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Area of Science:

  • Neuroscience
  • Psychiatry
  • Medical Imaging

Background:

  • Major depressive disorder (MDD) is primarily characterized by functional brain abnormalities rather than structural changes.
  • Regional homogeneity (ReHo) deficits observed in MDD may correlate with underlying regional hypoperfusion.
  • Identifying functional deficit patterns can lead to brain pattern-based biomarkers for MDD linked to pathophysiology.

Purpose of the Study:

  • To determine if cortical ReHo patterns serve as a replicable biomarker for MDD, potentially more sensitive than reduced cortical thickness.
  • To evaluate if the ReHo deficit pattern in MDD reflects regional cerebral blood flow (RCBF) deficit patterns.
  • To assess if a constructed regional vulnerability index (RVI) can act as a concise, brain pattern-based biomarker for MDD.

Main Methods:

  • Analysis of four datasets: UK Biobank (UKBB), Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA), Amish Connectome Project (ACP), and Ament Clinic Inc (ACI).
  • Inclusion of ReHo and structural measurements from UKBB and ENIGMA, with ReHo, structural, and RCBF data from ACP.
  • RCBF data measured via single-photon emission computed tomography in the ACI sample.

Main Results:

  • MDD participants exhibited lower cortical ReHo in specific brain regions (cingulum, superior temporal lobe, frontal lobe) without significant cortical thickness differences.
  • The regional pattern of ReHo MDD effect sizes strongly correlated with RCBF patterns in independent datasets (Pearson r = 0.52 and 0.46).
  • Functional RVIs (ReHo and RCBF) demonstrated stronger effect sizes (Cohen d = 0.33–0.90) compared to structural RVIs (Cohen d = 0.09–0.20).
  • Elevated ReHo-based RVI-MDD values were associated with increased depression symptom severity across cohorts.

Conclusions:

  • The ReHo MDD deficit pattern accurately reflects cortical hypoperfusion and is regionally specific in MDD.
  • A ReHo-based RVI shows potential as a sensitive functional biomarker for MDD.
  • These findings support the utility of functional neuroimaging biomarkers in understanding and diagnosing MDD.