Biology subjects

Ruff, C.

Publications and source records attributed to Ruff, C..

13 recordsLinked to original sources

bioRxiv2026

From APOE Genetics to AI-Designed Drug Candidates: An Integrated Pipeline for Oral, Brain-Penetrant ACAT1 Inhibitors in Alzheimer's Disease

For more than 55 million people living with dementia worldwide, no oral disease-modifying treatment is currently available. Alzheimers disease (AD) remains one of the most urgent unmet needs in neurology, with recent genetic evidence estimating that 72-93% of AD burden is attributable to common APOE allelic variation. Mechanistically, the APOE {varepsilon}4 isoform impairs cholesterol transport in the brain, promoting cholesteryl ester accumulation in microglia; these lipid-laden cells lose phagocytic capacity for amyloid-{beta} clearance and autophagy-mediated degradation of phosphorylated tau, linking a single upstream metabolic disruption to both hallmark pathologies of AD. ACAT1/SOAT1, the brains predominant cholesterol-esterifying enzyme, is an attractive therapeutic target: its inhibition reduces cholesteryl ester formation, restoring microglial A{beta} clearance, reducing A{beta} production, and promoting tau degradation via autophagy, supporting a multimodal mechanism not addressed by currently approved therapies. However, prior ACAT inhibitor programs were limited by isoform non-selectivity, excessive lipophilicity, and lack of CNS optimization. Addressing these constraints, we present the CuraGenAI Drug Discovery Platform for the design of oral, brain-penetrant ACAT1-targeted small-molecule candidates. The pipeline integrates scaffold-constrained generative chemistry with multi-objective ADMET optimization across more than 30 criteria. From approximately 6 million generated molecules, multi-stage filtering yielded approximately 7,300 CNS-optimized candidates with predicted oral brain-penetrant profiles distinct from prior ACAT clinical compounds. Three nominated lead candidates showed predicted BBB probabilities >0.93, favorable CNS drug-like physicochemical profiles, and stable ACAT1 binding poses, and are prioritized for synthesis and in vitro validation.

neuroscience↗

bioRxiv2025

Separable neurocomputational mechanisms underlying multisensory learning

Efficient control of behavior requires multisensory learning from information distributed across senses. However, most neurocomputational studies have focused on unisensory signals. Here, we identify distinct but interacting neurocomputational mechanisms that support learning of multisensory associations. We designed a task in which behaviorally relevant information was available only from combinations of visual cues with either auditory or tactile cues. In 58 participants undergoing fMRI, we dissociated three processes: multisensory statistical learning (SL), modeled as stimulus-locked Shannon surprise; reinforcement learning (RL), modeled as feedback-locked signed reward prediction errors (RPEs); and feedback-locked unsigned RPEs (uRPEs), reflecting surprise about reward outcomes. Behaviorally, response times scaled with Shannon surprise (SL) while accuracy improved with feedback (RL). Model-based fMRI revealed dissociable but complementary networks: RPEs engaged ventral striatum, vmPFC, and left angular gyrus; surprise recruited bilateral angular gyrus, dlPFC, and precuneus; and uRPEs involved insula, dorsomedial prefrontal, and lateral frontoparietal cortices. Several of these regions extend beyond canonical learning circuits and may contribute to learning when behavior depends on information distributed across sensory modalities. All three networks were modality-general, showing comparable strength for audiovisual and visuotactile learning. Notably, left angular gyrus tracked both Shannon surprise and RPE, suggesting its potential role for integrating structural and value information. These findings indicate that the brain engages distinct but complementary systems for structure-based, reward-based, and outcomesurprise computations. By combining behavioral modeling and fMRI with a novel task design, we provide a framework for dissecting the neurocomputational architecture of multisensory learning.

neuroscience↗

bioRxiv2025

Probability weighting arises from boundary repulsions of cognitive noise

In both risky choice and perception, people overweight small and underweight large probabilities. While prospect theory models this with a probability weighting function, and Bayesian noisy coding models attribute it to specific encoding functions or priors, we propose a more general account: Probability distortions arise from cognitive noise being repelled by the natural boundaries of probability (0,1). This boundary repulsion occurs in any encoding-decoding system that efficiently encodes, or Bayesian-decodes, bounded quantities, independent of specific priors or encoding functions. Our theory predicts: new, experimentally-induced boundaries should cause additional distortions; increasing cognitive noise should amplify distortions; and boundaries should reduce behavioral variability near them. We confirmed all predictions in three pre-registered experiments spanning risky choice and probability perception. Our findings further suggest that these changes originate largely during decoding. Our work provides a unified explanation for distorted and variable probability judgments, reframing them as consequences of bounded, noisy cognitive inference. SignificanceThe origin of probability weighting--a central feature of decision-making under risk--remains a longstanding puzzle. Does it arise from processes unique to risk, or does it reflect a more general cognitive mechanism? Here, we show that the classic probability weighting pattern is not domain-specific but instead emerges from a general property of noisy inference over bounded quantities, such as probabilities. Our account formalizes how resource-rational encoding and Bayesian optimal decoding naturally lead to interactions between cognitive noise and the 0-1 bounds of probability, giving rise to systematic distortions. Using pre-registered experimental manipulations across both risky lottery valuation and probability perception, we demonstrate that distortions in probability weighting and estimation are not fixed, intrinsic features, but rather predictable consequences of the interaction between noise and boundaries. This provides a mechanistic account of probability weighting and suggests a unifying explanation for its emergence across different cognitive domains. Similar mechanisms should extend to other naturally or contextually bounded quantities.

neuroscience↗

bioRxiv2025

Risk preferences causally rely on parietal magnitude representations: Evidence from combined TMS-fMRI

Risk preferences have traditionally been considered as stable traits that reflect subjective-valuation processes in prefrontal areas. More recently, however, it has been suggested that risk preferences may also be shaped by how choice problems are perceived and processed in perceptual brain regions. Specifically, the acuity of the parietal approximate number system (ANS), which encodes payoff magnitudes for different choice options, has been shown to correlate with both risk preferences and choice consistency. However, this correlational relationship leaves open the question whether parietal magnitude representations in fact causally underlie choice. Here, we provide direct evidence for such a key causal role of parietal magnitude representations in economic choice, using continuous theta-burst transcranial magnetic stimulation (cTBS), combined with functional MRI (fMRI) and numerical population receptive field (nPRF) modeling. Our stimulation protocol targeted numerosity-tuned regions in the right parietal cortex, identified through nPRF modeling of individual fMRI data (n=35; within-subject design). The stimulation successfully perturbed neural processing, as evidenced by decreased amplitude of numerical magnitude-tuned responses and less accurate multivariate decoding of presented magnitudes from unseen data that were not used for model fitting. In line with a perceptual account of risky choice, the reduction in neural information capacity was also reflected in noisier behavioral responses. Moreover, a computational cognitive model fitted to choice behavior revealed that perturbing the ANS specifically increased the noisiness of small-magnitude representations. This perturbation made small magnitudes to be perceived as larger than they actually are, leading to more risk-seeking behavior. Finally, individual estimates of the cTBS effect on cognitive noise correlated with the corresponding decrease in amplitude of numerical magnitude-tuned fMRI responses, further solidifying the role of the parietal ANS in economic choice. In conclusion, our study demonstrates that the precision of parietal magnitude representations causally influences economic decision-making, with noisier encoding promoting biased risk-taking as formalized in recent perceptual models of risky choice.

neuroscience↗

bioRxiv2022

Individual risk attitudes arise from noise in neurocognitive magnitude representations

Humans are generally risk averse: they prefer options with smaller certain outcomes over those with larger uncertain ones. This risk aversion is classically explained with a concave utility function, meaning that successive increases in monetary payoffs should increase subjective valuations by progressively smaller amounts. Here, we provide neural and behavioural evidence that risk aversion may also arise from a purely perceptual bias: The noisy logarithmic coding of numerical magnitudes can lead individuals to underestimate the size of larger monetary payoffs, leading to apparent risk aversion even when subjective valuation increases linearly with the estimated amount. A formal model of this process predicts that risk aversion should systematically increase when individuals represent numerical magnitudes more noisily. We confirmed this prediction by measuring both the mental and neural acuity of magnitude representations during a purely perceptual task and relating these measures to individual risk attitudes during separate financial decisions. Computational model fitting suggested that subjects based both types of choices on similar mental magnitude representations, with correlated precision across the separate perceptual and risky choices. Increased stimulus noise due to the presentation format of risky outcomes led to increased risk aversion, just as predicted by the model. The precision of the underlying neural magnitude representations was estimated with a numerical population receptive field model fitted to the fMRI data of the perceptual task. Subjects with more precise magnitude representations in parietal cortex indeed showed less variable behaviour and less risk-aversion in the separate financial choices. Our results highlight that individual patterns of economic behaviour may, at least partially, be determined by capacity limitations in perceptual processing rather than by processes that assign subjective values to monetary rewards.

neuroscience↗

bioRxiv2022

A unified neural account of contextual and individual differences in altruism

Altruism is critical for cooperation and productivity in human societies, but is known to vary strongly across contexts and individuals. The origin of these differences is largely unknown, but may in principle reflect variations in different types of neurocognitive processes that temporally unfold during altruistic decision making (ranging from initial perceptual processing via value computations to final integrative choice mechanisms). Here, we address this question by examining altruistic choices in different inequality contexts with computational modeling and EEG. Our results show that across all contexts and individuals, wealth distribution choices recruit a similar late decision process evident in model-predicted evidence accumulation signals over parietal regions. Contextual and individual differences in behavior related instead to initial processing of stimulus-locked inequality-related value information in centroparietal and centrofrontal sensors, as well as to gamma-band synchronization of these value-related signals with parietal response-locked evidence-accumulation signals. Our findings suggest separable biological bases for individual and contextual differences in altruism and emphasize that these reflect differences in processing of choice-relevant information.

neuroscience↗

bioRxiv2022

Neurocomputational evidence that conflicting prosocial motives guide distributive justice

In the history of humanity, most conflicts within and between societies have originated from perceived inequality in resource distribution. How humans achieve and maintain distributive justice has therefore been an intensely studied issue. However, most research on the corresponding psychological processes has focused on inequality aversion and has been largely agnostic of other motives that may either align or oppose this behavioral tendency. Here we provide behavioral, computational, and neuroimaging evidence that distribution decisions are guided by three distinct motives - inequality aversion, harm aversion, and rank reversal aversion - that interact and in fact can also deter individuals from pursuing equality. At the neural level, we show that these three motives are encoded by separate neural systems, compete for representation in various brain areas processing equality and harm signals, and are integrated in the striatum, which functions as a crucial hub for translating the motives to behavior. Our findings provide a comprehensive framework for understanding the cognitive and biological processes by which multiple prosocial motives are coordinated in the brain to guide redistribution behaviors. This framework enhances our understanding of the brain mechanisms underlying equality-related behavior, suggests possible neural origins of individual differences in social preferences, and provides a new pathway to understand the cognitive and neural basis of clinical disorders with impaired social functions.

neuroscience↗

bioRxiv2021

Disrupting inferior frontal cortex activity alters affect decoding efficiency from clear but not from ambiguous affective speech

The evaluation of socio-affective sound information is accomplished by the primate auditory cortex in collaboration with limbic and inferior frontal cortex (IFC)--often observed during affective voice classification. Partly opposing views have been proposed, with IFC either coding cognitive processing challenges in case of sensory ambiguity or representing categorical object and affect information for clear voices. Here, we presented clear and ambiguous affective speech to two groups of human participants during neuroimaging, while in one group we inhibited right IFC activity with transcranial magnetic stimulation. IFC activity inhibition led to faster affective decisions, more accurate choice probabilities, reduced auditory cortical activity and increased fronto-limbic connectivity for clear affective speech. This indicates a more intermediate functional property of the IFC than assumed--namely with normal activity representing a more deliberate form of affective sound processing (i.e., enforcing cognitive analysis) that flags categorical sound decisions with precaution (i.e., representation of categorical uncertainty). TeaserInferior frontal cortex enforces cognitive analyses during affect decisions with different levels of sensory ambiguity.

neuroscience↗

bioRxiv2021

A causal role for the right frontal eye fields in value comparison

Recent studies have suggested close functional links between visual attention and decision making. This suggests that the corresponding mechanisms may interface in brain regions known to be crucial for guiding visual attention - such as the frontal eye field (FEF). Here, we combined brain stimulation, eye tracking and computational approaches to explore this possibility. We show that inhibitory transcranial magnetic stimulation (TMS) over the right FEF has a causal impact on decision-making, reducing the effect of gaze dwell time on choice while also increasing reaction times. We computationally characterize this putative mechanism by using the attentional drift diffusion model (aDDM), which reveals that FEF inhibition reduces the relative discounting of the non-fixated option in the comparison process. Our findings establish an important causal role of the right FEF in choice, elucidate the underlying mechanism, and provide support for one of the key causal hypotheses associated with the aDDM.

bioinformatics↗

bioRxiv2020

Effective psychological treatment for PTSD changes the dynamics of specific large-scale brain networks

AO_SCPLOWBSTRACTC_SCPLOWVery little is known about the role of effective cognitive therapy in reversing imbalances in brain activity after trauma. We hypothesised that exaggerated threat perception characteristic of post-traumatic stress disorder (PTSD), and subsequent recovery from this disorder, are underpinned by changes in the dynamics of large-scale brain networks. Here, we use a novel data-driven approach with high temporal precision to find recurring brain networks from fMRI data and estimate when these networks become active during exposure to either trauma reminders or neutral pictures. We found that PTSD patients spend less time in two default mode sub-networks in contrast to trauma-exposed healthy controls, and that PTSD symptom severity correlates positively with time spent in the salience network during exposure to trauma reminders. The former are important for different aspects of self-referential processing and the latter for detection of threat. Importantly, the decreased time in the default mode sub-networks is rebalanced after successful cognitive therapy for PTSD. Our results show that remittance of PTSD through trauma-focused cognitive therapy is associated with the successful reinstatement of a healthy balance in self-referential and threat detection brain networks.

neuroscience↗

bioRxiv2019

Neuro-Computational Foundations of Moral Preferences

Moral preferences pervade many aspects of our lives, dictating how we ought to behave, whom we can marry, and even what we eat. Despite their relevance, one fundamental question remains unanswered: Where do individual moral preferences come from? It is often thought that all types of preferences reflect properties of domain-general neural decision mechanisms that employ a common \"neural currency\" to value choice options in many different contexts. This assumption, however, appears at odds with the observation that many humans consider it intuitively wrong to employ the same scale to compare moral value (e.g., of a human life) with material value (e.g., of money). In this paper, we directly challenge the common-currency hypothesis by comparing the neural mechanisms that represent moral and financial subjective values. In a study combining fMRI with a novel behavioral paradigm, we identify neural representations of the subjective values of human lives or financial payoffs by means of structurally identical computational models. Correlating isomorphic model variables from both domains with brain activity reveals specific patterns of neural activity that selectively represent values in the moral (in the rTPJ) or financial (in the vmPFC) domain. Thus, our findings show that human lives and money are valued in distinct neural currencies, supporting theoretical proposals that human moral behavior is guided by processes that are distinct from those underlying behavior driven by personal material benefit.

neuroscience↗

bioRxiv2018

Efficient coding of subjective value

Preference-based decisions are essential for survival, for instance when deciding what we should (not) eat. Despite their importance, choices based on preferences are surprisingly variable and can appear irrational in ways that have defied mechanistic explanations. Here we propose that subjective valuation results from an inference process that accounts for the information structure of values in the environment and that maximizes information in value representations in line with demands imposed by limited coding resources. A model of this inference process explains the variability in both subjective value reports and preference-based choices, and predicts a new preference illusion that we validate with empirical data. Interestingly, the same model also explains the level of confidence associated with these reports. Our results imply that preference-based decisions reflect information-maximizing transmission and statistically optimal decoding of subjective values by a limited-capacity system. These findings provide a unified account of how humans perceive and valuate the environment to optimally guide behavior.

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bioRxiv2017

Concurrent tACS-fMRI Reveals Causal Influence Of Power Synchronized Neural Activity On Resting State fMRI Connectivity

Resting state fMRI (rs-fMRI) is commonly used to study the brains intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSN). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices.\n\nThe two driving tACS signals were tailored to the individuals alpha rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either alpha oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS).\n\nPower-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared to phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brains intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity.\n\nSignificance StatementResting state fMRI has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes.\n\nIt was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power-synchronization mechanisms.\n\nThese novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology and Psychology.

neuroscience↗