Predicting individual traits from unperformed tasks

Gal, Shachar; Tik, Niv; Bernstein‐Eliav, Michal; Tavor, Ido

doi:10.1016/j.neuroimage.2022.118920

Cited by 13 publications

(24 citation statements)

References 42 publications

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“…We used unadjusted scores from ten cognitive tasks, including seven tasks from the NIH Toolbox (Dimensional Change Cart Sort, Flanker Task, List Sort Test, Picture Sequence Test, Picture Vocabulary Test, Pattern Completion Test, Oral Reading Recognition Test), and three tasks from the Penn Neurocognitive Battery (Penn Progressive Matrices, Penn Word Memory Test, Variable Short Penn Line Orientation Test). The G-scores prediction was performed using the Basis Brain Set pipeline (BBS) (Sripada et al, 2019; Sripada, Angstadt, Rutherford, Taxali, & Shedden, 2020), which has been previously found effective for predicting individual traits from connTask maps (Gal et al, 2022) (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…In each iteration, a model was trained on 4/5 of the data, and predictions were yielded for 1/5 of the data, while genetically related participants kept in either the training or the test datasets. For each input type (i.e., resting-state or movie-watching) and each task, the prediction of GCA scores from connTask maps included the following steps: (1) dimensionality of the training set data was reduced using PCA to a predetermined number of components, k. We used k=20 to maintain a similar ratio between the number of observations (participants) and the number of predictors (components) as in previous work (Gal et al, 2022). (2) These components were regressed against the individual data of each participant, test and training set alike, to yield an “expression score” for each component.…”

Section: Methodsmentioning

confidence: 99%

“…Task-activity prediction was considered successful if the predicted map was more similar to the true activation map of the same participant than to all other participants’ maps (Gal et al, 2022; Tavor et al, 2016; Tik et al, 2021). In these cases, the correlation matrix between true and predicted maps is diagonal-dominant.…”

Section: Methodsmentioning

confidence: 99%

“…In this case, only six task-contrasts do not allow sufficient data for fitting a reliable model. Therefore, we used twelve more task-contrasts from the HCP dataset, as in previous work on connTask maps (Gal et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…GCA scores were constructed using an exploratory factor analysis, following the procedure suggested by Dubois et al (2018) . The G-scores prediction was performed using the Basis Brain Set pipeline (BBS) (Sripada et al, 2019;Sripada, Angstadt, Rutherford, Taxali, & Shedden, 2020), which has been previously found effective for predicting individual traits from connTask maps (Gal et al, 2022) (Figure 1).…”

Section: Prediction Of Individual Traits Using Conntask Mapsmentioning

confidence: 99%

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Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

Gal

Coldham

Bernstein‐Eliav

et al. 2021

Preprint

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The search for an ‘ideal’ approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Prediction Of Individual Traits Using Conntask Mapsmentioning

confidence: 99%

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Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

Gal

Coldham

Bernstein‐Eliav

et al. 2021

Preprint

Self Cite

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Oxytocin enhances the triangular association among behavior, resting‐state, and task‐state functional connectivity

Zhang,

Chen,

Bao

et al. 2023

Human Brain Mapping

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Considerable advances in the role of oxytocin (OT) effect on behavior and the brain network have been made, but the effect of OT on the association between inter‐individual differences in functional connectivity (FC) and behavior is elusive. Here, by using a face‐perception task and multiple connectome‐based predictive models, we aimed to (1) determine whether OT could enhance the association among behavioral performance, resting‐state FC (rsFC), and task‐state FC (tsFC) and (2) if so, explore the role of OT in enhancing this triangular association. We found that in the OT group, the prediction performance of using rsFC or tsFC to predict task behavior was higher than that of the PL group. Additionally, the correlation coefficient between rsFC and tsFC was substantially higher in the OT group than in the PL group. The strength of these associations could be partly explained by OT altering the brain's FCs related to social cognition and face perception in both the resting and task states, mainly in brain regions such as the limbic system, prefrontal cortex, temporal poles, and temporoparietal junction. Taken together, these results provide novel evidence and a corresponding mechanism for how neuropeptides cause increased associations among inter‐individual differences across different levels (e.g., behavior and large‐scale brain networks in both resting and task‐state), and may inspire future research on the role of neuropeptides in the cross levels association of both clinical and nonclinical use.

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fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Leibovitz

Osin

Wolf

et al. 2022

Lecture Notes in Computer Science

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Predicting individual traits from unperformed tasks

Cited by 13 publications

References 42 publications

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

Oxytocin enhances the triangular association among behavior, resting‐state, and task‐state functional connectivity

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

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