Joshua Faskowitz

The paper shows that trait-relevant tasks improve personality prediction from structural-functional brain network coupling during resting state and specific tasks.

The paper examines how the brain adapts to novelty using fMRI data from repeated movie viewings, finding reduced brain activity dimensionality and links to intelligence and arousal.

This paper adapts network control theory to model spatially diffuse input signals in the human brain, showing reduced energy for state transitions and matching existing neurobiological maps.

The paper investigates if neuroticism is reflected in brain activity similarity using fMRI data from 174 participants, finding higher neuroticism correlates with greater brain response heterogeneity during trait-relevant scenes.

This study investigates how brain connectivity and dynamics across temporal and spatial scales relate to intelligence test performance using fMRI and EEG data.

The paper analyzes high-amplitude events in edge time series across the human lifespan, finding age-related changes in their patterns and associations with cognitive phenotypes.

This paper examines limitations in interpreting resting-state fMRI correlations, distinguishing measurement distortion from causal non-identifiability and their implications for biological interpretation.

This paper identifies amplitude-dependent co-fluctuation transitions along the sensorimotor-association axis using resting-state fMRI data across developmental stages and external stimuli.

This paper introduces a community detection algorithm for functional brain networks that accounts for higher-order interactions and identifies redundant modules, revealing new organizational patterns in the transmodal cortex.

The paper analyzes time-varying synergy and redundancy dominance in fMRI BOLD signals during rest, finding that whole-brain synergy is rare and that optimized subsets show recurrent functional systems.

The paper investigates how connectome architecture supports dual communication modes, showing that modular networks enable within-module diffusion and between-module routing across multiple organisms.

This study predicts human intelligence from brain connectivity with a focus on interpretability, finding that multiple brain connections across the system contribute to intelligence prediction.

The paper predicts general intelligence using structural-functional brain network coupling during task performance and resting state.

This study maps neuroanatomical changes in a human brain from preconception to two years postpartum, revealing decreases in gray matter and increases in white matter integrity.

The paper investigates how structural-functional brain network coupling relates to individual differences in general cognitive ability using data from the Human Connectome Project.

This study identifies bursty dynamics and modular subgraphs in mouse and human brain networks, showing cross-species consistency in co-fluctuation patterns and anatomical connectivity.

The paper measures brain irreversibility in ferrets during different behavioral states to reveal functional hierarchies in cortical dynamics.

The paper analyzes brain network events during movie-watching, identifying three synchronization classes and their distinct characteristics.

This commentary critiques Pang et al.'s 2023 study, arguing that their geometric mode approach to brain dynamics lacks specificity to brain shape and orientation, and does not sufficiently establish geometry as a more fundamental constraint than connectivity.

This paper introduces a coupled oscillator model that dynamically limits brain network communication, showing it better fits empirical functional connectivity than standard models.

The paper shows that 16 time frames of high brain connectivity can predict intelligence, but network-defining states do not, and results replicate in an independent sample.

This study uses multilayer modeling to analyze brain-behavior correlations across seven social support measures, finding that subcortical and control systems show varying community affiliations.

This study identifies hierarchical co-fluctuation patterns in brain activity using fMRI data from two dense-sampling studies, revealing multi-scale organization of brain systems.

The paper examines how hormonal fluctuations during the menstrual cycle affect brain network states using edge time series analysis in a single individual.

This paper introduces a method to weight anatomical brain connectivity data using multi-modal MRI data and evaluates its performance on the Human Connectome Project dataset.

This paper investigates the relationship between anatomical and functional modular organization of the human brain using a novel multi-layer framework across multiple subjects.

The paper curates and provides analysis-ready diffusion MRI data for pediatric brain white-matter research, including automated quality control using a trained neural network.

The paper investigates local brain system events and their contributions to global events, revealing distinct co-fluctuation patterns and their spatial distribution during resting-state and movie-watching scans.

The paper introduces a multi-policy communication framework for the human connectome that outperforms existing models and explains variance in time-varying functional connectivity.

This study investigates structure-function relationships in human brain networks across the lifespan, finding that local functional coupling is better predicted by structural features than global patterns, with age-related changes observed in sensorimotor regions.