Biology subjects

Wong-Lin, K.

Publications and source records attributed to Wong-Lin, K..

14 recordsLinked to original sources

bioRxiv2026

Neural latent representation of implicit perceptual decision confidence signatures from brain-wide intracranial EEG

Human perceptual decision confidence has been associated with brain-wide signalling, albeit with inconsistencies across studies. It remains unclear whether more robust neural latent representations exist, particularly for implicit confidence. We address this by applying complementary machine learning approaches to comprehensive, brain-wide intracranial electroencephalography (iEEG) data acquired without explicit confidence reports. Using explainable feature selection methods, we elucidated key spatiotemporal iEEG components, especially in the frontal cortex, that readily distinguished confidence characteristics related to decision correctness, difficulty, and speed, and that were transferable across motor modalities. Despite inconsistencies in the features identified across methods and conditions, dimensionality reductions revealed robust low-dimensional neural latent representations. Furthermore, their neural trajectories within a shared subspace enhanced the distinguishability of confidence characteristics. Overall, our results uncover low-dimensional spatial and temporal neural latent representations underlying human implicit perceptual decision confidence that are robust to analytical methods and decision conditions.

neuroscience↗

bioRxiv2026

Decision Confidence Neuron in Echo State Network for Continual Evaluation of EEG Motor Imagery Classification Quality

Echo state networks (ESNs) are efficient, neuro-inspired computational frameworks well suited to time-series data. However, ESN decision confidence is typically quantified in limited ways. We propose an explicit decision-confidence readout neuron, trained from decision readout outputs, to continuously monitor confidence as decisions form. In a simulated decision task, confidence activity increased with stimulus strength, linking greater discriminability to higher confidence. We then evaluated the model on EEG-based motor imagery classification, showing that confidence activity increased with decision accuracy and discriminated correct from error decisions, particularly in higher-performing participants, reflecting human-like metacognition. Overall, this approach enables continual monitoring of decision confidence, supporting more trustworthy ESN decisions, particularly in biomedical applications.

neuroscience↗

bioRxiv2025

A canonical gated neural circuit model for flexible perceptual decisions

Flexible perceptual decision-making requires rapid, context-dependent adjustments, yet the neural circuit mechanisms underlying its parsimonious representations remain unclear. Here, we propose a minimal mean-field neural circuit model that integrates sensory evidence and selects actions via distributed neuronal encoding, guided by data from a task that dissociates perceptual choice from motor response - abstract perceptual decision-making. The models nonlinear gating of action selective (AS) neurons replicates parietal cortical activity observed during task performance. Critically, recurrent excitation within the evidence integration (EI) population supports sensory evidence accumulation, working memory for sequential sampling, and reward rate optimisation. Moreover, the dynamics of EI and AS neuronal activities in the same model respectively mirror parietal neuronal activities related to sensory evidence encoding and ramping-to-threshold firing in a separate reaction-time task, while suggesting that decision readout engages both neuronal populations. The model also predicts decision interference in a novel two-stage decision version of the task, accounting for choice accuracy decrements observed in other experiments while predicting slower decisions. Together, these findings propose a minimal mean-field circuit-level mechanism unifying perceptual, memory-based, and abstract decision-making.

neuroscience↗

bioRxiv2025

Neural oscillation as a selective modulatory mechanism on decision confidence, speed and accuracy

Neural oscillations have been associated with decision-making processes, but their underlying network mechanisms remain unclear. This study investigates how neural oscillations influence decision network models of competing cortical columns with varying intrinsic and emergent timescales. Our findings reveal that decision networks with faster excitatory than inhibitory synapses are more susceptible to oscillatory modulations. Higher in-phase oscillation amplitude reduces decision confidence without affecting accuracy, while decision speed increases. In contrast, anti-phase modulation increases decision accuracy, confidence and speed. Increasing oscillation frequency reverses these effects. Changing oscillatory phase difference gradually modulates decision behaviour, with decision confidence affected nonlinearly. Moreover, neural resonance can further enhance modulatory susceptibility for network with faster excitatory than inhibitory synapses. These effects decouple decision accuracy, speed and confidence, challenging standard speed-accuracy trade-off. These phenomena can be explained by excitatory neural populations contributing more to in-phase modulation, while inhibitory neural populations to anti-phase modulation. State-space trajectories momentum swinging with respect to network steady states and decision uncertainty manifold further provide insights into the neural circuit mechanisms. Our work provides mechanistic insights into how neurobiological diversity shapes decision-making processes in the presence of ubiquitous neural oscillations. Significance StatementNeural oscillations shape how the brain balances decision speed, accuracy, and confidence. Here, we show that tuning oscillatory amplitude, frequency, or phase can selectively alter these decision measures. In some cases, these modulations even break the usual trade-off between speed and accuracy, revealing a more flexible decision-making mechanism than previously assumed. Our findings highlight how synaptic time scales and rhythmic brain activity can give rise to distinct patterns of decision performance. This insight may guide new strategies for improving decisional processes in both healthy populations and clinical conditions.

neuroscience↗

bioRxiv2025

Limitations of Dynamic Causal Modelling for Multistable Cortical Circuits

Dynamic causal modelling (DCM) is widely used to infer effective connectivity from neuroimaging data. However, its applicability to neural systems with complex, multistable dynamics remains uncertain--particularly when using the standard estimation approach based on variational Bayesian inference under the Laplace approximation. To investigate this limitation, we constructed biologically grounded cortical columnar neural mass models exhibiting three distinct multistable regimes: bistable fixed points associated with decision-making, coexisting oscillatory states through period-doubling bifurcations, and deterministic chaotic dynamics. These models were used to simulate local field potentials, which served as inputs to DCM. Bayesian model selection successfully identified the correct model architecture in all cases. However, Bayesian model averaging of the winning models failed to accurately estimate extrinsic connectivity parameters, leading to substantial discrepancies between the dynamics of the reconstructed and ground-truth systems. These results suggest that even when model selection is accurate, parameter estimation can break down under complex dynamics. Compared to previous applications of DCM to simpler neural systems, our study highlights significant limitations in its ability to capture the structure of multistable and globally nonlinear dynamics. We conclude that caution is warranted when applying variational Laplace-based DCM procedures to experimental paradigms involving bifurcations, chaotic trajectories, or other forms of dynamical complexity.

neuroscience↗

bioRxiv2024

Non-Negative Connectivity Causes Bow-Tie Architecture in Neural Circuits

Bow-tie or hourglass architecture is commonly found in biological neural networks. Recently, artificial neural networks with bow-tie architecture have been widely used in various machine-learning applications. However, it is unclear how bow-tie architecture in neural circuits can be formed. We address this by training multi-layer neural network models to perform classification tasks. We demonstrate that during network learning and structural changes, non-negative connections amplify error signals and quench neural activity particularly in the hidden layer, resulting in the emergence of the networks bow-tie architecture. We further show that such architecture has low wiring cost, robust to network size, and generalizable to different discrimination tasks. Overall, our work suggests a possible mechanism for the emergence of bow-tie neural architecture and its functional advantages.

neuroscience↗

bioRxiv2024

Effective connectivity predicts distributed neural coding of perceptual decision confidence, uncertainty and speed

Decision-making is often accompanied by a level of confidence regarding the accuracy of ones decision. Previous studies have indicated neural activity associated with perceptual decision confidence during sensory stimulus presentation. Choice-based reaction time (RT) has been suggested as an indirect but more objective measure of decision confidence - generally faster RT for higher confidence. However, it is unclear whether choice confidence and RT are mediated by distinct neural pathways, and whether their neural correlates are encoded nonlinearly. Within a perceptual decision-making task, we applied fMRI-informed EEG-based effective connectivity analysis via dynamic causal modelling (DCM) on event-related potentials and found the frontoparietal network for fast-vs-slow RT condition to be different from that of high-vs-low confidence rating condition. Furthermore, trial-by-trial DCM analysis predicted cortical layer-based, distributed and nonlinear coding of RT, confidence or uncertainty. Collectively, our study suggests that decision confidence and speed are instantiated by different dynamical networks distributed across cortical layers.

neuroscience↗

bioRxiv2021

Functional Differences in the Neural Substrates of Auditory Cognition as a Consequence of Music Training

Previous studies have demonstrated that musical deviants (syntactically irregular chords) elicit event related potentials/fields with negative polarity; specifically, the early right anterior negativity and the right anterior temporal negativity responses with peak latencies at ~200 ms and ~350 ms, respectively, post stimulus onset. Here, we investigated differences in the neural dynamics of the auditory perceptual system of individuals with music training compared to those with no music training. Magnetoencephalography was used to examine the neural response to a deviant sound when the auditory system was primed using stimulus entrainment to evoke an auditory gamma-band response between 31 Hz and 39 Hz, in 2 Hz steps. Participants responded to the harmonic relationship between the entrainment stimulus and the subsequent target stimulus. Gamma frequencies carry stimulus information; thus, the paradigm primed the auditory system with a known gamma frequency and evaluated any improvement in the brains response to a deviant stimulus. The entrainment stimuli did not elicit an early right anterior negativity response. Furthermore, the source location of the event-related field difference during the later right anterior temporal negativity response time-window varied depending on group and entrainment condition. In support of previous findings from research using this, and a functionally similar visual-priming paradigm, a 7 Hz phase modulation of gamma amplitude was found for non-musicians following 33 Hz stimulus entrainment. Overall, significant effects of gamma entrainment were found more frequently in the non-music brain. By contrast, musicians demonstrated a greater range of interactions with slower brain rhythms, indicative of increased top-down control. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/446932v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1097a58org.highwire.dtl.DTLVardef@13f3c74org.highwire.dtl.DTLVardef@99fae1org.highwire.dtl.DTLVardef@125d351_HPS_FORMAT_FIGEXP M_FIG C_FIG ConclusionO_LIMusicians auditory cognition relies more on top-down processes, while non-musicians rely on bottom-up processing, and therefore, their auditory cognition is facilitated by the entrained gamma-band response. C_LIO_LIFirst neural evidence in support of faster reaction-time responses due to an interaction in phase of an entrained gamma-band response of 33 Hz and a slower endogenous theta rhythm (~7 Hz) - consistently reported in previous research. C_LI

neuroscience↗

bioRxiv2020

Degeneracy and stability in neural circuits of neuromodulators

Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves be degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modelling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modelling is constrained by relevant experimental studies of the VTA or DRN system using e.g. electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed and mixed DRN-VTA neuronal signalling in reward and punishment tasks. The inability for this model to recapitulate all observed neuronal signalling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implication for the stable and robust maintenance of neuromodulatory functions.

neuroscience↗

bioRxiv2019

Cluster-permutation statistical analysis for high-dimensional brain-wide functional connectivity mapping

Brain functional connectivity (FC) analyses based on magnetoencephalographic (MEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, which leads to conservative hypothesis testing. We removed such constraint by extending cluster-permutation statistics for high-dimensional MEG-FC analyses. We demonstrated the feasibility of this approach by identifying MEG-FC resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimers disease. We found dense clusters of increased connectivity strength in MCI compared to healthy controls (hypersynchronization), in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These clusters mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere and could potentially be used as neuromarkers of early progression in Alzheimers disease. Our novel approach can be used to generate high-resolution statistical FC maps for neuroimaging studies in general.

neuroscience↗

bioRxiv2019

Changes-of-mind in the absence of new post-decision evidence

Decisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.\n\nAuthors SummaryWe used limited availability of sensory evidence during a standard motion discrimination task, and demonstrated that changes-of-mind could occur long after sensory information was no longer available. Unlike previous studies, our experiment further indicated that changes-of-mind were strongly linked to slow response time. We used a reduced version of a previously developed neural computational model of decision uncertainty and change-of-mind to account for these experimental observations. Importantly, our model showed that the replication of these experimental results required a strong link between change-of-mind and high decision uncertainty (i.e. low decision confidence), supporting the notion that change-of-mind are related to decision uncertainty or confidence.

neuroscience↗

bioRxiv2019

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on the group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimers disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and k-Nearest Neighbor (kNNreg) for regression and Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (kNNclass) for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to be consistently higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

neuroscience↗

bioRxiv2018

Neural Circuit Mechanism of Decision Uncertainty and Change-of-Mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind.

neuroscience↗

bioRxiv2018

A laboratory demand optimisation project in primary care

BackgroundThere is evidence of increasing use of laboratory tests with substantial variation between clinical teams which is difficult to justify on clinical grounds. The aim of this project was to assess the effect of a demand optimisation intervention on laboratory test requesting in primary care.\n\nMethodsThe intervention comprised educational initiatives, feedback to 55 individual practices on test request rates with ranking relative to other practices, and a small financial incentive for practices to engage and reflect on their test requesting activity. Data on test request numbers were collected from the laboratory databases for consecutive 12 month periods; pre-intervention 2011-12, intervention 2012-13, 2013-14, 2014-15, and post-intervention 2015-16.\n\nResultsThe intervention was associated with a 3.6% reduction in the mean number of profile test requests between baseline and 2015-16, although this was seen only in rural practices. In both rural and urban practices, there was a significant reduction in-between practice variability in request rates. The mean number of HbA1c requests increased from 1.9 to 3.0 per practice patient with diabetes. Variability in HbA1c request rates increased from 23.8% to 36.6%. At all considered time points, test request rates and variability were higher in rural than in urban areas.\n\nConclusionsThe intervention was associated with a reduction in both the volume and between practice variability of profile test requests, with differences noted between rural and urban practices. The increase in HbA1c requests may reflect a more appropriate rate of diabetes monitoring and also the adoption of HbA1c as a diagnostic test.\n\nStrengths & limitations of the studyO_LIWe assessed the effect of a laboratory demand optimisation intervention both on the value and between GP practice variability in laboratory test requesting.\nC_LIO_LIThe changes in laboratory test requesting were separately evaluated for rural and urban GP practices.\nC_LIO_LIOther factors (GP practice organisation, characteristics of general practitioners) potentially affecting between practice differences in laboratory test ordering were not taken into account due to data unavailability.\nC_LIO_LIThe demand management initiative was not accompanied by the cost-effectiveness analysis.\nC_LIO_LIThe demand optimisation intervention was conducted in a Northern Ireland (NI) Western Health and Social Care Trust and the findings have not been independently replicated in any other NI trusts.\nC_LI

pathology↗