62 research outputs found
Volume of subcortical brain regions in social anxiety disorder:mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group
There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = â0.077, pFWE = 0.037; right: d = â0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = â0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = â0.141, pFWE < 0.001; right: d = â0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.</p
Multi-Site Benchmark Classification of Major Depressive Disorder Using Machine Learning on Cortical and Subcortical Measures
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (Nâ=â5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects
Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brainâage) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brainâage has highlighted the need for robust and publicly available brainâage models preâtrained on data from large samples of healthy individuals. To address this need we have previously released a developmental brainâage model. Here we expand this work to develop, empirically validate, and disseminate a preâtrained brainâage model to cover most of the human lifespan. To achieve this, we selected the bestâperforming model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brainâage prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5â90âyears; 53.59% female). The preâtrained models were tested for crossâdataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8â80âyears; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9â25âyears; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two ageâbins (5â40 and 40â90âyears) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brainâage prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an openâscience, webâbased platform for individualized neuroimaging metrics
Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brainâage) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brainâage has highlighted the need for robust and publicly available brainâage models preâtrained on data from large samples of healthy individuals. To address this need we have previously released a developmental brainâage model. Here we expand this work to develop, empirically validate, and disseminate a preâtrained brainâage model to cover most of the human lifespan. To achieve this, we selected the bestâperforming model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brainâage prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5â90âyears; 53.59% female). The preâtrained models were tested for crossâdataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8â80âyears; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9â25âyears; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two ageâbins (5â40 and 40â90âyears) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brainâage prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an openâscience, webâbased platform for individualized neuroimaging metrics
DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features
Major depressive disorder (MDD) is a complex psychiatric disorder that
affects the lives of hundreds of millions of individuals around the globe. Even
today, researchers debate if morphological alterations in the brain are linked
to MDD, likely due to the heterogeneity of this disorder. The application of
deep learning tools to neuroimaging data, capable of capturing complex
non-linear patterns, has the potential to provide diagnostic and predictive
biomarkers for MDD. However, previous attempts to demarcate MDD patients and
healthy controls (HC) based on segmented cortical features via linear machine
learning approaches have reported low accuracies. In this study, we used
globally representative data from the ENIGMA-MDD working group containing an
extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a
comprehensive analysis with generalizable results. Based on the hypothesis that
integration of vertex-wise cortical features can improve classification
performance, we evaluated the classification of a DenseNet and a Support Vector
Machine (SVM), with the expectation that the former would outperform the
latter. As we analyzed a multi-site sample, we additionally applied the ComBat
harmonization tool to remove potential nuisance effects of site. We found that
both classifiers exhibited close to chance performance (balanced accuracy
DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher
classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was
found when the cross-validation folds contained subjects from all sites,
indicating site effect. In conclusion, the integration of vertex-wise
morphometric features and the use of the non-linear classifier did not lead to
the differentiability between MDD and HC. Our results support the notion that
MDD classification on this combination of features and classifiers is
unfeasible
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