Pattern recognition analysis of anterior cingulate cortex blood flow to classify depression polarity
J R C Almeida1, J Mourao-Miranda, H J Aizenstein
1J. R. C. Almeida, MD, PhD, Department of Psychiatry, University of Pittsburgh School of Medicine; J. Mourao-Miranda, PhD, Department of Computer Science, University College London; H. J. Aizenstein, MD, PhD, A. Versace, MD, Department of Psychiatry, University of Pittsburgh School of Medicine; F. A. Kozel, MD, MSCR, Department of Psychiatry and Neuroscience, University of South Florida, Tampa; H. Lu, PhD, Department of Psychiatry, University of Texas Southwestern, Dallas; A. Marquand, PhD, Department of Clinical Neuroscience, Institute of Psychiatry, King's College London; E. J. LaBarbara, BS, Department of Psychiatry, University of Pittsburgh School of Medicine, USA; M. Brammer, PhD, Department of Biostatistics, Institute of Psychiatry, King's College London; M. Trivedi, MD, Department of Psychiatry, University of Texas Southwestern, Dallas; D. J. Kupfer, MD, Department of Psychiatry, University of Pittsburgh School of Medicine; M. L. Phillips, MB, Bchir, MD (Cantab), MRCPsych, Department of Psychiatry, University of Pittsburgh School of Medicine and Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, UK.
Distinguishing bipolar depression from recurrent unipolar depression is difficult. Arterial spin labelling revealed that subgenual anterior cingulate cortex (ACC) blood flow can differentiate these conditions with 81% accuracy.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Differentiating bipolar disorder from unipolar depression presents a significant clinical challenge.
- Accurate diagnosis is crucial for effective treatment selection and patient outcomes.
- Neuroimaging may offer objective biomarkers to aid in differential diagnosis.
Purpose of the Study:
- To investigate the utility of resting-state anterior cingulate cortex (ACC) blood flow in distinguishing between bipolar and unipolar depression.
- To apply pattern recognition analysis to neuroimaging data for diagnostic classification.
Main Methods:
- Arterial spin labelling (ASL) was used to measure resting-state blood flow in subdivisions of the ACC.
- Participants included healthy females (n=18) and females with a current depressive episode (n=36): 18 with bipolar I disorder and 18 with recurrent unipolar depression.
- Pattern recognition analysis was employed to classify depressive subtypes based on ACC blood flow.
Main Results:
- Blood flow in the subgenual ACC demonstrated significant differences between unipolar and bipolar depression.
- Pattern recognition analysis using subgenual ACC blood flow achieved 81% accuracy in classifying unipolar versus bipolar depression.
- The classification achieved 83% sensitivity and 78% specificity.
Conclusions:
- Resting-state subgenual ACC blood flow is a potential neuroimaging biomarker for differentiating bipolar from unipolar depression.
- These findings may contribute to more accurate differential diagnosis in clinical practice.
- Further research is warranted to validate these findings in larger and more diverse populations.
