scholarly journals Precise topology of adjacent domain-general and sensory-biased regions in the human brain

2021 ◽  
Author(s):  
Moataz Assem ◽  
Sneha Shashidhara ◽  
Matthew F. Glasser ◽  
John Duncan
Keyword(s):  
Working Memory ◽  
Human Brain ◽  
Cognitive Demands ◽  
Visual Preference ◽  
Domain Specific ◽  
Domain Generality ◽  
Co Activation ◽  
Human Connectome Project ◽  
Anatomical Relationship ◽  
Multimodal Mri

AbstractRecent functional MRI studies identified sensory-biased regions across much of the association cortices and cerebellum. However, their anatomical relationship to multiple-demand (MD) regions, characterized as domain-general due to their co-activation during multiple cognitive demands, remains unclear. For a better anatomical delineation, we used multimodal MRI techniques of the Human Connectome Project to scan subjects performing visual and auditory versions of a working memory (WM) task. The contrast between hard and easy WM showed strong domain generality, with essentially identical patterns of MD activity for visual and auditory materials. In contrast, modality preferences were shown by contrasting easy WM with baseline; most MD regions showed visual preference while immediately adjacent to cortical MD regions, there were interleaved regions of both visual and auditory preference. The results may exemplify a general motif whereby domain-specific regions feed information into and out of an adjacent, integrative MD core.

scholarly journals Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

2019 ◽  
Author(s):  
Jianfeng Zhang ◽  
Zirui Huang ◽  
Shankar Tumati ◽  
Georg Northoff
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Peak Period ◽  
Physiological Noise ◽  
Activation Patterns ◽  
Global Signal ◽  
Co Activation ◽  
Human Connectome Project ◽  
Partial Dissociation ◽  
Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

scholarly journals Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

2021 ◽  
Vol 15 ◽  
Author(s):  
Sahin Hanalioglu ◽  
Siyar Bahadir ◽  
Ilkay Isikay ◽  
Pinar Celtikci ◽  
Emrah Celtikci ◽  
...  
Keyword(s):  
Human Brain ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Population Level ◽  
Similar Distribution ◽  
Group Level ◽  
Imaging Data ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Intuitive Method

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

scholarly journals Toward a neuro-inspired creative decoder

2020 ◽  
Author(s):  
Payel Das ◽  
Brian Quanz ◽  
Pin-Yu Chen ◽  
Jae-wook Ahn ◽  
Dhruv Shah
Keyword(s):  
Human Brain ◽  
Positive Control ◽  
Activation Pattern ◽  
Neuronal Activation ◽  
Direct Modulation ◽  
Human Evaluation ◽  
Latent Space ◽  
Co Activation ◽  
Creative Capacity ◽  
And Task

Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain. Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space. The proposed approach is fully unsupervised and can be used off- the-shelf. Several novelty metrics and human evaluation were used to evaluate the creative capacity of the deep decoder. Our experiments on different image datasets (MNIST, FMNIST, MNIST+FMNIST, WikiArt and CelebA) reveal that atypical co-activation of highly activated and weakly activated neurons in a deep decoder promotes generation of novel and meaningful artifacts.

scholarly journals Decision Making Processes and Outcomes

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Julie Hicks Patrick ◽  
Jenessa C. Steele ◽  
S. Melinda Spencer
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Information Search ◽  
Linear Regression Analysis ◽  
Individual Characteristics ◽  
Decision Quality ◽  
Quality Data ◽  
Solution Quality ◽  
Strategic Processing ◽  
Domain Specific

The primary aim of this study was to examine the contributions of individual characteristics and strategic processing to the prediction of decision quality. Data were provided by 176 adults, ages 18 to 93 years, who completed computerized decision-making vignettes and a battery of demographic and cognitive measures. We examined the relations among age, domain-specific experience, working memory, and three measures of strategic information search to the prediction of solution quality using a 4-step hierarchical linear regression analysis. Working memory and two measures of strategic processing uniquely contributed to the variance explained. Results are discussed in terms of potential advances to both theory and intervention efforts.

scholarly journals Strategic resource allocation in the human brain supports cognitive coordination of object and spatial working memory

2010 ◽  
Vol 32 (8) ◽  
pp. 1330-1348 ◽  
Author(s):  
Margaret C. Jackson ◽  
Helen M. Morgan ◽  
Kimron L. Shapiro ◽  
Harald Mohr ◽  
David E.J. Linden
Keyword(s):  
Working Memory ◽  
Resource Allocation ◽  
Human Brain ◽  
Spatial Working Memory ◽  
Strategic Resource

Multicomponent Working Memory System with Distributed Executive Control

2020 ◽  
pp. 150-174 ◽  
Author(s):  
André Vandierendonck
Keyword(s):  
Working Memory ◽  
Executive Control ◽  
Memory System ◽  
General Information ◽  
Long Term Memory ◽  
Term Memory ◽  
Episodic Buffer ◽  
Domain Specific ◽  
Current Task

The working memory model with distributed executive control accounts for the interactions between working memory and multi-tasking performance. The working memory system supports planned actions by relying on two capacity-limited domain-general and two time-limited domain-specific modules. Domain-general modules are the episodic buffer and the executive module. The episodic buffer stores multimodal representations and uses attentional refreshment to counteract information loss and to consolidate information in episodic long-term memory. The executive module maintains domain-general information relevant for the current task. The phonological buffer and the visuospatial module are domain specific; the former uses inner speech to maintain and to rehearse phonological information, whereas the latter holds visual and spatial representations active by means of image revival. For its operation, working memory interacts with declarative and procedural long-term memory, gets input from sensory registers, and uses the motor system for output.

Human Cognitive Processes

2009 ◽  
pp. 1-33
Author(s):  
Slava Kalyuga
Keyword(s):  
Working Memory ◽  
Cognitive Architecture ◽  
Human Cognition ◽  
Long Term Memory ◽  
Domain Specific ◽  
Knowledge Based ◽  
Domain Specific Knowledge ◽  
And Performance ◽  
Memory Knowledge ◽  
High Level

One of the major components of our cognitive architecture, working memory, becomes overloaded if more than a few chunks of information are processed simultaneously. For example, we all experience this cognitive overload when trying to keep in memory an unfamiliar telephone number or add two four-digit numbers in the absence of a pen and paper. Similar in nature processing limitations of working memory represent a major factor influencing the effectiveness of human learning and performance, particularly in complex environments that require concurrent performance of multiple tasks. The learner prior domain-specific knowledge structures and associated levels of expertise are considered as means of reducing these limitations and guiding high-level knowledge-based cognitive activities. One of the most important results of studies in human cognition is that the available knowledge is a single most significant learner cognitive characteristic that influences learning and cognitive performance. Understanding the key role of long-term memory knowledge base in our cognition is important to the successful management of cognitive load in multimedia learning.

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