Neural population partitioning and a concurrent brain-machine interface for sequential motor function

1] Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. [2] Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA. [3] Harvard Medical School, Boston, Massachusetts, USA.
Nature Neuroscience (Impact Factor: 16.1). 11/2012; 15(12). DOI: 10.1038/nn.3250
Source: PubMed


Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.

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Available from: Ziv Williams, Mar 17, 2014
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    • "In addition, PMd is thought to play an important role in integrating both effectorand spatial goal-related signals for reaching (Cisek et al. 2003; Hoshi and Tanji 2006; Pesaran et al. 2006; Beurze et al. 2010; Gallivan , McLean, Smith, et al. 2011; Gallivan, McLean, Flanagan, et al. 2013). When considering the planning and execution of limb-or hand-related movement sequences, however, the activity of PMd has not often been considered (though see Kettner et al. 1996; Shanechi et al. 2012; Wiestler and Diedrichsen 2013; Kornysheva and Diedrichsen 2014). Thus, a goal of the present study was to fully characterize the activity of PMd in the context of preparing different object-directed action sequences. "
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    ABSTRACT: Object-manipulation tasks (e.g., drinking from a cup) typically involve sequencing together a series of distinct motor acts (e.g., reaching toward, grasping, lifting, and transporting the cup) in order to accomplish some overarching goal (e.g., quenching thirst). Although several studies in humans have investigated the neural mechanisms supporting the planning of visually guided movements directed toward objects (such as reaching or pointing), only a handful have examined how manipulatory sequences of actions-those that occur after an object has been grasped-are planned and represented in the brain. Here, using event-related functional MRI and pattern decoding methods, we investigated the neural basis of real-object manipulation using a delayed-movement task in which participants first prepared and then executed different object-directed action sequences that varied either in their complexity or final spatial goals. Consistent with previous reports of preparatory brain activity in non-human primates, we found that activity patterns in several frontoparietal areas reliably predicted entire action sequences in advance of movement. Notably, we found that similar sequence-related information could also be decoded from pre-movement signals in object- and body-selective occipitotemporal cortex (OTC). These findings suggest that both frontoparietal and occipitotemporal circuits are engaged in transforming object-related information into complex, goal-directed movements. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
    Cerebral Cortex 01/2015; DOI:10.1093/cercor/bhu302 · 8.67 Impact Factor
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    • "Studies of different types of goaldirected movements, eg. movements to targets, sequential hand movements or movements following prescribed paths indicated that the serial order of sub- movement 1 , " aspects of movement " (meaning aspects of movement shape and target location) and movement fragments are represented in cortical activity [33] [1] [2] [32] [67] [31]. Evidence for important role of non-euclidian geometry, and in particular equi-affine [53] [27] [54] [57] [55] [41] [16] [11] [58] [56] [42] [52] [8] [43] and affine [4] [51], in production and perception of biological motion was pro- vided. "
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    ABSTRACT: Neuroscientific studies of the drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about empirically supported mathematical ideas behind splitting and merging geometric and temporal features which characterize biological movements. Movement primitives supposedly facilitate the efficiency of movements' representation in the brain and comply with different criteria for biological movements, among them kinematic smoothness and geometric constraint. Criterion for trajectories' maximal smoothness of arbitrary order n is employed, n = 3 is the case of the minimum-jerk model. I derive a class of differential equations obeyed by movement paths for which nth order maximally smooth trajectories have constant rate of accumulating geometric measurement along the drawn path. The geometric measurement is invariant under a class of geometric transformations and may be chosen to be an arc in certain geometry. For example the two-thirds power-law model corresponds to piece-wise con-stant speed of accumulating equi-affine arc. Equations' solutions presumably serve as candidates for geometric movement primitives. The derived class of differential equations consists of two parts. The first part is identical for all geometric parameterizations of the path. The second part is parametrization specific and is needed to identify whether a solution of the first part indeed represents a curve. Counter-examples are provided. Equations in different geometries in plane and in space and their known solutions are presented. A method for constructing trajectories based on primitives in different geometries is proposed. The derived class of differential equations is a novel tool for discovering candidates for geometric movement primitives.
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    • "When tested in the laboratory, computer or motor-based interfaces tend to only involve series of individual targets (Velliste et al 2008, Collinger et al 2012, Gilja et al 2012, Hauschild et al 2012). When multiple targets have been used, the effects of the greater task complexity themselves were not evaluated (O'Doherty et al 2011, Ifft et al 2013) or the cognitive component of the tasks have been minor (Hochberg et al 2006, Shanechi et al 2012), limiting clinical usefulness. These studies do not replicate the function of a modern GUI interface, i.e. selecting a remembered or desired target from a group of alternatives. "
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    ABSTRACT: Objective: To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach: A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like 'Face in a Crowd' task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the 'Crowd') using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a 'Crowd Off' condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results: Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance: Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.
    Journal of Neural Engineering 11/2014; 11(6):066014. DOI:10.1088/1741-2560/11/6/066014 · 3.30 Impact Factor
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