Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach

Sartori, Juliana; Reckziegel, Ramiro; Passos, Ives Cavalcante; Czepielewski, Letícia Sanguinetti; Fijtman, Adam; Sodré, Leonardo; Massuda, Raffael; Goi, Pedro Domingues; Vianna-Sulzbach, Miréia; Cardoso, Taiane de Azevedo; Kapczinski, Flávio; Mwangi, Benson; Gama, Clarissa Severino

doi:10.1016/j.jpsychires.2018.05.023

Cited by 49 publications

(17 citation statements)

References 64 publications

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“…Specific to BD, a previous study exploring neurobiological correlates of cognitive functioning reported that changes within both frontal cortex volume and right white matter volume predicted FAST scores, 43 supporting our finding of right‐lateralized changes centered in the frontal cortex. Another study examining networks constructed using cortical thickness‐derived structural covariance in BD ( n = 37) found that participants exhibited lower global efficiency, global strength and mean clustering coefficient compared to healthy controls 28 .…”

Section: Discussionsupporting

confidence: 89%

Network properties of intracortical myelin associated with psychosocial functioning in bipolar I disorder

et al. 2022

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Objective: Psychosocial functioning in bipolar disorder (BD) persists even during euthymia and has repeatedly been associated with illness progression and cognitive function. Its neurobiological correlates remain largely unexplored. Using a structural covariance approach, we explored whole cortex intracortical myelin (ICM) and psychosocial functioning in 39 BD type I and 58 matched controls.Method: T 1 -weighted images (3T) optimized for ICM measurement were analyzed using a surface-based approach. The ICM signal was sampled at cortical mid-depth using the MarsAtlas parcellation, and psychosocial functioning was measured via the Functioning Assessment Short Test (FAST). Following construction of structural covariance matrices, graph theoretical measures were calculated for each subject. Within BD and HC groups separately, correlations between network measures and FAST were explored. After accounting for multiple comparisons, significant correlations were tested formally using rank-based regressions accounting for sex differences. Results:In BD only, psychosocial functioning was associated with global efficiency (β = −0.312, p corr = 0.03), local efficiency in the right rostral dorsolateral prefrontal cortex (β = 0.545, p corr = 0.001) and clustering coefficient in this region (β = 0.497, p corr = 0.0002) as well as in the right ventromedial prefrontal cortex (β = 0.428, p corr = 0.002). All results excepting global efficiency remained significant after accounting for severity of depressive symptoms. In contrast, no significant associations between functioning and network measures were observed in the HC group. Conclusion:These results uncovered a novel brain-behaviour relationship between intracortical myelin signal changes and psychosocial functioning in BD.

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Section: Discussionsupporting

confidence: 89%

Network properties of intracortical myelin associated with psychosocial functioning in bipolar I disorder

et al. 2022

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“…RFE is an effective feature selection algorithm that uses the accuracy yielded by SVM to determine which features contribute most to the prediction results. 37 , 39 , 40 Training on the original, each feature was assigned a weight coefficient, and those features with the most negligible absolute weight were kicked out of the feature set. After multiple iterations, the features of small weight coefficients were removed until the remaining features reached optimal performance.…”

Section: Methodsmentioning

confidence: 99%

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study^®

et al. 2022

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Background Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health. Methods We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers. Results The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately. Conclusions The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.

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“…77,78 In the state of Rio Grande do Sul, knowledgeable clinical and basic science research groups based at the Federal University of Rio Grande do Sul (UFRGS) and the Pontifical Catholic University (PUC) have adopted the first model outlined in the previous section of this article, testing original hypotheses in a number of psychiatric neuroimaging studies carried out in collaboration with teams of imaging experts either from the local Clinics Hospital of Porto Alegre (HCPA), the PUC-based Brain Institute (InsCer), or Sã o Paulo-based centers. These groups have contributed to the development of the field of psychiatric neuroimaging in Brazil by leading studies on attention-deficit/hyperactivity disorder (ADHD), [79][80][81] child and adolescent development, 82,83 psychosis, 84 mood and anxiety disorders, [85][86][87] and autism (Table 1). 88 The same collaborative model has been applied by research groups based at other universities in Brazil.…”

Section: Psychiatric Neuroimaging In Brazilmentioning

confidence: 99%

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

Busatto

Rosa

Squarzoni

et al. 2021

Braz. J. Psychiatry

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The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.

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Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach

Cited by 49 publications

References 64 publications

Network properties of intracortical myelin associated with psychosocial functioning in bipolar I disorder

Network properties of intracortical myelin associated with psychosocial functioning in bipolar I disorder

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study^®

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

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Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach

Cited by 49 publications

References 64 publications

Network properties of intracortical myelin associated with psychosocial functioning in bipolar I disorder

Network properties of intracortical myelin associated with psychosocial functioning in bipolar I disorder

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study®

Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities

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Product

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Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study^®