Dataset Information

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging.

ABSTRACT:

Rationale and objectives

More than half of the bipolar depression (BD) subjects are misdiagnosed as unipolar depression (UD) due to lack of objective diagnostic criteria. We aimed to identify microstructural neuronal changes differentiating BD from UD groups using diffusion kurtosis imaging (DKI). The objective of the study is to identify an objective neuro-imaging marker to differentiate BD from UD.

Materials and methods

A prospective, cross-sectional study included total of 62 subjects with diagnosis of bipolar depression (n = 21), unipolar depression (n = 21), and healthy controls (n = 20). All subjects underwent diffusion-weighted imaging (b = 0,1000,2000) of the whole brain on 3-Tesla MR scanner. DKI data was analyzed using 189 region whole-brain atlas. Eight diffusion and kurtosis metrics including mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), and kurtosis fractional anisotropy (FKA) were measured against these 189 regions. Principle component analysis (PCA) was utilized to identify the most significant regions of the brain. ANOVA with post hoc tests was used for analyzing these regions.

Results

BD group showed increased MD, RD, decreased AK at the left amygdala and decreased MK and RK at the right hemi-cerebellum. UD group showed increased MK and RK at the right external capsule; and increased AK, MK, and RK at the right amygdala.

Conclusion

The right and left amygdala, right external capsule, and right hemi-cerebellum showed microstructural abnormalities capable of differentiating BD and UD groups. Diffusion imaging especially DKI can aid in management of depression patients.

SUBMITTER: Maralakunte M

PROVIDER: S-EPMC10034704 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Publications

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging.

Maralakunte Muniraju M Gupta Vivek V Grover Sandeep S Ahuja Chirag Kamal CK Sahoo Swapnajeet S Kishore Kamal K Vyas Sameer S Garg Gaurav G Singh Paramjeet P Govind Varan V

The neuroradiology journal 20220711 2

<h4>Rationale and objectives</h4>More than half of the bipolar depression (BD) subjects are misdiagnosed as unipolar depression (UD) due to lack of objective diagnostic criteria. We aimed to identify microstructural neuronal changes differentiating BD from UD groups using diffusion kurtosis imaging (DKI). The objective of the study is to identify an objective neuro-imaging marker to differentiate BD from UD.<h4>Materials and methods</h4>A prospective, cross-sectional study included total of 62 s ...[more]

PMID: 35817080

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Project description:Background and purposeDetecting microstructural changes due to chronic ischemia potentially enables early identification of patients at risk of cognitive impairment. In this study, diffusional kurtosis imaging and diffusion tensor imaging were used to investigate whether the former provides additional information regarding microstructural changes in the gray and white matter of adult patients with Moyamoya disease.Materials and methodsMR imaging (diffusional kurtosis imaging and DTI) was performed in 23 adult patients with Moyamoya disease and 23 age-matched controls. Three parameters were extracted from diffusional kurtosis imaging (mean kurtosis, axial kurtosis, and radial kurtosis), and 4, from DTI (fractional anisotropy, radial diffusivity, mean diffusivity, and axial diffusivity). Voxelwise analysis for these parameters was performed in the normal-appearing brain parenchyma. The association of these parameters with neuropsychological performance was also evaluated.ResultsVoxelwise analysis revealed the greatest differences in fractional anisotropy, followed, in order, by radial diffusivity, mean diffusivity, and mean kurtosis. In patients, diffusional kurtosis imaging parameters were decreased in the dorsal deep white matter such as the corona radiata and superior longitudinal fasciculus (P < .01), including areas without DTI abnormality. Superior longitudinal fasciculus fiber-crossing areas showed weak correlations between diffusional kurtosis imaging and DTI parameters compared with tissues with a single-fiber direction (eg, the corpus callosum). Diffusional kurtosis imaging parameters were associated with general intelligence and frontal lobe performance.ConclusionsAlthough DTI revealed extensive white matter changes, diffusional kurtosis imaging additionally demonstrated microstructural changes in ischemia-prone deep white matter with abundant fiber crossings. Thus, diffusional kurtosis imaging may be a useful adjunct for detecting subtle chronic ischemic injuries.

Project description:ImportanceBipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools.ObjectiveTo identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information.Design, setting, and participantsThis diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023.Main outcomes and measuresPatient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview.ResultsOf 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001).Conclusions and relevanceThis study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

Dataset Information

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging.

Rationale and objectives

Materials and methods

Results

Conclusion

Publications

Cross-sectional analysis of whole-brain microstructural changes in adult patients with bipolar and unipolar depression by diffusion kurtosis imaging.

Similar Datasets

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