Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Evidence suggests that osteocyte apoptosis is involved in the adaptive response of bone, although the specific role of osteocytes in the signaling mechanism is unknown. Here, we examined and correlated regional variability in indices of remodeling, modeling, osteocyte apoptosis, and osteocyte density in rabbit tibia midshafts. Histomorphometric analysis indicated that remodeling parameters (BMU activation frequency, osteon density, forming osteon density, and resorption cavity density) were lower in the cranial region compared to other quadrants. In addition, pericortical subregions displayed less remodeling relative to intracortical and endocortical ones. Modeling indices also demonstrated regional variability in that periosteal surfaces exhibited a greater extent of bone forming surface than endosteal ones across all anatomic quadrants. In contrast, endosteal surfaces demonstrated significantly greater surface mineral apposition rates compared to periosteal surfaces in caudal, medial, and lateral but not cranial quadrants. Using TUNEL analysis to detect osteocytes undergoing apoptosis, the density of apoptotic osteocytes was found to be lower in cranial quadrants relative to medial ones. In addition, the densities of osteocyte lacunae, empty lacunae, and total osteocytes were higher in lateral fields relative to caudal quadrants. There was a strong, statistically significant linear correlation between the remodeling indices and apoptotic osteocyte density, supporting the theory that osteocytes undergoing apoptosis produce signals that attract or direct bone remodeling. In contrast, the modeling parameters did not exhibit a correlation with apoptotic osteocytes, although there was a strong correlation between the modeling indices and the density of empty osteocyte lacunae, corroborating previous studies that have found that osteocytes inhibit bone formation. It was found that osteocyte density and osteocyte lacunar density did not significantly correlate with modeling or remodeling parameters, suggesting that cell viability should be examined in studies correlating bone turnover parameters with the functional role of osteocytes in bone adaptation.

The incidence of IFIS was lower than previously reported. Use of prophylactic intracameral lidocaine-epinephrine did not reduce the incidence of IFIS. A preoperative dilated pupil diameter smaller than 6.5 mm was significantly associated with an increased incidence of IFIS.

3 Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf AbstractTo acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple scanners. These effects have been shown to bias comparison between scanners, mask biologically meaningful associations, and 1

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites.These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our Russell T. Shinohara and Haochang Shou contributed equally to this work.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted ‘gold-standard’ subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

Oxidative damage of the lens causes disulfide bonds between cysteinyl residues of lens proteins and thiols such as glutathione and cysteine, which may lead to cataract. The effect of H 2 O 2 oxidation was determined by comparing bovine lenses incubated with and without 30 mM H 2 O 2 . The H 2 O 2 treatment decreased the glutathione and increased the protein-glutathione and proteincysteine disulfides in the lens. The molecular mass of the ␥B-crystallin isolated from lenses, not treated with H 2 O 2 , agreed with the published sequence (M r 20,966). Some lenses also had a less abundant ␥B-crystallin component 305 Da higher (M r 21,270), suggesting the presence of a glutathione adduct. The ␥B-crystallins from H 2 O 2 treated lenses had three components, the major one with one GSH adduct, another one with the mass of unmodified ␥B-crystallin, and a third with a mass consistent with addition of two GSH adducts. Mass spectrometric analysis of tryptic peptides of ␥B-crystallins from different lenses indicated that the ؉305 Da modifications were not at a specific cysteine. For the lenses incubated without H 2 O 2 , there was evidence of adducts at Cys-41 and in peptide 10 -31, which includes 3 cysteines. Analysis of modified peptide 10 -31 by tandem mass spectrometry showed GSH adducts at Cys-15, Cys-18, and Cys-22. In addition, ␥B-crystallins from H 2 O 2 -treated lenses had an adduct at Cys-109, partial oxidation at all 7 Met residues, and evidence for two disulfide bonds.Age-related cataract is one of the major causes of blindness in humans. The pathology of such a condition involves opacification and decreased transparency of the lens, which can lead to loss of vision. Although the mechanisms for age-related cataractogenesis are not understood, oxidation of the lens proteins, known as crystallins, is associated with cataract formation in humans (1, 2). Protein thiolation, which involves the formation of disulfide bonds between the cysteinyl residues of lens proteins and other low molecular weight thiols in the lens, is one of the modifications caused by oxidative stress of the lens (3). Thiols in the lens that participate in this reaction are GSH and cysteine, which form protein-S-S-glutathione (PSSG) 1 and protein-S-S-cysteine (PSSC), respectively (4). These proteinthiol products are referred to as mixed disulfides. The conformational changes caused by protein thiolation (5, 6) may allow some of the buried functional groups to be exposed and modified. This may cause proteins to aggregate and disrupt the close packing of the crystallins, decreasing their solubility (7) and leading to cataract formation. In the H 2 O 2 -induced cataract model, the progression of cataract is associated with sequential events involving first the formation of PSSG, followed by protein-protein disulfide cross-links, decreased protein solubility, and finally an increase in the formation of high molecular weight aggregates (8). Protein-thiol mixed disulfides accumulate in older human lenses (9) and in all types of human cataractous lenses ...

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