Biology subjects

Stocco, A.

Publications and source records attributed to Stocco, A..

9 recordsLinked to original sources

bioRxiv2025

The Role of the Basal Ganglia in the Human Cognitive Architecture: A Dynamic Causal Modeling Comparison Across Tasks and Individuals

Researchers agree input from the basal ganglia (BG) to the prefrontal cortex (PFC) plays an important role in cognition, but they disagree on its computational properties. Theoretical models characterize the majority of BG input as either direct (directly transmitting information to the PFC), or modulatory (indirectly influencing PFC activity through the gating of signals from other cortical areas). To determine the computational nature of these BG-PFC inputs in cognition, we tested three alternative connectivity configurations (Direct, Modulatory, and Mixed) within a large-scale cognitive architecture. This architecture, the Common Model of Cognition, has been independently validated using fMRI data from the Human Connectome Project (HCP). The Direct model reflected the standard CMC configuration, featuring a bidirectional, direct connection between the BG and PFC modules. The Modulatory model removed this direct link, instead incorporating a unidirectional connection from the PFC to the BG and two modulatory pathways from the BG to the PFC that passed through other cortical modules. The Mixed model included both direct and modulatory connections. Using fMRI data from 200 HCP participants performing six cognitive tasks and one resting-state session, we applied Dynamic Causal Modeling (DCM) to estimate and compare the influence of these different BG-PFC connectivity patterns. Here, we show that for each of the six cognitive tasks and resting state, the Mixed model consistently outperformed the Direct and Modulatory models. The next best model depended on the specific cognitive task, suggesting the ability for the BG to flexibly adapt to various task demands. Taken together, the current data provide evidence for a likely set of core computations that the BG uses to differentially regulate cortical activity. Author SummaryThis study aimed to understand the nature of regulatory input from the basal ganglia to the prefrontal cortex in cognition using a neuro-computational approach. Three model architectures of basal ganglia connectivity were tested using fMRI data from 200 individuals during cognitive tasks and resting state sessions. Our findings indicate that the Mixed architecture, in which the basal ganglia can affect the prefrontal cortex both directly and indirectly, outperformed the Direct and Modulatory architectures across various cognitive tasks and resting state sessions. The results suggest that the basal ganglia likely uses a set of core computations to regulate cortical activity, which is present during task and task-free behavior.

neuroscience↗

bioRxiv2025

Predicting Ecosystem Resilience Using Multi-Agent Reinforcement Learning

Twin models of natural ecosystems hold great promise for informing real-world decisions on sustainable land use and biodiversity conservation. However, existing simulations of animal behavior often rely on manually crafted rules, limiting their scalability and practical utility. Here, we present a flexible and scalable agent-based modeling approach that uses reinforcement learning--instead of hand-coded rules--to simulate animal behavior. We validate this approach across ten alpine ecosystems featuring wolves, chamois, and vegetation. By comparing model outputs with empirical data, we show that the simulations reproduce realistic ecological and behavioral patterns, including population dynamics, life history traits, and social interactions. We then use the model to assess ecosystem resilience under scenarios of habitat degradation, game hunting, and heat stress. Our framework paves the way for realistic simulations advancing our ability to predict ecosystem responses to disturbance and tipping points leading to biodiversity loss, in order to support conservation planning and guide the sustainable use of natural resources.

ecology↗

bioRxiv2023

Efficient Memory Encoding Explains the Interactions Between Hippocampus Size, Individual Experience, and Clinical Outcomes: A Computational Model

The relationship between hippocampal volume and memory function has produced mixed results in neuroscience research, which suggests an additional influencing mechanism. To explore the role of an experience-dependent encoding mechanism, we developed an autoencoder model of the cortex-hippocampus loop and examined how its memory representation is affected by a penalty that prioritizes sparseness. We trained our model with the Fashion MNIST database and a loss function to modify synapses via backpropagation of mean squared recall error. The model exhibited experience-dependent efficient encoding, representing frequently-repeated objects with fewer neurons and smaller loss penalties; representations of similar sizes were found for objects repeated equally. Our findings clarify perplexing results from neurodevelopmental studies by linking increased hippocampal size and memory impairments in autism spectrum disorder (ASD) to decreased sparseness, and explaining dementia symptoms of forgetting with varied neuronal integrity. Our findings propose a novel model that connects observed relationships between hippocampal size and memory to the environmental demands and the underlying biological constraints, contributing to the development of a larger theory on experience-dependent encoding and storage and its failure. Author SummaryThe hippocampus is the brain region where memories are initially formed. A larger hippocampus seems to be associated with better memory performance, but this is not a static relationship; studies suggest that its size might grows with demand (e.g., the need to memorize more information) and that, in certain conditions, larger or smaller volumes of the hippocampus are not associated with better or worse memory. We demonstrate that it is possible to make sense of these findings by assuming that the hippocampus balances the need to correctly remember with the need to minimize resources used to store it, and that the hippocampal size might change as a function of memory demands (the characteristics of the information to memorize) and the underlying biology (e.g., the presence of hippocampal cell damage, or a reduction in hippocampal GABA receptors). We test this hypothesis by building a neural network model; the model correctly predicts existing, puzzling findings in the literature, and can even capture subtle interactions between different phenomena, such as predicting that individuals with Autism Spectrum Disorder would be more susceptible to memory loss in dementia.

neuroscience↗

bioRxiv2023

Competing Decision-Making Systems Are Adaptively Chosen Based on Individual Differences in Brain Connectivity

Experiential decision-making can be explained as a result of either memory-based or reinforcement-based processes. Here, for the first time, we show that individual preferences between a memory-based and a reinforcement-based strategy, even when the two are functionally equivalent in terms of expected payoff, are adaptively shaped by individual differences in resting-state brain connectivity between the corresponding brain regions. Using computational cognitive models to identify which mechanism was most likely used by each participant, we found that individuals with comparatively stronger connectivity between memory regions prefer a memory-based strategy, while individuals with comparatively stronger connectivity between sensorimotor and habit-formation regions preferentially rely on a reinforcement-based strategy. These results suggest that human decision-making is adaptive and sensitive to the neural costs associated with different strategies.

neuroscience↗

bioRxiv2021

Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy (r = .78) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure. Author SummaryWhy do some people forget faster than others? This study investigates individual differences in long-term memory forgetting by linking them to patterns of brain connectivity. Although memory formation and retrieval are well-studied, much less is known about the brain processes that determine how memories are maintained or lost over time. A major challenge has been accurately measuring forgetting. To address this, we used an innovative method: fitting a computational model of memory to each participants accuracy and response time data during a learning task. This allowed us to estimate each persons unique rate of forgetting. We then examined how these rates related to resting-state brain connectivity. The results revealed that individual forgetting speeds were strongly associated with connectivity between the brains default mode network (DMN) and cortical sensory regions--areas thought to support long-term memory maintenance. These findings suggest that forgetting reflects how well memory traces are preserved in the brain, not just whether they can be retrieved.

neuroscience↗

bioRxiv2020

Reflections of Idiographic Long-Term Memory Characteristics In Resting-State Neuroimaging Data

Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individuals biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13-15 Hz) and upper (15-17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings.

neuroscience↗

bioRxiv2019

A Common Architecture for Human and Artificial Cognition Explains Brain Activity Across Domains

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.

neuroscience↗

bioRxiv2019

Comparing Alternative Computational Models of the Stroop Task Using Effective Connectivity Analysis of fMRI Data

Methodological advances have made it possible to generate fMRI predictions for cognitive architectures, such as ACT-R, thus expanding the range of model predictions and making it possible to distinguish between alternative models that produce otherwise identical behavioral patterns. However, for tasks associated with relatively brief response times, fMRI predictions are often not sufficient to compare alternative models. In this paper, we outline a method based on effective connectivity, which significantly augments the amount of information that can be extracted from fMRI data to distinguish between models. We show the application of this method in the case of two competing ACT-R models of the Stroop task. Although the models make, predictably, identical behavioral and BOLD time-course predictions, patterns of functional connectivity favor one model over the other. Finally, we show that the same data suggests directions in which both models should be revised.

neuroscience↗

bioRxiv2018

BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as \"Senders\" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Senders decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders decisions are transmitted via the Internet to the brain of a third subject, the \"Receiver,\" who cannot see the game screen. The Senders decisions are delivered to the Receivers brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and makes a decision using an EEG interface about either turning the block or keeping it in the same position. A second round of the game provides an additional chance for the Senders to evaluate the Receivers decision and send feedback to the Receivers brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game; (2) True/False positive rates of subjects decisions; (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the Tetris task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Senders signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that Receivers are able to learn which Sender is more reliable based solely on the information transmitted to their brains. Our results raise the possibility of future brain-to-brain interfaces that enable cooperative problem solving by humans using a \"social network\" of connected brains.

bioengineering↗