scholarly journals Pattern Similarity Analyses of FrontoParietal Task Coding: Individual Variation and Genetic Influences

2020 ◽  
Vol 30 (5) ◽  
pp. 3167-3183 ◽  
Author(s):  
Joset A Etzel ◽  
Ya’el Courtney ◽  
Caitlin E Carey ◽  
Maria Z Gehred ◽  
Arpana Agrawal ◽  
...  
Keyword(s):  
Individual Differences ◽  
Neural Coding ◽  
Memory Load ◽  
Brain Regions ◽  
Genetic Influences ◽  
Behavioral Performance ◽  
Activation Pattern ◽  
Human Connectome Project ◽  
Pattern Similarity ◽  
Perceptual Category

Abstract Pattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task functional magnetic resonance imaging (fMRI) dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 monozygotic twin, 78 dizygotic twin, 99 nontwin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.

scholarly journals Pattern similarity analyses of frontoparietal task coding: Individual variation and genetic influences

2019 ◽  
Author(s):  
Joset A. Etzel ◽  
Ya’el Courtney ◽  
Caitlin E. Carey ◽  
Maria Z. Gehred ◽  
Arpana Agrawal ◽  
...  
Keyword(s):  
Individual Differences ◽  
Neural Coding ◽  
Memory Load ◽  
Brain Regions ◽  
Genetic Influences ◽  
Behavioral Performance ◽  
Activation Pattern ◽  
Human Connectome Project ◽  
Pattern Similarity ◽  
Perceptual Category

AbstractPattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task fMRI dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 MZ (monozygotic) twin, 78 DZ (dizygotic) twin, 99 non-twin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically-selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding, and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.

scholarly journals A connectome-based neuromarker of the non-verbal number acuity and arithmetic skills

2021 ◽  
Author(s):  
Dai Zhang ◽  
Liqin Zhou ◽  
Anmin Yang ◽  
Shanshan Li ◽  
Chunqi Chang ◽  
...  
Keyword(s):  
Individual Differences ◽  
Number System ◽  
Brain Regions ◽  
Brain Network ◽  
Large Sample Size ◽  
Neural Basis ◽  
Mathematical Abilities ◽  
Human Connectome Project ◽  
Survival And Reproduction ◽  
Numerosity Perception

The approximate number system (ANS) is vital for survival and reproduction in animals and crucial in constructing abstract mathematical abilities in humans. Most previous neuroimaging studies focused on identifying discrete brain regions responsible for the ANS and characterizing their functions in numerosity perception. However, there lacks a neuromarker to characterize an individual's ANS acuity, especially one based on the whole-brain functional connectivity (FC). Here, we identified a distributed brain network (i.e., numerosity network) using a connectome-based predictive modeling (CPM) analysis on the resting-state functional magnetic resonance imaging (rs-fMRI) data based on a large sample size. The summed strength of all FCs within the numerosity network could reliably predict individual differences of the ANS acuity in behavior. Furthermore, in an independent dataset from the Human Connectome Project (HCP), we found that the summed FC strength within the numerosity network could also predict individual differences in arithmetic skills. Our findings illustrate that the numerosity network we identified could be an applicable neuromarker of the non-verbal number acuity and might serve as the neural basis underlying the known link between the non-verbal number acuity and mathematical abilities.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

Individual Uniqueness in the Neonatal Functional Connectome

2021 ◽  
Author(s):  
Qiushi Wang ◽  
Yuehua Xu ◽  
Tengda Zhao ◽  
Zhilei Xu ◽  
Yong He ◽  
...  
Keyword(s):  
Individual Differences ◽  
Individual Identification ◽  
Imaging Data ◽  
Functional Connectome ◽  
Middle Range ◽  
Human Connectome Project ◽  
The Individual ◽  
And Behavior ◽  
Identification Analysis ◽  
The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

scholarly journals Heritability and interindividual variability of regional structure-function coupling

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zijin Gu ◽  
Keith Wakefield Jamison ◽  
Mert Rory Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Functional Connectivity ◽  
Structure Function ◽  
Regional Differences ◽  
Regional Scale ◽  
Brain Regions ◽  
Diffusion Weighted Mri ◽  
Regional Structure ◽  
Human Connectome Project ◽  
Structural Connections ◽  
The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

scholarly journals Spatial Patterns for Discriminative Estimation

2019 ◽  
Author(s):  
Alberto Llera ◽  
Roselyne Chauvin ◽  
Peter Mulders ◽  
Jilly Naaijen ◽  
Maarten Mennes ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Brain Connectivity ◽  
Covariance Structure ◽  
Brain Regions ◽  
Linear Filters ◽  
Cognitive Changes ◽  
Experimental Conditions ◽  
Cognitive States ◽  
Human Connectome Project ◽  
Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

scholarly journals Effective Degrees of Freedom of the Pearson’s Correlation Coefficient under Autocorrelation

2018 ◽  
Author(s):  
Soroosh Afyouni ◽  
Stephen M. Smith ◽  
Thomas E. Nichols
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Degrees Of Freedom ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Pearson’S Correlation ◽  
Individual Level ◽  
Human Connectome Project ◽  
Pearson's Correlation ◽  
The Impact

AbstractThe dependence between pairs of time series is commonly quantified by Pearson’s correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher’s transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors – before or after Fisher’s transformation – becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardized Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical “xDF” method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

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