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

Brain Mechanisms of Social Cognition in Schizophrenia and Early Psychosis

Analysis plan for investigating and interpreting the functional brain mechanisms underlying social cognition in a cohort of individuals with schizophrenia and a cohort of individuals with early psychosis

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

Population-level normative models reveal race- and socioeconomic-related variability in cortical thickness of threat neurocircuitry

Abstract The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness

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