Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain

General cognitive ability (GCA) refers to a trait‐like ability that contributes to performance across diverse cognitive tasks. Identifying brain‐based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole‐brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N‐back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in 10‐fold cross‐validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain‐based prediction of GCA.We investigated prediction of general cognitive ability (GCA) based on fMRI task activation patterns with 15 task contrasts in the Human Connectome Project dataset. The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in ten10‐fold cross‐validation analysis. Additionally, we found that task contrasts that produce greater fronto‐parietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in GCA prediction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/2/hbm25007.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/1/hbm25007_am.pd

Automated brain masking of fetal functional MRI with open data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing

4.3 Normative Modeling with the Predictive Clinical Neuroscience Toolkit (PCNtoolkit)

https://osf.io/2c8s9 In this chapter we introduce normative modeling as a tool for mapping variation across large neuroimaging datasets. We provide practical guidance to illustrate how normative models can be used to map diverse patterns of individual differences found within the large datasets used to train the models. In other words, while normative modeling is a method often applied to big datasets containing thousands of subjects, it provides single subject inference and prediction. We use an open-source Python package, Predictive Clinical Neuroscience Toolkit (PCNtoolkit) and showcase several helpful tools (including an interface that does not require coding) to run a normative modeling analysis, evaluate the model fit, and visualize the results

Normative Modeling with the Predictive Clinical Neuroscience Toolkit (PCNtoolkit)

In this chapter we introduce normative modeling as a tool for mapping variation across large neuroimaging datasets. We provide practical guidance to illustrate how normative models can be used to map diverse patterns of individual differences found within the large datasets used to train the models. In other words, while normative modeling is a method often applied to big datasets containing thousands of subjects, it provides single subject inference and prediction. We use an open-source Python package, Predictive Clinical Neuroscience Toolkit (PCNtoolkit) and showcase several helpful tools (including an interface that does not require coding) to run a normative modeling analysis, evaluate the model fit, and visualize the results

Social Cognition and Functional Connectivity in Schizophrenia and Early Psychosis

Individuals with schizophrenia (SZ) experience pervasive, treatment-resistant impairments in social cognition that contribute to poor functional outcomes. However, the mechanisms of social cognitive dysfunction in SZ remain poorly understood, which impedes the design of novel interventions to improve outcomes. This pre-registered project (https://doi.org/10.17605/OSF.IO/JH5FC) examines the representation of social functioning in the brain’s functional architecture across early psychosis (EP) and SZ. The study contains two parts: a confirmatory and an exploratory portion. In the confirmatory portion, we identified specific resting-state brain connectivity disruptions evident in EP and SZ. We performed a seed-based connectivity analysis using brain regions associated with social cognitive dysfunction in SZ (based on a published review) to test whether aberrant functional connectivity observed in SZ was also present in EP. In the exploratory portion, we assessed the out-of-sample generalizability and precision of resting state connectivity-based predictive models of social cognition. We used machine learning to predict social cognition from whole-brain connectomes and established the generalizability of these brain-behavior relationships with out-of-sample testing and cross-validation (to handle confounding variables). Results reveal significant decreases between the left inferior frontal gyrus and intraparietal sulcus that were evident in SZ but not EP. This connectivity profile is significantly associated with social cognition/functioning in both SZ and EP. Null predictive modeling results reveal the importance of out-of-sample evaluation, proper null hypothesis testing, and confound removal procedures. Overall, this work provides insights into the brain's functional architecture in SZ and EP. This work suggests that IFG-IPS connectivity profiles could be an important prognostic biomarker of social impairments and may be a target for future interventions focused on improved treatment outcomes related to social functioning

Social Cognition and Functional Connectivity in Schizophrenia and Early Psychosis

Individuals with schizophrenia (SZ) experience pervasive, treatment-resistant impairments in social cognition that contribute to poor functional outcomes. However, the mechanisms of social cognitive dysfunction in SZ remain poorly understood, which impedes the design of novel interventions to improve outcomes. This pre-registered project examines the representation of social functioning in the brain’s functional architecture across early psychosis (EP) and SZ. The study contains two parts: a confirmatory and an exploratory portion. In the confirmatory portion, we identified specific resting-state brain connectivity disruptions evident in EP and SZ. We performed a seed-based connectivity analysis using brain regions associated with social cognitive dysfunction in SZ (based on a published review) to test whether aberrant functional connectivity observed in SZ was also present in EP. In the exploratory portion, we assessed the out-of-sample generalizability and precision of resting state connectivity-based predictive models of social cognition. We used machine learning to predict social cognition from whole-brain connectomes and established the generalizability of these brain-behavior relationships with out-of-sample testing and cross-validation (to handle confounding variables). Results reveal significant decreases between the left inferior frontal gyrus and intraparietal sulcus that were evident in SZ but not EP. This connectivity profile is significantly associated with social cognition/functioning in both SZ and EP. Null predictive modeling results reveal the importance of out-of-sample evaluation, proper null hypothesis testing, and confound removal procedures. Overall, this work provides insights into the brain's functional architecture in SZ and EP. This work suggests that IFG-IPS connectivity profiles could be an important prognostic biomarker of social impairments and may be a target for future interventions focused on improved treatment outcomes related to social functioning

Predictive Clinical Neuroscience Portal (PCNportal):instant online access to research-grade normative models for clinical neuroscientists [version 1; peer review: awaiting peer review]

Background: The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop.Methods: To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset.Results: At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively.Conclusions: This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine

The “social brain” is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study

<div><p>How the human brain processes social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of “social” were subject to an automated meta-analysis (<a href="http://neurosynth.org/" target="_blank">http://neurosynth.org</a>). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social–non-social contrast in the independent study showed a strong resemblance to the NeuroSynth map. ROI analyses revealed that a social effect was credible in 9 out of the 11 NeuroSynth regions in the independent dataset. The findings support the conclusion that the “social brain” is highly sensitive to the mere presence of social information.</p></div