An official website of the United States government
Abstract Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.
more »
« less
Nonergodicity and Simpson’s paradox in neurocognitive dynamics of cognitive control
Nonergodicity and Simpson’s paradox present significant, yet underappreciated challenges in neuroscience. Leveraging brain imaging and behavioral data from over 4,000 children and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying cognitive control at both between-subjects and within-subjects levels. Strikingly, we observed a reversal of associations of inhibitory control brain activations with dynamic behavioral measures when comparing between-subjects and within-subjects analyses, revealing the nonergodic nature of these processes. This nonergodicity was pervasive throughout the brain but most pronounced in the salience network. Additionally, within-subjects analysis uncovered dissociated brain representations of reactive and proactive control processes, as well as distinct brain-behavior associations for individuals who adaptively versus maladaptively regulated cognitive control. Our findings offer insights into dynamic neural mechanisms of cognitive control during a critical developmental period. This work highlights the importance of embracing nonergodicity in human neuroscience, with implications for both theoretical understanding and applications to AI and psychopathology.
more »
« less
- Award ID(s):
- 2024856
- PAR ID:
- 10559988
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.more » « less
-
Cognitive neuroscience is the interdisciplinary study of how cognitive and intellectual functions are processed and represented within the brain, which is critical to building understanding of core psychological and behavioural processes such as learning, memory, behaviour, perception, and consciousness. Understanding these processes not only offers relevant fundamental insights into brain-behavioural relations, but may also lead to actionable knowledge that can be applied in the clinical treatment of patients with various brain-related disabilities. This Handbook examines complex cognitive systems through the lens of neuroscience, as well as providing an overview of development and applications within cognitive and systems neuroscience research and beyond. Containing 35 original, state of the art contributions from leading experts in the field, this Handbook is essential reading for researchers and students of cognitive psychology, as well as scholars across the fields of neuroscientific, behavioural and health sciences. Part 1: Attention, Learning and Memory; Part 2: Language and Communication; Part 3: Emotion and Motivation; Part 4: Social Cognition; Part 5: Cognitive Control and Decision Making; and Part 6: Intelligence.more » « less
-
Abstract In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functional connectivity analysis. However, while functional connectivity offers valuable insights, it fails to consider the diverse timescales of coupling between different brain regions. This gap in understanding leaves a significant aspect of brain dynamics unexplored in neuroimaging research. We propose an innovative approach that delves into the dynamic coupling/connectivity timescales of brain regions relative to one another, focusing on how brain region couplings stretch or shrink over time, rather than relying solely on functional connectivity measures. Our method introduces a novel metric called “warping elasticity,” which utilizes dynamic time warping (DTW) to capture the temporal nuances of connectivity. Unlike traditional methods, our approach allows for (potentially nonlinear) dynamic compression and expansion of the time series, offering a more intricate understanding of how coupling between brain regions evolves. Through the adaptive windows employed by the DTW method, we can effectively capture transient couplings within varying connectivity timescales of brain network pairs. In extensive evaluations, our method exhibits high replicability across subjects and diverse datasets, showcasing robustness against noise. More importantly, it uncovers statistically significant distinctions between healthy control (HC) and schizophrenia (SZ) groups through the identification of warp elasticity states. These states are cluster centroids, representing the warp elasticity across subjects and time, offering a novel perspective on the dynamic nature of brain connectivity, distinct from conventional metrics focused solely on functional connectivity. For instance, controls spend more time in a warp elasticity state characterized by timescale stretching of the visual domain relative to other domains, suggesting disruptions in the visual cortex. Conversely, patients show increased time spent in a warp elasticity state with stretching timescales in higher cognitive areas relative to sensory regions, indicative of prolonged cognitive processing of sensory input. Overall, our approach presents a promising avenue for investigating the temporal dynamics of brain network interactions in functional magnetic resonance imaging (fMRI) data. By focusing on the elasticity of connectivity timescales, rather than adhering to functional connectivity metrics, we pave the way for a deeper understanding of neuropsychiatric disorders in neuroscience research.more » « less
-
The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain–behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.1101/2024.07.05.602273
