during ballistic arm movement is mainly the result of input from the moving limb (peripheral feedback) and not the result of input from other structures of the central nervous system (internal feedback).
1. This study examines the neuronal activity of motor cortical cells associated with the production of arm trajectories during drawing movements. Three monkeys were trained to perform two tasks. The first task ("center----out" task) required the animal to move its arm in different directions from a center start position to one of eight targets spaced at equal angular intervals and equal distances from the origin. Movements to each target were in a constant direction, and the average rate of neuronal discharge with movements to different targets varied in a characteristic pattern. A cosine tuning function was used to map each cell's discharge rate to the direction of arm movement. This function spanned all movement directions, with a peak firing rate in the cell's preferred direction. 2. The second task ("tracing" task) required the animal to trace curved figures consisting of sine waves of different spatial frequencies and amplitudes. Both the speed and direction changed continuously throughout these movements. The cosine tuning function derived from the center----out task was used to model the activity of the cell during the tracing of sinusoids in the second task. Sinusoidal data were divided into 20-ms bins; instantaneous direction, speed, and discharge rate were analyzed bin by bin. This provided a way to compare directly the tuning parameters during a task with constant direction to a task where the direction varied continuously. 3. Movement direction as it changed during the tracing task was an important factor in the discharge pattern of cells that had discharge patterns that could be represented by the cosine tuning function. 4. The modulation of discharge rate during figure tracing depended on both the cell's preferred direction and the orientation of the figure. The activity of cells with preferred directions perpendicular to the axis of the sinusoidal figure was most modulated, whereas the activity of those cells with preferred directions aligned to the figure's axis was least modulated. 5. The cells with modulated activity tended to have firing rates that differed from the predicted cosine tuning function during the sinusoidal movements for those portions of the trajectory where the movement direction was in the cell's preferred direction. 6. Finger speed during figure tracing varied inversely with path curvature with the same relation that has been found during human drawing. To assess the relation of instantaneous speed to discharge rate, the component of the discharge pattern related to direction was subtracted from the total discharge.(ABSTRACT TRUNCATED AT 400 WORDS)
Because reaching movements have a clear objective--to bring the hand to the spatial location of an object--they are well suited
to study how the central nervous system plans a purposeful act from sensory input to motor output. Most models of movement
planning propose a serial hierarchy of analytic steps. However, the central nervous system is organized into densely interconnected
populations of neurons. This paradox between the apparent serial order of central nervous system function and its complex
internal organization is strikingly demonstrated by recent behavioral, modeling, and neurophysiological studies of reaching
movements.
A previous study reported that proximal-arm related area 5 neurons showed continuously-graded changes in activity during unloaded arm movements in different directions (Kalaska et al. 1983), which resembled the responses of primary motor cortex cells in several respects (Georgopoulos et al. 1982). We report here that loading the arm reveals an important difference between cell activity in the two areas. Loads were continuously applied to the arm in different directions. The loads produced large continuously-graded changes in muscle activity but did not alter the handpath or joint angle changes of the arm during the movements. The activity of most area 5 cells was only weakly affected by the loads, and the overall pattern of population activity was virtually unaltered under all load conditions. This indicates that area 5 activity encodes the invariant spatial parameters (kinematics) of the movements. In contrast, many motor cortex cells showed large changes in activity during loading, and so signal the changing forces, torques or muscle activity (movement dynamics; Kalaska et al. 1989).
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1. Bedingham and Tatton recently reported that in cats trained not to resist imposed limb perturbations, some motor cortex (area 4) neurons responded predominantly to acceleration or jerk (the third derivative of position). The questions arose whether motor cortex neurons responding to higher derivatives of limb displacement exist in the primate in a resist-perturbation task and, if so, whether discharge of such neurons responds to the same kinematics in active (voluntary) movements. 2. To answer these questions we studied the discharge patterns of 203 motor cortex neurons that responded to torque pulse perturbations about the elbow and fired during active elbow flexions and extensions in four monkeys. Detailed analysis was performed on 66 neurons that responded reciprocally in both situations. 3. Reciprocal neurons discharged at short latency (20-40 ms) for one direction of arm perturbation. For the opposite direction they were initially silent or inhibited and then discharged at a variety of latencies but in apparent relation to limb kinematics. Based on the timing and overall pattern of their discharge the majority of neurons (68%) were classified as being acceleration-like. 4. Twenty-four (36%) of these reciprocal neurons had only sensory (kinematic)-like properties in active movements, i.e., they discharged after (and not before) movement onset. Discharge of these neurons followed the timing, but not the magnitude, of acceleration (20 neurons) or velocity (4 neurons). The discharge of these neurons also had a static component as the arm was held stationary. 5. Twenty-nine (44%) of reciprocal neurons commenced firing before movement onset for one direction of active movement, while for the opposite direction their discharge occurred after movement onset. Thus their discharge appeared to be muscle-related: both when the muscle was contracting as an agonist and stretched as an antagonist. 6. Although in these tasks discharge of MCNs could be generated either by sensory feedback or by motor responses, the strong response sensitivity of many neurons to acceleration supports the hypothesis that feedback based on higher derivatives of limb displacement could represent a "predictive" control system for accurate regulation of limb motion.
The activity of identified tecto-reticulo-spinal neurons (TRSNs) was studied in alert head-fixed cats during orienting towards moving visual stimuli. Eye movements and dorsal neck muscle activity were recorded simultaneously. Burst parameters of TRSNs showing visuomotor properties were analysed quantitatively. It could be demonstrated that some neurons generate presaccadic bursts whose instantaneous frequency profile is closely correlated with the profile of saccadic eye velocity. This correlation could be revealed only under conditions in which cats made orienting saccades to 'catch' a target moving in the preferred direction of the neuron's visual receptive field. Latency between bursts and saccades varied depending upon the degree of attention toward the target and saccade direction.
In eight awake cats, elbow flexion movements were imposed by a computer-controlled torque motor using three different classes of angular displacement inputs: force step-load displacements; sinusoidal displacements; and constant-velocity ramp displacements. Microelectrode recordings were obtained from 309 pericruciate neurons in areas 4 and 3a. Average response histograms for single-unit activity coupled with computer simulation of the imposed movements have shown in a neuronal population (n = 81), selected for receptive fields that were directly related to elbow movements, that both the magnitude and temporal features of the responses can be characterized by the coefficients of a third-order differential equation describing the movement's angular kinematics (i.e., position, velocity, acceleration, and jerk). To compare the responses of different neurons the coefficients were normalized to the angular velocity coefficient, which was assigned a weighted value of 1.0. The neurons' average responses were "predictable" by the normalized coefficients regardless of the imposed movements' temporal characteristics. Two distinct and spatially separate pericruciate areas containing neurons that responded to the imposed forearm movements were located: 1) one within area 4 at the lateral extent of the cruciate sulcus, which contained neurons that responded with predominant jerk and acceleration coefficients, exhibited either cutaneous or deep receptive fields, and demonstrated low microstimulation current thresholds to activate forelimb muscles; 2) a second, more laterally located area near the 3a/4 border in the postsygmoid gyrus, which contained neurons that responded with predominant velocity coefficients, and comparatively small jerk acceleration, and position coefficients, exhibited either cutaneous or deep receptive fields, and demonstrated high microstimulation thresholds (greater than 20 microA). Due to the sensitivity of the higher derivatives to changes in motion, the relative magnitude and time course of the average firing probability of area 4 neurons with prominent acceleration and jerk coefficients were dominated by these kinematic features during the more rapidly imposed movements. The findings are in accord with a hypothesis proposing that motor cortical neurons in area 4 form a sufficient substrate for a "predictive" feedback organization, and may constitute an essential component of a system capable of regulating errors in angular joint movements despite the relatively long conduction delays and the slow time course of muscle tension production inherent to mammalian neuromuscular systems.
Onset times of neural responses (NRs) in relation to visually triggered muscle activity were compared for precentral and postcentral neurons in monkeys carrying out learned movements for juice reward. Each neuron was observed for 2 reciprocal movements, and of 563 neurons studied, 418 had NRs for both movements, giving rise to a total of 981 NRs whose onset times were determined. Of the 563 neurons, 210 were in postcentral and 353 in precentral cortex. Of the 353 precentral neurons, 93 had antidromic responses to stimulation of the medullary pyramid and were therefore classified as pyramidal tract neurons (PTNs). Precentral NR onset times preceded muscular discharge by about 60 ms, whereas postcentral NRs began concurrently with muscular discharge. Within the group of precentral neurons, NR onset times were the same for PTNs as compared to non PTNs. While showing a clear temporal differentiation between pre and postcentral areas, the results are ambiguous as to whether or not some postcentral activity may occur prior to the first muscular activity.
Recordings have been obtained simultaneously from several, individually selected neurons in the motor cortex of unanesthetized
monkey as the animal performed simple arm movements. With the use of comparatively simple quantitative procedures, the activity
of small sets of cells was found to be adequate for rather accurate real-time prediction of the time course of various response
measurements. In addition, the results suggest that hypotheses concerning the response variables "controlled" by cortical
motor systems may well depend upon whether or not the temporal relations between simultaneously active neurons are taken into
account.
Unitary discharge was recorded from 157 cells in area 5 of 2 monkeys trained to perform rapid movements of the contralateral arm. Ninety-six cells were task-related. The earliest movement-related modulation in discharge for the large majority of cells (92%) followed the onset of electromyographic (EMG) activity. The discharge pattern of almost all units for which discharge was recorded during movements in opposite directions varied with direction, most often in a nonreciprocal manner. Discharge was correlated with peak velocity in 23% of the excited cells (n = 52). Almost the entire population of cells correlated with velocity were located in the upper part of the anterior bank of the intraparietal sulcus, suggesting that there may be at least two different functional subregions within the arm representation of area 5. Forty percent of the movement-related units had a short latency response to a small, brief perturbation of the elbow which served as one of the movement cues. These sensory responses were labile, not being present in every trial for a large number of cells. Thirty-six percent of the perturbation-sensitive cells were classified as reaction time (RT)-dependent on the basis of a correlation between RT and either the magnitude or the frequency of occurrence of the response. The response was clearly dependent on the subsequent motor response being absent when movement was extinguished. This dependence of the sensory response on the subsequent movement is a property which might represent a neural substrate for somatic sensory attention. The results also support the idea that the RT-dependent cells may be involved in the initiation of the shortest RT movements in response to the somaesthetic cue.
The relations between the direction of two-dimensional arm movements and single cell discharge in area 5 were investigated during 49 penetrations into the superior parietal lobule of 3 monkeys. A significant variation of cell discharge with the direction of movement was observed in 182 of 212 cells that were related to arm movements. In 151/182 of these cells the frequency of discharge was highest during movements in a preferred direction, and decreased in an orderly fashion with movements made in directions farther and farther away from the preferred one; in 112/151 cells this variation in discharge was a sinusoidal function of the direction of movement. Preferred directions differed for different cells so that directional tuning curves overlapped partially. These results are similar to those described for cells in the motor cortex (Georgopoulos et al. 1982): this suggests that directional information may be processed in a similar way in these structures. Many cells in area 5 changed activity before the onset of movement, and several did so before the earliest electromyographic changes (63% and 35%, respectively, of the cells that showed an increase in activity with movements in the preferred direction). However, the distribution of onset times of the parietal cells lagged the corresponding one of the motor cortical cells by about 60 ms. This suggests that the early changes observed in the parietal cortex might represent a corollary discharge from the precentral motor fields, whereas later activity might reflect peripheral as well as central events.
We describe the relations between active maintenance of the hand at various positions in a two-dimensional space and the frequency of single cell discharge in motor cortex (n = 185) and area 5 (n = 128) of the rhesus monkey. The steady-state discharge rate of 124/185 (67%) motor cortical and 105/128 (82%) area 5 cells varied with the position in which the hand was held in space ("static spatial effect"). The higher prevalence of this effect in area 5 was statistically significant. In both structures, static effects were observed at similar frequencies for cells that possessed as well as for those that lacked passive driving from the limb. The results obtained by a quantitative analysis were similar for neurons of the two cortical areas studied. It was found that of the neurons with a static effect, the steady-state discharge rate of 78/124 (63%) motor cortical and 63/105 (60%) area 5 cells was a linear function of the position of the hand across the two-dimensional space, so that the neuronal "response surface" was adequately described by a plane (R2 greater than or equal to 0.7, p less than 0.05, F-test in analysis of variance). The preferred orientations of these response planes differed for different cells. These results indicate that individual cells in these areas do not relate uniquely a particular position of the hand in space. Instead, they seem to encode spatial gradients at certain orientations.(ABSTRACT TRUNCATED AT 250 WORDS)
The activity of 41 pyramidal tract neurons (PTNs) within the hand-arm area of the monkey motor cortex was studied during a rapid wrist flexion movement at specified velocities. The discharge rate during the movement showed significant correlations to the movement velocity in 21 PTNs, but the rate before the movement onset did not show correlations. Therefore the PTN activity before the movement onset in the rapid wrist flexion is not coding the velocity of the movement.
Unit recordings were performed in the postcentral cortex and focused on area 5 of awake monkeys during the execution of a learned movement of the contralateral forearm so that the time relationship between the motor act and any modification of neuronal activity could be precisely correlated. Recordings were obtained from intact animals (561 neurons) and after deafferentation (C1-T7) of the trained limb (344 neurons). Of the movement-related neurons in normal animals, 243 cells were located in area 5 and these cells were divided into two populations. The first population (66% of movement-related neurons) presented modifications of activity after the onset of movement and receptive fields, often complex, were identifiable for these somaesthetic-like cells. No such neurons were found in the same cortical area after deafferentation. The second population (34% of movement-related neurons) presented modifications of activity related to movement but these changes occurred well before the onset of movement, up to 280 ms before. These cells were also characterized by an absence of sensory modulation and they represented the entire population of movement-related neurons recorded in area 5 after deafferentation (124 neurons). The first population appears to subserve a complex somaesthetic function. The second population is subject to purely central influences which, in part, may be due to corollary discharge or internal feedback. However, this population most likely represents a command apparatus for movement located 'upstream' to the motor cortex.
1. Single-unit neuronal activity was recorded in the primary motor and superior precentral premotor areas of two rhesus monkeys during an arm reaching task. The task involved moving a cursor displayed on a video terminal using a draftsman's arm-type manipulandum. From a centrally located start box the animal was required to move to 1 of 48 target boxes at eight different directions (0-360 degrees in 45 degrees intervals) and six distances (1.4-5.4 cm in 0.8-cm increments). Both direction and distance for the upcoming movement were unpredictable. 2. The activity of 197 arm movement-related cells was recorded and evaluated for each of the 48 targets. Histological examination showed the cells to be primarily in the primary motor cortex or in the premotor area around the superior precentral sulcus. Each cell's discharge was aligned on movement onset and averaged over five trials for each target. Movement kinematics including hand path velocity were also determined. The task time was divided into three epochs, a premovement period (PT), a movement period (MT), and total time (TT = PT+MT). For each epoch the average firing was correlated with the direction and distance of the movement using various regression procedures. 3. An analysis of variance (ANOVA) showed that the majority of neurons were modulated significantly by movement direction in each of the three time periods, PT (73.7%), MT (68.3%), and TT (78.5%). The relationship of the firing to direction was fit to a cosine tuning function for each significantly modulated cell. In 86.3% of the cells the firing was correlated significantly with a cosine function of movement direction in TT. A cell's preferred direction varied little for different movement distances. The mean difference in preferred direction for the smallest possible change in distance (0.8 cm) was 12.8 +/- 11.4 degrees (SD) and 17.1 +/- 14.7 degrees for the largest change in distance (4.0 cm). 4. Correlation analysis revealed that the activity of the majority of cells was modulated significantly by distance along at least one direction in each of the three time periods, PT (46.8%), MT (68.8%), and TT (67.7%). Subsequently, a univariate linear regression model was used to quantify a cell's discharge as a function of distance. For the regressions of firing with distance with a statistically significant correlation (r > 0.8), the mean slope was 3.59 +/- 0.17 spikes.s-1.cm-1 for the total time. The existence of a significant distance modulation was not invariably correlated with a cell's preferred movement direction.(ABSTRACT TRUNCATED AT 400 WORDS)
1. Monkeys were trained to trace sinusoids with their index fingers on a planar surface. During this task, both the direction and speed of movement varied continuously. Activity of individual units in the precentral gyrus contralateral to the moving arm was recorded as the task was performed. These cells responded to passive movement of the shoulder and/or elbow. The relation between discharge rate and movement direction for these individual cells could be described with a cosine tuning function. 2. Data recorded as the sinusoid was traced were divided into 100 bins as each cell was studied during the experiment. In each bin, the activity of a particular cell was represented by a vector. The vector ("cell vector") pointed in the direction of finger movement that corresponded to the highest rate of neuronal discharge. This direction, referred to as the preferred direction, corresponded to the peak of the cosine tuning function. The direction of the vector was constant between bins, but the magnitude of this cell's vector was a function of the instantaneous discharge rate. 3. This cell vector is a hypothetical contribution of a single cell to the population response comprised of 554 similarly derived vectors from different cells. The population response was represented as the vector that resulted from forming the sum of the vector contributions from the individual cells. A separate calculation was made for each bin, resulting in 100 population vectors for each sinusoid. 4. Within a given time series of population vectors, their lengths and directions varied in a consistent relation to the tangential velocity of the drawing movement.(ABSTRACT TRUNCATED AT 250 WORDS)
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