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Cancer-related cognitive impairment (CRCI) is often related to chemotherapy. Increased chronic inflammation is believed to play a key role in the development of CRCI related to chemotherapy but studies assessing this hypothesis specifically in patients receiving chemotherapy are rare.We assessed several cognitive domains using the Cambridge Neuropsychological Test Automated Battery (CANTAB) in twenty-two breast cancer patients currently receiving chemotherapy. We also measured inflammatory cytokine and receptor (MCP-1, TNF-α, sTNFRI, sTNFRII) concentrations in patient sera using Luminex assays. These concentrations were log-transformed to obtain a normal distribution. Associations between log-transformed cytokines and cognition were evaluated using Pearson correlations and linear regression, taking into account relevant covariates.Increased concentrations of sTNFRI and sTNFRII were associated with poorer performance on the CANTAB Delayed Matching to Sample (DMS, tests visual memory). Increasing sTNFRI levels were negatively correlated with DMS percent correct (r=-0.47, p=0.029) and DMS percent correct after a 12 second (s) delay (r=-0.65, p=0.001). Increasing levels of sTNFRII negatively correlated with DMS percent correct after 12s delay (r=-0.57, p=0.006). After controlling for relevant demographic (i.e. age, education) and clinical variables (i.e. disease stage, regimen type), we found that increased sTNFRI remained significantly related to decline on the DMS at the 12s delay (p=0.018).This preliminary study shows a significant association between higher sTNFRI and lower scores on the short-term visual memory delayed match to sample test in breast cancer patients receiving chemotherapy, supporting the hypothesis that sTNFRI is involved in CRCI.

12070 Background: Cognitive impairment is a prevalent side effect of chemotherapy. We have previously shown that chemotherapy treatment is associated with worse performance on the Rapid Visual Processing test (RVP), an objective measure of sustained attention, over time compared to non-cancer controls. Better understanding of the biologic mechanisms underlying cognitive impairment in cancer patients is needed. The pro-inflammatory cytokine tumor necrosis factor alpha (TNFα) has been implicated in increasing blood brain barrier (BBB) permeability, which in turn is associated with cognitive impairment. This study assessed the relationships of TNFα and S100β, a biomarker of BBB permeability, to each other and to RVP performance over time. Methods: We analyzed a subset of participants (n = 89 patients, n = 52 controls, mean age = 60) from a prospective longitudinal study of women with breast cancer receiving chemotherapy and non-cancer controls. TNFα and S100β were measured in serum pre-chemotherapy (T1, ≤7 days before first treatment) and post-chemotherapy (T2, ≤1 month after last treatment) and at corresponding times for controls. Sustained attention was assessed by total correct rejections on the RVP test at T1 and T2. Separate linear regression models including all participants were used to relate 1) baseline TNFα and S100β levels to change in RVP performance over time, 2) change in TNFα and S100β to change in RVP performance over time, and 3) change in TNFα to change in S100β. Models were adjusted for age. 4) T-tests were used to compare the TNFα and S100β change scores (T1 to T2) of patients vs controls. Results: Greater increase (T1 to T2) in the pro-inflammatory cytokine TNFα was associated with worse cognition, measured by performance on RVP over time (p = 0.02). Higher baseline S100β, a biomarker of BBB permeability, was associated with worse performance on RVP over time (p = 0.09). Increase in TNFα was associated with increase in S100β (p = 0.11). S100β increased from T1 to T2 in patients relative to controls (p = 0.09). Conclusions: These results suggest that higher TNFα may be related to increases in blood brain barrier permeability and worse cognition. Future studies will further define the link between inflammation, blood brain barrier permeability and chemotherapy-related cognitive decline, with the goal of informing the development of new interventions. Funding: R01CA231014, T32CA102618, DP2CA195765, UG1CA189961.

An iterative learning algorithm for performing Multi-Channel Coherence Analysis (MCCA) is developed in this paper. MCCA is an extension of the well-known Canonical Correlation Analysis (CCA) that allows for more than two data channels to be analyzed. This paper discusses a standard method for performing MCCA and compares it to a newly developed data-driven and iterative approach. The proposed algorithm is then tested on two examples and its performance is evaluated in terms of estimation errors with respect to the values obtained using the standard MCCA algorithm. The first example uses a synthesized data set while the second example uses a real data set based on multi-spectral satellite imagery of the Earth's surface.

Developing an effective detection and classification system for use with buried underwater objects is a challenging problem. In this paper, multichannel canonical correlation analysis (MCCA) is used for feature extraction from multiple sonar returns of buried underwater objects using data collected by the new generation Buried Object Scanning Sonar (BOSS) system. Comparisons are made between the classification results of features extracted by the proposed algorithm and those extracted by the two-channel canonical correlation analysis (CCA) algorithm on the SAX '04 data set. Extracted features are subsequently used in the development of classification systems able to differentiate between mine-like and non-mine-like objects. This study compares different feature extraction algorithms and classification schemes, and the results are presented in terms of classification rates and overall detection/classification performance. The results show that, for the SAX '04 data set, the features extracted via MCCA yield higher correct classification rates than feature extracted using CCA while simultaneously reducing structural complexity.

Multichannel canonical correlation analysis (MCCA) is used in this paper for feature extraction from multiple sonar returns off of buried underwater objects using data collected by the new generation buried object scanning sonar (BOSS) system. Comparisons are made between the classification results of features extracted by the proposed algorithm and those extracted by the two-channel canonical correlation analysis (CCA) algorithm. This study compares different feature extraction and classification algorithms, and the results are presented in terms of confusion matrices. The results show that MCCA yields higher correct classification rates than CCA while reducing the classifier's structural complexity.

When modeling thermal imaging systems to predict their performance, there are three functional areas that must be specifically characterized: the object being imaged; the intervening atmosphere; and the sensor itself. This paper will discuss two computer simulations presently being used by the U.S. Army Missile Command (MICOM) which integrate models of these three functional areas to calculate the performance of a variety of thermal imaging systems for a range of atmospheres, scenarios, targets, and sensors. Common to both approaches is: use of the Johnson bar criteria for detection, recognition and identification; the use (direct or indirect) of the AFGL LOWTRAN code to model the atmosphere; and, finally, some methodologies for modeling staring focal plane arrays (FPAs) as the sensor detection element. The three functional areas will be discussed first, followed by discussions of the two approaches; the Fire Control Sensor Simulator (FCSS), an engagement model; and, the MICOM Infrared Imaging System Performance Model (MIISPM), a more detailed model emphasizing the sensor itself. The MIISPM is based upon the Night Vision Laboratory Static Performance Model (NVLSPM), but has been enhanced in several important ways, discussed in the paper. While both approaches provide as output the same performance criteria (probability of detection, recognition and identification, as a function of range), each model has its own strengths for different aspects of the sensor performance problem: for the FCSS, targets and scenarios; for the MIISPM, details of the sensor. Both approaches will be presented and compared in terms of the results, run time efficiency, and required hardware. Both models presently run on IBM or IBM compatible personal computers.