Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery.

Importance: Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients. Objective: To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery. Design, Setting, and Participants: This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. Exposure: Body composition derived from automated analysis of multislice abdominal CT scans. Main Outcomes and Measures: The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program. Results: The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS. Conclusions and Relevance: In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.

Background: Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN). Methods: Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach. Results: The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping. Conclusion: In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Neuroimaging feature extraction using a neural network classifier for imaging genetics.

BACKGROUND: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. RESULTS: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. CONCLUSIONS: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

Safety, tolerability, and effectiveness of the sodium-glucose cotransporter 2 inhibitor (SGLT2i) dapagliflozin in combination with standard chemotherapy for patients with advanced, inoperable pancreatic adenocarcinoma: a phase 1b observational study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Thus, there is an urgent need for safe and effective novel therapies. PDAC's excessive reliance on glucose metabolism for its metabolic needs provides a target for metabolic therapy. Preclinical PDAC models have demonstrated that targeting the sodium-glucose co-transporter-2 (SGLT2) with dapagliflozin may be a novel strategy. Whether dapagliflozin is safe and efficacious in humans with PDAC is unclear. METHODS: We performed a phase 1b observational study (ClinicalTrials.gov ID NCT04542291; registered 09/09/2020) to test the safety and tolerability of dapagliflozin (5 mg p.o./day × 2 weeks escalated to 10 mg p.o./day × 6 weeks) added to standard Gemcitabine and nab-Paclitaxel (GnP) chemotherapy in patients with locally advanced and/or metastatic PDAC. Markers of efficacy including Response Evaluation Criteria in Solid Tumors (RECIST 1.1) response, CT-based volumetric body composition measurements, and plasma chemistries for measuring metabolism and tumor burden were also analyzed. RESULTS: Of 23 patients who were screened, 15 enrolled. One expired (due to complications from underlying disease), 2 dropped out (did not tolerate GnP chemotherapy) during the first 4 weeks, and 12 completed. There were no unexpected or serious adverse events with dapagliflozin. One patient was told to discontinue dapagliflozin after 6 weeks due to elevated ketones, although there were no clinical signs of ketoacidosis. Dapagliflozin compliance was 99.4%. Plasma glucagon increased significantly. Although abdominal muscle and fat volumes decreased; increased muscle-to-fat ratio correlated with better therapeutic response. After 8 weeks of treatment in the study, partial response (PR) to therapy was seen in 2 patients, stable disease (SD) in 9 patients, and progressive disease (PD) in 1 patient. After dapagliflozin discontinuation (and chemotherapy continuation), an additional 7 patients developed the progressive disease in the subsequent scans measured by increased lesion size as well as the development of new lesions. Quantitative imaging assessment was supported by plasma CA19-9 tumor marker measurements. CONCLUSIONS: Dapagliflozin is well-tolerated and was associated with high compliance in patients with advanced, inoperable PDAC. Overall favorable changes in tumor response and plasma biomarkers suggest it may have efficacy against PDAC, warranting further investigation.

Segmentation-guided domain adaptation and data harmonization of multi-device retinal optical coherence tomography using cycle-consistent generative adversarial networks.

BACKGROUND: Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using another device, resulting in a lack of generalizability. This study addresses this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks (CycleGAN), especially when the ground-truth labels are only available in the source domain. METHODS: A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain to the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with integration of different loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics were evaluated. RESULTS: Regarding the segmentation Dice score, the proposed model ssppg achieves a significant improvement of 46.24% compared to without adaptation and reaches 87.4% of the upper limit of the segmentation performance. Furthermore, image quality metrics, including FID and KID scores, indicate that adapted images with better segmentation also have better image qualities. CONCLUSION: The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the labor cost of manual labeling, incorporates prior anatomic information to regulate and guide domain adaptation, and provides insights into improving segmentation qualities in image domains without labels.

Body composition and lung cancer-associated cachexia in TRACERx.

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC.

Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum.

BACKGROUND: The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear. OBJECTIVE: To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points. METHODS: We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up. RESULTS: In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12. CONCLUSION: These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.

White-matter abnormalities in presymptomatic GRN and C9orf72 mutation carriers.

A large proportion of familial frontotemporal dementia is caused by TAR DNA-binding protein 43 (transactive response DNA-binding protein 43 kDa) proteinopathies. Accordingly, carriers of autosomal dominant mutations in the genes associated with TAR DNA-binding protein 43 aggregation, such as Chromosome 9 open reading frame 72 (C9orf72) or progranulin (GRN), are at risk of later developing frontotemporal dementia. Brain imaging abnormalities that develop before dementia onset in mutation carriers may serve as proxies for the presymptomatic stages of familial frontotemporal dementia due to a genetic cause. Our study objective was to investigate brain MRI-based white-matter changes in predementia participants carrying mutations in C9orf72 or GRN genes. We analysed mutation carriers and their family member controls (noncarriers) from the University of British Columbia familial frontotemporal dementia study. First, a total of 42 participants (8 GRN carriers; 11 C9orf72 carriers; 23 noncarriers) had longitudinal T1-weighted MRI over ∼2 years. White-matter signal hypointensities were segmented and volumes were calculated for each participant. General linear models were applied to compare the baseline burden and the annualized rate of accumulation of signal abnormalities among mutation carriers and noncarriers. Second, a total of 60 participants (9 GRN carriers; 17 C9orf72 carriers; 34 noncarriers) had cross-sectional diffusion tensor MRI available. For each participant, we calculated the average fractional anisotropy and mean, radial and axial diffusivity parameter values within the normal-appearing white-matter tissues. General linear models were applied to compare whether mutation carriers and noncarriers had different trends in diffusion tensor imaging parameter values as they neared the expected age of onset. Baseline volumes of white-matter signal abnormalities were not significantly different among mutation carriers and noncarriers. Longitudinally, GRN carriers had significantly higher annualized rates of accumulation (estimated mean: 15.87%/year) compared with C9orf72 carriers (3.69%/year) or noncarriers (2.64%/year). A significant relationship between diffusion tensor imaging parameter values and increasing expected age of onset was found in the periventricular normal-appearing white-matter region. Specifically, GRN carriers had a tendency of a faster increase of mean and radial diffusivity values and C9orf72 carriers had a tendency of a faster decline of fractional anisotropy values as they reached closer to the expected age of dementia onset. These findings suggest that white-matter changes may represent early markers of familial frontotemporal dementia due to genetic causes. However, GRN and C9orf72 mutation carriers may have different mechanisms leading to tissue abnormalities.

AAV5-miHTT-mediated huntingtin lowering improves brain health in a Huntington's disease mouse model.

Huntingtin (HTT)-lowering therapies show great promise in treating Huntington's disease. We have developed a microRNA targeting human HTT that is delivered in an adeno-associated serotype 5 viral vector (AAV5-miHTT), and here use animal behaviour, MRI, non-invasive proton magnetic resonance spectroscopy and striatal RNA sequencing as outcome measures in preclinical mouse studies of AAV5-miHTT. The effects of AAV5-miHTT treatment were evaluated in homozygous Q175FDN mice, a mouse model of Huntington's disease with severe neuropathological and behavioural phenotypes. Homozygous mice were used instead of the more commonly used heterozygous strain, which exhibit milder phenotypes. Three-month-old homozygous Q175FDN mice, which had developed acute phenotypes by the time of treatment, were injected bilaterally into the striatum with either formulation buffer (phosphate-buffered saline + 5% sucrose), low dose (5.2 × 109 genome copies/mouse) or high dose (1.3 × 1011 genome copies/mouse) AAV5-miHTT. Wild-type mice injected with formulation buffer served as controls. Behavioural assessments of cognition, T1-weighted structural MRI and striatal proton magnetic resonance spectroscopy were performed 3 months after injection, and shortly afterwards the animals were sacrificed to collect brain tissue for protein and RNA analysis. Motor coordination was assessed at 1-month intervals beginning at 2 months of age until sacrifice. Dose-dependent changes in AAV5 vector DNA level, miHTT expression and mutant HTT were observed in striatum and cortex of AAV5-miHTT-treated Huntington's disease model mice. This pattern of microRNA expression and mutant HTT lowering rescued weight loss in homozygous Q175FDN mice but did not affect motor or cognitive phenotypes. MRI volumetric analysis detected atrophy in four brain regions in homozygous Q175FDN mice, and treatment with high dose AAV5-miHTT rescued this effect in the hippocampus. Like previous magnetic resonance spectroscopy studies in Huntington's disease patients, decreased total N-acetyl aspartate and increased myo-inositol levels were found in the striatum of homozygous Q175FDN mice. These neurochemical findings were partially reversed with AAV5-miHTT treatment. Striatal transcriptional analysis using RNA sequencing revealed mutant HTT-induced changes that were partially reversed by HTT lowering with AAV5-miHTT. Striatal proton magnetic resonance spectroscopy analysis suggests a restoration of neuronal function, and striatal RNA sequencing analysis shows a reversal of transcriptional dysregulation following AAV5-miHTT in a homozygous Huntington's disease mouse model with severe pathology. The results of this study support the use of magnetic resonance spectroscopy in HTT-lowering clinical trials and strengthen the therapeutic potential of AAV5-miHTT in reversing severe striatal dysfunction in Huntington's disease.

Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis.

Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.

Effects of Myopia and Glaucoma on the Neural Canal and Lamina Cribrosa Using Optical Coherence Tomography.

PRCIS: Glaucoma was associated with axial bowing and rotation of Bruchs membrane opening (BMO) and anterior laminar insertion (ALI), skewed neural canal, and deeper anterior lamina cribrosa surface (ALCS). Longer axial length was associated with wider, longer, and more skewed neural canal and flatter ALCS. PURPOSE: Investigate the effects of myopia and glaucoma in the prelaminar neural canal and anterior lamina cribrosa using 1060-nm swept-source optical coherence tomography. PATIENTS: 19 control (38 eyes) and 38 glaucomatous subjects (63 eyes). MATERIALS AND METHODS: Participants were imaged with swept-source optical coherence tomography, and the images were analyzed for the BMO and ALI dimensions, prelaminar neural canal dimensions, and ALCS depth. RESULTS: Glaucomatous eyes had more bowed and nasally rotated BMO and ALI, more horizontally skewed prelaminar neural canal, and deeper ALCS than the control eyes. Increased axial length was associated with a wider, longer, and more horizontally skewed neural canal and a decrease in the ALCS depth and curvature. CONCLUSION: Our findings suggest that glaucomatous posterior bowing or cupping of lamina cribrosa can be significantly confounded by the myopic expansion of the neural canal. This may be related to higher glaucoma risk associated with myopia from decreased compliance and increased susceptibility to IOP-related damage of LC being pulled taut.

Brain Imaging Abnormalities in Mixed Alzheimer's and Subcortical Vascular Dementia.

BACKGROUND: A large proportion of Alzheimer's disease (AD) patients have coexisting subcortical vascular dementia (SVaD), a condition referred to as mixed dementia (MixD). Brain imaging features of MixD presumably include those of cerebrovascular disease and AD pathology, but are difficult to characterize due to their heterogeneity. OBJECTIVE: To perform an exploratory analysis of conventional and non-conventional structural magnetic resonance imaging (MRI) abnormalities in MixD and to compare them to those observed in AD and SVaD. METHODS: We conducted a cross-sectional, region-of-interest-based analysis of 1) hyperintense white-matter signal abnormalities (WMSA) on T2-FLAIR and hypointense WMSA on T1-weighted MRI; 2) diffusion tensor imaging; 3) quantitative susceptibility mapping; and 4) effective transverse relaxation rate (R2*) in N = 17 participants (AD:5, SVaD:5, MixD:7). General linear model was used to explore group differences in these brain imaging measures. RESULTS: Model findings suggested imaging characteristics specific to our MixD group, including 1) higher burden of WMSAs on T1-weighted MRI (versus both AD and SVaD); 2) frontal lobar preponderance of WMSAs on both T2-FLAIR and T1-weighted MRI; 3) higher fractional anisotropy values within normal-appear white-matter tissues (versus SVaD, but not AD); and 4) lower R2* values within the T2-FLAIR WMSA areas (versus both AD and SVaD). CONCLUSION: These findings suggest a preliminary picture of the location and type of brain imaging characteristics associated with MixD. Future imaging studies may employ region-specific hypotheses to distinguish MixD more rigorously from AD or SVaD.

Müller cell degeneration and microglial dysfunction in the Alzheimer's retina.

Amyloid beta (Aß) deposits in the retina of the Alzheimer's disease (AD) eye may provide a useful diagnostic biomarker for AD. This study focused on the relationship of Aß with macroglia and microglia, as these glial cells are hypothesized to play important roles in homeostasis and clearance of Aß in the AD retina. Significantly higher Aß load was found in AD compared to controls, and specifically in the mid-peripheral region. AD retina showed significantly less immunoreactivity against glial fibrillary acidic protein (GFAP) and glutamine synthetase (GS) compared to control eyes. Immunoreactivity against ionized calcium binding adapter molecule-1 (IBA-1), a microglial marker, demonstrated a higher level of microgliosis in AD compared to control retina. Within AD retina, more IBA-1 immunoreactivity was present in the mid-peripheral retina, which contained more Aß than the central AD retina. GFAP co-localized rarely with Aß, while IBA-1 co-localized with Aß in more layers of control than AD donor retina. These results suggest that dysfunction of the Müller and microglial cells may be key features of the AD retina.

Body composition from single versus multi-slice abdominal computed tomography: Concordance and associations with colorectal cancer survival.

BACKGROUND: Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi-slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared. METHODS: A total of 2871 patients with colorectal cancer diagnosed during 2012-2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid-L3 cross-sectional areas and multi-slice T12-L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi-slice segmentation platform. To facilitate comparison between single-slice and multi-slice measurements, sex-specific z-scores were calculated. Pearson correlation coefficients and Bland-Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage. RESULTS: Single-slice area and multi-slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P < 0.001; SAT R = 0.97, P < 0.001; VAT R = 0.98, P < 0.001; IMAT R = 0.89, P < 0.001). Bland-Altman plots had a bias of 0 (SE: 0.00), indicating high average agreement between measures. The limits of agreement were narrowest for VAT ( ± 0.42 SD) and SAT ( ± 0.44 SD), and widest for SKM ( ± 0.78 SD) and IMAT ( ± 0.92 SD). The HRs had overlapping CIs, and similar magnitudes and direction of effects; for example, a 1-SD increase in SKM area was associated with an 18% decreased risk of death (HR = 0.82; 95% CI: 0.72-0.92), versus 15% for volume from T12 to L5 (HR = 0.85; 95% CI: 0.75-0.96). CONCLUSIONS: Single-slice L3 areas and multi-slice T12-L5 abdominal volumes of SKM, VAT, SAT and IMAT are highly correlated. Associations between area and volume measures with all-cause mortality were similar, suggesting that they are equivalent tools for population studies if body composition is assessed at a single timepoint. Future research should examine longitudinal changes in multi-slice tissues to improve individual risk prediction.

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration.

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

Surface-based abnormalities of the executive frontostriatial circuit in pediatric TBI.

Childhood traumatic brain injury (TBI) is one of the most common causes of acquired disability and has significant implications for executive functions (EF), such as impaired attention, planning, and initiation that are predictive of everyday functioning. Evidence has suggested attentional features of executive functioning require behavioral flexibility that is dependent on frontostriatial circuitry. The purpose of this study was to evaluate surface-based deformation of a specific frontostriatial circuit in pediatric TBI and its role in EF. Regions of interest included: the dorsolateral prefrontal cortex (DLPFC), caudate nucleus, globus pallidus, and the mediodorsal nucleus of the thalamus (MD). T1-weighted magnetic resonance images were obtained in a sample of children ages 8-13 with complicated mild, moderate, or severe TBI (n = 32) and a group of comparison children with orthopedic injury (OI; n = 30). Brain regions were characterized using high-dimensional surface-based brain mapping procedures. Aspects of EF were assessed using select subtests from the Test of Everyday Attention for Children (TEA-Ch). General linear models tested group and hemisphere differences in DLPFC cortical thickness and subcortical shape of deep-brain regions; Pearson correlations tested relationships with EF. Main effects for group were found in both cortical thickness of the DLPFC (F1,60 = 4.30, p = 0.042) and MD mean deformation (F1,60 = 6.50, p = 0.01) all with lower values in the TBI group. Statistical surface maps revealed significant inward deformation on ventral-medial aspects of the caudate in TBI relative to OI, but null results in the globus pallidus. No significant relationships between EF and any region of interest were observed. Overall, findings revealed abnormalities in multiple aspects of a frontostriatial circuit in pediatric TBI, which may reflect broader pathophysiological mechanisms. Increased consideration for the role of deep-brain structures in pediatric TBI can aid in the clinical characterization of anticipated long-term developmental effects of these individuals.

Network-wise concordance of multimodal neuroimaging features across the Alzheimer's disease continuum.

Background: Concordance between cortical atrophy and cortical glucose hypometabolism within distributed brain networks was evaluated among cerebrospinal fluid (CSF) biomarker-defined amyloid/tau/neurodegeneration (A/T/N) groups. Method: We computed correlations between cortical thickness and fluorodeoxyglucose metabolism within 12 functional brain networks. Differences among A/T/N groups (biomarker normal [BN], Alzheimer's disease [AD] continuum, suspected non-AD pathologic change [SNAP]) in network concordance and relationships to longitudinal change in cognition were assessed. Results: Network-wise markers of concordance distinguish SNAP subjects from BN subjects within the posterior multimodal and language networks. AD-continuum subjects showed increased concordance in 9/12 networks assessed compared to BN subjects, as well as widespread atrophy and hypometabolism. Baseline network concordance was associated with longitudinal change in a composite memory variable in both SNAP and AD-continuum subjects. Conclusions: Our novel study investigates the interrelationships between atrophy and hypometabolism across brain networks in A/T/N groups, helping disentangle the structure-function relationships that contribute to both clinical outcomes and diagnostic uncertainty in AD.

Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease.

BACKGROUND: The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). OBJECTIVE: The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. METHODS: We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. RESULTS: Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. CONCLUSION: MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.