Biology subjects

Sajda, P.

Publications and source records attributed to Sajda, P..

12 recordsLinked to original sources

bioRxiv2026

Pupil and Neural Dynamics Reveal Belief-Dependent Decision Making Under Ambiguity

Humans make thousands of decisions under ambiguity every day, where most times the probabilities of getting beneficial outcomes are not fully known. Although ambiguity aversion is well characterized at the aggregate level, it remains unclear how individuals internally represent ambiguous outcomes and how these representations shape behavior and physiology. To address these questions, we investigate value-based decision-making under risk and ambiguity using a multimodal approach that combines behavioral, pupillometric, and electroencephalographic (EEG) data. We show that individuals adopt distinct decision strategies that reflect different internal beliefs about unknown outcomes. These belief differences are expressed not only in choice but also in pupil-linked arousal during decision formation. Crucially, physiological differences across strategies persist under an objective valuation rule for ambiguous options but disappear when valuation is computed using each individuals subjective beliefs, indicating that arousal tracks subjective belief rather than objective task structure. Together, our findings show that ambiguity aversion is not a uniform bias but reflects heterogeneous internal belief models, and that multimodal physiology provides a window into the mechanisms through which these beliefs guide value-based choice.

neuroscience↗

bioRxiv2025

Alpha modulation of spiking activity across multiple brain regions in mice performing a tactile selective detection task

Many cognitive and sensory processes are characterized by strong relationships between the timing of neuronal spiking and the phase of ongoing local field potential oscillations. The coupling of neuronal spiking in neocortex to the phase of alpha oscillations (8-12 Hz) has been well studied in nonhuman primates but remains largely unexplored in other mammals. How this alpha modulation of spiking differs between brain areas and cell types, as well as its role in sensory processing and decision making, are not well understood. We used Neuropixels 1.0 probes to chronically record neural activity from somatosensory cortex, prefrontal cortex, striatum, and amygdala in mice performing a whisker-based selective detection task. We observed strong spontaneous alpha modulation of single-neuron spiking activity during inter-trial intervals while mice performed the task. The prevalence and strength of alpha phase modulation differed significantly across regions and between cell types. Phase modulated neurons exhibited stronger responses to both go and no-go stimuli, as well as stronger motor- and reward-related changes in firing rate, than their unmodulated counterparts. The increased responsiveness of phase modulated neurons suggests they are innervated by more diverse populations. Alpha modulation of neuronal spiking during baseline activity also correlated with task performance. In particular, many neurons exhibited strong alpha modulation before correct trials, but not before incorrect trials. These data suggest that dysregulation of spiking activity with respect to alpha oscillations may characterize lapses in attention.

neuroscience↗

bioRxiv2024

Phase synchrony between prefrontal noradrenergic and cholinergic signals indexes inhibitory control

Inhibitory control is a critical executive function that allows animals to suppress their impulsive behavior in order to achieve certain goals or avoid punishment. We investigated norepinephrine (NE) and acetylcholine (ACh) dynamics and population neuronal activity in the prefrontal cortex (PFC) during inhibitory control. Using fluorescent sensors to measure extracellular levels of NE and ACh, we simultaneously recorded prefrontal NE and ACh dynamics in mice performing inhibitory control tasks. The prefrontal NE and ACh signals exhibited strong coherence at 0.4-0.8 Hz. Although inhibition of locus coeruleus (LC) neurons projecting to the PFC impaired inhibitory control, inhibiting LC neurons projecting to the basal forebrain (BF) caused a more profound impairment, despite an approximately 30% overlap between LC neurons projecting to the PFC and BF, as revealed by our tracing studies. The inhibition of LC neurons projecting to the BF did not diminish the difference in prefrontal NE/ACh signals between successful and failed trials; instead, it abolished the difference in NE-ACh phase synchrony between successful and failed trials, indicating that NE-ACh phase synchrony is a task-relevant neuromodulatory feature. Chemogenetic inhibition of cholinergic neurons that project to the LC region did not impair inhibitory control, nor did it abolish the difference in NE-ACh phase synchrony between successful or failed trials, further confirming the relevance of NE-ACh phase synchrony to inhibitory control. To understand the possible effect of NE-ACh synchrony on prefrontal population activity, we employed Neuropixels to record from the PFC during inhibitory control. The inhibition of LC neurons projecting to the BF not only reduced the number of prefrontal neurons encoding inhibitory control, but also disrupted population firing patterns representing inhibitory control, as revealed by a demixed principal component (dPCA) analysis. Taken together, these findings suggest that the LC modulates inhibitory control through its collective effect with cholinergic systems on population activity in the prefrontal cortex. Our results further indicate that NE-ACh phase synchrony is a critical neuromodulatory feature with important implications for cognitive control.

neuroscience↗

bioRxiv2023

Uncovering Invariant Representations in Functional Neuroimaging with Deep Metric Learning

With the increasing ability to record neuroimaging with higher spatial and temporal resolution, there is a growing need for methods that reduce these high-dimensional representations into latent low-dimensional structures that are discriminative and/or predictive of behavior, disease, or in general experimental context. We propose a metric learning framework to extract meaningful latent structures from high-dimensional fMRI data. This method learns the latent embeddings that reduce the intra-group variability while maximizing the inter-group variability. In addition, our method leverages advances in few-shot learning approaches to adapt to small sample-size fMRI datasets, allowing one to learn the latent structure from just a few samples per context. We evaluate our work on two publicly available fMRI datasets and report superior results compared to popular alternative approaches such as Principal Component Analysis (84.7% vs. 60%; 21.8% vs. 8.3%). We provide the Python code as open-source at Github.

bioinformatics↗

bioRxiv2022

Pupil-linked phasic arousal relates to the reduction of response inhibition: inferences from a simultaneous pupillometry and EEG-fMRI study

Attention reorienting is a critical cognitive function which drives how we respond to novel and unexpected stimuli. In recent years, arousal has been linked to attention reorienting. The timing and spatial organization of the interactions between the arousal and reorienting systems, however, remain only partially revealed. Here, we investigate the dynamics between the two systems through simultaneous recordings of pupillometry, EEG, and fMRI of healthy human subjects while they performed an auditory target detection task. We used pupil diameter and activity in the noradrenergic locus coeruleus to infer arousal, and found these measures linked to distinct cortical activity at various temporal stages of the reorienting response. Specifically, our results provide the first demonstration in humans of how phasic pupil-linked arousal relates to the reduction of response inhibition, an inference which otherwise would remain hidden without the help of simultaneous multi-modal acquisitions.

neuroscience↗

bioRxiv2022

Functional and effective connectivity between dorsolateral prefrontal and subgenual anterior cingulate cortex depends on the timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG

BACKGROUNDThe communication through coherence model posits that brain rhythms are synchronized across different frequency bands and that effective connectivity strength between interacting regions depends on their phase relation. Evidence to support the model comes mostly from electrophysiological recordings in animals while evidence from human data is limited. METHODSHere, an fMRI-EEG-TMS (fET) instrument capable of acquiring simultaneous fMRI and EEG during noninvasive single pulse TMS applied to dorsolateral prefrontal cortex (DLPFC) was used to test whether prefrontal EEG alpha phase moderates TMS-evoked top-down influences on subgenual, rostral and dorsal anterior cingulate cortex (ACC). Results in healthy volunteers (n=11) were compared to those from patients with major depressive disorder (MDD) (n=17) collected as part of a ongoing clinical trial investigation. RESULTSIn both groups, TMS-evoked functional connectivity between DLPFC and subgenual ACC (sgACC) depended on the EEG alpha phase. TMS-evoked DLPFC to sgACC effective connectivity (EC) was moderated by EEG alpha phase in healthy volunteers, but not in the MDD patients. Top-down EC was inhibitory for TMS onsets during the upward slope of the alpha wave relative to TMS timed to the downward slope of the alpha wave. Prefrontal EEG alpha phase dependent effects on TMS-evoked fMRI BOLD activation of the rostral anterior cingulate cortex were detected in the MDD patient group, but not in the healthy volunteer group. DISCUSSIONResults demonstrate that TMS-evoked top-down influences vary as a function of the prefrontal alpha rhythm, and suggest clinical applications whereby TMS is synchronized to the brains internal rhythms in order to more efficiently engage deep therapeutic targets.

neuroscience↗

bioRxiv2022

Spatiotemporal dissociation of brain network connectivity in salience processing: A simultaneous pupillometry-EEG-fMRI study

The processing of salient stimuli involves a wide range of both bottom-up and top-down processes. Previous neuroimaging studies have identified multiple brain areas and networks for salience processing, including the salience network (SN), dorsal attention network (DAN), and the locus coeruleus-norepinephrine (LC-NE) neuromodulatory system. However, interactions among these networks and the cortico-subcortical systems in salience processing remain unclear. Here, we simultaneously recorded pupillometry, electroencephalogram (EEG), and functional magnetic resonance imaging (fMRI) during an auditory oddball paradigm. Using EEG-informed fMRI analysis, we temporally dissociated the target stimulus evoked activation, allowing us to identify the cascades of cortical areas associated with salience processing. Furthermore, functional connectivity analysis uncovered spatiotemporal functional network organizations of these salience processing neural correlates. Using pupillometry as a psychophysiological marker of LC-NE activity, we also assessed brain-pupil relationships. With state-space modeling of target modulated effective connectivity, we found that the target evoked pupillary response is associated with the network causal couplings from late to early subsystems, as well as the network switching initiated by the SN. These findings indicate that the SN might cooperate with pupil-indexed brainstem neuromodulatory systems, such as the LC-NE system, in the reorganization and dynamic switching of cortical networks, and shed light on the implications of their integrative framework in various cognitive processes and neurological diseases.

neuroscience↗

bioRxiv2021

Spatiospectral brain networks reflective of improvisational experience

Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical cord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

neuroscience↗

bioRxiv2019

Decomposing Simon task BOLD activation using a drift-diffusion model framework

The Simon effect is observed in spatial conflict tasks where the response time of subjects is increased if stimuli are presented in a lateralized manner so that they are incongruous with the response information that they represent symbolically. Previous studies have used fMRI to investigate this phenomenon, and while some have been driven by considerations of an underlying model, none have attempted to directly tie model and BOLD response together. It is likely that this is due to Simon models having been predominantly descriptive of the phenomenon rather than capturing the full spectrum of behavior at the level of individual subjects. Sequential sampling models (SSM) which capture full response distributions for correct and incorrect responses have recently been extended to capture conflict tasks.\n\nIn this study we use our freely available framework for fitting and comparing non-standard SSM to fit the Simon effect SSM (SE-SSM) to behavioral data. This model extension includes specific estimates of automatic response bias and conflict monitoring based deployment of attention to individual subject behavioral data. We apply this approach in order to investigate whether our task specific model parameters have a correlate in BOLD response. Under the assumption that the SE-SSM reflects aspects of neural processing in this task, we go on to examine the BOLD correlates with the within trial expected decision-variable.\n\nWe find that the SE-SSM captures the behavioral data and that our two conflict specific model parameters have clear across subject BOLD correlates, while other model parameters, as well as more standard behavioral measures do not. We also find that examining BOLD in terms of the expected decision-variable leads to a specific pattern of activation that would not be otherwise possible to extract.

neuroscience↗

bioRxiv2018

Regulation of arousal via on-line neurofeedback improves human performance in a demanding sensory-motor task

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use on-line neurofeedback to shift an individuals arousal toward this optimal state. Specifically, we use a brain computer interface (BCI) that uses information in the electroencephalogram (EEG) to generate a neurofeedback signal that dynamically adjusts an individuals arousal state when they are engaged in a boundary avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality (VR), and which creates cognitive conditions that escalate arousal and quickly results in task failure -- e.g. missing or crashing into the boundary. We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart rate variability show that the neurofeedback indeed reduces arousal. Our work is the first demonstration of a BCI system that uses on-line neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.

neuroscience↗

bioRxiv2018

Parameterized expertise: Evidence for gradations of musical expertise from electroencephalographic monitoring of auditory decision-making

A musicians nervous system is thought to specialize to their mode of music production. For instance, the pianists control of hands and arms develops to facilitate greater dexterity at the keyboard, while the cellist develops control to play notes using both the fret board and/or bow. Our previous work, employing an anomalous musical event (AME) detection task, identified neural and behavioral correlates that differentiated between a specific class of musicians, cellists, and those without professional musical training and expertise. Here we investigate a fine-grain differentiation between musicians having different modes of musical production, specifically in terms of how these differences are manifested in the neural correlates identified in the AME task. We show, using electroencephalography (EEG), that both event related potentials (ERPs) and single-trial analysis of the EEG can grade musical expertise by mode of sound production. Important is that these fine-grained EEG correlates are observable absent any motor response or active music production by the individuals. We find evidence that these grades of expertise are mediated by different sensory-motor interactions emblematic of the sound production mode. More broadly, our results show that neural markers can both define types of musical expertise and decompose their source components when behavioral differences are either minute or indistinguishable.

neuroscience↗

bioRxiv2018

Spatio-temporally specific transcranial magnetic stimulation to test the locus of perceptual decision making in the human brain

Previous research modeling EEG, fMRI and behavioral data has identified three spatially distributed brain networks that activate in temporal sequence, and are thought to enable perceptual decision-making during face-versus-car categorization. These studies have linked late activation (>300ms post stimulus onset) in the lateral occipital cortex (LOC) to object discrimination processes. We applied paired-pulse transcranial magnetic stimulation (ppTMS) to LOC at different temporal latencies with the specific prediction, based on these studies, that ppTMS beginning at 400ms after stimulus onset would slow reaction time (RT) performance. Thirteen healthy adults performed a two-alternative forced choice task selecting whether a car or face was present on each trial amidst visual noise pre-titrated to approximate 79% accuracy. ppTMS, with pulses separated by 50ms, was applied at one of five stimulus onset asynchronies: -200, 200, 400, 450, or 500ms, and a sixth no-stimulation condition. As predicted, TMS at 400ms resulted in significant slowing of RTs, providing causal evidence in support of LOC contribution to perceptual decision processing. In addition, TMS delivered at -200ms resulted in faster RTs, indicating early stimulation may result in performance enhancement. These findings build upon correlational EEG and fMRI observations and demonstrate the use of TMS in predictive validation of psychophysiological models.

neuroscience↗