Differentiating between bipolar and unipolar depression in functional and structural MRI studies

Kyu Man Han, Domenico De Berardis, Michele Fornaro, Yong Ku Kim

Research output: Contribution to journalReview article

11 Citations (Scopus)

Abstract

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.

Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalProgress in Neuro-Psychopharmacology and Biological Psychiatry
Volume91
DOIs
Publication statusPublished - 2019 Apr 20

Fingerprint

Depressive Disorder
Bipolar Disorder
Magnetic Resonance Imaging
Gyrus Cinguli
Prefrontal Cortex
Amygdala
Reward
Neuroimaging
Emotions
Parietal Lobe
Corpus Callosum
Brain
Temporal Lobe
Thalamus
Cognition
Hippocampus

Keywords

  • bipolar depression
  • bipolar disorder
  • functional imaging
  • magnetic resonance imaging
  • major depressive disorder
  • mood disorder
  • structural imaging
  • unipolar depression

ASJC Scopus subject areas

  • Pharmacology
  • Biological Psychiatry

Cite this

Differentiating between bipolar and unipolar depression in functional and structural MRI studies. / Han, Kyu Man; De Berardis, Domenico; Fornaro, Michele; Kim, Yong Ku.

In: Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 91, 20.04.2019, p. 20-27.

Research output: Contribution to journalReview article

@article{9f3c0fe608c846daac3ee6f1bd5f9896,
title = "Differentiating between bipolar and unipolar depression in functional and structural MRI studies",
abstract = "Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.",
keywords = "bipolar depression, bipolar disorder, functional imaging, magnetic resonance imaging, major depressive disorder, mood disorder, structural imaging, unipolar depression",
author = "Han, {Kyu Man} and {De Berardis}, Domenico and Michele Fornaro and Kim, {Yong Ku}",
year = "2019",
month = "4",
day = "20",
doi = "10.1016/j.pnpbp.2018.03.022",
language = "English",
volume = "91",
pages = "20--27",
journal = "Progress in Neuro-Psychopharmacology and Biological Psychiatry",
issn = "0278-5846",
publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - Differentiating between bipolar and unipolar depression in functional and structural MRI studies

AU - Han, Kyu Man

AU - De Berardis, Domenico

AU - Fornaro, Michele

AU - Kim, Yong Ku

PY - 2019/4/20

Y1 - 2019/4/20

N2 - Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.

AB - Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.

KW - bipolar depression

KW - bipolar disorder

KW - functional imaging

KW - magnetic resonance imaging

KW - major depressive disorder

KW - mood disorder

KW - structural imaging

KW - unipolar depression

UR - http://www.scopus.com/inward/record.url?scp=85062804726&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062804726&partnerID=8YFLogxK

U2 - 10.1016/j.pnpbp.2018.03.022

DO - 10.1016/j.pnpbp.2018.03.022

M3 - Review article

VL - 91

SP - 20

EP - 27

JO - Progress in Neuro-Psychopharmacology and Biological Psychiatry

JF - Progress in Neuro-Psychopharmacology and Biological Psychiatry

SN - 0278-5846

ER -