Literature review and protocol for a prospective multicentre cohort study on multimodal prediction of seizure recurrence after unprovoked first seizure.

INTRODUCTION: Epilepsy is a common neurological disorder characterised by recurrent seizures. Almost half of patients who have an unprovoked first seizure (UFS) have additional seizures and develop epilepsy. No current predictive models exist to determine who has a higher risk of recurrence to guide treatment. Emerging evidence suggests alterations in cognition, mood and brain connectivity exist in the population with UFS. Baseline evaluations of these factors following a UFS will enable the development of the first multimodal biomarker-based predictive model of seizure recurrence in adults with UFS. METHODS AND ANALYSIS: 200 patients and 75 matched healthy controls (aged 18-65) from the Kingston and Halifax First Seizure Clinics will undergo neuropsychological assessments, structural and functional MRI, and electroencephalography. Seizure recurrence will be assessed prospectively. Regular follow-ups will occur at 3, 6, 9 and 12 months to monitor recurrence. Comparisons will be made between patients with UFS and healthy control groups, as well as between patients with and without seizure recurrence at follow-up. A multimodal machine-learning model will be trained to predict seizure recurrence at 12 months. ETHICS AND DISSEMINATION: This study was approved by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen's University (DMED-2681-22) and the Nova Scotia Research Ethics Board (1028519). It is supported by the Canadian Institutes of Health Research (PJT-183906). Findings will be presented at national and international conferences, published in peer-reviewed journals and presented to the public via patient support organisation newsletters and talks. TRIAL REGISTRATION NUMBER: NCT05724719.

Interaction between decision-making and motor learning when selecting reach targets in the presence of bias and noise.

Motor errors can have both bias and noise components. Bias can be compensated for by adaptation and, in tasks in which the magnitude of noise varies across the environment, noise can be reduced by identifying and then acting in less noisy regions of the environment. Here we examine how these two processes interact when participants reach under a combination of an externally imposed visuomotor bias and noise. In a center-out reaching task, participants experienced noise (zero-mean random visuomotor rotations) that was target-direction dependent with a standard deviation that increased linearly from a least-noisy direction. They also experienced a constant bias, a visuomotor rotation that varied (across groups) from 0 to 40 degrees. Critically, on each trial, participants could select one of three targets to reach to, thereby allowing them to potentially select targets close to the least-noisy direction. The group who experienced no bias (0 degrees) quickly learned to select targets close to the least-noisy direction. However, groups who experienced a bias often failed to identify the least-noisy direction, even though they did partially adapt to the bias. When noise was introduced after participants experienced and adapted to a 40 degrees bias (without noise) in all directions, they exhibited an improved ability to find the least-noisy direction. We developed two models-one for reach adaptation and one for target selection-that could explain participants' adaptation and target-selection behavior. Our data and simulations indicate that there is a trade-off between adaptation and selection. Specifically, because bias learning is local, participants can improve performance, through adaptation, by always selecting targets that are closest to a chosen direction. However, this comes at the expense of improving performance, through selection, by reaching toward targets in different directions to find the least-noisy direction.

Grasping with a Twist: Dissociating Action Goals from Motor Actions in Human Frontoparietal Circuits.

In daily life, prehension is typically not the end goal of hand-object interactions but a precursor for manipulation. Nevertheless, functional MRI (fMRI) studies investigating manual manipulation have primarily relied on prehension as the end goal of an action. Here, we used slow event-related fMRI to investigate differences in neural activation patterns between prehension in isolation and prehension for object manipulation. Sixteen (seven males and nine females) participants were instructed either to simply grasp the handle of a rotatable dial (isolated prehension) or to grasp and turn it (prehension for object manipulation). We used representational similarity analysis (RSA) to investigate whether the experimental conditions could be discriminated from each other based on differences in task-related brain activation patterns. We also used temporal multivoxel pattern analysis (tMVPA) to examine the evolution of regional activation patterns over time. Importantly, we were able to differentiate isolated prehension and prehension for manipulation from activation patterns in the early visual cortex, the caudal intraparietal sulcus (cIPS), and the superior parietal lobule (SPL). Our findings indicate that object manipulation extends beyond the putative cortical grasping network (anterior intraparietal sulcus, premotor and motor cortices) to include the superior parietal lobule and early visual cortex.SIGNIFICANCE STATEMENT A simple act such as turning an oven dial requires not only that the CNS encode the initial state (starting dial orientation) of the object but also the appropriate posture to grasp it to achieve the desired end state (final dial orientation) and the motor commands to achieve that state. Using advanced temporal neuroimaging analysis techniques, we reveal how such actions unfold over time and how they differ between object manipulation (turning a dial) versus grasping alone. We find that a combination of brain areas implicated in visual processing and sensorimotor integration can distinguish between the complex and simple tasks during planning, with neural patterns that approximate those during the actual execution of the action.

Visual search for reach targets in actionable space is influenced by movement costs imposed by obstacles.

Real world search tasks often involve action on a target object once it has been located. However, few studies have examined whether movement-related costs associated with acting on located objects influence visual search. Here, using a task in which participants reached to a target object after locating it, we examined whether people take into account obstacles that increase movement-related costs for some regions of the reachable search space but not others. In each trial, a set of 36 objects (4 targets and 32 distractors) were displayed on a vertical screen and participants moved a cursor to a target after locating it. Participants had to fixate on an object to determine whether it was a target or distractor. A rectangular obstacle, of varying length, location, and orientation, was briefly displayed at the start of the trial. Participants controlled the cursor by moving the handle of a robotic manipulandum in a horizontal plane. The handle applied forces to simulate contact between the cursor and the unseen obstacle. We found that search, measured using eye movements, was biased to regions of the search space that could be reached without moving around the obstacle. This result suggests that when deciding where to search, people can incorporate the physical structure of the environment so as to reduce the movement-related cost of subsequently acting on the located target.

Whole-brain dynamics of human sensorimotor adaptation.

Humans vary greatly in their motor learning abilities, yet little is known about the neural processes that underlie this variability. We identified distinct profiles of human sensorimotor adaptation that emerged across 2 days of learning, linking these profiles to the dynamics of whole-brain functional networks early on the first day when cognitive strategies toward sensorimotor adaptation are believed to be most prominent. During early learning, greater recruitment of a network of higher-order brain regions, involving prefrontal and anterior temporal cortex, was associated with faster learning. At the same time, greater integration of this "cognitive network" with a sensorimotor network was associated with slower learning, consistent with the notion that cognitive strategies toward adaptation operate in parallel with implicit learning processes of the sensorimotor system. On the second day, greater recruitment of a network that included the hippocampus was associated with faster learning, consistent with the notion that declarative memory systems are involved with fast relearning of sensorimotor mappings. Together, these findings provide novel evidence for the role of higher-order brain systems in driving variability in adaptation.

The influence of movement-related costs when searching to act and acting to search.

Real-world search behavior often involves limb movements, either during search or after search. Here we investigated whether movement-related costs influence search behavior in two kinds of search tasks. In our visual search tasks, participants made saccades to find a target object among distractors and then moved a cursor, controlled by the handle of a robotic manipulandum, to the target. In our manual search tasks, participants moved the cursor to perform the search, placing it onto objects to reveal their identity as either a target or a distractor. In all tasks, there were multiple targets. Across experiments, we manipulated either the effort or time costs associated with movement such that these costs varied across the search space. We varied effort by applying different resistive forces to the handle, and we varied time costs by altering the speed of the cursor. Our analysis of cursor and eye movements during manual and visual search, respectively, showed that effort influenced manual search but did not influence visual search. In contrast, time costs influenced both visual and manual search. Our results demonstrate that, in addition to perceptual and cognitive factors, movement-related costs can also influence search behavior.NEW & NOTEWORTHY Numerous studies have investigated the perceptual and cognitive factors that influence decision making about where to look, or move, in search tasks. However, little is known about how search is influenced by movement-related costs associated with acting on an object once it has been visually located or acting during manual search. In this article, we show that movement time costs can bias visual and manual search and that movement effort costs bias manual search.

Distinct patterns of cortical manifold expansion and contraction underlie human sensorimotor adaptation.

Sensorimotor learning is a dynamic, systems-level process that involves the combined action of multiple neural systems distributed across the brain. Although much is known about the specialized cortical systems that support specific components of action (such as reaching), we know less about how cortical systems function in a coordinated manner to facilitate adaptive behavior. To address this gap, our study measured human brain activity using functional MRI (fMRI) while participants performed a classic sensorimotor adaptation task and used a manifold learning approach to describe how behavioral changes during adaptation relate to changes in the landscape of cortical activity. During early adaptation, areas in the parietal and premotor cortices exhibited significant contraction along the cortical manifold, which was associated with their increased covariance with regions in the higher-order association cortex, including both the default mode and fronto-parietal networks. By contrast, during Late adaptation, when visuomotor errors had been largely reduced, a significant expansion of the visual cortex along the cortical manifold was associated with its reduced covariance with the association cortex and its increased intraconnectivity. Lastly, individuals who learned more rapidly exhibited greater covariance between regions in the sensorimotor and association cortices during early adaptation. These findings are consistent with a view that sensorimotor adaptation depends on changes in the integration and segregation of neural activity across more specialized regions of the unimodal cortex with regions in the association cortex implicated in higher-order processes. More generally, they lend support to an emerging line of evidence implicating regions of the default mode network (DMN) in task-based performance.

Spontaneous Behavioural Recovery Following Stroke Relates to the Integrity of Parietal and Temporal Regions.

Stroke is a devastating disease that results in neurological deficits and represents a leading cause of death and disability worldwide. Following a stroke, there is a degree of spontaneous recovery of function, the neural basis of which is of great interest among clinicians in their efforts to reduce disability following stroke and enhance rehabilitation. Conventionally, work on spontaneous recovery has tended to focus on the neural reorganization of motor cortical regions, with comparably little attention being paid to changes in non-motor regions and how these relate to recovery. Here we show, using structural neuroimaging in a macaque stroke model (N = 31) and by exploiting individual differences in spontaneous behavioural recovery, that the preservation of regions in the parietal and temporal cortices predict animal recovery. To characterize recovery, we performed a clustering analysis using Non-Human Primate Stroke Scale (NHPSS) scores and identified a good versus poor recovery group. By comparing the preservation of brain volumes in the two groups, we found that brain areas in integrity of brain areas in parietal, temporal and somatosensory cortex were associated with better recovery. In addition, a decoding approach performed across all subjects revealed that the preservation of specific brain regions in the parietal, somatosensory and medial frontal cortex predicted recovery. Together, these findings highlight the importance of parietal and temporal regions in spontaneous behavioural recovery.

Neural excursions from manifold structure explain patterns of learning during human sensorimotor adaptation.

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here, we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on 2 consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects' patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that subject differences in patterns of learning and relearning are related to reconfiguration processes occurring in cognitive and sensorimotor networks.

Human Variation in Error-Based and Reinforcement Motor Learning Is Associated With Entorhinal Volume.

Error-based and reward-based processes are critical for motor learning and are thought to be mediated via distinct neural pathways. However, recent behavioral work in humans suggests that both learning processes can be bolstered by the use of cognitive strategies, which may mediate individual differences in motor learning ability. It has been speculated that medial temporal lobe regions, which have been shown to support motor sequence learning, also support the use of cognitive strategies in error-based and reinforcement motor learning. However, direct evidence in support of this idea remains sparse. Here we first show that better overall learning during error-based visuomotor adaptation is associated with better overall learning during the reward-based shaping of reaching movements. Given the cognitive contribution to learning in both of these tasks, these results support the notion that strategic processes, associated with better performance, drive intersubject variation in both error-based and reinforcement motor learning. Furthermore, we show that entorhinal cortex volume is larger in better learning individuals-characterized across both motor learning tasks-compared with their poorer learning counterparts. These results suggest that individual differences in learning performance during error and reinforcement learning are related to neuroanatomical differences in entorhinal cortex.

Human somatosensory cortex is modulated during motor planning.

Recent data and motor control theory argues that movement planning involves preparing the neural state of primary motor cortex (M1) for forthcoming action execution. Theories related to internal models, feedback control, and predictive coding also emphasize the importance of sensory prediction (and processing) prior to (and during) the movement itself, explaining why motor-related deficits can arise from damage to primary somatosensory cortex (S1). Motivated by this work, here we examined whether motor planning, in addition to changing the neural state of M1, changes the neural state of S1, preparing it for the sensory feedback that arises during action. We tested this idea in two human functional MRI studies (N=31, 16 female) involving delayed object manipulation tasks, focusing our analysis on pre-movement activity patterns in M1 and S1. We found that the motor effector to be used in the upcoming action could be decoded, well before movement, from neural activity in M1 in both studies. Critically, we found that this effector information was also present, well before movement, in S1. In particular, we found that the encoding of effector information in area 3b (S1 proper) was linked to the contralateral hand, similarly to that found in M1, whereas in areas 1 and 2 this encoding was present in both the contralateral and ipsilateral hemispheres. Together, these findings suggest that motor planning not only prepares the motor system for movement, but also changes the neural state of the somatosensory system, presumably allowing it to anticipate the sensory information received during movement.SIGNIFICANCE STATEMENT:Whereas recent work on motor cortex has emphasized the critical role of movement planning in preparing neural activity for movement generation, it has not investigated the extent to which planning also modulates the activity in adjacent primary somatosensory cortex (S1). This reflects a key gap in knowledge, given that recent motor control theories emphasize the importance of sensory feedback processing in effective movement generation. Here we find, through a convergence of experiments and analyses, that the planning of object manipulation tasks, in addition to modulating the activity in motor cortex, changes the state of neural activity in different subfields of human S1. We suggest that this modulation prepares S1 for the sensory information it will receive during action execution.

Reaching for known unknowns: Rapid reach decisions accurately reflect the future state of dynamic probabilistic information.

Everyday tasks such as catching a ball appear effortless, but in fact require complex interactions and tight temporal coordination between the brain's visual and motor systems. What makes such interceptive actions particularly impressive is the capacity of the brain to account for temporal delays in the central nervous system-a limitation that can be mitigated by making predictions about the environment as well as one's own actions. Here, we wanted to assess how well human participants can plan an upcoming movement based on a dynamic, predictable stimulus that is not the target of action. A central stationary or rotating stimulus determined the probability that each of two potential targets would be the eventual target of a rapid reach-to-touch movement. We examined the extent to which reach movement trajectories convey internal predictions about the future state of dynamic probabilistic information conveyed by the rotating stimulus. We show that movement trajectories reflect the target probabilities determined at movement onset, suggesting that humans rapidly and accurately integrate visuospatial predictions and estimates of their own reaction times to effectively guide action.

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

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's 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.

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's 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.

Motor memories in manipulation tasks are linked to contact goals between objects.

Skillful manipulation requires forming memories of object dynamics, linking applied force to motion. Although it has been assumed that such memories are linked to objects, a recent study showed that people can form separate memories when these are linked to different controlled points on an object (Heald JB, Ingram JN, Flanagan JR, Wolpert DM. Nat Hum Behav 2: 300-311, 2018). In that study, participants controlled the handle of a robotic device to move a virtual bar with circles (control points) on the left and right sides. Participants were instructed to move either the left or right control point to a target on the left or right, respectively, such that the required movement was constant. When these control points were paired with opposing force fields, adaptation was observed. In this previous study, both the controlled point and the target changed between contexts. To assess which of these factors is critical for learning, here, we used a similar paradigm but with a bar that automatically rotated as it was moved. In the first experiment, the bar rotated, such that the left and right control points moved to a common target. In the second experiment, the bar rotated such that a single control point moved to a target located on either the left or right. In both experiments, participants were able to learn opposing force fields applied in the two contexts. We conclude that separate memories of dynamics can be formed for different "contact goals," involving a unique combination of the controlled point on an object and the target location this point "contacts."NEW & NOTEWORTHY Skilled manipulation requires forming memories of object dynamics, previously assumed to be associated with entire objects. However, we recently demonstrated that people can form multiple motor memories when explicitly instructed to move different locations on an object to different targets. Here, we show that separate motor memories can be learned for different contact goals, which involve a unique combination of a control point and target.

Dynamic Reconfiguration, Fragmentation, and Integration of Whole-Brain Modular Structure across Depths of Unconsciousness.

General anesthetics are routinely used to induce unconsciousness, and much is known about their effects on receptor function and single neuron activity. Much less is known about how these local effects are manifest at the whole-brain level nor how they influence network dynamics, especially past the point of induced unconsciousness. Using resting-state functional magnetic resonance imaging (fMRI) with nonhuman primates, we investigated the dose-dependent effects of anesthesia on whole-brain temporal modular structure, following loss of consciousness. We found that higher isoflurane dose was associated with an increase in both the number and isolation of whole-brain modules, as well as an increase in the uncoordinated movement of brain regions between those modules. Conversely, we found that higher dose was associated with a decrease in the cohesive movement of brain regions between modules, as well as a decrease in the proportion of modules in which brain regions participated. Moreover, higher dose was associated with a decrease in the overall integrity of networks derived from the temporal modules, with the exception of a single, sensory-motor network. Together, these findings suggest that anesthesia-induced unconsciousness results from the hierarchical fragmentation of dynamic whole-brain network structure, leading to the discoordination of temporal interactions between cortical modules.

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.

Models, movements, and minds: bridging the gap between decision making and action.

Decision making is a fundamental cognitive function, which not only determines our day-to-day choices but also shapes the trajectories of our movements, our lives, and our societies. While immense progress has been made in recent years on our understanding of the mechanisms underlying decision making, research on this topic is still largely split into two halves. Good-based models largely state that decisions are made between representations of abstract value associated with available options; while action-based models largely state that decisions are made at the level of action representations. These models are further divided between those that state that a decision is made before an action is specified, and those that regard decision making as an evolving process that continues until movement completion. Here, we review computational models, behavioral findings, and results from neural recordings associated with these frameworks. In synthesizing this literature, we submit that decision making is best understood as a continuous, graded, and distributed process that traverses a landscape of behaviorally relevant options, from their presentation until movement completion. Identifying and understanding the intimate links between decision making and action processing has important implications for the study of complex, goal-directed behaviors such as social communication, and for elucidating the underlying mechanisms by which decisions are formed.

Selective Modulation of Early Visual Cortical Activity by Movement Intention.

The primate visual system contains myriad feedback projections from higher- to lower-order cortical areas, an architecture that has been implicated in the top-down modulation of early visual areas during working memory and attention. Here we tested the hypothesis that these feedback projections also modulate early visual cortical activity during the planning of visually guided actions. We show, across three separate human functional magnetic resonance imaging (fMRI) studies involving object-directed movements, that information related to the motor effector to be used (i.e., limb, eye) and action goal to be performed (i.e., grasp, reach) can be selectively decoded-prior to movement-from the retinotopic representation of the target object(s) in early visual cortex. We also find that during the planning of sequential actions involving objects in two different spatial locations, that motor-related information can be decoded from both locations in retinotopic cortex. Together, these findings indicate that movement planning selectively modulates early visual cortical activity patterns in an effector-specific, target-centric, and task-dependent manner. These findings offer a neural account of how motor-relevant target features are enhanced during action planning and suggest a possible role for early visual cortex in instituting a sensorimotor estimate of the visual consequences of movement.

Separate motor memories are formed when controlling different implicitly specified locations on a tool.

Skillful manipulation requires forming and recalling memories of the dynamics of objects linking applied force to motion. It has been assumed that such memories are associated with entire objects. However, we often control different locations on an object, and these locations may be associated with different dynamics. We have previously demonstrated that multiple memories can be formed when participants are explicitly instructed to control different visual points marked on an object. A key question is whether this novel finding generalizes to more natural situations in which control points are implicitly defined by the task. To answer this question, we used objects with no explicit control points and tasks designed to encourage the use of distinct implicit control points. Participants moved a handle, attached to a robotic interface, to control the position of a rectangular object ("eraser") in the horizontal plane. Participants were required to move the eraser straight ahead to wipe away a column of dots ("dust"), located to either the left or right. We found that participants adapted to opposing dynamics when linked to the left and right dust locations, even though the movements required for these two contexts were the same. Control conditions showed this learning could not be accounted for by contextual cues or the fact that the task goal required moving in a straight line. These results suggest that people naturally control different locations on manipulated objects depending on the task context and that doing so affords the formation of separate motor memories. NEW & NOTEWORTHY Skilled manipulation requires forming motor memories of object dynamics, which have been assumed to be associated with entire objects. However, we recently demonstrated that people can form multiple memories when explicitly instructed to control different visual points on an object. In this article we show that this novel finding generalizes to more natural situations in which control points are implicitly defined by the task.