scholarly journals Inter-subject phase synchronization and the dynamics of human cognition

2017 ◽  
Author(s):  
Taylor Bolt ◽  
Jason S. Nomi ◽  
Shruti G. Vij ◽  
Catie Chang ◽  
Lucina Q. Uddin
Keyword(s):  
Cognitive Processes ◽  
Phase Synchronization ◽  
Brain Activity ◽  
Methodological Approach ◽  
Brain Networks ◽  
Human Cognition ◽  
Bold Signal ◽  
Top Down ◽  
Whole Brain ◽  
Human Connectome Project

AbstractMassive whole-brain blood-oxygen-level dependent (BOLD) signal modulation (up to 95% of brain voxels) in response to task stimuli has recently been reported in functional MRI investigations. These findings have two implications. First, they highlight inability of a conventional ‘top-down’ general linear model approach to capture all forms of task-driven brain activity. Second, as opposed to a static ‘active’ or ‘non-active’ localization theory of the neural implementation of cognitive processes, functional neuroimaging should develop and pursue dynamical theories of cognition involving the dynamic interactions of all brain networks, in line with psychological constructionist theories of cognition. In this study, we describe a novel exploratory, bottom-up approach that directly estimates task-driven brain activity regardless of whether it follows an a priori reference function. Leveraging the property that task-driven brain activity is associated with reductions in BOLD signal variability, we combine the tools of instantaneous phase synchronization and independent component analysis to characterize whole-brain task-driven activity in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this novel framework to task fMRI data from a motor, theory of mind and working memory task provided through the Human Connectome Project. We discovered a large number of brain networks that dynamically synchronized to various features of the task scan, some overlapping with areas identified as ‘active’ in the top-down GLM approach. Using the results provided through this novel approach, we provide a more comprehensive description of cognitive processes whereby task-related brain activity is not restricted to dichotomous ‘active’ or ‘non-active’ inferences, but is characterized by the temporal dynamics of brain networks across time.Significance StatementThis study describes the results of a novel exploratory methodological approach that allows for direct estimation of task-driven brain activity in terms of group-wise similarity in temporal signal dynamics, as opposed to the conventional approach of identifying task-driven brain activity with a hypothesized temporal pattern. This approach applied to three different task paradigms yielded novel insights into the brain activity associated with these tasks in terms of time-varying, low-frequency dynamics of replicable synchronization networks. We suggest that this exploratory methodological approach provides a framework in which the complexity and dynamics of the neural mechanisms underlying cognitive processes can be captured more comprehensively.

scholarly journals Deep learning models of cognitive processes constrained by human brain connectomes

2021 ◽  
Author(s):  
Yu Zhang ◽  
Nicolas et Farrugia ◽  
Pierre Bellec
Keyword(s):  
Deep Learning ◽  
Cognitive Processes ◽  
Brain Activity ◽  
Neural Substrates ◽  
Brain Regions ◽  
Human Cognition ◽  
Experimental Conditions ◽  
Cognitive States ◽  
Learning Techniques ◽  
Human Connectome Project

Brain decoding aims to infer cognitive states from recordings of brain activity. Current literature has mainly focused on isolated brain regions engaged in specific experimental conditions, but ignored the integrative nature of cognitive processes recruiting distributed brain networks. To tackle this issue, we propose a connectome-based graph neural network to integrate distributed patterns of brain activity in a multiscale manner, ranging from localized brain regions, to a specific brain circuit/network and towards the full brain. We evaluate the decoding model using a large task-fMRI database from the human connectome project. By implementing connectomic constraints and multiscale interactions in deep graph convolutions, the model achieves high accuracy of decoding 21 cognitive states (93%, chancel level: 4.8%) and shows high robustness against adversarial attacks on the graph architecture. Our study bridges human connectomes with deep learning techniques and provides new avenues to study the underlying neural substrates of human cognition at scale.

scholarly journals Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

2016 ◽  
Author(s):  
Timothy N. Rubin ◽  
Oluwasanmi Koyejo ◽  
Krzysztof J. Gorgolewski ◽  
Michael N. Jones ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Brain Activity ◽  
Human Cognition ◽  
Brain Images ◽  
Whole Brain ◽  
Context Sensitive ◽  
Cognitive States ◽  
Small Set

AbstractA central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

scholarly journals The Relation Between White Matter Microstructure and Network Complexity: Implications for Processing Efficienc

2018 ◽  
Author(s):  
Ian M. McDonough ◽  
Jonathan T. Siegel
Keyword(s):  
White Matter ◽  
Time Scales ◽  
Brain Structure ◽  
Brain Networks ◽  
Simulation Models ◽  
Multiscale Entropy ◽  
Fast Time ◽  
Bold Signal ◽  
White Matter Microstructure ◽  
Human Connectome Project

AbstractBrain structure has been proposed to facilitate as well as constrain functional interactions within brain networks. Simulation models suggest that integrity of white matter (WM) microstructure should be positively related to the complexity of BOLD signal—a measure of network interactions. Using 121 young adults from the Human Connectome Project, we empirically tested whether greater WM integrity would be associated with greater complexity of the BOLD signal during rest via multiscale entropy. Multiscale entropy measures the lack of predictability within a given time series across varying time scales, thus being able to estimate fluctuating signal dynamics within brain networks. Using multivariate analysis techniques (Partial Least Squares), we found that greater WM integrity was associated with greater network complexity at fast time scales, but less network complexity at slower time scales. These findings implicate two separate pathways through which WM integrity affects brain function in the prefrontal cortex—an executive-prefrontal pathway and a perceptuo-occipital pathway. In two additional samples, the main patterns of WM and network complexity were replicated. These findings support simulation models of WM integrity and network complexity and provide new insights into brain structure-function relationships.

scholarly journals Data-driven models reveal the organization of diverse cognitive functions in the brain

2019 ◽  
Author(s):  
Tomoya Nakai ◽  
Shinji Nishimoto
Keyword(s):  
Cognitive Processes ◽  
Brain Activity ◽  
Quantitative Model ◽  
Task Type ◽  
Hierarchical Organization ◽  
Human Cognition ◽  
Cognitive Tasks ◽  
Cortical Organization ◽  
Brain Functions ◽  
Complete Representations

AbstractOur daily life is realized by the complex orchestrations of diverse brain functions including perception, decision, and action. One of the central issues in cognitive neuroscience is to reveal the complete representations underlying such diverse functions. Recent studies have revealed representations of natural perceptual experiences using encoding models1–5. However, there has been little attempt to build a quantitative model describing the cortical organization of multiple active, cognitive processes. Here, we measured brain activity using functional MRI while subjects performed over 100 cognitive tasks, and examined cortical representations with two voxel-wise encoding models6. A sparse task-type encoding model revealed a hierarchical organization of cognitive tasks, their representation in cognitive space, and their mapping onto the cortex. A cognitive factor encoding model utilizing continuous intermediate features by using metadata-based inferences7 predicted brain activation patterns for more than 80 % of the cerebral cortex and decoded more than 95 % of tasks, even under novel task conditions. This study demonstrates the usability of quantitative models of natural cognitive processes and provides a framework for the comprehensive cortical organization of human cognition.

scholarly journals State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes

2018 ◽  
Author(s):  
Yu Takagi ◽  
Jun-ichiro Hirayama ◽  
Saori C Tanaka
Keyword(s):  
Functional Connectivity ◽  
Individual Variation ◽  
Brain Networks ◽  
Life Outcomes ◽  
Behavioral Measures ◽  
Experimental Conditions ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Brain Functional Connectivity

AbstractRecent functional magnetic resonance imaging (fMRI) studies have increasingly revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have been inherently limited to state-specific characterizations of related brain networks and their functions, several recent studies have examined the potential state-unspecific nature of functional brain networks, such as their global similarities across different experimental conditions (i.e., states) including both task and rest. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variation in whole-brain functional connectivity patterns, called “Common Neural Modes (CNMs)”, from a large fMRI dataset including eight task/rest states, obtained from the Human Connectome Project. Furthermore, we tested how CNMs account for variability in individual behavioral measures. The results revealed that three CNMs were robustly extracted under various different preprocessing conditions. Each of these CNMs was significantly correlated with different aspects of behavioral measures of both fluid and crystalized intelligence. The three CNMs were also able to predict several life outcomes, such as income and life satisfaction, achieving the highest performance when combined with behavioral intelligence measures as inputs. Our findings highlight the importance of state-unspecific brain networks to characterize fundamental individual variation.

scholarly journals Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders

2020 ◽  
Author(s):  
Daniel S Barron ◽  
Siyuan Gao ◽  
Javid Dadashkarimi ◽  
Abigail S Greene ◽  
Marisa N Spann ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Psychiatric Disorders ◽  
Memory Performance ◽  
Brain Networks ◽  
Memory Deficits ◽  
Computational Method ◽  
Long Term Memory ◽  
Whole Brain ◽  
Human Connectome Project

Abstract Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.

scholarly journals Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

2021 ◽  
Vol 118 (23) ◽  
pp. e2022288118
Author(s):  
Rong Wang ◽  
Mianxin Liu ◽  
Xinhong Cheng ◽  
Ying Wu ◽  
Andrea Hildebrandt ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Structural Equation ◽  
Large Scale ◽  
Functional Organization ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Brain Networks ◽  
Deep Understanding ◽  
Human Cognition ◽  
Equation Modeling

Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.

scholarly journals Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

2019 ◽  
Author(s):  
João F. Guassi Moreira ◽  
Katie A. McLaughlin ◽  
Jennifer A. Silvers
Keyword(s):  
Emotion Regulation ◽  
Skill Acquisition ◽  
Network Architecture ◽  
Evolutionary Biology ◽  
Brain Activity ◽  
Brain Networks ◽  
Adult Life ◽  
Brain Network ◽  
Network Activity ◽  
Whole Brain

AbstractThe ability to regulate emotions is key to goal attainment and wellbeing. Although much has been discovered about how the human brain develops to support the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework in conjunction with machine learning to: (i) parse the neural underpinnings of emotion regulation skill acquisition while accounting for age, and (ii) build a working taxonomy of brain network activity supporting emotion regulation in a sample of youth (N = 70, 34 female). We were able to predict emotion regulation ability, but not age, using network activity metrics from whole-brain networks during an emotion regulation task. Further, by leveraging analytic techniques traditionally used in evolutionary biology (e.g., cophenetic correlations), we were able to demonstrate that brain networks evince reliable taxonomic organization to meet emotion regulation demands in youth. This work shows that meaningful information about emotion regulation development is encoded in whole-brain network activity, suggesting that brain activity during emotion regulation encodes unique information about regulatory skill acquisition in youth but not domain-general maturation.Significance StatementThe acquisition of emotion regulation is critical for healthy functioning in later adult life. To date, little is known about how brain networks support the developmental acquisition of emotion regulation skills. This is noteworthy because brain networks have been increasingly shown to provide highly useful information about neural activity. Here we show that brain activity during an emotion regulation task encodes information about regulatory abilities over and above age. These results suggest emotion regulation skills are dependent on neural specialization of domain-specific systems, whereas age is encoded via domain-general systems.

scholarly journals Instantaneous Brain Dynamics Mapped to a Continuous State Space

2017 ◽  
Author(s):  
Jacob Billings ◽  
Alessio Medda ◽  
Sadia Shakil ◽  
Xiaohong Shen ◽  
Amrit Kashyap ◽  
...  
Keyword(s):  
State Space ◽  
Dimensional Space ◽  
Brain Activity ◽  
Dimensional Structure ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Continuous State ◽  
Brain States ◽  
Space Dynamics

AbstractMeasures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain’s dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting and task data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. We also demonstrate that resting brain activity includes brain states that are very similar to those adopted during some tasks, as well as brain states that are distinct from experimentally-defined tasks. Back-projection of segmented brain states onto the brain’s surface reveals the patterns of brain activity that support each experimental state.

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