PMID- 38250018
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240123
IS  - 1662-5196 (Print)
IS  - 1662-5196 (Electronic)
IS  - 1662-5196 (Linking)
VI  - 17
DP  - 2023
TI  - The hemodynamic response function as a type 2 diabetes biomarker: a data-driven 
      approach.
PG  - 1321178
LID - 10.3389/fninf.2023.1321178 [doi]
LID - 1321178
AB  - 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.
CI  - Copyright (c) 2024 Guimaraes, Serranho, Duarte, Crisostomo, Moreno, Gomes, 
      Bernardes and Castelo-Branco.
FAU - Guimaraes, Pedro
AU  - Guimaraes P
AD  - University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational 
      Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), 
      Coimbra, Portugal.
FAU - Serranho, Pedro
AU  - Serranho P
AD  - University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational 
      Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), 
      Coimbra, Portugal.
AD  - Department of Sciences and Technology, Universidade Aberta, Lisbon, Portugal.
FAU - Duarte, Joao V
AU  - Duarte JV
AD  - University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational 
      Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), 
      Coimbra, Portugal.
AD  - University of Coimbra, Faculty of Medicine (FMUC), Coimbra, Portugal.
FAU - Crisostomo, Joana
AU  - Crisostomo J
AD  - University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational 
      Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), 
      Coimbra, Portugal.
FAU - Moreno, Carolina
AU  - Moreno C
AD  - Department of Endocrinology, University Hospital of Coimbra (CHUC), Coimbra, 
      Portugal.
FAU - Gomes, Leonor
AU  - Gomes L
AD  - Department of Endocrinology, University Hospital of Coimbra (CHUC), Coimbra, 
      Portugal.
FAU - Bernardes, Rui
AU  - Bernardes R
AD  - University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational 
      Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), 
      Coimbra, Portugal.
AD  - University of Coimbra, Clinical Academic Center of Coimbra (CACC), Faculty of 
      Medicine (FMUC), Coimbra, Portugal.
FAU - Castelo-Branco, Miguel
AU  - Castelo-Branco M
AD  - University of Coimbra, Coimbra Institute for Biomedical Imaging and Translational 
      Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), 
      Coimbra, Portugal.
AD  - University of Coimbra, Clinical Academic Center of Coimbra (CACC), Faculty of 
      Medicine (FMUC), Coimbra, Portugal.
LA  - eng
PT  - Journal Article
DEP - 20240105
PL  - Switzerland
TA  - Front Neuroinform
JT  - Frontiers in neuroinformatics
JID - 101477957
PMC - PMC10796780
OTO - NOTNLM
OT  - deep learning
OT  - functional magnetic resonance imaging
OT  - hemodynamic response
OT  - neuroimaging
OT  - type 2 diabetes
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2024/01/22 06:41
MHDA- 2024/01/22 06:42
PMCR- 2023/01/01
CRDT- 2024/01/22 05:10
PHST- 2023/10/13 00:00 [received]
PHST- 2023/12/14 00:00 [accepted]
PHST- 2024/01/22 06:42 [medline]
PHST- 2024/01/22 06:41 [pubmed]
PHST- 2024/01/22 05:10 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fninf.2023.1321178 [doi]
PST - epublish
SO  - Front Neuroinform. 2024 Jan 5;17:1321178. doi: 10.3389/fninf.2023.1321178. 
      eCollection 2023.