Biology subjects

Nakuci, J.

Publications and source records attributed to Nakuci, J..

11 recordsLinked to original sources

bioRxiv2026

Spatial collinearity constrains multivariate molecular-enriched network estimation

Analyses of neuroimaging data increasingly leverage the distribution of neurotransmitter receptors derived from Positron Emission Tomography (PET) to bridge the gap between micro- and macro-scale brain function. However, these receptor maps are highly spatially overlapping which can give rise to interpretive and analytical challenges. Here, we systematically investigate the impact of spatial collinearity among PET maps in the context of Receptor-Enriched Analysis of functional Connectivity by Targets (REACT), a method that uses receptor maps as spatial regressors to derive subject-level molecular-enriched functional connectivity networks. Exhaustive combinatorial analysis across 19 receptor and transporter maps showed that collinearity scales rapidly with the number of receptors modelled simultaneously, and that this was relatively stable across parcellation scales, reflecting the intrinsic organisation of neurotransmitter systems. Using test-retest fMRI data from the Human Connectome Project, we demonstrate that modelling greater numbers of receptors degrades the reliability of molecular-enriched networks derived from conventional multivariate REACT models, and that collinearity among receptor maps drives this degradation. An alternative univariate approach, in which each receptor is modelled independently, yielded more reliable networks and, when applied to a within-subjects study of LSD compared to placebo, better recovered the role of the 5HT-2A receptor in LSDs neural effects. These findings identify spatial collinearity as a fundamental constraint on multivariate molecular-enriched network estimation and support univariate modelling as a more robust default for this class of analysis.

neuroscience↗

bioRxiv2025

Brain-like variability in convolutional neural networks reveals evidence-,uncertainty- and bias-driven decision-making

Modern AI systems can be accurate and confident, but this alone does not reveal whether a decision is well supported by the input. This creates a trust problem because confidence reports how decisive a model is, but not what supports that decisiveness. Here we show that neural-network confidence can be decomposed into input-dependent feature support and input-independent offset support. We formalize this decomposition through the Bias Dominance Index (BDI), a layer-resolved measure quantifying the relative contribution of input-independent offsets to the decision margin, revealing whether confidence is primarily feature-supported or bias-driven. Across convolutional neural networks, a vision transformer and a transformer language model, BDI shows that high confidence can coexist with bias-driven decisions. Layer-resolved analyses map where bias-driven support across network depth. Perturbation analyses further show that the bias component can stabilize performance when readout weights are degraded. Finally, we operationalize decision composition into an acceptance rule that combines confidence and BDI for mechanism-aware auditing and triage. Together, these results position BDI as a general diagnostic of decision composition that distinguishes feature-supported from bias-driven decisions across model families.

neuroscience↗

bioRxiv2024

The neuroreceptors and transporters underlying spontaneous brain activity

Determining the neuromodulators driving brain activity is critical for understanding cognition and neuropathology. Neuromodulators act through neuroreceptors in a coordinated manner, yet their interconnected dynamics are often overlooked. We show that a neuroreceptor-based modeling framework using cortical density maps of 19 neuroreceptors and transporters from Positron Emission Tomography (PET) can model BOLD-derived brain activity. This framework reconstructs activity across four datasets (N = 314) identifying two neuroreceptor modules linked to higher-order associative or somatomotor and visual networks. Applying the framework to independent datasets, we recover the binding profiles of LSD and Modafinil, demonstrating consistency with known pharmacological and neurobiological associations. Additionally, it uncovered associations between neuroreceptors, transporters, and altered brain activity in neuropsychiatric disorders. These findings demonstrate the frameworks potential to elucidate neuromodulatory mechanisms and advance our understanding of brain function across diverse states and conditions.

neuroscience↗

bioRxiv2024

Quantifying the influence of biophysical factors in shaping brain communication through remnant functional networks

Functional connectivity (FC) reflects brain-wide communication essential for cognition, yet the role of underlying biophysical factors in shaping FC remains unclear. We quantify the influence of physical factors--structural connectivity (SC) and Euclidean distance (DC), which capture anatomical wiring and regional distance--and molecular factors--gene expression similarity (GC) and neuroreceptor congruence (RC), representing neurobiological similarity--on resting- state FC. We assess how these factors impact graph-theoretic and gradient features, capturing pairwise and higher-order interactions. By generating remnant functional networks after selectively removing connections tied to specific factors, we show that molecular factors, particularly RC, dominate graph-theoretic features, while gradient features are shaped by a mix of molecular and physical factors, especially GC and DC. SC has a surprisingly minor role. We also link FC alterations to biophysical factors in schizophrenia, bipolar disorder, and ADHD, with physical factors differentiating these groups. These insights are key for understanding FC across various applications, including task performance, development, and clinical conditions.

neuroscience↗

bioRxiv2024

Behavior can be decoded across the cortex when individual differences are considered

Group-level analyses have typically linked behavioral signatures to a constrained set of brain areas. Here we show that two behavioral metrics - reaction time (RT) and confidence - can be decoded across the cortex when each individual is considered separately. Subjects (N=50) completed a perceptual decision-making task with confidence. We built models decoding trial-level RT and confidence separately for each subject using the activation patterns in one brain area at a time after splitting the entire cortex into 200 regions of interest (ROIs). First, we developed a simple test to determine the robustness of decoding performance, which showed that several hundred trials per subject are required for robust decoding. We then examined the decoding performance at the group and subject levels. At the group level, we replicated previous results by showing that both RT and confidence could be decoded from a small number of ROIs (12.0% and 3.5%, respectively). Critically, at the subject level, both RT and confidence could be decoded from most brain regions even after Bonferroni correction (90.0% and 72.5%, respectively). Surprisingly, we observed that many brain regions exhibited opposite brain-behavior relationships across individuals, such that, for example, higher activations predicted fast RTs in some subjects but slow RTs in others. All results were replicated in a second dataset. These findings show that behavioral signatures can be decoded from a much broader range of cortical areas than previously recognized and suggest the need to study the brain-behavior relationship at both the group and subject levels.

neuroscience↗

bioRxiv2023

Multiple brain activation patterns for the same task

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibited strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.

neuroscience↗

bioRxiv2023

Brain signatures indexing variation in internal processing during perceptual decision-making

Brain activity is highly variable even while performing the same cognitive task with consequences for performance. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimentally inducing changes (e.g., via attention manipulation) to identify neuronal and behavioral consequences or studying spontaneous changes in ongoing brain dynamics. However, changes in internal processing could arise from many factors, such as variation in strategy or arousal, that are independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials and relate this activity to behavioral performance. Subjects (N = 25) performed a motion direction discrimination task with six interleaved levels of motion coherence. Modularity-maximization based clustering identified two discrete spatial-temporal clusters, or subtypes, of trials with different patterns of brain activity. Surprisingly, even though Subtype 1 occurred more frequently with lower motion coherence, it was nonetheless associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight trial-to-trial variability in decision processes usually masked to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior. Highlights Brain activity is highly variable. We find multiple and distinct stimulus-driven patterns in EEG. With changes in decision-making and drift-diffusion parameters. These results suggest a new way to identify brain states relevant to behavior. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/523502v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@e6e448org.highwire.dtl.DTLVardef@95512eorg.highwire.dtl.DTLVardef@104a976org.highwire.dtl.DTLVardef@11191e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Idiosyncratic relation between human brain activity and behavior

Research in neuroscience often assumes universal neural mechanisms, but increasing evidence points towards sizeable individual differences in brain activations. What remains unclear is the extent of the idiosyncrasy and whether different types of analyses are associated with different levels of idiosyncrasy. Here we develop a new method for addressing these questions. The method consists of computing the within-subject reliability and subject-to-group similarity of brain activations and submitting these values to a computational model that quantifies the relative strength of group- and subject-level factors. We apply this method to a perceptual decision-making task (N=50) and find that activations related to task, reaction time (RT), and confidence are influenced equally strongly by group- and subject-level factors. Both group- and subject-level factors are dwarfed by a noise factor, though higher levels of smoothing increases their contributions relative to noise. Overall, our method allows for the quantification of group- and subject-level factors of brain activations and thus provides a more detailed understanding of the idiosyncrasy levels in brain activations.

neuroscience↗

bioRxiv2022

Within- and between-subject reproducibility and variability in multi-modal, longitudinal brain networks

Network analysis provides new and important insights into the function of complex systems such as the brain by examining structural and functional networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. Although network models can shed light on cognition and pathology, questions remain regarding the importance of these findings, due in part to the reproducibility of the core measurements and subsequent modeling strategies. In order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first investigate the reproducibility of individual brain connections and network measures and find that across all modalities, within-subject reproducibility is higher than between-subject reproducibility, reaffirming the ability to detect individual differences in network structure in both structural and functional human brain networks. We see high variability in the reproducibility of pairwise connections between brain regions, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than networks derived from other frequency bands. Further, reproducible connections correspond to strong connections. Structural networks show a higher reliability in network statistics than functional networks, and certain measures such as synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the analysis of either structural or functional networks to study individual differences should depend on whether or not one wants to take into account state dependencies of the observed networks.

neuroscience↗

bioRxiv2022

Single trial variability in neural activity during a working memory task: A window into multiple distinct information processing sequences

Successful encoding, maintenance, and retrieval of information stored in working memory requires persistent coordination of activity among multiple brain regions. It is generally assumed that the pattern of such coordinated activity remains consistent for a given task. Thus, to separate this task-relevant signal from noise, multiple trials of the same task are completed, and the neural response is averaged across trials to generate an event-related potential (ERP). However, from trial to trial, the neuronal activity recorded with electroencephalogram (EEG) is actually spatially and temporally diverse, conflicting with the assumption of a single pattern of activity for a given task. Here, we show that variability in neuronal activity among single time-locked trials arises from the presence of multiple forms of stimulus dependent synchronized activity (i.e., distinct ERPs). We develop a data-driven classification method based on community detection to identify three discrete spatio-temporal clusters, or subtypes, of trials with different patterns of activation that are further associated with differences in decision-making processes. These results demonstrate that differences in the patterns of neural activity during working memory tasks represent fluctuations in the engagement of distinct brain networks and cognitive processes, suggesting that the brain can choose from multiple mechanisms to perform a given task. Significance StatementWorking memory is a complex cognitive ability requiring coordinated activity among multiple brain regions to encode, maintain, and retrieve information. It is generally assumed that the pattern of coordination among brain regions remains consistent and one can average data across multiple trials of the same task. We instead show that there is significant variability in the patterns of brain activity among trials of the same task and develop a method to classify brain activity into distinct subtypes of responses, each with a different spatial and temporal pattern. The subtypes are associated with differences in decision-making processes, suggesting that the brain can use multiple mechanisms to perform a given task.

neuroscience↗

bioRxiv2021

Differential patterns of change in brain connectivity resulting from traumatic brain injury

BackgroundTraumatic brain injury (TBI) damages white matter tracts, disrupting brain network structure and communication. There exists a wide heterogeneity in the pattern of structural damage associated with injury, as well as a large heterogeneity in behavioral outcomes. However, little is known about the relationship between changes in network connectivity and clinical outcomes. MethodsWe utilize the rat lateral fluid-percussion injury (FPI) model of severe TBI to study differences in brain connectivity in 8 animals that received the insult and 11 animals that received only a craniectomy. Diffusion Tensor Imaging (DTI) is performed 5 weeks after the injury and network theory is used to investigate changes in white matter connectivity. ResultsWe find that 1) global network measures are not able to distinguish between healthy and injured animals; 2) injury induced alterations predominantly exist in a subset of connections (subnetworks) distributed throughout the brain; and 3) injured animals can be divided into subgroups based on changes in network motifs - measures of local structural connectivity. Additionally, alterations in predicted functional connectivity indicate that the subgroups have different propensities to synchronize brain activity, which could relate to the heterogeneity of clinical outcomes. DiscussionThese results suggest that network measures can be used to quantify progressive changes in brain connectivity due to injury and differentiate among subpopulations with similar injuries but different pathological trajectories. Impact StatementWhite matter tracts are important for efficient communication between brain regions and their connectivity pattern underlies proper brain function. Traumatic brain injury (TBI) damages white matter tracts and changes brain connectivity, but how specific changes relate to differences in clinical/behavioral outcomes is not known. Using network theory to study injury related changes in structural connectivity, we find that local measures of network structure can identify subgroups of injured rats with different types of changes in brain structure. Our results suggest that these different patterns of change could relate to differences in clinical outcomes.

neuroscience↗