SUMMARY The hippocampus in schizophrenia is characterized by both hypermetabolism and reduced size. It remains unknown whether these abnormalities are mechanistically linked. Here, in addressing these questions we used MRI tools that can map hippocampal metabolism and structure in patients and mouse models. In at-risk patients, hypermetabolism was found to begin in CA1 and spread to the subiculum after psychosis onset. CA1 hypermetabolism at baseline predicted hippocampal atrophy, which occured during progression to psychosis, most prominently in similar regions. Next, we used ketamine to model conditions of acute psychosis in mice. Acute ketamine reproduced a regional pattern of hippocampal hypermetabolism, while repeated exposure shifted the hippocampus to a hypermetabolic state with concurrent atrophy and pathology in parvalbumin-expressing interneurons. Parallel in vivo experiments using LY379268 and direct measurements of extracellular glutamate showed that glutamate drives both neuroimaging abnormalities. These findings show that hippocampal hypermetabolism leads to atrophy in psychotic disorder and suggest glutamate as a pathogenic driver.

OBJECTIVE This study was performed to determine the condylar morphological variation of osteoarthritic (OA) and asymptomatic temporomandibular joints (TMJ) and to determine its correlation with pain intensity and duration. STUDY DESIGN Three dimensional surface models of mandibular condyles were constructed from Cone-Beam CT images of 29 female patients with TMJ OA (Research Diagnostic Criteria for Temporomandibular Disorders Group III) and 36 female asymptomatic subjects. Shape Correspondence was used to localize and quantify the condylar morphology. Statistical analysis was performed with MANCOVA analysis using Hotelling T2 metric based on covariance matrices, and Pearson correlation. RESULTS OA condylar morphology was statistically significantly different from the asymptomatic condyles (p<0.05). 3D morphological variation of the OA condyles was significantly correlated with pain intensity and duration. CONCLUSION 3D quantification of condylar morphology revealed profound differences between OA and asymptomatic condyles and the extent of the resorptive changes paralleled pain severity and duration.

Objective To assess 3D morphological variations and local and systemic biomarker profiles in subjects with a diagnosis of temporomandibular joint osteoarthritis (TMJ OA). Design Twenty-eight patients with long-term TMJ OA (39.9 ± 16 years), 12 patients at initial diagnosis of OA (47.4 ± 16.1 years), and 12 healthy controls (41.8 ± 12.2 years) were recruited. All patients were female and had cone beam CT scans taken. TMJ arthrocentesis and venipuncture were performed on 12 OA and 12 age-matched healthy controls. Serum and synovial fluid levels of 50 biomarkers of arthritic inflammation were quantified by protein microarrays. Shape Analysis MANCOVA tested statistical correlations between biomarker levels and variations in condylar morphology. Results Compared with healthy controls, the OA average condyle was significantly smaller in all dimensions except its anterior surface, with areas indicative of bone resorption along the articular surface, particularly in the lateral pole. Synovial fluid levels of ANG, GDF15, TIMP-1, CXCL16, MMP-3 and MMP-7 were significantly correlated with bone apposition of the condylar anterior surface. Serum levels of ENA-78, MMP-3, PAI-1, VE-Cadherin, VEGF, GM-CSF, TGFβb1, IFNγg, TNFαa, IL-1αa, and IL-6 were significantly correlated with flattening of the lateral pole. Expression levels of ANG were significantly correlated with the articular morphology in healthy controls. Conclusions Bone resorption at the articular surface, particularly at the lateral pole was statistically significant at initial diagnosis of TMJ OA. Synovial fluid levels of ANG, GDF15, TIMP-1, CXCL16, MMP-3 and MMP-7 were correlated with bone apposition. Serum levels of ENA-78, MMP-3, PAI-1, VE-Cadherin, VEGF, GM-CSF, TGFβ1, IFNγ, TNFα, IL-1α, and IL-6 were correlated with bone resorption.

Hippocampal pathology is central to Alzheimer’s disease (AD) and other forms of dementia such as frontotemporal lobar degeneration (FTLD). Autopsy studies have shown that certain hippocampal subfields are more vulnerable than others to AD and FTLD pathology, in particular the subiculum and cornu ammonis 1 (CA1). We conducted shape analysis of hippocampi segmented from structural T1 MRI images on clinically diagnosed dementia patients and controls. The subjects included 19 AD and 35 FTLD patients (13 frontotemporal dementia [FTD], 13 semantic dementia [SD] and 9 progressive nonfluent aphasia [PNFA]) and 21 controls. Compared to controls, SD displayed severe atrophy of the whole left hippocampus. PNFA and FTD also displayed atrophy on the left side, restricted to the hippocampal head in FTD. AD finally displayed most atrophy in left hippocampal body with relative sparing of the hippocampal head. Consistent with pathological studies most deformation was found in CA1 and subiculum areas in FTLD and AD.

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.