2 results
Larger putamen in individuals at risk and with manifest bipolar disorder
- Florian Thomas-Odenthal, Frederike Stein, Christoph Vogelbacher, Nina Alexander, Andreas Bechdolf, Felix Bermpohl, Kyra Bröckel, Katharina Brosch, Christoph U. Correll, Ulrika Evermann, Irina Falkenberg, Andreas Fallgatter, Kira Flinkenflügel, Dominik Grotegerd, Tim Hahn, Martin Hautzinger, Andreas Jansen, Georg Juckel, Axel Krug, Martin Lambert, Gregor Leicht, Karolina Leopold, Susanne Meinert, Pavol Mikolas, Christoph Mulert, Igor Nenadić, Julia-Katharina Pfarr, Andreas Reif, Kai Ringwald, Philipp Ritter, Thomas Stamm, Benjamin Straube, Lea Teutenberg, Katharina Thiel, Paula Usemann, Alexandra Winter, Adrian Wroblewski, Udo Dannlowski, Michael Bauer, Andrea Pfennig, Tilo Kircher
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- Journal:
- Psychological Medicine , First View
- Published online by Cambridge University Press:
- 27 May 2024, pp. 1-11
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Background:
Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.
Methods:In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.
Results:Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.
Conclusions:Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.
Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features
- Pavol Mikolas, Michael Marxen, Philipp Riedel, Kyra Bröckel, Julia Martini, Fabian Huth, Christina Berndt, Christoph Vogelbacher, Andreas Jansen, Tilo Kircher, Irina Falkenberg, Martin Lambert, Vivien Kraft, Gregor Leicht, Christoph Mulert, Andreas J. Fallgatter, Thomas Ethofer, Anne Rau, Karolina Leopold, Andreas Bechdolf, Andreas Reif, Silke Matura, Felix Bermpohl, Jana Fiebig, Thomas Stamm, Christoph U. Correll, Georg Juckel, Vera Flasbeck, Philipp Ritter, Michael Bauer, Andrea Pfennig
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- Journal:
- Psychological Medicine / Volume 54 / Issue 2 / January 2024
- Published online by Cambridge University Press:
- 22 May 2023, pp. 278-288
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Background
Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.
MethodsFollowing a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).
ResultsFor BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.
ConclusionsIndividuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.