Biology subjects

Nicholas, J.

Publications and source records attributed to Nicholas, J..

13 recordsLinked to original sources

bioRxiv2026

Flexible decisions arise from resource-rational memory sampling

Flexible decision making depends on retrieving and recombining memories. Yet because this process unfolds covertly, its governing principles remain unknown. Here we use gaze reinstatement to uncover the hidden dynamics and computational logic of memory retrieval during flexible behavior. As people deliberated on a blank screen, they directed their gaze toward the encoding locations of decision-relevant experiences, and these fixations shaped their evolving choice. A task-optimized recurrent neural network captured both their behavior and gaze patterns by learning to balance retrieval costs against expected gains in decision quality. These results demonstrate that flexible decisions emerge from a resource-rational process in which memories are sampled to construct decision variables on the fly.

neuroscience↗

bioRxiv2026

Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations

High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKBs original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

bioinformatics↗

bioRxiv2026

The Periaqueductal Gray Selectively Supports Reversal Learning During a Flexible Discrimination Task in Mice

Flexible, goal-directed behavior depends on the ability to update value representations in response to changing contingencies. This ability depends on distributed brain networks, calling for use of whole-brain imaging. Widely used in human research, whole-brain imaging in rodents has been a major challenge that has only recently been properly addressed. Using functional MRI in behaving mice performing a go/no-go odor discrimination task, we compared neural activity during initial cue-reward learning (acquisition) and subsequent contingency reversal. To link neural activity to underlying learning processes, we modeled value updating using a model-free reinforcement-learning algorithm. Trial-by-trial estimates of state-action values allowed us to dissociate acquisition from reversal-related signals, revealing that ventral striatal responses tracked expected value during acquisition, whereas reversal learning additionally recruited the periaqueductal gray (PAG), a midbrain structure classically linked to threat processing and aversive learning. PAG activity closely followed model-derived signatures of reversal learning, implicating it in the suppression of previously rewarded actions and in updating behavior in the absence of explicit punishment. These findings reveal a previously unrecognized computational role for the PAG in value-based decision-making and cognitive flexibility, and substantiate task-fMRI as a powerful tool to study the rodent brain at a mesoscale resolution.

neuroscience↗

bioRxiv2025

Episodic memory facilitates flexible decision making via access to detailed events

Our experiences contain countless details that may be important in the future, yet we rarely know which will matter and which wont. This uncertainty poses a difficult challenge for adaptive decision making, as failing to preserve relevant information can prevent us from making good choices later on. One solution to this challenge is to store detailed memories of individual experiences that can be flexibly accessed whenever their details become relevant. By allowing us to store and recall specific events in vivid detail, the human episodic memory system provides exactly this capacity. Yet whether and how this ability supports adaptive behavior is poorly understood. Here we aimed to determine whether people use detailed episodic memories to make decisions when future task demands are uncertain. We hypothesized that the episodic memory systems ability to store events in great detail allows us to reference any of these details if they later become relevant. We tested this hypothesis using a novel decision making task where participants encoded individual events with multiple features and later made decisions based on these features to maximize their earnings. Across five experiments (total n = 535), we found that participants referenced episodic memories during decisions in feature-rich environments, and that they did so specifically when it was unclear at encoding which features would be needed in the future. Overall, these findings reveal a fundamental adaptive function of episodic memory, showing how its rich representational capacity enables flexible decision making under uncertainty.

neuroscience↗

bioRxiv2024

Two routes to value-based decisions in Parkinsons disease: differentiating incremental reinforcement learning from episodic memory

Patients with Parkinsons disease are impaired at incremental reward-based learning. It is typically assumed that this impairment reflects a loss of striatal dopamine. However, many open questions remain about the nature of reward-based learning deficits in Parkinsons. Recent studies have found that a combination of different cognitive and computational strategies contribute even to simple reward-based learning tasks, suggesting a possible role for episodic memory. These findings raise critical questions about how incremental learning and episodic memory interact to support learning from past experience and what their relative contributions are to impaired decision-making in Parkinsons disease. Here we addressed these questions by asking patients with Parkinsons disease (n=26) both on and off their dopamine replacement medication and age- and education-matched healthy controls (n=26) to complete a task designed to isolate the contributions of incremental learning and episodic memory to reward-based learning and decision-making. We found that Parkinsons patients performed as well as healthy controls when using episodic memory, but were impaired at incremental reward-based learning. Dopamine replacement medication remediated this deficit while enhancing subsequent episodic memory for the value of motivationally relevant stimuli. These results demonstrate that Parkinsons patients are impaired at learning about reward from trial-and-error when episodic memory is properly controlled for, and that learning based on the value of single experiences remains intact in patients with Parkinsons disease.

neuroscience↗

bioRxiv2023

Proactive and reactive construction of memory-based preferences

We are often faced with decisions we have never encountered before, requiring us to infer possible outcomes before making a choice. Computational theories suggest that one way to make these types of decisions is by accessing and linking related experiences stored in memory. Past work has shown that such memory-based preference construction can occur at a number of different timepoints relative to the moment a decision is made. Some studies have found that memories are integrated at the time a decision is faced (reactively) while others found that memory integration happens earlier, when memories were initially encoded (proactively). Here we offer a resolution to this inconsistency, demonstrating that these two strategies tradeoff rationally as a function of the associative structure of memory. We use fMRI to decode patterns of brain responses unique to categories of images in memory and find that proactive memory access is more common and allows more efficient inference. However, we also find that participants use reactive access when choice options are linked to a larger number of memory associations. Together, these results indicate that the brain judiciously conducts proactive inference by accessing memories ahead of time when conditions make this strategy more favorable.

neuroscience↗

bioRxiv2022

The role of the cerebellum in learning to predict reward: evidence from cerebellar ataxia

Recent findings in animals have challenged the traditional view of the cerebellum solely as the site of motor control, suggesting that the cerebellum may also be important for learning to predict reward from trial-and-error feedback. Yet, evidence for the role of the cerebellum in reward learning in humans is lacking. Moreover, open questions remain about which specific aspects of reward learning the cerebellum may contribute to. Here we address this gap through an investigation of multiple forms of reward learning in individuals with cerebellum dysfunction, represented by cerebellar ataxia cases. Nineteen participants with cerebellar ataxia and 57 age- and sex-matched healthy controls completed two separate tasks that required learning about reward contingencies from trial-and-error. To probe the selectivity of reward learning processes, the tasks differed in their underlying structure: while one task measured incremental reward learning ability alone, the other allowed participants to use an alternative learning strategy based on episodic memory alongside incremental reward learning. We found that individuals with cerebellar ataxia were profoundly impaired at reward learning from trial-and-error feedback on both tasks, but retained the ability to learn to predict reward based on episodic memory. These findings provide evidence from humans for a specific and necessary role for the cerebellum in incremental learning of reward associations based on reinforcement. More broadly, the findings suggest that alongside its role in motor learning, the cerebellum likely operates in concert with the basal ganglia to support reinforcement learning from reward.

neuroscience↗

bioRxiv2022

Uncertainty alters the balance between incremental learning and episodic memory

A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.

neuroscience↗

bioRxiv2021

Plasma amyloid beta ratios in autosomal dominant Alzheimers disease: the influence of genotype

In-vitro studies of autosomal dominant Alzheimers disease (ADAD) implicate longer A{beta} peptides in pathogenesis, however less is known about the behaviour of ADAD mutations in-vivo. In this cross-sectional cohort study, we used liquid chromatography-tandem mass spectrometry to analyse 66 plasma samples from ADAD family members who were at-risk of inheriting a mutation or were already symptomatic. We tested for differences in plasma A{beta}42:38, 38:40 and 42:40 ratios between Presenilin1 (PSEN1) and Amyloid Precursor Protein (APP) carriers. We examined the relationship between plasma and in-vitro models of A{beta} processing and, among PSEN1 carriers, tested for associations with parental age at onset (AAO). 39 participants were mutation carriers (28 PSEN1 and 11 APP). Age- and sex-adjusted models showed marked differences in plasma A{beta} between APP and PSEN1: higher A{beta}42:38 in PSEN1 versus APP (p<0.001) and non-carriers (p<0.001); higher A{beta}38:40 in APP versus PSEN1 (p<0.001) and non-carriers (p<0.001), while A{beta}42:40 was higher in APP and PSEN1 compared to non-carriers (both p<0.001). A{beta} profiles were reasonably consistent in plasma and cell lines. Within PSEN1, sex-adjusted models demonstrated negative associations between (i)A{beta}42:40 (ii)A{beta}42:38 and parental AAO. In-vivo differences in A{beta} processing between APP and PSEN1 provide insights into ADAD pathophysiology which can inform therapy development.

neuroscience↗

bioRxiv2020

Multi-Modal Biomarkers of Cerebral Edema in Low Resolution MRI

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images (MRI). These methods, however, may not translate to low resolution images acquired on MRI scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria were imaged using low-field 0.35 Tesla MRI. We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to cerebral malaria.

neuroscience↗

bioRxiv2018

Activation of NIX-mediated mitophagy and replication by an interferon regulatory factor homologue of human herpesvirus

Viral control of mitochondrial quality and content has emerged as an important mechanism for counteracting the host response to virus infection. Despite the knowledge of this crucial function of some viruses, little is known about how herpesviruses regulate mitochondrial homeostasis during infection. Human herpesvirus 8 (HHV-8) is an oncogenic virus causally related to AIDS-associated malignancies. Here, we found that HHV-8-encoded viral interferon regulatory factor 1 (vIRF-1) promotes mitochondrial clearance by activating mitophagy to support virus replication. Genetic interference with vIRF-1 expression or targeting to the mitochondria inhibits HHV-8 replication-induced mitophagy and leads to an accumulation of mitochondria. Moreover, vIRF-1 binds directly to a mitophagy receptor, NIX, on the mitochondria and activates NIX-mediated mitophagy to promote mitochondrial clearance. Genetic and pharmacological interruption of vIRF-1/NIX-activated mitophagy inhibits HHV-8 productive replication. In short, our findings uncover an essential role of vIRF-1 in mitophagy activation for successful lytic replication of HHV-8.

microbiology↗

bioRxiv2018

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: applied to GENFI study

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimers disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure.\n\nIn this paper we propose a spatiotemporal analysis pipeline based Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects.\n\nWe found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

neuroscience↗

bioRxiv2018

Application of mechanistic methods to clinical trials in multiple sclerosis: the simvastatin case

The analysis of clinical trials is limited to pre-specified outcomes, thereby precluding a mechanistic understanding of the treatment response. Multivariate mechanistic models can elucidate the causal chain of events by simultaneous analysis of multimodal data that link intermediate variables to outcomes of interest. A double-blind, randomised, controlled, phase 2 clinical trial in secondary progressive multiple sclerosis (MS-STAT, NCT00647348) demonstrated that simvastatin (80mg/day) over two years reduced the brain atrophy rate and was associated with beneficial effects on cognitive and disability outcomes. Therefore, this trial offers an opportunity to apply mechanistic models to investigate the hypothesised pathways that link simvastatin to clinical outcome measures, either directly or indirectly via changes in serum total cholesterol levels and to determine which is the more likely.

neuroscience↗