scholarly journals Analysis of the Human Connectome Data Supports the Notion of A “Common Model of Cognition” for Human and Human-Like Intelligence Across Domains

2019 ◽  
Author(s):  
Andrea Stocco ◽  
Catherine Sibert ◽  
Zoe Steine-Hanson ◽  
Natalie Koh ◽  
John E. Laird ◽  
...  
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Causal Modeling ◽  
Human Cognition ◽  
Functional Architecture ◽  
Hub And Spoke ◽  
Cognitive Domains ◽  
Fmri Analysis

AbstractThe 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. The results suggest that a common, general architecture underpins human cognition across multiple cognitive domains, from the overall functional architecture of the human brain to higher-level thought processes.

Neuroimaging and modeling

2014 ◽  
Vol 11 (04) ◽  
pp. 245-253
Author(s):  
S. B. Eickhoff ◽  
D. Bzdok
Keyword(s):  
Psychiatric Disorders ◽  
Large Scale ◽  
Effective Connectivity ◽  
Network Models ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Causal Modeling ◽  
Functional Coupling ◽  
Response Patterns ◽  
Functional Relationships

SummaryThe links between symptomatic phenomenology of psychiatric disorders and their neurobiological pathophysiology are still understood only in fragments. While functional and structural neuroimaging methods have leveraged our knowledge of regional dysfunction in psychiatric disorders, emerging novel approaches more focused on brain networks or neural patterns promise additional forward progress. Activation likelihood estimation (ALE) performs quantitative large-scale aggregation of neuroimaging findings. Resting-state (RS) correlation captures networks of functional relationships between regions in the idling, non-goal-focused brain. In contrast, meta-analytic connectivity modeling (MACM) captures functional coupling between brain regions in the context of experimental paradigms. These methods may furthermore be exploited to provide data-driven parcellations (CBP, connectivity-based parcellation) of larger brain regions into distinct functional modules. Dynamic causal modeling (DCM), in turn, allows for the automatic selection among a set of connectional network models to delineate effective connectivity dynamics during experimental paradigms. Finally, machine learning (ML) allows for the automatic detection and prediction of diagnosis/treatment-response patterns in massive datasets. Capitalizing on this toolbox of computational modeling methods might considerably further psychiatry and thus benefit patients with mental disorders.

scholarly journals Altered Prefrontal–Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment

2020 ◽  
Vol 11 ◽  
Author(s):  
Jing Zhang ◽  
Zixiao Li ◽  
Xingxing Cao ◽  
Lijun Zuo ◽  
Wei Wen ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Basal Ganglia ◽  
Cognitive Decline ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Neuronal Model ◽  
Causal Modeling ◽  
Healthy Controls

We investigated the association between poststroke cognitive impairment and a specific effective network connectivity in the prefrontal–basal ganglia circuit. The resting-state effective connectivity of this circuit was modeled by employing spectral dynamic causal modeling in 11 poststroke patients with cognitive impairment (PSCI), 8 poststroke patients without cognitive impairment (non-PSCI) at baseline and 3-month follow-up, and 28 healthy controls. Our results showed that different neuronal models of effective connectivity in the prefrontal–basal ganglia circuit were observed among healthy controls, non-PSCI, and PSCI patients. Additional connected paths (extra paths) appeared in the neuronal models of stroke patients compared with healthy controls. Moreover, changes were detected in the extra paths of non-PSCI between baseline and 3-month follow-up poststroke, indicating reorganization in the ipsilesional hemisphere and suggesting potential compensatory changes in the contralesional hemisphere. Furthermore, the connectivity strengths of the extra paths from the contralesional ventral anterior nucleus of thalamus to caudate correlated significantly with cognitive scores in non-PSCI and PSCI patients. These suggest that the neuronal model of effective connectivity of the prefrontal–basal ganglia circuit may be sensitive to stroke-induced cognitive decline, and it could be a biomarker for poststroke cognitive impairment 3 months poststroke. Importantly, contralesional brain regions may play an important role in functional compensation of cognitive decline.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Unilateral thalamic glioma disrupts large-scale functional architecture of human brain during resting state

2019 ◽  
Vol Volume 15 ◽  
pp. 947-956
Author(s):  
Sirui Li ◽  
Lei Gao ◽  
Ying Liu ◽  
Yawen Ao ◽  
Haibo Xu
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Large Scale ◽  
Functional Architecture ◽  
Thalamic Glioma

scholarly journals The neurophysiological architecture of semantic dementia: spectral dynamic causal modelling of a neurodegenerative proteinopathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Elia Benhamou ◽  
Charles R. Marshall ◽  
Lucy L. Russell ◽  
Chris J. D. Hardy ◽  
Rebecca L. Bond ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Semantic Dementia ◽  
Older Individuals ◽  
World Knowledge ◽  
Causal Modelling ◽  
Dynamic Causal Modelling ◽  
Semantic Impairment

Abstract The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral dynamic causal modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level dynamic causal modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.

scholarly journals Large-scale DCMs for resting-state fMRI

2017 ◽  
Vol 1 (3) ◽  
pp. 222-241 ◽  
Author(s):  
Adeel Razi ◽  
Mohamed L. Seghier ◽  
Yuan Zhou ◽  
Peter McColgan ◽  
Peter Zeidman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Effective Connectivity ◽  
Directed Graphs ◽  
Resting State Fmri ◽  
Causal Modeling ◽  
Large Graphs ◽  
Model Inversion ◽  
Graph Theoretic

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

scholarly journals Dynamic connectivity analyses of intracranial EEG during recognition memory reveal various large-scale functional networks

2020 ◽  
Author(s):  
Jakub Kopal ◽  
Jaroslav Hlinka ◽  
Elodie Despouy ◽  
Luc Valton ◽  
Marie Denuelle ◽  
...  
Keyword(s):  
Recognition Memory ◽  
Large Scale ◽  
Visual Recognition ◽  
Effective Connectivity ◽  
Memory Task ◽  
Brain Regions ◽  
Neural Basis ◽  
Intracranial Eeg ◽  
Large Scale Networks ◽  
The Right

Recognition memory is the ability to recognize previously encountered events, objects, or people. It is characterized by its robustness and rapidness. Even this relatively simple ability requires the coordinated activity of a surprisingly large number of brain regions. These spatially distributed, but functionally linked regions are interconnected into large-scale networks. Understanding memory requires an examination of the involvement of these networks and the interactions between different regions while memory processes unfold. However, little is known about the dynamical organization of large-scale networks during the early phases of recognition memory. We recorded intracranial EEG, which affords high temporal and spatial resolution, while epileptic subjects performed a visual recognition memory task. We analyzed dynamic functional and effective connectivity as well as network properties. Various networks were identified, each with its specific characteristics regarding information flow (feedforward or feedback), dynamics, topology, and stability. The first network mainly involved the right visual ventral stream and bilateral frontal regions. It was characterized by early predominant feedforward activity, modular topology, and high stability. It was followed by the involvement of a second network, mainly in the left hemisphere, but notably also involving the right hippocampus, characterized by later feedback activity, integrated topology, and lower stability. The transition between networks was associated with a change in network topology. Overall, these results confirm that several large-scale brain networks, each with specific properties and temporal manifestation, are involved during recognition memory. Ultimately, understanding how the brain dynamically faces rapid changes in cognitive demand is vital to our comprehension of the neural basis of cognition.

scholarly journals Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ni Shu ◽  
Yaou Liu ◽  
Yunyun Duan ◽  
Kuncheng Li
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Functional Integration ◽  
Brain Regions ◽  
Hemispheric Asymmetries ◽  
Characteristic Path Length ◽  
Topological Measures ◽  
Structural Connections ◽  
The Brain

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

scholarly journals Persistent Homology-Based Topological Analysis on the Gestalt Patterns during Human Brain Cognition Process

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zaisheng Liu ◽  
Fei Ni ◽  
Rongpeng Li ◽  
Honggang Zhang ◽  
Chang Liu ◽  
...  
Keyword(s):  
Human Brain ◽  
Random Sequence ◽  
Persistent Homology ◽  
Topological Analysis ◽  
Brain Regions ◽  
Human Cognition ◽  
Eeg Signals ◽  
Neural Signals ◽  
Neuropsychological Characteristics ◽  
The Brain

The neuropsychological characteristics inside the brain are still not sufficiently understood in previous Gestalt psychological analyses. In particular, the extraction and analysis of human brain consciousness information itself have not received enough attention for the time being. In this paper, we aim to investigate the features of EEG signals from different conscious thoughts. Specifically, we try to extract the physiologically meaningful features of the brain responding to different contours and shapes in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG). The experimental results show that more brain regions in the frontal lobe are involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images. Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) is significantly different from that evoked by the ordered Gestalt (GST) images in several frequency bands, which indicate that the human cognition of the shape and contour of images can be separated to some extent through topological analysis. This implies the feasibility to digitize the neural signals while preserving the whole and local features of the original signals, which are further verified by our extensive experiments. In general, this paper evaluates and quantifies cognitively related neural correlates by persistent homology features of EEG signals, which provides an approach to realizing the digitization of neural signals. Preliminary verification of the analyzability of human consciousness signals provides reliable research ideas and directions for the realization of feature extraction and analysis of human brain consciousness cognition.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

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