James A. Waltz

Normative modeling of brain morphometry in Clinical High-Risk for Psychosis

This study examines brain morphometric deviations in individuals at clinical high-risk for psychosis and finds they largely overlap with healthy variation and are not strongly associated with symptom severity or conversion to psychosis.

Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery

This paper introduces an adaptive dense subgraph discovery method to identify disease-related connectomic subnetworks from brain connectome data, showing improved performance in detecting schizophrenia-related subnetworks.

White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

The paper uses machine learning to predict brain age from diffusion tensor imaging in schizophrenia patients and finds accelerated white matter aging is associated with cognitive deficits.

Bayes Estimate of Primary Threshold in Cluster-wise fMRI Inferences

The paper proposes a data-driven Bayesian method to select an optimal primary threshold in cluster-wise fMRI analysis, improving statistical power while controlling false discovery rate.

Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification

The paper introduces a link prediction model using ERGM with outcome misclassification to handle incomplete network data, showing improved performance on social and brain networks.

Impaired expected value computations in schizophrenia are associated with a reduced ability to integrate reward probability and magnitude of recent outcomes.

The paper investigates how schizophrenia affects the integration of reward probability and magnitude, finding that patients rely more on stimulus-response learning than expected value-based learning.

Impaired expected value computations coupled with overreliance on prediction error learning in schizophrenia

This study investigates decision-making impairments in schizophrenia by analyzing reward prediction error and expected value computations using computational modeling.