Biology subjects

Pappas, I.

Publications and source records attributed to Pappas, I..

16 recordsLinked to original sources

bioRxiv2026

The Prefrontal Cortex Organizes Visual Evidence by Representational Abstraction

Perceptual decisions often depend on both lower-level visual features and higher-level object representations, yet natural images entangle these levels of abstraction. We used deep neural network features to dissociate lower- and higher-level visual similarity during a naturalistic image-similarity judgment task. Participants relied on both forms of evidence, with greater weighting of higher-level similarity. Across prefrontal cortex, visual evidence was systematically organized by representational level: lower- and higher-level similarity were expressed in distinct prefrontal regions during comparison and remained spatially differentiated as they were transformed into decision signals. This organization was behaviorally relevant, with prefrontal activation predicting which level of evidence guided choice when the two conflicted. Together, these findings identify representational level as an organizing principle of prefrontal cortex during visual decisions.

neuroscience↗

bioRxiv2025

Arousal increases locus coeruleus blood flow, salience-related brain responses, and modulates negative-valence attentional biases

The amygdala helps prioritize emotional over neutral information. However, it responds similarly to positive and negative stimuli, and so is unlikely to be the source of valence-specific effects within affective networks. We hypothesized that the locus coeruleus (LC) is a key contributor to negative biases in attention. Using ultra-high field 7T magnetic resonance imaging, we tested how arousal modulates processing of emotional faces during an oddball task in twenty-two young adults during two separate sessions. Arousal induced by isometric handgrip increased LC cerebral blood flow and amplified brain responses to target and angry faces, but not to happy faces. The amygdala exhibited valence-general responses that were not modulated by arousal. LC connectivity with the default mode network decreased during processing target and angry faces, and arousal further modulated responses in the salience network and visual cortex. Behaviorally, arousal enhanced recognition of angry faces only when allocating attentional resources and memory performance was linked to left LC brain activity. These findings highlight the LC as a key structure through which arousal shapes valence processing, biases attention, and informs mechanisms related to affective disorders.

neuroscience↗

bioRxiv2025

Temporal fMRI Dynamics Map Dopamine Physiology

Spatial variations in dopamine function are linked to cognition and substance use disorders but are challenging to characterize with current methods. Because dopamine influences blood vessel dilation, we hypothesized that hemodynamic latency, which reflects BOLD signal timing, could serve as an indirect marker of dopamine physiology. Across four datasets, we found a topography of hemodynamic latencies that precisely distinguished the nucleus accumbens, a dopaminergic region implicated in motivation and substance abuse, from other striatal regions. Using PET, genetics, and pharmacology, we found that hemodynamic latencies are robustly related to dopamine function and dopamine-linked behavior. In individuals with cocaine use disorder, we observed a spatial gradient of altered hemodynamic latencies in the striatum. This pattern independently predicted nicotine use, revealing a conserved physiological profile associated with addictive substance use. Hemodynamic latencies map regional, individual, and pathological differences linked to dopamine, opening new avenues for indirectly assessing the role of dopamine in healthy cognition and disease.

neuroscience↗

bioRxiv2025

Recurrent Neural Network Exploration Strategies During Reinforcement Learning Depend on Network Capacity

Artificial neural networks constitute simplified computational models of neural circuits that might help understand how the biological brain solves and represents complex tasks. Previous research revealed that recurrent neural networks (RNNs) with 48 hidden units show human-level performance in restless four-armed bandit tasks but differ from humans with respect to the task strategy employed. Here we systematically examined the impact of network capacity (no. of hidden units) on computational mechanisms and performance. Computational modeling was applied to investigate and compare network behavior between capacity levels as well as between RNNs and human learners. Using a task frequently employed in human cognitive neuroscience work as well as in animal systems neuroscience work, we show that high-capacity networks displayed increased directed exploration and attenuated random exploration relative to low-capacity networks. RNNs with 576 hidden units approached "human-like" exploration strategies, but the overall switch rate and the level of perseveration still deviated from human learners. In the context of the resource-rational framework, which posits a trade-off between reward and policy complexity, human learners may devote more resources to solving the task, albeit without performance benefits over RNNs. Taken together, this work reveals the importance of network capacity on exploration strategies during reinforcement learning and therefore contributes to the goal of building neural networks that behave "human-like" to possibly gain insights into computational mechanisms in human brains.

animal behavior and cognition↗

bioRxiv2025

Emotional arousal enhances narrative memories through functional integration of large-scale brain networks

Emotional events tend to be vividly remembered. While growing evidence suggests that emotions have their basis in brain-wide network interactions, it is unclear if and how these whole-brain dynamics contribute to memory encoding. We combined fMRI, graph theory, text analyses, and pupillometry in a naturalistic context where participants recalled complex narratives in their own words. Across three independent datasets, emotionally arousing moments during narrative perception were associated with an integrated brain state characterized by increased cohesion across functional modules, which in turn predicted the fidelity of subsequent recall. Network integration mediated the influence of emotional arousal on recall fidelity, with consistent within- and between-network interactions supporting the mediation across datasets. Together, these results suggest that emotional arousal enhances memory encoding via strengthening functional integration across brain networks. Our findings advance a cross-level understanding of emotional memories that bridges large-scale brain network dynamics, affective states and ongoing cognition.

neuroscience↗

bioRxiv2024

A meta reinforcement learning account of behavioral adaptation to volatility in recurrent neural networks

Natural environments often exhibit various degrees of volatility, ranging from slowly changing to rapidly changing contingencies. How learners adapt to changing environments is a central issue in both reinforcement learning theory and psychology. For example, learners may adapt to changes in volatility by increasing learning if volatility increases, and reducing it if volatility decreases. Computational neuroscience and neural network modeling work suggests that this adaptive capacity may result from a meta-reinforcement learning process (implemented for example in the prefrontal cortex), where past experience endows the system with the capacity to rapidly adapt to environmental conditions in new situations. Here we provide direct evidence for a meta-reinforcement learning account of adaptation to environmental volatility. Recurrent neural networks (RNNs) were trained on a restless four-armed bandit reinforcement learning problem under three different training regimes (low volatility training only, medium volatility training only, or meta-volatility training across a range of volatilities). We show that, in contrast to RNNs trained in the low volatility regime, RNNs trained under the medium or meta-volatility regimes exhibited a superior adaption to environmental volatility. This was reflected in a better performance, and computational modeling of the networks behavior revealed a more adaptive adjustment of learning and exploration to varying levels of volatility during test. Results extend the meta-RL account to volatility adaptation and confirm past experience as a crucial factor of adaptive decision-making.

neuroscience↗

bioRxiv2024

Age-Related Decline in BBB Function is More Pronounced in Males than Females

The blood-brain barrier (BBB) plays a pivotal role in protecting the central nervous system (CNS), shielding it from potential harmful entities. A natural decline of BBB function with aging has been reported in both animal and human studies, which may contribute to cognitive decline and neurodegenerative disorders. Limited data also suggest that being female may be associated with protective effects on BBB function. Here we investigated age and sex-dependent trajectories of perfusion and BBB water exchange rate (kw) across the lifespan in 186 cognitively normal participants spanning the ages of 8 to 92 years old, using a non-invasive diffusion prepared pseudo-continuous arterial spin labeling (DP-pCASL) MRI technique. We found that the pattern of BBB kw decline with aging varies across brain regions. Moreover, results from our DP-pCASL technique revealed a remarkable decline in BBB kw beginning in the early 60s, which was more pronounced in males. In addition, we observed sex differences in parietal and temporal regions. Our findings provide in vivo results demonstrating sex differences in the decline of BBB function with aging, which may serve as a foundation for future investigations into perfusion and BBB function in neurodegenerative and other brain disorders. Significance statementThe blood-brain barrier (BBB) serves as a critical protection mechanism for the CNS. A natural decline of BBB function with aging has been reported in both animal and human studies, with possible differences in BBB function by sex. Using our MRI technique, DP-pCASL that measures water exchange rate (kw) without contrast in 186 participants from diverse race and age groups, we identified age and sex-specific patterns in BBB kw especially in parietal and temporal regions. We observed of a decline in kw beginning in the early 60s, especially in males. Our study unveils the dynamic spatiotemporal pattern of kw differences with age and sex, which serve as a foundation for understanding aberrations of BBB function in neurodegenerative and other brain disorders.

neuroscience↗

bioRxiv2023

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

neuroscience↗

bioRxiv2023

Human-level reinforcement learning performance of recurrent neural networks is linked to hyperperseveration, not directed exploration

A key feature of animal and human decision-making is to balance the exploration of unknown options for information gain (directed exploration) versus selecting known options for immediate reward (exploitation), which is often examined using restless bandit tasks. Recurrent neural network models (RNNs) have recently gained traction in both human and systems neuroscience work on reinforcement learning, due to their ability to show meta-learning of task domains. Here we comprehensively compared the performance of a range of RNN architectures as well as human learners on restless four-armed bandit problems. The best-performing architecture (LSTM network with computation noise) exhibited human-level performance. Computational modeling of behavior first revealed that both human and RNN behavioral data contain signatures of higher-order perseveration, i.e., perseveration beyond the last trial, but this effect was more pronounced in RNNs. In contrast, human learners, but not RNNs, exhibited a positive effect of uncertainty on choice probability (directed exploration). RNN hidden unit dynamics revealed that exploratory choices were associated with a disruption of choice predictive signals during states of low state value, resembling a win-stay-loose-shift strategy, and resonating with previous single unit recording findings in monkey prefrontal cortex. Our results highlight both similarities and differences between exploration behavior as it emerges in meta-learning RNNs, and computational mechanisms identified in cognitive and systems neuroscience work.

neuroscience↗

bioRxiv2022

Cerebral perfusion in post-stroke aphasia and its relation to residual language abilities

Stroke alters blood flow to the brain resulting in damaged tissue and cell death. Moreover, the disruption of cerebral blood flow (perfusion) can be observed in areas surrounding and distal to the lesion. These structurally preserved but sub-optimally perfused regions may also affect recovery. Thus, to better understand aphasia recovery the relationship between cerebral perfusion and language needs to be systematically examined. In the current study, we aimed to evaluate 1) how stroke affects perfusion outside of lesioned areas in chronic aphasia, and 2) how perfusion in specific cortical areas and perilesional tissue relates to language outcomes in aphasia. We analyzed perfusion data from a large sample of participants with chronic aphasia due to left hemisphere stroke (n=43) and age-matched healthy controls (n=25). We used anatomically-defined regions of interest that covered the frontal, parietal, and temporal areas of the perisylvian cortex in both hemispheres, areas typically known to support language, along with several control regions not implicated in language processing. For the aphasia group we also looked at three regions of interest in the perilesional tissue. We compared perfusion levels between the two groups and investigated the relationship between perfusion levels and language subtest scores while controlling for demographic and lesion variables. First, we observed that perfusion levels outside the lesioned areas were significantly reduced in frontal and parietal regions in the left hemisphere in people with aphasia compared to the control group, while no differences were observed for the right hemisphere regions. Second, we found that perfusion in the left temporal lobe (and most strongly in the posterior part of both superior and middle temporal gyri) and inferior parietal areas (supramarginal gyrus) was significantly related to residual expressive and receptive language abilities. In contrast, perfusion in the frontal regions did not show such a relationship; no relationship with language was also observed for perfusion levels in control areas and all right hemisphere regions. Third, perilesional perfusion was only marginally related to language production abilities. Cumulatively, the current findings demonstrate that blood flow is reduced beyond the lesion site in chronic aphasia and that hypoperfused neural tissue in critical temporoparietal language areas has a negative impact on behavioral outcomes. These results, using perfusion imaging, underscore the critical and general role that left hemisphere posterior temporal regions play in various expressive and receptive language abilities. Overall, the study highlights the importance of exploring perfusion measures in stroke.

neuroscience↗

bioRxiv2021

Consciousness is supported by near-critical cortical electrodynamics

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition, and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, for the first time, we empirically identify the specific critical point near which conscious cortical dynamics operate as the edge-of-chaos critical point, or the boundary between periodicity/stability and chaos/instability. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magne-toencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, GABAergic anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of cortical dynamics away from this critical point; conversely, we show that psychedelics may increase the information-richness of cortical activity by tuning cortical electrodynamics closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC), and show that assessing the proximity of cortical electrodynamics to the edge-of-chaos critical point may be clinically useful as a new biomarker of consciousness. Significance StatementWhat changes in the brain when we lose consciousness? One possibility is that the loss of consciousness corresponds to a transition of the brains electric activity away from edge-of-chaos criticality, or the knifes edge in between stability and chaos. Recent mathematical developments have produced novel tools for testing this hypothesis, which we apply for the first time to cortical recordings from diverse brain states. We show that the electric activity of the cortex is indeed poised near the boundary between stability and chaos during conscious states and transitions away from this boundary during unconsciousness, and that this transition disrupts cortical information processing.

neuroscience↗

bioRxiv2021

Resting-state based prediction of task-related activation in patients with disorders of consciousness

BackgroundAssessment of the level of awareness of people with disorders of consciousness (DOC) is clinically challenging, motivating several studies to combine brain imaging with machine learning to improve this process. While this work has shown promise, it has limited clinical utility, as misdiagnosis of DOC patients is relatively high. As machine learning algorithms rely on accurately labelled data, any error in diagnosis will be learned by the algorithm, resulting in an equally limited diagnostic tool. The goal of the present study is to overcome this problem by stratifying patients, not by diagnosis, but by their capacity to perform volitional tasks during functional magnetic resonance imaging (fMRI) scanning. MethodsA total of 71 patients were assessed for inclusion. They were excluded for the final analysis if they had large focal brain damage, excessive head motion during scanning, or suboptimal MRI preprocessing. Patients underwent both resting-state and task-based fMRI scanning. Univariate fMRI analysis was performed to determine if an individual patient had brain activity consistent with having retained volitional capacity (VC). Differences in resting brain network connectivity between patients with VC and patients without volitional capacity (non-VC) were measured. Connectivity data was then entered as input to a deep learning framework. We used a deep graph convolutional neural network (DGCNN) on connectivity data to identify a specific brain network that most significantly differentiates patients. FindingsWe included 30 patients in our final analysis. Univariate analysis revealed that 13 patients displayed signs of VC, while 17 did not. We found that resting-state connectivity between frontoparietal control and salience network was significantly different between VC and non-VC patients (T(28) = 3.347, p = 0.0023, Bonferroni corrected p = 0.042). Furthermore, we found that using frontoparietal control network connectivity as input to the DGCNN resulted in the best classification performance (test accuracy = 0.85; ROC AUC = 0.92). InterpretationWe found that the DGCNN performed best at discriminating between patients with VC when using only the frontoparietal control network as input to the model. The use of this deep learning method is a significant advance since its inherent flexibility permits the inclusion of both whole-brain and network-specific properties as input, allowing us to classify patients as either having or not having VC. This inclusion of multi-scale inputs (e.g. whole-brain and network-level) facilitates model interpretability and increases our understanding of the neurobiology of DOC. The results propose that the integrity of frontoparietal control network, a brain network well known to play a key role in executive functions and cognitive control, is essential for volitional capacity preservation in patients with DOC. The study also lays groundwork for development of a biomarker to aid in the diagnosis of DOC patients. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSDisorders of consciousness (DOC) are a group of severe brain disorders characterised by damage to the neural systems underlying wakefulness and awareness. DOC are often caused by traumatic brain injury, hypoxia, or neurodegenerative diseases. The motor and cognitive impairments in DOC patients make providing an accurate diagnosis very challenging. Diagnosis is primarily made at the bedside by assessing a patients response to motor commands.

neuroscience↗

bioRxiv2020

A Precuneal Causal Loop Mediates External and Internal Information Integration in the Human Brain

AO_SCPLOWBSTRACTC_SCPLOWHuman brains interpret external stimuli based on internal representations. One untested hypothesis is that the default-mode network (DMN) while responsible for internally oriented cognition can also encode externally oriented information. The unique neuroanatomical and functional fingerprint of the posterior part of the DMN supports a prominent role for the precuneus in this process. By utilising imaging data during two tasks from 100 participants, we found that the precuneus is functionally divided into dorsal and ventral subdivisions, each one differentially connecting to internally and externally oriented networks. The strength and direction of their connectivity is modulated by task difficulty in a manner dictated by the balance of internal versus external cognitive demands. Our study provides evidence that the medial posterior part of the DMN may drive interactions between large-scale networks, potentially allowing access to stored representations for moment to moment interpretation of an ever-changing environment.

neuroscience↗

bioRxiv2020

Connectome Harmonic Decomposition of Human Brain Dynamics Reveals a Landscape of Consciousness

AbstractA central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Departing from the predominant location- centric view in neuroimaging, here we provide an alternative perspective on the neural signatures of human consciousness: one that is intrinsically centered on how the distributed network architecture of the human structural connectome shapes functional activation across scales. We decompose cortical dynamics of resting-state functional MRI into fundamental distributed patterns of structure- function association: the harmonic modes of the human structural connectome. We contrast wakefulness with a wide spectrum of states of consciousness, spanning chronic disorders of consciousness but also pharmacological perturbations of consciousness induced with the anaesthetic propofol and the psychoactive drugs ketamine and LSD. Decomposing this wide spectrum of states of consciousness in terms of "connectome harmonics" reveals a generalisable structure-function signature of loss of consciousness, whether due to anaesthesia or brain injury. A mirror-reverse of this harmonic signature characterises the altered state induced by LSD or ketamine, reflecting psychedelic-induced decoupling of brain function from structure. The topology and neuroanatomy of the human connectome are crucial for shaping the repertoire of connectome harmonics into a fine-tuned indicator of consciousness, correlating with physiological and subjective scores across datasets and capable of discriminating between behaviourally indistinguishable sub-categories of brain-injured patients, tracking the presence of covert consciousness. Overall, connectome harmonic decomposition identifies meaningful relationships between neurobiology, brain function, and conscious experience.

neuroscience↗

bioRxiv2020

Differential Contributions of Static and Time-Varying Functional Connectivity to Human Behavior

Measures of human brain functional connectivity acquired during the resting-state track critical aspects of behavior. Recently, fluctuations in resting-state functional connectivity patterns - typically averaged across in traditional analyses - have been considered for their potential neuroscientific relevance. There exists a lack of research on the differences between traditional "static" measures of functional connectivity and newly-considered "time-varying" measures as they relate to human behavior. Using functional magnetic resonance imagining (fMRI) data collected at rest, and a battery of behavioral measures collected outside the scanner, we determined the degree to which each modality captures aspects of personality and cognitive ability. Measures of time-varying functional connectivity were derived by fitting a Hidden Markov Model. To determine behavioral relationships, static and time-varying connectivity measures were submitted separately to canonical correlation analysis. A single relationship between static functional connectivity and behavior existed, defined by measures of personality and stable behavioral features. However, two relationships were found when using time-varying measures. The first relationship was similar to the static case. The second relationship was unique, defined by measures reflecting trialwise behavioral variability. Our findings suggest that time-varying measures of functional connectivity are capable of capturing unique aspects of behavior to which static measures are insensitive. Author SummaryCorrelated patterns of brain activity measured in the absence of any prescribed task show meaningful temporal fluctuations. However, the manner by which such fluctuations track aspects of human behavior remains unresolved. The current report takes a data-driven approach to characterize how time-varying patterns of human brain functional connectivity differ from traditional static measures in their ability to track aspects of personality and cognitive ability. We determine that time-varying patterns of functional connectivity not only track similar aspects of behavior as do static measures, but also unique behavioral qualities as well, specifically those that reflect behavioral variability. These results highlight the importance and relevance of examining time-varying measures of functional connectivity.

neuroscience↗

bioRxiv2019

Consciousness & Brain Functional Complexity in Propofol Anaesthesia

The brain is possibly the most complex system known to mankind, and its complexity has been called upon to explain the emergence of consciousness. However, complexity can take many forms: here, we investigate measures of algorithmic and process complexity in both the temporal and topological dimension, testing them on functional MRI data obtained from individuals undergoing various levels of sedation with the anaesthetic agent propofol, in two separate datasets. We demonstrate that the various measures are differently able to discriminate between levels of sedation, with temporal measures showing higher sensitivity. Further, we show that all measures are strongly related to a single underlying construct explaining most of the variance, as assessed by Principal Component Analysis, which we interpret as a measure of overall complexity of our data. This overall complexity was also able to discriminate between levels of sedation, supporting the hypothesis that consciousness is related to complexity - independent of how the latter is measured.

neuroscience↗