Biology subjects

Mushtaq, F.

Publications and source records attributed to Mushtaq, F..

14 recordsLinked to original sources

bioRxiv2026

Regulatory plasticity of the rdar biofilm morphotype in clinical uropathogenic Escherichia coli and its modulation by ciprofloxacin

Red, dry and rough (rdar) biofilm formation in Escherichia coli is characterized by the coordinated production of extracellular cellulose and amyloid curli fimbriae. Although rdar biofilm formation has been extensively characterized in laboratory strains, its clinical relevance and implications for antimicrobial treatment remain poorly understood. Here, we systematically investigate rdar biofilm formation of 150 consecutively isolated E. coli strains recovered from patients with urinary tract infections and correlate it to antimicrobial resistance. Genetic analysis of rdar regulation revealed distinct nucleotide signatures within the csgD promoter region that discriminate semi-constitutive rdar expression from temperature-dependent phenotypes, highlighting regulatory plasticity among clinical isolates. Whole-genome sequencing-based phylogenetic analysis further demonstrated that rdar-positive isolates are distributed across diverse E. coli phylogroups and sequence types, indicating that rdar biofilm formation is not restricted to specific clonal lineages. Strikingly, phenotypic assays revealed that the fluoroquinolone antibiotic ciprofloxacin suppresses rdar biofilm formation and associated extracellular matrix architecture in ciprofloxacin-resistant isolates at subinhibitory concentrations, suggesting that ciprofloxacin modulates biofilm-associated pathways beyond its canonical bactericidal targets. Together, our findings establish the rdar morphotype as a clinically relevant biofilm phenotype in uropathogenic E. coli and reveal an antibiofilm activity of ciprofloxacin that is uncoupled from antibiotic resistance. These results underscore the importance of considering antibiotic-mediated modulation of biofilm behavior when interpreting treatment responses and designing strategies to combat persistent urinary tract infections.

microbiology↗

bioRxiv2026

Co-Designing Research Priorities in Developmental Neuroscience: A Community-Based Participatory Approach

Children and adolescents, the primary participants in developmental neuroscience research, are rarely consulted or included in the research design process. By neglecting their perspectives and needs, we risk limiting the relevance and effectiveness of translational research that may emerge from this work. Here, we report on a process of co-design with adolescents to inform the generation of a new population neuroimaging research programme. We employed a two-study community-based participatory approach and used mixed methods to identify the research priorities of this community. In the first study, 79 secondary school students from four schools were introduced to fundamental neuroscience concepts during five one-hour workshops. In groups, they then designed research studies, centred around topics they deemed important, and were asked to present their ideas through poster presentations, role playing as neuroscientists. Reflexive thematic analysis was conducted on these posters, extracting ten key themes. In the second study, 376 students from four different schools, ranked these ten themes in order of importance. Mental health and stress consistently emerged as the highest priorities. Further exploratory analysis revealed that the priority for exploring everyday routines and understanding anti-social behaviours varied across schools and age groups, while the importance of mental health varied significantly between genders. This work provides an illustration of how to implement place-based participatory approaches at the early stages of a developmental neuroscience research project to maximise inclusivity and potential downstream translation and impact.

neuroscience↗

bioRxiv2025

An 'Aha!' moment precedes the strategic response to a visuomotor rotation

Strategic behaviour in sensorimotor adaptation tasks is typically described as either a gradual error minimisation process or as a process of learning through trial-and-error. The former predicts a gradual monotonic reduction in error, until some asymptote, while the latter predicts behavioural exploration to discover an efficacious solution. Another explanation is that a sufficiently rich understanding of the task culminates in an "Aha!" moment, and that this richer understanding naturally entails a new strategy. This predicts some period of perseveration in baseline behaviour, followed by an abrupt shift to a new strategic solution. To avoid obfuscation caused by the motor system, we first investigate these hypotheses in a strategy-only aiming game, where participants aim and fire a cannon at targets. Most participants exhibited a period of baseline perseveration followed by a single-trial shift to a distinct strategy. This strategy typically appears to produce the correct compensatory magnitude for a noisily estimated perturbation, but also substantial variability in compensatory sign. We then applied the same analyses to reaching data from a visuomotor rotation task that inhibited implicit adaptation through delaying feedback presentation. Similarly, we found that participants typically perseverated in reaching towards the target before suddenly switching, in a single trial, to good performance in compensatory magnitude. We fit our generative "Aha!" model to 1337 participants across several datasets and show that it faithfully captures these phenomena. Our findings highlight the need to update existing models to account for the abrupt, insight-driven shifts that characterise individual human responses to sensorimotor perturbations.

neuroscience↗

bioRxiv2024

Theta transcranial alternating current stimulation is not effective in improving working memory performance

There is an extensive body of research showing a significant relationship between frontal midline theta activity in the 4-8 Hz range and working memory (WM) performance. Transcranial alternating current stimulation (tACS) is recognized for inducing lasting changes in brain oscillatory activity. Across two experiments, we tested whether WM could be improved through tACS of dorsomedial prefrontal cortex and anterior cingulate cortex, by affecting executive control networks associated with frontal midline theta. In Experiment 1, following either a 20-minute verum or sham stimulation applied to Fpz-CPz at 1 mA and 6 Hz, 31 participants performed WM tasks, while EEG was recorded. The tasks required participants to either mentally manipulate memory items or retain them in memory as they were originally presented. No significant effects were observed in behavioral performance, and we found no change in theta activity during rest and task following stimulation. However, alpha activity during retention or manipulation of information in WM was less strongly enhanced during the delay period following verum stimulation as compared with sham. In Experiment 2 (N = 25), tACS was administered during the task in two separate sessions. Here, we changed the order of the stimulation blocks: a 25-minute task block was either accompanied first by sham stimulation and then by verum stimulation, or vice versa. Again, we found no improvements in WM through either tACS after-effects or online stimulation. Taken together, our results demonstrate that theta frequency tACS applied at the midline is not an effective method for enhancing WM.

neuroscience↗

bioRxiv2024

De Novo Sensorimotor Skill Learning Through Reuse of Movement Components

From tying ones shoelaces to driving a car, complex skills involving the coordination of multiple muscles are common in everyday life; yet relatively little is known about how these skills are learned. Recent studies have shown that new sensorimotor skills involving re-mapping familiar body movements to unfamiliar outputs cannot be learned by adjusting pre-existing controllers, and that new task-specific controllers must instead be learned "de novo". To date, however, few studies have investigated de novo learning in scenarios requiring continuous and coordinated control of relatively unpractised body movements. In this study, we used a myoelectric interface to investigate how a novel controller is learned when the task involves an unpractised combination of relatively untrained continuous muscle contractions. Over five sessions on five consecutive days, participants learned to trace a series of trajectories using a computer cursor controlled by the activation of two muscles. The timing of the generated cursor trajectory and its shape relative to the target improved for conditions trained with post-trial visual feedback. Improvements in timing transferred to all untrained conditions, but improvements in shape transferred only to untrained conditions requiring the trained order of muscle activation. All muscle outputs in the final session could already be generated during the first session, suggesting that participants learned the new task by improving the selection of existing motor commands. These results show that generating novel motor behaviours need not involve learning to generate new motor commands. Significance StatementReal-world skills often involve continuous coordination of multiple muscles. Such skills cannot be learned by adjusting an existing control policy, instead requiring a new controller to be learned "de novo". It remains unclear how new controllers are learned for tasks involving unfamiliar combinations of body movements. In this study, we used a novel human-computer interface task to test this. Over five sessions, participants learned to trace a series of cursor trajectories by coordinating the activation of two muscles. We found that participants tended to reuse the same muscle contractions for trained and untrained variants of the task, and that performance improvements were attributable to improvements in the choice of muscle contractions from a pre-existing repertoire. Our results demonstrate that learning of new complex movements does not necessarily require learning to generate new patterns of muscle activity.

neuroscience↗

bioRxiv2023

Visuomotor memory is not bound to visual motion

Sensory feedback plays a critical role in motor control and motor learning, with both processes adjusting outgoing motor commands relative to the error between actual sensory feedback and the predictions of a forward model. However, models of motor control rarely specify the exact nature of these predictions. We hypothesized that large differences in low-level perceptual feedback would delineate contextual boundaries for motor memory, as sufficiently different feedback would require a distinct mapping between motor commands and sensory states. We tested this hypothesis by measuring transfer of visuomotor adaptation across contexts where hand movements caused visual motion in opposite directions. Instead of observing that visual feedback is bound to distinct internal models, we found nearly complete transfer of learning across the contexts, with evidence that the motor memory was bound to the planned displacement of the hand, rather than visual features of the task space.

neuroscience↗

bioRxiv2023

Kinematic markers of skill in first-person shooter video games

Video games present a unique opportunity to study motor skill. First-person shooter (FPS) games have particular utility because they require visually-guided hand movements that are similar to widely studied planar reaching tasks. However, there is a need to ensure the tasks are equivalent if FPS games are to yield their potential as a powerful scientific tool for investigating sensorimotor control. Specifically, research is needed to ensure that differences in visual feedback of a movement do not affect motor learning between the two contexts. In traditional tasks, a movement will translate a cursor across a static background, whereas FPS games use movements to pan and tilt the view of the environment. To this end, we designed an online experiment where participants used their mouse or trackpad to shoot targets in both contexts. Kinematic analysis showed player movements were nearly identical between conditions, with highly correlated spatial and temporal metrics. This similarity suggests a shared internal model based on comparing predicted and observed displacement vectors, rather than primary sensory feedback. A second experiment, modelled on FPS-style aim-trainer games, found movements exhibited classic invariant features described within the sensorimotor literature. We found that two measures of mouse control, the mean and variability in distance of the primary sub-movement, were key predictors of overall task success. More broadly, these results show that FPS games offer a novel, engaging, and compelling environment to study sensorimotor skill, providing the same precise kinematic metrics as traditional planar reaching tasks. Significance statementSensorimotor control underpins human behaviour and is a predictor of education, health, and socioemotional wellbeing. First-person shooter (FPS) games hold promise for studying sensorimotor control at scale, but the visual feedback provided differs from traditional laboratory tasks. There is a need to ensure they provide measures that relate to traditional tasks. We designed an experiment where the visual contingency of movements could be varied whilst participants shot targets. Participants movements were similar between contexts, suggesting the use of a common internal model despite the sensory differences. A second experiment observed canonical learning patterns with practice and found two measures of mouse control strongly predicted overall performance. Our results highlight the opportunity offered by FPS games to study situated skilled behaviour.

neuroscience↗

bioRxiv2022

Measuring motion-to-photon latency for sensorimotor experiments with virtual reality systems

Consumer virtual reality (VR) systems are increasingly being deployed in research to study sensorimotor behaviours, but properties of such systems require verification before being used as scientific tools. The motion-to-photon latency (the lag between a user making a movement and the movement being displayed within the display) is a particularly important metric as temporal delays can degrade sensorimotor performance. Extant approaches to quantifying this measure have involved the use of bespoke software and hardware and produce a single measure of latency and ignore the effect of the motion prediction algorithms used in modern VR systems. This reduces confidence in the generalisability of the results. We developed a novel, system-independent, high-speed camera-based latency measurement technique to co-register real and virtual controller movements, allowing assessment of how latencies change through a movement. We applied this technique to measure the motion-to-photon latency of controller movements in the HTC Vive, Oculus Rift, Oculus Rift S, and Valve Index, using the Unity game engine and SteamVR. For the start of a sudden movement, all measured headsets had mean latencies between 21-42ms. Once motion prediction could account for the inherent delays, the latency was functionally reduced to 2-13ms, and our technique revealed this reduction occurs within [~]25-58ms of movement onset. Our findings indicate that sudden accelerations (e.g. movement onset, impacts and direction changes) will increase latencies and lower spatial accuracy. Our technique allows researchers to measure these factors and determine the impact on their experimental design before collecting sensorimotor data from VR systems.

neuroscience↗

bioRxiv2022

Proactive inhibition of goal-directed movements involves explicit changes to movement planning

Inhibition can be implemented reactively, withholding movement in response to a stop-signal, or by proactive changes to movement planning when a stop-signal is expected. Previous studies have typically employed simple button presses, finding proactive delays to movement onset when a stop-signal might appear. Here, we consider inhibition in the context of more complex, goal-directed movements, such as the swing of a bat. Thus, we observe two additional dimensions of movement planning under proactive control, movement duration and end-point error. We found, in addition to onset delay, movements were briefer and arrived later when a stop-signal might appear. This challenges a classical theoretical dichotomy, suggesting proactive inhibition is underlay by both response suppression and delayed facilitation. Moreover, participants were aware of delays to onset and arrival, but reported magnitudes were smaller than observed. This suggests proactive inhibition operates as an explicitly retrievable compensatory strategy whose finer details are implicitly tuned.

neuroscience↗

bioRxiv2020

Getting stuck in a rut as an emergent feature of a dynamic decision-making system

Human sensorimotor decision-making has a tendency to get stuck in a rut, being biased towards selecting a previously implemented action structure ( hysteresis). Existing explanations cannot provide a principled account of when hysteresis will occur. We propose that hysteresis is an emergent property of a dynamical system learning from the consequences of its actions. To examine this, 152 participants moved a cursor to a target on a tablet device whilst avoiding an obstacle. Hysteresis was observed when the obstacle moved sequentially across the screen between trials, but not with random obstacle placement. Two further experiments (n = 20) showed an attenuation when time and resource constraints were eased. We created a simple computational model capturing dynamic probabilistic estimate updating that showed the same patterns of results. This provides the first computational demonstration of how sensorimotor decision-making can get stuck in a rut through the dynamic updating of its probability estimates. Significance StatementHumans show a bias to select the organisational structure of a recently carried out action, even when an alternative option is available with lower costs. This hysteresis is said to be more efficient than creating a new plan and it has been interpreted as a design feature within decision-making systems. We suggest such teleological arguments are redundant, with hysteresis being a naturally emergent property of a dynamic control system that evolved to operate effectively in an uncertain and partially observable world. Empirical experimentation and simulations from a first principle computational model of decision-making were consistent with our hypothesis. The identification of such a mechanism can inform robotics research, suggesting how robotic agents can show human-like flexibility in complex dynamic environments.

neuroscience↗

bioRxiv2019

Distinct Processing of Selection and Execution Errors in Neural Signatures of Outcome Monitoring

Losing a point in tennis could result from poor shot selection or faulty stroke execution. To explore how the brain responds to these different types of errors, we examined feedback-locked EEG activity while participants completed a modified version of a standard three-armed bandit probabilistic reward task. Our task framed unrewarded outcomes as either the result of errors of selection or errors of execution. We examined whether amplitude of a medial frontal negativity (the Feedback-Related Negativity; FRN) was sensitive to the different forms of error attribution. Consistent with previous reports, selection errors elicited a large FRN relative to rewards and amplitude of this signal correlated behavioral adjustment following these errors. A different pattern was observed in response to execution errors. These outcomes produced a larger FRN, a frontocentral attenuation in activity preceding this component, and a subsequent enhanced error positivity in parietal sites. Notably, the only correlations with behavioral adjustment were with the early frontocentral attenuation and amplitude of the parietal signal; FRN differences between execution errors and rewarded trials did not correlate with subsequent changes in behavior. Our findings highlight distinct neural correlates of selection and execution error processing, providing insight into how the brain responds to the different classes of error that determine future action.

neuroscience↗

bioRxiv2019

Exploring Disturbance as a Force for Good in Motor Learning

Disturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such active inference is driven by surprise. We used these insights to create a formal model that explains why disturbance helps learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning.

neuroscience↗

bioRxiv2018

Neural Signatures of Prediction Errors in a Decision-making Task are Modulated by Action Execution Failures

Decisions must be implemented through actions, and actions are prone to error. As such, when an expected outcome is not obtained, an individual should not only be sensitive to whether the choice itself was suboptimal, but also whether the action required to indicate that choice was executed successfully. The intelligent assignment of credit to action execution versus action selection has clear ecological utility for the learner. To explore this scenario, we used a modified version of a classic reinforcement learning task in which feedback indicated if negative prediction errors were, or were not, associated with execution errors. Using fMRI, we asked if prediction error computations in the human striatum, a key substrate in reinforcement learning and decision making, are modulated when a failure in action execution results in the negative outcome. Participants were more tolerant of non-rewarded outcomes when these resulted from execution errors versus when execution was successful but the reward was withheld. Consistent with this behavior, a model-driven analysis of neural activity revealed an attenuation of the signal associated with negative reward prediction error in the striatum following execution failures. These results converge with other lines of evidence suggesting that prediction errors in the mesostriatal dopamine system integrate high-level information during the evaluation of instantaneous reward outcomes.

neuroscience↗

bioRxiv2018

Studying Human Behaviour with Virtual Reality: The Unity Experiment Framework

Virtual Reality systems offer a powerful tool for human behaviour research. The ability to create three-dimensional visual scenes and measure responses to the visual stimuli enables the behavioural researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behaviour might wish to violate Newtonian mechanics, so objects move in novel 3D trajectories. The same researcher may wish to collect such data with hundreds of participants outside the laboratory, and the use of a VR headset makes this a realistic proposition. The difficulty facing the researcher is that sophisticated 3D graphics engines (e.g. Unity) have been created for game designers rather than behavioural scientists. In order to overcome this barrier, we have created a set of tools and programming syntaxes that allow logical encoding of the common experimental features required by the behavioural scientist. The Unity Experiment Framework (UXF) allows the researcher to readily implement several forms of data collection, and provides researchers with the ability to easily modify independent variables. UXF does not offer any stimulus presentation features, so the full power of the Unity game engine can be exploited. We use a case study experiment, measuring postural sway in response to an oscillating virtual room, to show how UXF can replicate and advance upon behavioural research paradigms. We show that UXF can simplify and speed up development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioural research data.

neuroscience↗