The impairment of axonal transport by overexpression or hyperphosphorylation of tau is well documented for in vitro conditions; however, only a few studies on this phenomenon have been conducted in vivo, using invasive procedures, and with contradictory results. Here we used the non-invasive, Manganese-Enhanced Magnetic Resonance Imaging technique (MEMRI), to study for the first time a pure model of tauopathy, the JNPL3 transgenic mouse line, which overexpresses a mutated (P301L) form of the human tau protein. We show progressive impairment in neuronal transport as tauopathy advances. These findings are further supported by a significant correlation between the severity of the impairment in neuronal transport assessed by MEMRI, and the degree of abnormal tau assessed by histology. Unlike conventional techniques that focus on axonal transport measurement, MEMRI can provide a global analysis of neuronal transport, i.e. from dendrites to axons and at the macroscopic scale of fiber tracts. Neuronal transport impairment has been shown to be a key pathogenic process in Alzheimer's disease and numerous other neurodegenerative disorders. Hence, MEMRI provides a promising set of functional biomarkers to be used during preclinical trials to facilitate the selection of new drugs aimed at restoring neuronal transport in neurodegenerative diseases.

Immunotherapy targeting hyperphosphorylated tau is a promising prospect to mitigate the neurodegenerative effects of tauopathies. Assessing the effectiveness of such immunotherapies often involves sacrifice of the animal. However, Manganese-Enhanced Magnetic Resonance Imaging (MEMRI) permits the longitudinal study of neuronal function with minimal risk to the animal. We hypothesize that tract-tracing MEMRI in a mouse model of tau pathology should enable non-invasive monitoring of various tau targeting therapies aimed at improving neuronal integrity. Twenty-five homozygous JNPL3 tangle transgenic mice underwent MEMRI at 6 months of age. Thirteen of the mice received tau immunotherapy with Tau379-408[P-Ser396,404] in alum adjuvant from 3 months of age, and twelve controls received an adjuvant alone. Imaging studies were performed on a 7-T micro-MRI. Mice were imaged pre-injection, then injected in one nostril with a solution of 2.5 M MnCl 2, under isoflurane anesthesia. Image sets were acquired at 1, 4, 8, 12, 24, 36 and 48 hours, and finally at 7 days (Fig 1). The datasets were processed using ImageJ. Normalized measurements for each mouse were plotted and fitted to a tract tracing bolus model using MATLAB. Fitting enabled the estimation of the timing (Pt) and intensity (Pv) of the bolus peak of Mn, and maximal slope of uptake (Sv). A significant increase in maximal slope of manganese uptake, Sv, was observed in the mitral cell layer (35%, P <.005) and glomerular layer (36%, P <0.02) in treated JNPL3 mice compared to identical controls. There was also a significant increase in bolus peak value, Pv, in the mitral layer in the treated group (7%, P = 0.02). Furthermore, in the immunized mice, there was a strong trend for a decrease in the time to peak value, Pt (−9%P = 0.10), in the mitral cell layer, compared to the controls. Utilizing MEMRI's non-invasive, longitudinal measurements from 1 hour to 7 days, allowed us to detect substantial improvements in neuronal transport following tau immunotherapy. We are analyzing tau pathology in olfactory sections from these mice to assess the correlation of these benefits with clearance of tau lesions, which we have shown previously to occur with this treatment. (Row 1: 1, 4, 8, 12 hours | Row 2: 24, 36, 48 hours, 7 days)

Axonal transport perturbations are known to play a critical role in the pathological progression of Alzheimer's disease (AD); and Manganese-Enhanced MRI (MEMRI) provides a unique, non-invasive tool allowing for the in vivo evaluation of transport deficits in preclinical studies. In this paper, we provide a brief history of MEMRI, and review the current literature describing its biological basis. We propose a model of how manganese transport reflects both axonal and dendritic transport (termed "neuronal transport"), and potentially, mitochondrial trafficking in neurons. A framework for the analysis of MEMRI data is provided. It summarizes the significance of the various parameters describing manganese transport and the pathophysiological events that can alter their relevance, such as neuronal loss, gliosis and excitotoxicity. Lastly, we review publications describing different animal models of AD pathology that suggest the expression of either mutated human tau or mutated human amyloid beta alters neuronal transport, as measured by MEMRI. In this way, MEMRI correlates the in vitro observation of impaired axonal transport and mitochondrial mislocalization related to AD lesions, with direct in vivo data. Therefore, MEMRI has the potential to become a unique tool for assessing the effect of new AD treatments aimed at restoring neuronal transport and mitochondrial trafficking.

Manganese-Enhanced MRI (MEMRI) allows for a non-invasive in vivo measurement of neuronal transport. Previous MEMRI studies on an animal model of Aß deposition (Tg2576 mice) have demonstrated that the expression of human mutant APP induces in vivo perturbations of neuronal transport. The aim of our study was to characterize neuronal transport in an accelerated mouse model of Aß amyloidosis, which expresses 5 mutations of APP and PS1 human genes (5XFAD). Thirteen Tg and 15 WT mice were imaged on a 7T magnet at 3 or 6 months of age. We used a tract-tracing MEMRI protocol with 9 imaging time points, 1 prior to and 8 following nasal injection of MnCl2. Two regions of interest (ROI) were defined on MR images, corresponding to the glomerular layer and the mitral cell layer of the olfactory bulb. In each ROI, the evolution of signal intensity profile was used to estimate the maximum (Smax), maximal slope (Vmax) and time to maximal slope (T2Vmax) of the relative manganese concentration. Unexpectedly, WT mice demonstrated an age-associated impairment of neuronal transport, assessed by a decrease in Smax (p < 0.01), decrease in Vmax (p < 0.01) and increase in T2Vmax (p < 0.005) between 3 and 6 months of age. Surprisingly, this age-related impairment was lacking in the Tg group, in which neuronal transport parameters remained stable between 3 and 6 months of age. At 6 months, T2Vmax was significantly lower in the mitral cell layer of Tg mice as compared to WT controls (p < 0.05), suggesting a faster neuronal transport in the Tg group.

STUDY Benjamin Winthrop Little, Umer Khan, Hameetha Rajamohamedsait, Lindsay K Hill, Leslie Pendery, Dung Minh Hoang, Einar M Sigurdsson, and Youssef Z Wadghiri Physiology & Neuroscience, New York University School of Medicine, New York, New York, United States, Radiology, New York University School of Medicine, New York, New York, United States, Psychiatry, New York University School of Medicine, New York, New York, United States

Functional alterations of axonal transport have been suggested to occur in the early stages of Alzheimer's disease. In vitro studies have shown a deleterious effect of tauopathy on axonal transport. We hypothesized that Tract-Tracing-Manganese-Enhanced-MRI (TT-MEMRI) should detect early axonal transport impairments in a mouse model of tauopathy. Ten JNLP3 (P301L) transgenic mice (Tg) and 5 wild-type mice (WT) were imaged on a 7T magnet at 3 and 6 months of age, using a TT-MEMRI protocol with 9 imaging time points (1 prior to and 8 following nasal injection of MnCl2). Four regions of interest (ROI) were defined on MR images, corresponding to 4 consecutive areas of the olfactory system (glomerular and mitral cell layers, anterior and posterior part of the piriform cortex). In each ROI, the evolution of signal intensity profile, indicative of manganese propagation, was used to estimate the peak value (Pv) and time to peak (Pt) of the relative manganese concentration. By fitting to a one-dimensional flow-diffusion model, we calculated the 3 parameters expected to contribute to manganese propagation: the flow velocity, reflecting its active transport in neurons; the diffusion coefficient, reflecting its passive stochastic propagation in neurons; and the leakage rate, reflecting the clearance of manganese from the main olfactory fiber system, through ion channels, synapses, or collateral axons. A decrease of Pv and increase of Pt was observed in the older Tg mice compared to both age-matched WT (Fig. 1A) and young Tg mice. This decrease was significant in females (p < 0.05-0.01), who develop tauopathy earlier than males (Fig. 1B&C). Older Tg mice showed trends of increased diffusion and decreased leakage and velocity when compared to both old WT and young Tg mice. These 2 last parameters can contribute to the increase in Pt in the older Tg mice. This study provides the first in vivo evidence of impairment of axonal transport in a model of tauopathy, assessed non-invasively by TT-MEMRI. It also indicates that manganese propagation reflects not only active axonal transport but also passive diffusion and extra-neuronal leakage.

Accurate and less invasive personalized predictive medicine relieves many breast cancer patients from agonizingly complex surgical treatments, their colossal costs and primarily letting the patient to forgo the morbidity of a treatment that proffers no benefit. Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management. Support Vector Machines (SVMs) are shown to be powerful tools for analyzing data sets where there are complicated nonlinear interactions between the input data and the information to be predicted. In this paper, we have targeted this strength of SVMs to analyze the potential of classification through feature vectors for predicting the survival chances of a breast cancer patient. Experiments were performed using different types of SVM algorithms analyzing their classification efficiency using different kernel parameters. SEER breast cancer data set (1973-2003), the most comprehensible source of information on cancer incidence in United States, is considered. Sensitivity, specificity and accuracy parameters along with RoC curves have been used to explain the performance of each SVM algorithm with different kernel types.

Breast cancer prognosis poses a great challenge to the researchers. Recently, there have been breakthroughs in the field of bioinformatics and because of that a new realm of breast cancer prognosis has opened. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer prognosis. In this paper we present a survey of those models that are being used to enhance the breast cancer prognosis prediction. Firstly, we introduce these models and secondly we give an overview of the current research being carried out using these models. We specify different level of accuracies being claimed by different researchers. Lastly, we conclude that despite the ongoing research efforts towards achieving better capabilities for prediction system, we still need much more to build a more accurate and less invasive prognostic system that can benefit the mankind.

Data analysis systems, intended to assist a physician, are highly desirable to be accurate, human interpretable and balanced, with a degree of confidence associated with final decision. In cancer prognosis, such systems estimate recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such a prognostic system, this paper proposes to investigate a hybrid scheme based on fuzzy decision trees, as an efficient alternative to crisp classifiers that are applied independently. Experiments were performed using different combinations of: number of decision tree rules, types of fuzzy membership functions and inference techniques. For this purpose, SEER breast cancer data set (1973–2003), the most comprehensible source of information on cancer incidence in United States, is considered. Performance comparisons suggest that, for cancer prognosis, hybrid fuzzy decision tree classification is more robust and balanced than independently applied crisp classification; moreover it has a potential to adapt for significant performance enhancement.

Accurate and less invasive personalized predictive medicine can spare many breast cancer patients from receiving complex surgical biopsies, unnecessary adjuvant treatments and its expensive medical cost. Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such knowledge based prognostic system, this paper examines potential hybridization of accuracy and interpretability in the form of Fuzzy Logic and Decision Trees, respectively. Effect of rule weights on fuzzy decision trees is investigated to be an alternative to membership function modifications for performance optimization. Experiments were performed using different combinations of: number of decision tree rules, types of fuzzy membership functions and inference techniques for breast cancer survival analysis. SEER breast cancer data set (1973-2003), the most comprehensible source of information on cancer incidence in United States, is considered. Performance comparisons suggest that predictions of weighted fuzzy decision trees (wFDT) are more accurate and balanced, than independently applied crisp decision tree classifiers; moreover it has a potential to adapt for significant performance enhancement.