scholarly journals Harnessing Human Connectome Project Data for Replication Studies: A Demonstration for Resting State-Task Network Correspondence

2017 ◽  
Author(s):  
Lisa D. Nickerson
Keyword(s):  
Open Access ◽  
Resting State ◽  
Major Barrier ◽  
Replication Studies ◽  
Human Connectome Project ◽  
Neuroimaging Data ◽  
Neuroimaging Study ◽  
Methodological Problems ◽  
Release Data ◽  
Project Data

AbstractAfter a series of reports uncovered various methodological problems with functional magnetic resonance imaging (fMRI) research, considerable attention has been given to principles and practices to improve reproducibility of neuroimaging findings, including promotion of openness, transparency, and data sharing. However, much less attention has been given to use of open access neuroimaging datasets to conduct replication studies. A major barrier to reproducing neuroimaging studies is their high cost, in money and labor, and utilizing such datasets is an obvious solution for breaking down this barrier. The Human Connectome Project (HCP) is an open access dataset consisting of extensive behavioral and neuroimaging data from over 1,100 individuals and there are numerous ongoing HCP-harmonized studies of lifespan and disease that will ultimately release data through HCP infrastructure. To bring attention to the HCP and related projects as an important resource for conducting replication studies, I used the HCP to conduct a replication of a highly cited neuroimaging study that showed correspondence between resting state and task brain networks.

scholarly journals Resting-State Functional Brain Connectivity Best Predicts the Personality Dimension of Openness to Experience

2018 ◽  
Vol 1 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Yanting Han ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Personality Traits ◽  
Resting State ◽  
Small Sample ◽  
Openness To Experience ◽  
Fmri Data ◽  
Personality Factors ◽  
Personality Factor ◽  
Personality Dimension ◽  
Human Connectome Project ◽  
Neuroimaging Data

AbstractPersonality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 884 young healthy adults in the Human Connectome Project database. We attempted to predict personality traits from the “Big Five,” as assessed with the Neuroticism/Extraversion/Openness Five-Factor Inventory test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness, and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two intersubject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 hr of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; three denoising strategies; two alignment schemes; three models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O:r=.24,R2=.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR:r=.26,R2=.044). Other factors (Extraversion, Neuroticism, Agreeableness, and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the Neuroticism/Extraversion/Openness Five-Factor Inventory factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=.27,R2=.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

scholarly journals Resting-state functional brain connectivity best predicts the personality dimension of openness to experience

2017 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Yanting Han ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Personality Traits ◽  
Resting State ◽  
Fluid Intelligence ◽  
Small Sample ◽  
Openness To Experience ◽  
Personality Factors ◽  
Personality Factor ◽  
Personality Dimension ◽  
Human Connectome Project ◽  
Neuroimaging Data

AbstractPersonality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the “Big Five”, as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

scholarly journals Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

2021 ◽  
Author(s):  
Ying-Qiu Zheng ◽  
Seyedeh-Rezvan Farahibozorg ◽  
Weikang Gong ◽  
Hossein Rafipoor ◽  
Saad Jbabdi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Predictive Accuracy ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Human Connectome Project ◽  
Wide Range ◽  
Dual Regression ◽  
Project Data ◽  
Test Retest Reliability

Modelling and predicting individual differences in task-evoked FMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble leaner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of tfMRI scans, suggesting that it has potential to supplement traditional task localisers.

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.

Perceived friendship and binge drinking in young adults: A study of the Human Connectome Project data

2021 ◽  
Vol 224 ◽  
pp. 108731
Author(s):  
Guangfei Li ◽  
Yu Chen ◽  
Thang M. Le ◽  
Simon Zhornitsky ◽  
Wuyi Wang ◽  
...  
Keyword(s):  
Young Adults ◽  
Binge Drinking ◽  
Human Connectome Project ◽  
Project Data

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

scholarly journals Estimating the reproducibility & transparency of smoking cessation behaviour change interventions

2020 ◽  
Author(s):  
Emma Norris ◽  
Yiwei He ◽  
Rachel Loh ◽  
Robert West ◽  
Susan Michie
Keyword(s):  
Open Access ◽  
Behaviour Change ◽  
Conflict Of Interest ◽  
Open Data ◽  
Smoking Behaviour ◽  
Funding Source ◽  
Behaviour Change Intervention ◽  
Behaviour Change Interventions ◽  
Replication Studies ◽  
Randomised Controlled

Introduction: Activities promoting research reproducibility and transparency are crucial for generating trustworthy evidence. Evaluation of smoking interventions is one area where vested interests may motivate reduced reproducibility and transparency. Aims: Assess markers of transparency and reproducibility in smoking behaviour change intervention evaluation reports.Methods: One hundred evaluation reports of smoking behaviour change intervention randomised controlled trials published in 2018-2019 were identified. Reproducibility markers of pre-registration, protocol sharing, data-, materials- and analysis script-sharing, replication of a previous study and open access publication were coded in identified reports. Transparency markers of funding and conflict of interest declarations were also coded. Coding was performed by two researchers, with inter-rater reliability calculated using Krippendorff’s alpha.Results: Seventy-one percent of reports were open access and 73% pre-registered. However, only 13% provided accessible materials, 7% accessible data and 1% accessible analysis scripts. No reports were replication studies. Ninety-four percent of reports provided a funding source statement and eighty-eight percent of reports provided a conflict of interest statement.Conclusions: Open data, materials, analysis and replications are rare in smoking behaviour change interventions, whereas funding source and conflict of interest declarations are common. Future smoking research should be more reproducible to enable knowledge accumulation.

scholarly journals Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

scholarly journals Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

2018 ◽  
Author(s):  
Ayumu Yamashita ◽  
Noriaki Yahata ◽  
Takashi Itahashi ◽  
Giuseppe Lisi ◽  
Takashi Yamada ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Functional Mri ◽  
Resting State ◽  
Sampling Bias ◽  
Measurement Bias ◽  
Limited Capacity ◽  
Signal To Noise ◽  
Multiple Imaging ◽  
Neuroimaging Data ◽  
Biological Sampling

AbstractWhen collecting large neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent the greatest barrier when acquiring multi-site neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multi-site, multi-disorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. Effects on resting-state functional MRI connectivity because of both bias types were greater than or equal to those because of psychiatric disorders. Furthermore, our findings indicated that each site can sample only from among a subpopulation of participants. This result suggests that it is essential to collect large neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using traveling-subject dataset and achieved the reduction of the measurement bias by 29% and the improvement of the signal to noise ratios by 40%.

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