Dopamine dysfunction in depression: application of texture analysis to dopamine transporter single-photon emission computed tomography imaging.
Takehiro TamuraGenichi SugiharaKyoji OkitaYohei MukaiHiroshi MatsudaHiroki ShiwakuShunsuke TakagiHiromitsu DaisakiUkihide TateishiHidehiko TakahashiPublished in: Translational psychiatry (2022)
Dopamine dysfunction has been associated with depression. However, results of recent neuroimaging studies on dopamine transporter (DAT), which reflect the function of the dopaminergic system, are inconclusive. The aim of this study was to apply texture analysis, a novel method to extract information about the textural properties of images (e.g., coarseness), to single-photon emission computed tomography (SPECT) imaging in depression. We performed SPECT using 123 I-ioflupane to measure DAT binding in 150 patients with major depressive disorder (N = 112) and bipolar disorder (N = 38). The texture features of DAT binding in subregions of the striatum were calculated. We evaluated the relationship between the texture feature values (coarseness, contrast, and busyness) and severity of depression, and then examined the effects of medication and diagnosis on such relationship. Furthermore, using the data from 40 healthy subjects, we examined the effects of age and sex on the texture feature values. The degree of busyness of the limbic region in the left striatum linked to the severity of depression (p = 0.0025). The post-hoc analysis revealed that this texture feature value was significantly higher in both the severe and non-severe depression groups than in the remission group (p = 0.001 and p = 0.028, respectively). This finding remained consistent after considering the effect of medication. The effects of age and sex in healthy individuals were not evident in this texture feature value. Our findings imply that the application of texture analysis to DAT-SPECT may provide a state-marker of depression.
Keyphrases
- contrast enhanced
- depressive symptoms
- major depressive disorder
- bipolar disorder
- computed tomography
- sleep quality
- deep learning
- machine learning
- magnetic resonance imaging
- uric acid
- magnetic resonance
- oxidative stress
- healthcare
- high resolution
- pet ct
- metabolic syndrome
- prefrontal cortex
- social media
- rheumatoid arthritis
- mass spectrometry
- health information
- single cell
- artificial intelligence
- dna binding
- drug induced