Daniel H. Wolf

The PMADS Project tracks neurobiological and clinical changes in high-risk individuals across the perinatal period to identify biomarkers linking brain plasticity to PMADs risk.

The paper introduces a longitudinal data resource combining multimodal imaging and cognitive phenotyping to study brain development and EF variation in adolescents.

This study investigates how dopamine and serotonin affect persistence for delayed rewards in humans, finding that serotonin increases persistence while dopamine has mixed effects.

This study examines the relationship between cannabis use, glutamate levels in the anterior cingulate cortex, and symptom severity across the psychosis spectrum using 7T proton magnetic resonance spectroscopy.

The paper introduces an open data resource combining neuroimaging and behavioral measures to study mood and sleep in adolescents and young adults.

The paper identifies robust sex differences in personalized functional brain network topography in youth, linked to X-linked genes and brain regions involved in psychiatric disorders.

pNet is an open-source toolbox for creating personalized functional networks from fMRI data, offering multiple interfaces and methods to enhance reliability and reproducibility.

This study uses network control theory to examine how alprazolam affects persistence energy during emotion processing in schizophrenia relatives, revealing compensatory mechanisms and neuromodulatory relationships.

The paper estimates the whole-cortex excitation-inhibition ratio using fMRI and shows it declines during youth, with lower ratios linked to better cognitive performance in children.

This study links individual differences in delay discounting to specific patterns of dorsal prefrontal cortex connectivity in youth using resting-state fMRI.

The paper uses network control theory to show that bottom-up signaling across the sensory-fugal axis of the human connectome requires less energy than top-down signaling.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This study links individual mobility patterns to mood, sleep, and brain connectivity by identifying unique mobility footprints.

This study uses pharmacological fMRI to show a reduction in the excitation:inhibition ratio in association cortex during adolescence, linked to psychopathology.

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

This study identifies altered functional brain dynamics in 22q11.2DS during facial affect processing using fMRI, showing preserved early visual and insular activation but diminished motor feedback and emotional memory signals.

This study examines how neighborhood socioeconomic status affects executive system activation during a working memory task in youth and identifies mediating brain activity patterns.

The paper uses machine learning to analyze functional topography in youth brains, finding sex differences linked to X-linked genes and specific brain networks.

The paper models cortical brain deviations from normative neurodevelopment to predict psychiatric symptoms, finding that transdiagnostic psychopathology dimensions better predict symptoms than specific disorder measures.

The paper shows that reduced value signals in the ventromedial prefrontal cortex are linked to motivational and hedonic deficits across mood and psychotic disorders.

This study identifies three neurocognitive subtypes of depression in youth using machine learning, showing distinct neural signatures despite similar clinical symptoms.

The study replicates and extends the Parieto-Frontal Integration Theory (PFIT) in youths, showing age-related strengthening of brain parameter-cognitive performance associations and suggesting an expanded network incorporating motivation and affect regions.

This study shows how functional topography of brain networks changes with age and relates to executive function and brain organization in youth.

The paper harmonizes 10,232 structural brain MRIs from 18 studies to create a normative reference of brain development and aging across 3 to 96 years.

This study identifies two neurobiologically distinct subtypes of youth with internalizing symptoms using structural brain imaging and machine learning.

The paper identifies brain-based dimensions of psychopathology using sCCA in youths, finding four dimensions linked to functional dysconnectivity that replicate in an independent dataset.

This study links accelerated cortical thinning in structural brain networks to irritability in youth, highlighting associations in emotion-related brain regions.

The paper examines the association between cortical thickness and impulsive choice in adolescents, finding reduced cortical thickness in specific brain regions linked to impulsive decision-making.