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Purpose To describe characteristics of the vitreomacular interface (VMI) in traumatic macular holes (TMH) compared to idiopathic macular holes (IMH) using immunofluorescence and electron microscopy, and to correlate with clinical data. Methods For immunocytochemical and ultrastructural analyses, premacular tissue with internal limiting membrane (ILM) and epiretinal membrane (ERM) was harvested during vitrectomy from 5 eyes with TMH and 5 eyes with IMH. All specimens were processed as flat mounts for phase-contrast microscopy, interference and fluorescence microscopy, and transmission electron microscopy (TEM). Primary antibodies were used against microglial and macroglial cells. Clinical data was retrospectively evaluated. Results Surgically excised premacular tissue of eyes with TMH showed a less pronounced positive immunoreactivity for anti-glutamine synthetase, anti-vimentin and anti-IBA1 compared to eyes with IMH. Cell nuclei staining of the flat-mounted specimens as well as TEM presented a lower cell count in eyes with TMH compared to IMH. All detected cells were found on the vitreal side of the ILM. No collagen fibrils were seen in specimens of TMH. According to patients’ age, intraoperative data as well as spectral-domain optical coherence tomography (SD-OCT) analysis revealed an attached posterior vitreous in the majority of TMH cases (60%), whereas all eyes with IMH presented posterior vitreous detachment. Conclusion The vitreomacular interface in TMH and IMH shows significant differences. In TMH, glial cells are a rare finding on the vitreal side of the ILM.

Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between predictions and clinical variables. 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20,484 vertices) were extracted. Linear Support Vector Machine was used for classification within a repeated nested cross-validation framework. Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy of 66.2% and an Area Under the Curve of 72%. By stratifying our sample for MRI scanner, we increased generalizability across sites. Temporal brain areas resulted as the most influential in the classification. The predictive decision scores significantly correlated with age at onset, duration of treatment, and positive symptoms. In conclusion, although far from the threshold of clinical relevance, temporal cortical thickness proved to classify between FEP subjects and healthy individuals. The assessment of site-dependent variables permitted an increase in the across-site generalizability, thus attempting to address an important machine learning limitation.

Background Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness data, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. By leveraging the multi-site nature of the study, we further investigated how different demographical and site-dependent variables affected predictions. Finally, we assessed relationships between the predictions and clinical variables. Methods 428 subjects (147 females, mean age 27.14) with FEP and 448 (230 females, mean age 27.06) healthy controls were enrolled in 8 centers by the ClassiFEP group. All subjects underwent a structural MRI (sMRI) session and were clinically assessed. Cortical thickness parcellation (68 areas) and full cortical maps (20484 vertices) were extracted. Supervised (linear Support Vector Machine) classification was used to differentiate FEP from HC, within a repeated nested Cross-Validation (CV) framework through the NeuroMiner software. In both inner and outer CVs, a 10-fold CV cycle was employed. We performed repeated nested CV at the outer cross-validation cycle by randomly permuting the participants within their groups (10 permutations) and repeating the CV cycle for each of these permutations. As feature preprocessing, regression of covariates (age, sex, and site), Principal Component Analysis and Scaling were applied. All preprocessing steps were implemented within the CV. Further analyses were conducted by stratifying the sample for MRI scanner, sex and by performing random resampling with increasingly reduced sample sizes. Results Vertex-wise thickness maps outperformed parcellation-based methods with a balanced accuracy (BAC) of 66.2% and an Area Under the Curve of 72%, compared to a BAC of 59% and an Area Under the Curve of 61% obtained with the ROI-based approach. The two BACs were significantly different based on the McNemar’s Test. By stratifying our sample for MRI scanner, we increased the overall BAC to more than 70% and we also increased generalizability across sites. Temporal areas resulted the most influential regions in the classification. The predictive decision scores presented significant correlations with age at onset, duration of treatment and the presence of affective vs non-affective psychosis. Discussion Cortical thickness could represent a valid measure to classify FEP subjects, showing temporal areas as potential markers in the early stages of psychosis. The assessment of site-dependent variables allowed us to increase the across-site generalizability of the model, thus attempting to address an important machine learning limitation, especially in the framework of large multi-site cohort and big data analysis.