ComBat Harmonization: Empirical Bayes versus fully Bayes approaches

Reynolds, Maxwell; Chaudhary, Tigmanshu; Torbati, Mahbaneh Eshaghzadeh; Tudorascu, Dana L.; Batmanghelich, Kayhan

doi:10.1016/j.nicl.2023.103472

Cited by 5 publications

(2 citation statements)

References 57 publications

(62 reference statements)

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“…In order to eliminate systematic deviations in the eleven center GMVs, combat harmonization model was carried out before statistics, in which the center IDs were defined as the batch variable, and group, age, sex and total intracranial volume (TIV) were considered as biological covariates ( 40 , 41 ). Then, we employed a general linear model (GLM) to construct an interaction model between the group (schizophrenia vs. healthy controls) and the volume of the amygdala subregions.…”

Section: Methodsmentioning

confidence: 99%

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia

Chang,

Liu,

Lin

et al. 2024

Front. Psychiatry

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ObjectiveAlthough extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia.MethodsTo address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV.ResultsOur findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure.ConclusionOur research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.

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Section: Methodsmentioning

confidence: 99%

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia

Chang,

Liu,

Lin

et al. 2024

Front. Psychiatry

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“…From Mendeley data [26], selecting the Microarray Gene Expression Cancer (MGEC) dataset allowed us to assess the efficacy of our unique approach. With an impressive array of more than 14,124 features grouped within six distinct classifications, this dataset provides a rare chance to rigorously evaluate the effectiveness of our proposed technique on specific categories of malignancy data.…”

Section: Datasetmentioning

confidence: 99%

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Venkatesan,

Kuppusamy Murugesan,

Chandrasekaran

et al. 2023

Diagnostics

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Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

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Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia

Tsugawa,

Honda,

Noda

et al. 2023

Schizophrenia Bulletin

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Background and Hypothesis Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. Study Design We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. Study Results Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. Conclusions These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.

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ComBat Harmonization: Empirical Bayes versus fully Bayes approaches

Cited by 5 publications

References 57 publications

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia

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