The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach

Pedro Guimarães; Pedro Serranho; João V Duarte; Joana Crisóstomo; Carolina Moreno; Leonor Gomes; Rui Bernardes; Miguel Castelo-Branco

doi:10.3389/fninf.2023.1321178

The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach

Front Neuroinform. 2024 Jan 5:17:1321178. doi: 10.3389/fninf.2023.1321178. eCollection 2023.

Authors

Pedro Guimarães¹, Pedro Serranho^{1

2}, João V Duarte^{1

3}, Joana Crisóstomo¹, Carolina Moreno⁴, Leonor Gomes⁴, Rui Bernardes^{1

5}, Miguel Castelo-Branco^{1

5}

Affiliations

¹ University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), Coimbra, Portugal.
² Department of Sciences and Technology, Universidade Aberta, Lisbon, Portugal.
³ University of Coimbra, Faculty of Medicine (FMUC), Coimbra, Portugal.
⁴ Department of Endocrinology, University Hospital of Coimbra (CHUC), Coimbra, Portugal.
⁵ University of Coimbra, Clinical Academic Center of Coimbra (CACC), Faculty of Medicine (FMUC), Coimbra, Portugal.

Abstract

Introduction: There is a need to better understand the neurophysiological changes associated with early brain dysfunction in Type 2 diabetes mellitus (T2DM) before vascular or structural lesions. Our aim was to use a novel unbiased data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients, focusing on their potential as biomarkers.

Methods: We meshed task-based event-related (visual speed discrimination) functional magnetic resonance imaging with DL to show, from an unbiased perspective, that T2DM patients' blood-oxygen-level dependent response is altered. Relevance analysis determined which brain regions were more important for discrimination. We combined explainability with deconvolution generalized linear model to provide a more accurate picture of the nature of the neural changes.

Results: The proposed approach to discriminate T2DM patients achieved up to 95% accuracy. Higher performance was achieved at higher stimulus (speed) contrast, showing a direct relationship with stimulus properties, and in the hemispherically dominant left visual hemifield, demonstrating biological interpretability. Differences are explained by physiological asymmetries in cortical spatial processing (right hemisphere dominance) and larger neural signal-to-noise ratios related to stimulus contrast. Relevance analysis revealed the most important regions for discrimination, such as extrastriate visual cortex, parietal cortex, and insula. These are disease/task related, providing additional evidence for pathophysiological significance. Our data-driven design allowed us to compute the unbiased HRF without assumptions.

Conclusion: We can accurately differentiate T2DM patients using a data-driven classification of the HRF. HRF differences hold promise as biomarkers and could contribute to a deeper understanding of neurophysiological changes associated with T2DM.

Keywords: deep learning; functional magnetic resonance imaging; hemodynamic response; neuroimaging; type 2 diabetes.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. FCT DSAIPA/DS/0041/2020, FCT/UIDB/4950/2020 and FCT/UIDP/4950/2020 and EASD Innovative Outcomes 2019.