Planning Ahead: Object-Directed Sequential Actions Decoded from Human Frontoparietal and Occipitotemporal Networks.

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

Motor, not visual, encoding of potential reach targets

SummaryWe often encounter situations in which there are multiple potential targets for action, as when, for example, we hear the request “could you pass the …” at the dinner table. It has recently been shown that, in such situations, activity in sensorimotor brain areas represents competing reach targets in parallel prior to deciding between, and then reaching towards, one of these targets [1]. One intriguing possibility, consistent with the influential notion of action ‘affordances’ [2], is that this activity reflects movement plans towards each potential target [3]. However, an equally plausible explanation is that this activity reflects an encoding of the visual properties of the potential targets (for example, their locations or directions), prior to any target being selected and the associated movement plan being formed. Notably, previous work showing spatial averaging behaviour during reaching, in which initial movements are biased towards the midpoint of the spatial distribution of potential targets [4–6], remains equally equivocal concerning the motor versus visual encoding of reach targets. Here, using a rapid reaching task that disentangles these two competing accounts, we show that reach averaging behaviour reflects the parallel encoding of multiple competing motor plans. This provides direct evidence for theories proposing that the brain prepares multiple available movements before selecting between them [3]

Human decision making anticipates future performance in motor learning.

It is well-established that people can factor into account the distribution of their errors in motor performance so as to optimize reward. Here we asked whether, in the context of motor learning where errors decrease across trials, people take into account their future, improved performance so as to make optimal decisions to maximize reward. One group of participants performed a virtual throwing task in which, periodically, they were given the opportunity to select from a set of smaller targets of increasing value. A second group of participants performed a reaching task under a visuomotor rotation in which, after performing a initial set of trials, they selected a reward structure (ratio of points for target hits and misses) for different exploitation horizons (i.e., numbers of trials they might be asked to perform). Because movement errors decreased exponentially across trials in both learning tasks, optimal target selection (task 1) and optimal reward structure selection (task 2) required taking into account future performance. The results from both tasks indicate that people anticipate their future motor performance so as to make decisions that will improve their expected future reward

Motor Planning Modulates Neural Activity Patterns in Early Human Auditory Cortex

It is well established that movement planning recruits motor-related cortical brain areas in preparation for the forthcoming action. Given that an integral component to the control of action is the processing of sensory information throughout movement, we predicted that movement planning might also modulate early sensory cortical areas, readying them for sensory processing during the unfolding action. To test this hypothesis, we performed 2 human functional magnetic resonance imaging studies involving separate delayed movement tasks and focused on premovement neural activity in early auditory cortex, given the area\u27s direct connections to the motor system and evidence that it is modulated by motor cortex during movement in rodents. We show that effector-specific information (i.e., movements of the left vs. right hand in Experiment 1 and movements of the hand vs. eye in Experiment 2) can be decoded, well before movement, from neural activity in early auditory cortex. We find that this motor-related information is encoded in a separate subregion of auditory cortex than sensory-related information and is present even when movements are cued visually instead of auditorily. These findings suggest that action planning, in addition to preparing the motor system for movement, involves selectively modulating primary sensory areas based on the intended action

Interpolatory methods for H∞\mathcal{H}_\infty model reduction of multi-input/multi-output systems

We develop here a computationally effective approach for producing high-quality $\mathcal{H}_\infty$-approximations to large scale linear dynamical systems having multiple inputs and multiple outputs (MIMO). We extend an approach for $\mathcal{H}_\infty$ model reduction introduced by Flagg, Beattie, and Gugercin for the single-input/single-output (SISO) setting, which combined ideas originating in interpolatory $\mathcal{H}_2$-optimal model reduction with complex Chebyshev approximation. Retaining this framework, our approach to the MIMO problem has its principal computational cost dominated by (sparse) linear solves, and so it can remain an effective strategy in many large-scale settings. We are able to avoid computationally demanding $\mathcal{H}_\infty$ norm calculations that are normally required to monitor progress within each optimization cycle through the use of "data-driven" rational approximations that are built upon previously computed function samples. Numerical examples are included that illustrate our approach. We produce high fidelity reduced models having consistently better $\mathcal{H}_\infty$ performance than models produced via balanced truncation; these models often are as good as (and occasionally better than) models produced using optimal Hankel norm approximation as well. In all cases considered, the method described here produces reduced models at far lower cost than is possible with either balanced truncation or optimal Hankel norm approximation

Parallel specification of competing sensorimotor control policies for alternative action options.

Recent theory proposes that the brain, when confronted with several action possibilities, prepares multiple competing movements before deciding among them. Psychophysical supporting evidence for this idea comes from the observation that when reaching towards multiple potential targets, the initial movement is directed towards the average location of the targets, consistent with multiple prepared reaches being executed simultaneously. However, reach planning involves far more than specifying movement direction; it requires the specification of a sensorimotor control policy that sets feedback gains shaping how the motor system responds to errors induced by noise or external perturbations. Here we found that, when a subject is reaching towards multiple potential targets, the feedback gain corresponds to an average of the gains specified when reaching to each target presented alone. Our findings provide evidence that the brain, when presented with multiple action options, computes multiple competing sensorimotor control policies in parallel before implementing one of them

Muting, not fragmentation, of functional brain networks under general anesthesia

© 2021 Changes in resting-state functional connectivity (rs-FC) under general anesthesia have been widely studied with the goal of identifying neural signatures of consciousness. This work has commonly revealed an apparent fragmentation of whole-brain network structure during unconsciousness, which has been interpreted as reflecting a break-down in connectivity and a disruption of the brain\u27s ability to integrate information. Here we show, by studying rs-FC under varying depths of isoflurane-induced anesthesia in nonhuman primates, that this apparent fragmentation, rather than reflecting an actual change in network structure, can be simply explained as the result of a global reduction in FC. Specifically, by comparing the actual FC data to surrogate data sets that we derived to test competing hypotheses of how FC changes as a function of dose, we found that increases in whole-brain modularity and the number of network communities – considered hallmarks of fragmentation – are artifacts of constructing FC networks by thresholding based on correlation magnitude. Taken together, our findings suggest that deepening levels of unconsciousness are instead associated with the increasingly muted expression of functional networks, an observation that constrains current interpretations as to how anesthesia-induced FC changes map onto existing neurobiological theories of consciousness

Lattice-Boltzmann and finite-difference simulations for the permeability for three-dimensional porous media

Numerical micropermeametry is performed on three dimensional porous samples having a linear size of approximately 3 mm and a resolution of 7.5 $\mu$m. One of the samples is a microtomographic image of Fontainebleau sandstone. Two of the samples are stochastic reconstructions with the same porosity, specific surface area, and two-point correlation function as the Fontainebleau sample. The fourth sample is a physical model which mimics the processes of sedimentation, compaction and diagenesis of Fontainebleau sandstone. The permeabilities of these samples are determined by numerically solving at low Reynolds numbers the appropriate Stokes equations in the pore spaces of the samples. The physical diagenesis model appears to reproduce the permeability of the real sandstone sample quite accurately, while the permeabilities of the stochastic reconstructions deviate from the latter by at least an order of magnitude. This finding confirms earlier qualitative predictions based on local porosity theory. Two numerical algorithms were used in these simulations. One is based on the lattice-Boltzmann method, and the other on conventional finite-difference techniques. The accuracy of these two methods is discussed and compared, also with experiment.Comment: to appear in: Phys.Rev.E (2002), 32 pages, Latex, 1 Figur

Grip force when reaching with target uncertainty provides evidence for motor optimization over averaging.

When presented with competing potential reach targets and required to launch a movement before knowing which one will be cued as the target, people initially reach in the average target direction. Although this spatial averaging could arise from executing a weighted average of motor plans for the potential targets, it could also arise from planning a single, optimal movement. To test between these alternatives we used a task in which participants were required to reach to either a single target or towards two potential targets while grasping an object. A robotic device applied a lateral elastic load to the object requiring large grip forces for reaches to targets either side of midline and a minimal grip force for midline movements. As expected, in trials with two targets located either side of midline, participants initially reached straight ahead. Critically, on these trials the initial grip force was minimal, appropriate for the midline movement, and not the average of the large grip forces required for movements to the individual targets. These results indicate that under conditions of target uncertainty, people do not execute an average of planned actions but rather a single movement that optimizes motor costs