Imaging Phenotypes Research Articles

The effect of Lewy body (co-)pathology on the clinical and imaging phenotype of amnestic patients.

Lewy body (LB) pathology is present as a co-pathology in approximately 50% of Alzheimer's disease (AD) dementia patients and may even represent the main neuropathologic substrate in a subset of patients with amnestic impairments. However, the degree to which LB pathology affects the neurodegenerative course and clinical phenotype in amnestic patients is not well understood. Recently developed α-synuclein seed amplification assays (αSyn-SAAs) provide a unique opportunity for further investigating the complex interplay between AD and LB pathology in shaping heterogeneous regional neurodegeneration patterns and clinical trajectories among amnestic patients. We studied 865 patients from the ADNI cohort with clinical diagnoses of aMCI (N=661) or AD dementia (N=211), who had CSF and FDG-PET data available. CSF samples were analyzed for peptide levels of Aβ1-42 and p-tau181, and αSyn positivity was evaluated using a novel αSyn-SAA. Based on positive/negative results on the different biomarkers, subjects were classified as "AD-LB-" (N=304), "AD+LB-" (N=335), "AD+LB+" (N=158) and "AD-LB+" (N=68). We analyzed group differences in regional FDG-PET patterns, demographics, APOE4 genotype, baseline and longitudinal domain-specific cognitive profiles (memory vs executive function/visuospatial performance), as well as risk for developing hallucinations. AD+LB+ showed worse global cognition (MMSE: p=0.005) and declined faster (p<0.001) than AD+LB-, but both groups exhibited similar memory-predominant cognitive profiles. In FDG-PET, AD+LB+ showed more severe hypometabolism compared to AD+LB-, but both groups were characterized by largely identical patterns of temporo-parietal hypometabolism. By contrast, AD-LB+ were less globally impaired (p<0.001) but characterized by a markedly more dysexecutive and visuospatial profile (p<0.003) and a strikingly different posterior-occipital pattern of hypometabolism. APOE4 positivity was similar between AD+LB+ and AD+LB- (72% vs. 75%, p=0.28) but lower in AD-LB+ (28%, p<0.001). On a group level, AD+LB+, AD+LB-, and AD-LB+ showed similar risks of developing hallucinations, but patients with a LB-like posterior-occipital hypometabolism pattern had a significantly higher risk compared to those showing an AD-typical temporo-parietal pattern (HR=2.58, p=0.004). In conclusion, LB co-pathology in AD was associated with more severe hypometabolism and faster cognitive decline, but did not affect the regional hypometabolic pattern or cognitive profile. By contrast, patients with relatively pure LB pathology showed a more executive/visuospatial-predominant cognitive profile and a distinct posterior-occipital hypometabolism pattern characteristic for LB disease. These findings indicate that the presence of LB pathology may have different consequences for the clinical phenotype depending on AD co-morbidity, which may have critical implications for accurate diagnosis and prognosis of patients presenting with amnestic syndromes.

Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach

We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used. A comprehensive mask fusion approach was developed to select relevant image crops of diseased tissue. These fusion masks, which were guided by multiple sequences, helped collect information from the regions that seem disease-free to radiologists in standard MRI sequences while harboring pathology. Integrating the information in different MRI sequences and leveraging the high entropic capacity of deep neural networks, we built a 3D ROI-based custom CNN classifier for the automatic prediction of MGMT methylation status of glioblastoma in multi-parametric MRI. Single sequence-based classifiers reached intermediate predictive performance with 0.65, 0.71, 0.77, and 0.82 accuracy for T1W, T2W, T1 contrast-enhanced, and FLAIR sequences, respectively. The multiparametric classifier using T1 contrast-enhanced and FLAIR images reached 0.88 accuracy. The accuracy of the four-input model that used all sequences was 0.81. The best model reached 0.90 ROC AUC value. Integrating human knowledge in the form of relevant target selection was a useful approach in MGMT methylation status prediction in MRI. Exploration of means to integrate radiology knowledge into the models and achieve human-machine collaboration may help to develop better models. MGMT methylation status of glioblastoma is an important prognostic marker and is also important for treatment decisions. The preoperative non-invasive predictive ability and the explanation tools of the developed model may help clinicians to better understand imaging phenotypes of MGMT methylation status of glial tumors.

Tracing human trait evolution through integrative genomics and temporal annotations.

Understanding the evolution of human traits is a fundamental yet challenging question. In a recent Cell Genomics article, Kun etal.1 integrate large-scale genomic and phenotypic data, including deep-learning-derived imaging phenotypes, with temporal annotations to estimate the timing of evolutionary changes that led to differences in traits between modern humans and primates or hominin ancestors.

Prevalence and risk factors of covert brain infarction: A community-based cross-sectional study.

Covert brain infarction (CBI) is common and poses a potential and non-negligible burden of disease worldwide. The prevalence and risk factors for CBI have been reported inconsistently in previous studies. This study aims to ascertain the prevalence and risk factors of CBI and its imaging phenotypes in community-dwelling adults. The study population was derived from the baseline survey of a population-based cohort from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study, involving adults aged 50-75 years from Lishui City, Southeast China. The 3.0T magnetic resonance imaging (MRI) was performed to access CBI and detect intracranial and extracranial vascular lesions. The prevalence rates of CBI and three imaging phenotypes were stratified separately by age, sex, atherosclerotic burden, and artery stenosis. The intracranial and extracranial atherosclerotic burden was graded by summing atherosclerosis scores. Multivariable logistic regression with a stepwise selection method was used to identify independent CBI risk factors. A total of 2947 participants (mean age of 61.1 ± 6.6 years, 53.8% women) were included. CBI prevalence among study subjects was 9.6%, and the most common subtype was other chronic infarction (5.6%), followed by cavitatory lesions (4.6%) and acute cerebral infarction (0.5%). In multivariable analysis, older age (odds ratio (OR): 1.59, 95% confidence interval (CI): 1.40-1.83), hypertension (OR: 1.45, 95% CI: 1.08-1.94), higher levels of low-density lipoprotein cholesterol (LDL-C) (OR: 1.17, 95% CI: 1.04-1.32), homocysteine (OR: 1.12, 95% CI: 1.01-1.23) and diastolic blood pressure (DBP) (OR: 1.22, 95% CI: 1.06-1.41), intracranial artery plaque (OR: 1.56, 95% CI: 1.16-2.10), and severe extracranial atherosclerotic burden (OR: 6.57, 95% CI: 1.67-25.79) were associated with a higher CBI odds. There is a linear relationship between age, DBP, LDL-C, and CBI odds, while homocysteine shows a nonlinear relevancy. Age, DBP, homocysteine, and LDL-C elevation increase CBI risk. CBI prevalence in this Chinese community-based population was not low. Age, hypertension, intracranial artery plaque, extracranial atherosclerotic burden, homocysteine, LDL-C, and DBP were found to be the risk factors of CBI.

Genetic analyses identify brain functional networks associated with the risk of Parkinson's disease and drug-induced parkinsonism.

Brain functional networks are associated with parkinsonism in observational studies. However, the causal effects between brain functional networks and parkinsonism remain unclear. We aimed to assess the potential bidirectional causal associations between 191 brain resting-state functional magnetic resonance imaging (rsfMRI) phenotypes and parkinsonism including Parkinson's disease (PD) and drug-induced parkinsonism (DIP). We used Mendelian randomization (MR) to assess the bidirectional associations between brain rsfMRI phenotypes and parkinsonism, followed by several sensitivity analyses for robustness validation. In the forward MR analyses, we found that three rsfMRI phenotypes genetically determined the risk of parkinsonism. The connectivity in the visual network decreased the risk of PD (OR = 0.391, 95% CI = 0.235 ~ 0.649, P= 2.83 × 10-4, P_FDR= 0.039). The connectivity of salience and motor networks increased the risk of DIP (OR = 4.102, 95% CI = 1.903 ~ 8.845, P= 3.17 × 10-4, P_FDR= 0.044). The connectivity of limbic and default mode networks increased the risk of DIP (OR = 14.526, 95% CI = 3.130 ~ 67.408, P= 6.32 × 10-4, P_FDR= 0.0437). The reverse MR analysis indicated that PD and DIP had no effect on brain rsfMRI phenotypes. Our findings reveal causal relationships between brain functional networks and parkinsonism, providing important interventional and therapeutic targets for different parkinsonism.

Divergent Presentation of GRIN2B Neurodevelopmental Disorder in Monozygotic Twins: Case Report with Unique Imaging Phenotypes.

We describe a set of monozygotic twins with Glutamate Ionotropic Receptor N-methyl-D-aspartate Type Subunit 2B-related neurodevelopmental disorder (GRIN2B-ND) who exhibited distinct clinical and imaging characteristics due to a de novo heterozygous pathogenic variant in the GRIN2B gene (c.2453T > C, p.Met818Thr). Twin A displayed extensive symmetric malformation of cortical development (MCD) resembling polymicrogyria, accompanied by shallow sulci, dilated lateral ventricles, and dysplastic appearances of the basal ganglia, corpus callosum, and hippocampi. In twin B, malformative features, such as reduced brain volume, MCD, shallow sulci, and dilated lateral ventricle, were confined to the left hemisphere. In combination with previously published data, our report highlights variable phenotypes associated with the p.(Met818Thr) pathogenic variant, specifically with a potential for asymmetric or even unilateral presentation. We discuss the potential interplay between genetic and environmental factors underlying this phenomenon within the context of monozygotic twins. In addition, we also highlight the importance of recognizing potential genetic underpinnings in the assessment of apparently unilateral brain malformations.

High-throughput tracking enables systematic phenotyping and drug repurposing in C. elegans disease models.

There are thousands of Mendelian diseases with more being discovered weekly and the majority have no approved treatments. To address this need, we require scalable approaches that are relatively inexpensive compared to traditional drug development. In the absence of a validated drug target, phenotypic screening in model organisms provides a route for identifying candidate treatments. Success requires a screenable phenotype. However, the right phenotype and assay may not be obvious for pleiotropic neuromuscular disorders. Here, we show that high-throughput imaging and quantitative phenotyping can be conducted systematically on a panel of C. elegans disease model strains. We used CRISPR genome-editing to create 25 worm models of human Mendelian diseases and phenotyped them using a single standardised assay. All but two strains were significantly different from wild-type controls in at least one feature. The observed phenotypes were diverse, but mutations of genes predicted to have related functions led to similar behavioural differences in worms. As a proof-of-concept, we performed a drug repurposing screen of an FDA-approved compound library, and identified two compounds that rescued the behavioural phenotype of a model of UNC80 deficiency. Our results show that a single assay to measure multiple phenotypes can be applied systematically to diverse Mendelian disease models. The relatively short time and low cost associated with creating and phenotyping multiple strains suggest that high-throughput worm tracking could provide a scalable approach to drug repurposing commensurate with the number of Mendelian diseases.

Evaluation of pharyngeal and laryngeal squamous cell carcinoma after neoadjuvant immunochemotherapy by narrow band imaging

Objective: To investigate the changes in the narrow band imaging (NBI) phenotypes of oropharyngeal, hypopharyngeal, and laryngeal squamous cell carcinoma after neoadjuvant immunochemotherapy, and to explore the clinical value of NBI endoscopy in re-evaluation and follow-up of pharyngeal and laryngeal squamous cell carcinoma after neoadjuvant immunochemotherapy. Methods: Twenty-nine patients diagnosed with locally advanced pharyngeal or laryngeal squamous cell carcinoma in the First Affiliated Hospital of Xiamen University from November 2021 to January 2024 and receiving 2 cycles of neoadjuvant immunochemotherapy were selected, including 26 males and 3 females, aged 43-80 years. Regular NBI and white light (WL) endoscopy examinations, as well as imaging examinations such as CT scans, were performed. After the neoadjuvant immunochemotherapy, 36 specimens from suspected lesions of the pharynx or the larynx were obtained through surgical resection. The NBI findings of the pharyngeal or laryngeal lesions and their relationships with corresponding pathological and imaging results were analyzed with kappa test, Pearson correlation analysis, McNemar test, and Wilcoxon Signed Rank Test. Results: After neoadjuvant immunochemotherapy, primary lesions showed pathological complete response (pCR) in 9 cases, partial response (PR) in 19 cases, and progressive disease (PD) in one case. The NBI phenotypes of pharyngeal and laryngeal malignant tumors after treatment showed high consistency (kappa=0.818, P<0.01) and significant correlations (r=0.852, P<0.01) with their pathological results. WL endoscopic examinations showed inconsistency (kappa=0.239, P=0.12) with their pathological results. NBI endoscopy was more effective in identifying benign lesions after treatment than WL endoscopy (P=0.031). In cases of both PR and pCR of the primary lesion, there were significant differences in NBI phenotypes before and after neoadjuvant immunochemotherapy (all P<0.05), with phenotypes transitioning to lower malignancy and benign lesions, respectively. Among 19 cases with PR, 16 showed concentric/eccentric regression, and 3 showed multipoint focal regression. NBI endoscopy was more accurate in diagnosing benign or malignant lesions, pCR or PR after treatment compared to CT (all P<0.05). Conclusions: The present NBI microvascular classification of the pharyngeal and laryngeal lesions is still applicable for the diagnosis and evaluation in pharyngeal and laryngeal squamous cell carcinoma after neoadjuvant immunochemotherapy. The changes in NBI phenotypes and ranges can indicate tumor regression, progression, and recurrence. NBI is more accurate in diagnosing pharyngeal and laryngeal squamous cell carcinoma after neoadjuvant immunochemotherapy compared to WL endoscopy and imaging examinations such as CT.

A clinical study exploring the prediction of microvascular invasion in hepatocellular carcinoma through the use of combined enhanced CT and MRI radiomics.

To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI). A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue. The 93 patients were randomly divided into training and validation groups in 7:3 ratio. The imaging data of patients, including CT and MRI, were collected and processed using 3D Slicer to delineate the region of interest (ROI) for each tumor. Radiomics features were extracted from CT and MRI of patients using Python. Lasso regression analysis was used to select optimal radiomics features for MVI in the training group. The optimal radiomics features of CT and MRI were selected to establish the prediction model. The predictive performance of the model was evaluated using the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). After univariate and multivariate analyses, it was found that tumor diameter was significantly different between the MVI positive and negative groups. After extracting 2153 imaging phenotyping features from the CT and MRI images of the 93 patients using Python, ten standardized coefficient non-zero imaging phenotyping features were finally determined by Lasso regression analysis in the CT and MRI images. A comprehensive predictive model with clinical variable and optimal radiomics features was established. The area under the curve (AUC) of the training group was 0.916 (95%CI: 0.843-1.000), sensitivity: 95.2%, specificity: 79.2%. In the validation group, the predictive model diagnosed MVI with AUC = 0.816 (95%CI: 0.642-0.990), sensitivity: 84.2%, and specificity: 75.0%. The joint model that integrated the optimal radiomics features with clinical variables has good diagnostic performance for MVI of HCC and specific clinical applicability.

Imaging phenotype reveals that disulfirams induce protein insolubility in the mitochondrial matrix

The cell painting assay is useful for understanding cellular phenotypic changes and drug effects. To identify other aspects of well-known chemicals, we screened 258 compounds with the cell painting assay and focused on a mitochondrial punctate phenotype seen with disulfiram. To elucidate the reason for this punctate phenotype, we looked for clues by examining staining steps and gene knockdown as well as examining protein solubility and comparing cell lines. From these results, we found that the punctate phenotype was caused by protein insolubility in the mitochondrial matrix. Interestingly, the punctate phenotype of disulfiram was sensitive to the relative expression of LonP1, a protease in the mitochondrial matrix that regulates proteostasis, suggesting that the punctate phenotype manifests when the protein quality control capacity in the mitochondrial matrix is exceeded. Moreover, we discovered that disulfiram and its derivatives, which have all been reported to increase acetaldehyde in the blood after the in vivo intake of alcohol, induced a punctate phenotype as well. The investigated punctate phenotype not only provides a novel clue for elucidating the common mechanism of action among disulfiram derivatives but is also a novel example of chemical perturbation of proteostasis in the mitochondrial matrix.

VASARI 2.0: a new updated MRI VASARI lexicon to predict grading and IDH status in brain glioma

IntroductionPrecision medicine refers to managing brain tumors according to each patient’s unique characteristics when it was realized that patients with the same type of tumor differ greatly in terms of survival, responsiveness to treatment, and toxicity of medication. Precision diagnostics can now be advanced through the establishment of imaging biomarkers, which necessitates quantitative image acquisition and processing. The VASARI (Visually AcceSAble Rembrandt Images) manual annotation methodology is an ideal and suitable way to determine the accurate association between genotype and imaging phenotype. Our work proposes an updated version of the VASARI score that is derived by changing the evaluation ranges of its components in an effort to increase the diagnostic accuracy of the VASARI manual annotation system and to find neuroimaging biomarkers in neuro-oncology with increasing reliability.Materials and methodsWe gathered the histological grade and molecular status of 126 patients with glioma (Men/Women = 75/51; mean age: 55.30) by a retrospective analysis. Two residents and three neuroradiologists blindedly examined each patient using all 25 VASARI characteristics, after having appropriately modified the reference ranges in order to implement an innovative VASARI lexicon (VASARI 2.0). It was determined how well the observers agreed. A box plot and a bar plot were used in a statistical analysis to assess the distribution of the observations. After that, we ran a Wald test and univariate and multivariate logistic regressions. To find cutoff values that are predictive of a diagnosis, we also computed the odds ratios, confidence intervals, and evaluation matrices using receiver operating characteristic curves for each variable. Finally, we performed a Pearson correlation test to evaluate whether the variable grades and IDH were correlated.ResultsAn excellent Intraclass Correlation Coefficient (ICC) estimate was obtained. In this study, five features were part of the predictive model for determining glioma grade: F4, enhancement quality [area under the curve (AUC): 0.87]; F5, tumor-enhancing proportion (AUC: 0.70); F6, tumor–non-enhancing proportion (AUC: 0.89); F7, necrosis proportion (AUC: 0.79); and F17, diffusion characteristics (AUC: 0.75). Furthermore, six features were found to predict IDH mutation status: F4, enhancement quality (AUC: 0.904); F5, tumor-enhancing proportion (AUC: 0.73); F6, tumor–non-enhancing proportion (AUC: 0.91); F7, necrosis proportion (AUC: 0.84); F14, proportion of edema (AUC: 0.75); and diffusion characteristics F17 (AUC: 0.79). VASARI 2.0 models showed good performances according to the AUC values, which are also compared with traditional VASARI scores.Discussion and conclusionGlioma grade and isocitrate dehydrogenase (IDH) status can be predicted using specific magnetic resonance imaging (MRI) features, which have significant prognostic consequences. The accuracy of texture-derived metrics from preoperative MRI gliomas and machine learning analysis for predicting grade, IDH status, and their correlation can be enhanced by the suggested new and updated VASARI manual annotation system. To help with therapy selection and enhance patient care, we intend to create prediction models that incorporate these MRI findings with additional clinical data.

Investigation of choroid plexus variability in schizophrenia-spectrum disorders—insights from a multimodal study

Previous studies have suggested that choroid plexus (ChP) enlargement occurs in individuals with schizophrenia-spectrum disorders (SSD) and is associated with peripheral inflammation. However, it is unclear whether such an enlargement delineates a biologically defined subgroup of SSD. Moreover, it remains elusive how ChP is linked to brain regions associated with peripheral inflammation in SSD. A cross-sectional cohort of 132 individuals with SSD and 107 age-matched healthy controls (HC) underwent cerebral magnetic resonance imaging (MRI) and clinical phenotyping to investigate the ChP and associated regions. A case-control comparison of ChP volumes was conducted, and structural variance was analyzed by employing the variability ratio (VR). K-means clustering analysis was used to identify subgroups with distinct patterns of the ventricular system, and the clusters were compared in terms of demographic, clinical, and immunological measures. The relationship between ChP volumes and brain regions, previously associated with peripheral inflammation, was investigated. We did not find a significant enlargement of the ChP in SSD compared to HC but detected an increased VR of ChP and lateral ventricle volumes. Based on these regions, we identified 3 clusters with differences in cognitive measures and possibly inflammatory markers. Larger ChP volume was associated with higher volumes of hippocampus, putamen, and thalamus in SSD but not in HC. This study suggests that ChP variability, but not mean volume, is increased in individuals with SSD, compared to HC. Larger ChP volumes in SSD were associated with higher volumes of regions previously associated with peripheral inflammation.

Deriving Mendelian Randomization-based Causal Networks of Brain Imaging Phenotypes and Bipolar Disorder.

Neuroanatomical variation in individuals with bipolar disorder (BD) has been previously described in observational studies. However, the causal dynamics of these relationships remain unexplored. We performed Mendelian Randomization of 297 structural and functional neuroimaging phenotypes from the UK BioBank and BD using genome-wide association study summary statistics. We found 28 significant causal relationship pairs after multiple testing corrections containing BD as a term, 27 of which described neuroimaging phenotype effects on BD. We applied an inverse sparse regression algorithm to estimate the direct effect of phenotypes conditional on all other causal effects, finding that white matter tract phenotypes have larger absolute effects on BD than vice versa. We found that white matter phenotypes have significantly larger out-degrees than non-white matter tract phenotypes, and that the effect of neuroimaging variation on BD is larger than vice versa. Our results provide support for the hypothesis that neuroanatomical variation, specifically in white matter tracts such as the superior and inferior longitudinal fasciculi, is a cause rather than a consequence of BD.

Dietary N-6 Polyunsaturated Fatty Acid Intake and Brain Health in Middle-Aged and Elderly Adults.

Dietary intake of polyunsaturated fatty acids (PUFA) plays a significant role in the onset and progression of neurodegenerative diseases. Since the neuroprotective effects of n-3 PUFA have been widely validated, the role of n-6 PUFA remains debated, with their underlying mechanisms still not fully understood. In this study, 169,295 participants from the UK Biobank were included to analyze the associations between dietary n-6 PUFA intake and neurodegenerative diseases using Cox regression models with full adjustments for potential confounders. In addition, multiple linear regression models were utilized to estimate the impact of n-6 PUFA intake on brain imaging phenotypes. Results indicated that low dietary n-6 PUFA intake was associated with increased risks of incident dementia (hazard ratio [95% confidence interval] = 1.30 [1.13, 1.49]), Parkinson's disease (1.42 [1.16, 1.74]), and multiple sclerosis (1.65 [1.03, 2.65]). Moreover, the low intake was linked to diminished volumes of various brain structures, including the hippocampus (β [95% confidence interval] = -0.061 [-0.098, -0.025]), thalamus (-0.071 [-0.105, -0.037]), and others. White matter integrity was also found to be compromised in individuals with low n-6 PUFA intake. These findings enhanced our understanding of how dietary n-6 PUFA intake might affect neurological health, thereby providing epidemiological evidence for future clinical and public health interventions.

Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

Brain imaging is a high-content modality that offers dense insights into the structure and pathology of the brain. Existing genetic association studies of brain imaging, typically focusing on a number of individual image-derived phenotypes (IDPs), have successfully identified many genetic loci. Previously, we have created a 128-dimensional Unsupervised Deep learning derived Imaging Phenotypes (UDIPs), and identified multiple loci from single-phenotype genome-wide association studies (GWAS) for individual UDIP dimensions, using data from the UK Biobank (UKB). However, this approach may miss genetic associations where one single nucleotide polymorphism (SNP) is moderately associated with multiple UDIP dimensions. Here, we present Joint Analysis of multi-phenotype GWAS (JAGWAS), a new tool that can efficiently calculate multivariate association statistics using single-phenotype summary statistics for hundreds of phenotypes. When applied to UDIPs of T1 and T2 brain magnetic resonance imaging (MRI) on discovery and replication cohorts from the UKB, JAGWAS identified 195/168 independently replicated genomic loci for T1/T2, 6 times more than those from the single-phenotype GWAS. The replicated loci were mapped into 555/494 genes, and 217/188 genes overlapped with the expression quantitative trait loci (eQTL) of brain tissues. Gene enrichment analysis indicated that the genes mapped are closely related to neurobiological functions. Our results suggested that multi-phenotype GWAS is a powerful approach for genetic discovery using high-dimensional UDIPs.

Standardized Response Assessment in Patients with Advanced Cholangiocarcinoma Treated with Personalized Therapy.

Background/Objectives: Current guidelines recommend Cisplatin/Gemcitabine/Durvalumab as first-line treatment for inoperable or recurrent cholangiocarcinoma (CCA). Molecular tumor boards (MTB) have the expertise to support organ-specific tumor boards with evidence-based treatment recommendations for subsequent lines of treatment, based on genomic tumor data and scientific evidence. This study evaluates the adoption of an MTB at a comprehensive cancer center in Germany and whether actionable genetic alterations are associated with specific imaging phenotypes. Methods: Patients with CCA referred to MTB were enrolled from May 2019 to September 2021. For comparison, a cohort of patients from a second center was included. Data on treatment recommendations, regimens, and survival were collected from prospective registries. Baseline and follow-up contrast-enhanced CT were analyzed according to RECIST 1.1. The chi-square test and t-test were used to compare categorical and continuous variables. Results: 583 patients were referred to the MTB, and 92 patients (47 female/51%) with a mean age of 60.3 ± 11.2 were referred for CCA treatment. 65/92 patients harbored 1-3 targetable mutations. Liver metastases were more frequently observed in patients with targetable mutations (84% vs. 62%). Metastasis to the liver and lung was associated with increased sums of diameters (93 mm and 111 mm vs. 40/73 mm in patients with no liver/lung metastasis). The number of metastases in individual organs was unrelated to treatment targets. Follow-up was available for 25 patients with a median time until imaging progression of 23 weeks. Progression occurred as target progression in 63%, nontarget progression in 13%, and appearance of new lesions in 63%. Conclusions: Most patients with CCA harbored targetable mutations, some were related to disease patterns on imaging. The pattern of treatment response and progression was as diverse as the metastatic spread.

Associations between MRI radiomic phenotypes and clinical outcomes in endometrial cancer: Implications for preoperative risk stratification.

This study aimed to investigate the correlation between imaging phenotypes of endometrial cancer (EC) and clinical, pathologic, and molecular characteristics, as well as disease-free survival (DFS). The clinical, pathologic, and molecular characteristics, along with MRI radiomics features, of 356 patients with EC were collected retrospectively. The patients were divided into 2 groups based on radiomics features using unsupervised machine learning. The obtained characteristics and DFS of patients were compared between the various imaging phenotypes. The lesions with deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), cervical stromal invasion (CSI), lymph node metastasis, aggressive histologic type, advanced postoperative International Federation of Gynecology and Obstetrics (FIGO) stage, overexpression of p53, and absent expression of estrogen receptor or progesterone receptor were associated with poor DFS. Two clusters were identified and defined as imaging phenotype 1 and 2, respectively. Compared with phenotype 2, phenotype 1 exhibited a higher correlation with DMI (33.7% vs 13.0%), LVSI (23.8% vs 9.2%), CSI (16.3% vs 3.8%), aggressive histologic type (36.0% vs 17.4%), and advanced FIGO stage (IB or higher, 43.6% vs 22.3%) (p<0.001). The incidence of p53 overexpression was higher in phenotype 1 than in phenotype 2 (20.2% vs 8.5%, p=0.022). Survival analysis exhibited a higher risk of poor DFS in phenotype 1 than in phenotype 2 (log-rank p=0.002). EC imaging phenotypes identified through MRI radiomics features were associated with pathologic, molecular characteristics, and DFS, suggesting potential for preoperative risk stratification.

White Matter Imaging Phenotypes Mediate the Negative Causality of Mitochondrial DNA Copy Number on Sleep Apnea: A Bidirectional Mendelian Randomization Study and Mediation Analysis.

Sleep apnea (SA), associated with absent neural output, is characterised by recurrent episodes of hypoxemia and repeated arousals during sleep, resulting in decreased sleep quality and various health complications. Mitochondrial DNA copy number (mtDNA-CN), an easily accessible biomarker in blood, reflects mitochondrial function. However, the causal relationship between mtDNA-CN and SA remains unclear. This study aimed to investigate the causality between mtDNA-CN and SA while identifying potential mediating brain imaging phenotypes (BIPs). Two-sample bidirectional Mendelian randomisation (MR) analysis was performed to estimate the causal relationship between mtDNA-CN and SA, with further validation using Bayesian framework-based MR analysis. A two-step approach was employed to evaluate causal relationships between BIPs, mtDNA-CN and SA, utilising the "product of coefficients" method to assess the mediating effects of BIPs. Multiple testing errors were corrected using the Benjamini-Hochberg method. Genetically predicted mtDNA-CN had a negative causal effect on SA (OR = 0.859, 95% CI = 0.785-0.939, P = 3.20×10-4), whereas SA did not have a causal effect on mtDNA-CN (OR = 1.0056, 95% CI = 0.9954-1.0159, P = 0.2825). Among 3935 BIPs, two features related to white matter microstructure served as partial mediators: the second eigenvalue from diffusion MRI data analysed by tract-based spatial statistics in the right posterior thalamic radiation, with a mediation proportion of 11.37% (P = 0.0450), and fractional anisotropy in the right sagittal stratum, with a mediation proportion of 12.79% (P = 0.0323). This study demonstrated a causal relationship between mtDNA-CN and SA, with specific brain white matter microstructure phenotypes potentially acting as mediators. These findings highlight the potential of mtDNA-CN as a biomarker for SA and underscore its relevance in guiding future therapeutic strategies targeting mitochondrial health and brain white matter microstructure.

EEG biomarkers of Epileptiform Activity in Relation to Alzheimer’s Disease Biomarkers

AbstractBackgroundGrowing evidence suggests that the imbalance between excitability and inhibitory neural activity is a key aspect of cognitive decline. Subclinical epileptiform activity (SEA) has been indicated as a marker of increased cortical excitability. While SEA is considered as a benign EEG sign in the elderly population, recent studies demonstrated its role in the progression of Alzheimer’s disease. The aim of our study was to investigate the relevance and phenotype of SEA in clinically healthy elderly population.MethodWe studied 62 elderly subjects determined as healthy based on physical, neuropsychology, neuroimaging (MRI), and serological workup. All subjects underwent 24‐h Holter electroencephalography (EEG), visual analysis was conducted by two independent raters. Subjects were classified into EEG positive (EEG+) and negative (EEG‐) groups based on detecting epileptiform activity on their EEG. Neuropsychology battery and structural MRI results were compared between the groups.ResultsEEG analysis showed epileptiform activity in 26% of cases. EEG positivity was associated with significantly lower verbal and category fluency subscores on Addenbrooke Cognitive Examination (p=0,03 and p=0,01). This group also performed worse on both Trail Making Test A and B (p =0,02 and 0,003 respectively). Age and education had a significant effect on neuropsychological performance in both groups. Structural MR analysis showed significant group differences in various areas, particularly in the parietotemporal regions (p=0,001). Interestingly, EEG+ patients had larger external temporal brain volume bilaterally (p&lt;0,001) and reduced medial parietal volume (p=0,001).ConclusionPresence of SEA among healthy elderly shows a discrete but significant association with decreased cognitive performance in certain subdomains, as well as a special imaging phenotype. Our results underscore the relevance of data suggesting that cortical hyperexcitability associate with parietal atrophy and preserved temporal structures. In Alzheimer’s disease, the detailed representation corresponds to the hippocampal sparing phenotype. Considering our findings, SEA might associate with an increased risk for cognitive decline even in healthy individuals.

‘Diet’ deep canonical correlation analysis for high‐dimensional genetics study of brain imaging phenotypes in Alzheimer’s disease

‘Diet’ deep canonical correlation analysis for high‐dimensional genetics study of brain imaging phenotypes in Alzheimer’s disease