Biobehavioral correlates of an fMRI index of striatal tissue iron in depressed patients.
Rebecca B PriceBrenden C Tervo-ClemmensBenjamin PannyMichelle DegutisAngela GriffoMary L WoodyPublished in: Translational psychiatry (2021)
Dopaminergic function is a critical transdiagnostic neurophysiological dimension with broad relevance in psychiatry. Normalized T2*-weighted (nT2*w) imaging has been previously investigated as a method to quantify biological properties of tissue in the striatum (e.g., tissue iron), providing a widely available, in vivo marker with potential relevance to dopaminergic function; but no prior study to our knowledge has examined this neuroimaging marker in clinical depression. In a treatment-seeking, clinically depressed sample (n = 110), we quantified tissue iron (nT2*w) in striatal regions. We assessed test-retest reliability and correlated values with dimensional features across levels of analysis, including demographic/biological (sex, age, Body Mass Index), neuroanatomical (hippocampal atrophy, which was quantified using a recently validated machine-learning algorithm), and performance-based (Affective Go/NoGo task performance) indices with relevance to depressive neurocognition. Across patients, decreased tissue iron concentration (as indexed by higher nT2*w) in striatal regions correlated with indices of decreased cognitive-affective function on the Affective Go/NoGo task. Greater caudate nT2*w also correlated with greater hippocampal atrophy. Striatal tissue iron concentrations were robustly lower in female patients than males but gender differences did not explain relations with other neurocognitive variables. A widely available fMRI index of striatal tissue properties, which exhibited strong psychometric properties and can be readily quantified from most fMRI datasets irrespective of study-specific features such as task design, showed relevance to multiple biobehavioral markers of pathophysiology in the context of moderate-to-severe, treatment-resistant depression. Striatal tissue iron may play a role in dimensional and subgroup-specific features of depression, with implications for future research on depression heterogeneity.
Keyphrases
- functional connectivity
- end stage renal disease
- machine learning
- ejection fraction
- bipolar disorder
- parkinson disease
- chronic kidney disease
- depressive symptoms
- peritoneal dialysis
- prognostic factors
- magnetic resonance imaging
- healthcare
- magnetic resonance
- iron deficiency
- high resolution
- psychometric properties
- risk assessment
- patient reported outcomes
- patient reported
- clinical trial
- weight gain
- artificial intelligence
- sleep quality
- subarachnoid hemorrhage
- big data
- stress induced
- photodynamic therapy
- high intensity
- network analysis
- high speed