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Beyond Functional Connectivity: Disentangling Neural and Hemodynamic Network Dysfunction in Alcohol Use Disorder

September 10, 2026

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Project Description

This project will establish a systems-level framework for alcohol use disorder (AUD) by moving beyond conventional functional connectivity to disentangle neural-network organization, systemic/cerebrovascular physiology, and their interaction in resting-state fMRI. Leveraging T1-weighted and resting-state datasets, the fellow will use Dr. Tong’s expertise in systemic low-frequency BOLD physiology and lag-aware modeling to extract subject-specific hemodynamic components, including oscillation amplitudes, delay maps, propagation gradients, and physiological contributions. In parallel, Dr. Goñi’s expertise in functional connectomics, network science, geometry, dynamic connectivity, and dimensional AUD phenotyping will guide construction of conventional and physiologically corrected connectomes. Each participant will therefore be represented by two layers—a neural functional network and a hemodynamic propagation network—whose cross-layer coupling will be quantified through geometry-aware distances, gradient and community alignment, spectral similarity, and spatial null models. Associations with alcohol consumption, AUD-related problems, family history, impulsivity, age, sex, and education will test whether observed abnormalities are predominantly neural, predominantly vascular, or reflect pathological coupling or decoupling. The most transformative outcome would be identification of reduced neural–hemodynamic alignment despite preserved global physiology and connectivity strength, revealing a previously invisible AUD phenotype. This integrative, recruitment-free strategy is feasible, mechanistically informative, and readily generalizable to aging, neurodegeneration, pain, and other disorders.

Start Date

Summer 2027

Postdoc Qualifications

  • PhD in biomedical engineering, neuroscience, computational neuroscience, applied mathematics, computer science, statistics, or a closely related field.
  • Strong experience analyzing resting-state fMRI data, including preprocessing, quality control, nuisance regression, and functional-connectivity estimation.
  • Knowledge of BOLD physiology, cerebrovascular effects, systemic low-frequency oscillations, or physiological-noise modeling.
  • Experience with lag-aware analysis, time-delay estimation, signal propagation, or related time-series methods.
  • Expertise in network neuroscience, including connectome construction, graph measures, community detection, gradients, and spectral methods.
  • Familiarity with dynamic functional connectivity and multilayer or multimodal network analysis.
  • Strong quantitative background in statistical modeling, multivariate analysis, dimensional phenotyping, and correction for multiple comparisons.
  • Experience using spatial null models and accounting for cortical geometry and spatial autocorrelation.
  • Proficiency in Python, MATLAB, R, or comparable scientific-computing environments.
  • Ability to develop reproducible analytical pipelines for large neuroimaging datasets.
  • Experience with tools such as FSL, AFNI, SPM, FreeSurfer, fMRIPrep, Nilearn, or comparable software.
  • Familiarity with Riemannian geometry, covariance-based representations, or geometry-aware distances would be advantageous.
  • Experience with substance-use, psychiatric, or behavioral phenotyping—particularly alcohol use disorder—is desirable.
  • Ability to integrate physiological, imaging, demographic, and behavioral data.
  • Strong scientific writing and visualization skills, with evidence of peer-reviewed publications.
  • Commitment to open science, including documented code, version control, data provenance, and reproducible reporting.
  • Capacity to work independently while collaborating effectively across neuroimaging, physiology, network-science, and clinical teams.
  • Interest in translating methodological developments beyond AUD to aging, neurodegeneration, pain, and other health conditions.

Co-advisors

Joaquín Goñi (IE) and Yunjie Tong (BME)

Bibliography

Resting State Functional Connectivity Patterns Associate with Alcohol Use Disorder Characteristics: Insights from the Triple Network Model. (2026) D Guerrero, M Dzemidzic, M Moghaddam, M Liu, A Avena-Koenigsberger, J Harezlak, DA Kareken, MH Plawecki, MA Cyders, J Goñi. NeuroImage Clinical 49, 103939.

Functional Connectome Fingerprinting Through Tucker Tensor Decomposition. (2025) V Carvalho, M Liu, J Harezlak, AM Estrada Gomez, J Goñi. Applied Sciences. Special Issue Brain Functional Connectivity: Prediction, Dynamics, and Modeling 15(9), 4821.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder (2025) M Moghaddam, M Dzemidzic, D Guerrero, M Liu, J Alessi, MH Plawecki, J Harezlak, D Kareken, J Goñi. Network Neuroscience 9(1), 38-60.

Effect of brief rest on hemodynamics and CSF oscillations across age. (2025) VV Nair, AM Wright, T Xu, E Foster, X Zhou, Y Tong, Q Wen NeuroImage, 121531

An fMRI approach to assess intracranial arterial-to-venous cardiac pulse delay in aging (2025) AM Wright, T Xu, J Koo, Y Zhao, Y Tong, Q Wen Imaging Neuroscience 3, IMAG. a. 969