Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model.

INTRODUCTION: Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS: Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS: A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION: Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification. HIGHLIGHTS: Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.

Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods.

PURPOSE: Amyloid positron emission tomography (PET) with [18F]florbetaben (FBB) is an established tool for detecting Aß deposition in the brain in vivo based on visual assessment of PET scans. Quantitative measures are commonly used in the research context and allow continuous measurement of amyloid burden. The aim of this study was to demonstrate the robustness of FBB PET quantification. METHODS: This is a retrospective analysis of FBB PET images from 589 subjects. PET scans were quantified with 15 analytical methods using nine software packages (MIMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ) that used several metrics to estimate Aß load (SUVR, centiloid, amyloid load, and amyloid index). Six analytical methods reported centiloid (MIMneuro, standard centiloid, Neurology Toolkit, SPM8 (PET only), CapAIBL, NMF). All results were quality controlled. RESULTS: The mean sensitivity, specificity, and accuracy were 96.1 ± 1.6%, 96.9 ± 1.0%, and 96.4 ± 1.1%, respectively, for all quantitative methods tested when compared to histopathology, where available. The mean percentage of agreement between binary quantitative assessment across all 15 methods and visual majority assessment was 92.4 ± 1.5%. Assessments of reliability, correlation analyses, and comparisons across software packages showed excellent performance and consistent results between analytical methods. CONCLUSION: This study demonstrated that quantitative methods using both CE marked software and other widely available processing tools provided comparable results to visual assessments of FBB PET scans. Software quantification methods, such as centiloid analysis, can complement visual assessment of FBB PET images and could be used in the future for identification of early amyloid deposition, monitoring disease progression and treatment effectiveness.

A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [18F]flutemetamol amyloid PET images.

BACKGROUND: [18F]flutemetamol PET scanning provides information on brain amyloid load and has been approved for routine clinical use based upon visual interpretation as either negative (equating to none or sparse amyloid plaques) or amyloid positive (equating to moderate or frequent plaques). Quantitation is however fundamental to the practice of nuclear medicine and hence can be used to supplement amyloid reading methodology especially in unclear cases. METHODS: A total of 2770 [18F]flutemetamol images were collected from 3 clinical studies and 6 research cohorts with available visual reading of [18F]flutemetamol and quantitative analysis of images. These were assessed further to examine both the discordance and concordance between visual and quantitative imaging primarily using thresholds robustly established using pathology as the standard of truth. Scans covered a wide range of cases (i.e. from cognitively unimpaired subjects to patients attending the memory clinics). Methods of quantifying amyloid ranged from using CE/510K cleared marked software (e.g. CortexID, Brass), to other research-based methods (e.g. PMOD, CapAIBL). Additionally, the clinical follow-up of two types of discordance between visual and quantitation (V+Q- and V-Q+) was examined with competing risk regression analysis to assess possible differences in prediction for progression to Alzheimer's disease (AD) and other diagnoses (OD). RESULTS: Weighted mean concordance between visual and quantitation using the autopsy-derived threshold was 94% using pons as the reference region. Concordance from a sensitivity analysis which assessed the maximum agreement for each cohort using a range of cut-off values was also estimated at approximately 96% (weighted mean). Agreement was generally higher in clinical cases compared to research cases. V-Q+ discordant cases were 11% more likely to progress to AD than V+Q- for the SUVr with pons as reference region. CONCLUSIONS: Quantitation of amyloid PET shows a high agreement vs binary visual reading and also allows for a continuous measure that, in conjunction with possible discordant analysis, could be used in the future to identify possible earlier pathological deposition as well as monitor disease progression and treatment effectiveness.

Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease.

Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid-ß42, total tau, phosphorylated tau), [18F ]florbetapir, hippocampal volume and brain-age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two-way interaction models. The best performing model included an interaction between amyloid-ß-PET and P-tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker 'space', providing information for personalised or stratified healthcare or clinical trial design.

Mitochondrial complex I density is associated with IQ and cognition in cognitively healthy adults: an in vivo [18F]BCPP-EF PET study.

BACKGROUND: Mitochondrial function plays a key role in regulating neurotransmission and may contribute to general intelligence. Mitochondrial complex I (MC-I) is the largest enzyme of the respiratory chain. Recently, it has become possible to measure MC-I distribution in vivo, using a novel positron emission tomography tracer [18F]BCPP-EF, thus, we set out to investigate the association between MC-I distribution and measures of cognitive function in the living healthy brain. RESULTS: Analyses were performed in a voxel-wise manner and identified significant associations between [18F]BCPP-EF DVRCS-1 in the precentral gyrus and parietal lobes and WAIS-IV predicted IQ, WAIS-IV arithmetic and WAIS-IV symbol-digit substitution scores (voxel-wise Pearson's correlation coefficients transformed to Z-scores, thresholded at Z = 2.3 family-wise cluster correction at p < 0.05, n = 16). Arithmetic scores were associated with middle frontal and post-central gyri tracer uptake, symbol-digit substitution scores were associated with precentral gyrus tracer uptake. RAVLT recognition scores were associated with [18F]BCPP-EF DVRCS-1 in the middle frontal gyrus, post-central gyrus, occipital and parietal regions (n = 20). CONCLUSIONS: Taken together, our findings support the theory that mitochondrial function may contribute to general intelligence and indicate that interindividual differences in MC-I should be a key consideration for research into mitochondrial dysfunction in conditions with cognitive impairment.

A Visual Interpretation Algorithm for Assessing Brain Tauopathy with 18F-MK-6240 PET.

In vivo characterization of pathologic deposition of tau protein in the human brain by PET imaging is a promising tool in drug development trials of Alzheimer disease (AD). 6-(fluoro-18F)-3-(1H-pyrrolo[2,3-c]pyridin-1-yl)isoquinolin-5-amine (18F-MK-6240) is a radiotracer with high selectivity and subnanomolar affinity for neurofibrillary tangles that shows favorable nonspecific brain penetration and excellent kinetic properties. The purpose of the present investigation was to develop a visual assessment method that provides both an overall assessment of brain tauopathy and regional characterization of abnormal tau deposition. Methods: 18F-MK-6240 scans from 102 participants (including cognitively normal volunteers and patients with AD or other neurodegenerative disorders) were reviewed by an expert nuclear medicine physician masked to each participant's diagnosis to identify common patterns of brain uptake. This initial visual read method was field-tested in a separate, nonoverlapping cohort of 102 participants, with 2 additional naïve readers trained on the method. Visual read outcomes were compared with semiquantitative assessments using volume-of-interest SUV ratio. Results: For the visual read, the readers assessed 8 gray-matter regions per hemisphere as negative (no abnormal uptake) or positive (1%-25% of the region involved, 25%-75% involvement, or >75% involvement) and then characterized the tau binding pattern as positive or negative for evidence of tau and, if positive, whether brain uptake was in an AD pattern. The readers demonstrated agreement 94% of the time for overall positivity or negativity. Concordance on the determination of regional binary outcomes (negative or positive) showed agreement of 74.3% and a Fleiss κ of 0.912. Using clinical diagnosis as the ground truth, the readers demonstrated a sensitivity of 73%-79% and specificity of 91%-93%, with a combined reader-concordance sensitivity of 80% and specificity of 93%. The average SUV ratio in cortical regions showed a robust correlation with visually derived ratings of regional involvement (r = 0.73, P < 0.0001). Conclusion: We developed a visual read algorithm for 18F-MK-6240 PET offering determination of both scan positivity and the regional degree of cortical involvement. These cross-sectional results show strong interreader concordance on both binary and regional assessments of tau deposition, as well as good sensitivity and excellent specificity supporting use as a tool for clinical trials.

TauIQ: A Canonical Image Based Algorithm to Quantify Tau PET Scans.

Recently, AmyloidIQ was introduced as a new canonical image-based algorithm to quantify amyloid PET scans and demonstrated increased power over traditional SUV ratio (SUVR) approaches when assessed in cross-sectional and longitudinal analyses. We build further on this mathematical framework to develop a TauIQ algorithm for the quantitative analysis of the more complex spatial distribution displayed by tau PET radiotracers. Methods: Cross-sectional (n = 615) and longitudinal (n = 149) 18F-flortaucipir data were obtained from the Alzheimer's Disease Neuroimaging Initiative along with necessary adjunct amyloid PET and T1-weighted structural MRI data. A subset of these data were used to derive a chronological tau dataset, using AmyloidIQ analysis of associated amyloid PET data to calculate the subject's temporal position in the canonical AD disease process, from which canonical images for the nonspecific and specific binding components of 18F-flortaucipir in AD were calculated. These 2 canonical images were incorporated into the TauIQ algorithm that enables the quantification of both global and local tau outcome measures using an image-based regression and statistical parametric analysis of the initial residual image. Performance of the TauIQ algorithm was compared with SUVR approaches for cross-sectional analyses, longitudinal analyses, and correlation with clinical measures (Alzheimer Disease Assessment Scale-Cognitive Subscale [ADAS-Cog], Clinical Dementia Rating scale-sum of boxes [CDR-SB], and Mini-Mental State Examination [MMSE]). Results: TauIQ successfully calculated global tau load (TauL) in all 791 scans analyzed (range, -3.5% to 185.2%; mean ± SD, 23% ± 20.5%) with a nonzero additional local tau component being required in 31% of all scans (cognitively normal [CN], 22%; mild cognitive impairment [MCI], 35%; dementia, 72%). TauIQ was compared with the best SUVR approach in the cross-sectional analysis (TauL increase in effect size: CN- vs. CN+, +45%; CN- vs. MCI+, -5.6%; CN- vs. dementia+, +2.3%) (+/- indicates amyloid-positive or -negative) and correlation with clinical scores (TauL increase in r2: CDR-SB+, 7%; MMSE+, 38%; ADAS-Cog+, 0%). TauIQ substantially outperformed SUVR approaches in the longitudinal analysis (TauIQ increase in power: CN+, >3.2-fold; MCI+, >2.2-fold; dementia+, >2.9-fold). Conclusion: TauL as calculated by TauIQ provides a superior approach for the quantification of tau PET data. In particular, it provides a substantial improvement in power for longitudinal analyses and the early detection of tau deposition and thus should have significant value for clinical imaging trials in AD that are investigating the attenuation of tau deposition with novel therapies.

Boosting the diagnostic power of amyloid-ß PET using a data-driven spatially informed classifier for decision support.

BACKGROUND: Amyloid-ß (Aß) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aß deposition is a necessary cause or response to the cellular pathology of Alzheimer's disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD. METHODS: Voxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer's Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology. RESULTS: This classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr. CONCLUSIONS: The diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aß PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.

Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge.

The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data.

Amyloid Load: A More Sensitive Biomarker for Amyloid Imaging.

Amyloid-ß (Aß) plays a key role in the pathogenesis of Alzheimer disease (AD) and can be imaged in vivo using 18F-florbetapir PET. A composite SUV ratio (SUVr) is a commonly used outcome measure for quantifying the global Aß burden; however, sensitivity is suboptimal and can lead to low power in clinical trials. We introduce amyloid load, AßL, as a novel biomarker to quantify the global Aß burden along with an automated algorithm for its calculation (AmyloidIQ). AßL is evaluated on cross-sectional and longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative. The cross-sectional data consisted of 769 subjects across the disease spectrum (211 healthy controls, 223 patients with early mild cognitive impairment, 204 with late mild cognitive impairment, and 132 with AD). The distributions of AßL in the 4 different classifications were compared, and the same analyses were applied to a composite SUVr outcome measure. The effect sizes (Hedges g) between all but one classification were higher for AßL than for composite SUVr, with the mean difference being 46%. Of the patients with early mild cognitive impairment, 147 had a 2-y follow-up scan, and the effect size between baseline and follow-up for AßL was 0.49, compared with 0.36 for a composite SUVr, demonstrating an equivalent increase in power for longitudinal data. These results offer evidence that AßL will be a valuable outcome measure in future Aß imaging studies, providing a substantial increase in power over currently used SUVr methods.

Dopamine D2/D3 receptor abnormalities after traumatic brain injury and their relationship to post-traumatic depression.

OBJECTIVE: To investigate dopamine D2/D3 receptor availability following traumatic brain injury (TBI) and their relationship to the presence of DSM-IV Major Depressive Disorder (MDD) and patterns of axonal injury. METHODS: Twelve moderate-severe TBI patients and 26 controls were imaged using [11C]PHNO positron emission tomography (PET) and structural magnetic resonance imaging (MRI). TBI patients and a second group of 32 controls also underwent diffusion tensor imaging (DTI) and neuropsychological assessment. Patients included six with post-injury MDD (TBI-MDD) and six without (TBI-NON). Non-displaceable binding potential (BPND) [11C]PHNO values were used to index D2/D3 receptor availability, and were calculated using a reference region procedure. Differences in BPND were examined using voxelwise and region-of-interest analyses. White matter microstructure integrity, quantified by fractional anisotropy (FA), was assessed and correlated with BPND. RESULTS: Lower [11C]PHNO BPND was found in the caudate across all TBI patients when compared to controls. Lower [11C]PHNO BPND was observed in the caudate of TBI-MDD patients and increased [11C]PHNO BPND in the Amygdala of TBI-NON patients compared to controls. There were no significant differences in [11C]PHNO BPND between TBI-MDD and TBI-NON patients. Furthermore, DTI provided evidence of axonal injury following TBI. The uncinate fasciculus and cingulum had abnormally low FA, with the uncinate particularly affected in TBI-MDD patients. Caudate [11C]PHNO BPND correlated with FA within the nigro-caudate tract. CONCLUSIONS: [11C]PHNO BPND is abnormal following TBI, which indicates post-traumatic changes in D2/D3 receptors. Patterns of [11C]PHNO BPND seen in patients with and without MDD suggest that further research would be beneficial to determine whether the use of dopaminergic treatment might be effective in the treatment of post-traumatic depression.

In vivo detection of cerebral tau pathology in long-term survivors of traumatic brain injury.

Traumatic brain injury (TBI) can trigger progressive neurodegeneration, with tau pathology seen years after a single moderate-severe TBI. Identifying this type of posttraumatic pathology in vivo might help to understand the role of tau pathology in TBI pathophysiology. We used flortaucipir positron emission tomography (PET) to investigate whether tau pathology is present many years after a single TBI in humans. We examined PET data in relation to markers of neurodegeneration in the cerebrospinal fluid (CSF), structural magnetic resonance imaging measures, and cognitive performance. Cerebral flortaucipir binding was variable, with many participants with TBI showing increases in cortical and white matter regions. At the group level, flortaucipir binding was increased in the right occipital cortex in TBI when compared to healthy controls. Flortaucipir binding was associated with increased total tau, phosphorylated tau, and ubiquitin carboxyl-terminal hydrolase L1 CSF concentrations, as well as with reduced fractional anisotropy and white matter tissue density in TBI. Apolipoprotein E (APOE) ε4 genotype affected the relationship between flortaucipir binding and time since injury, CSF ß amyloid 1-42 (Aß42) concentration, white matter tissue density, and longitudinal Mini-Mental State Examination scores in TBI. The results demonstrate that tau PET is a promising approach to investigating progressive neurodegeneration associated with tauopathy after TBI.

Spatiotemporal Distribution of ß-Amyloid in Alzheimer Disease Is the Result of Heterogeneous Regional Carrying Capacities.

ß-amyloid (Aß) accumulation in the brain is 1 of 2 pathologic hallmarks of Alzheimer disease (AD), and the spatial distribution of Aß has been studied extensively ex vivo. Methods: We applied mathematical modeling to Aß in vivo PET imaging data to investigate competing theories of Aß spread in AD. Results: Our results provided evidence that Aß accumulation starts in all brain regions simultaneously and that its spatiotemporal distribution is due to heterogeneous regional carrying capacities (regional maximum possible concentration of Aß) for the aggregated protein rather than to longer-term spreading from seed regions. Conclusion: The in vivo spatiotemporal distribution of Aß in AD can be mathematically modeled using a logistic growth model in which the Aß carrying capacity is heterogeneous across the brain but the exponential growth rate and time of half maximal Aß concentration are constant.

Imaging Aß and tau in early stage Alzheimer's disease with [18F]AV45 and [18F]AV1451.

BACKGROUND: AD is a progressive neurodegenerative disorder that is associated with the accumulation of two different insoluble protein aggregates, Aß plaques and hyperphosphorylated tau. This study aimed to investigate the optimal acquisition and quantification of [18F]AV45 and [18F]AV1451 to image Aß and tau, respectively, in subjects with AD. Fifteen subjects with early stage AD underwent a T1-weighted structural MRI and two dynamic PET scans to image Aß (60 min, [18F]AV45) and tau (120 min, [18F]AV1451). Both dynamic BPND and static SUVR outcome measures were calculated and compared for 12 out of 15 subjects who completed 60 min of the Aß PET scan and at least 110 min of the tau PET scan. The SRTM and reference Logan graphical analysis were applied to the dynamic data to estimate regional BPND values and SUVR ratios from the static data. Optimal acquisition windows were explored for both the dynamic and static acquisitions. In addition, the spatial correlation between regional Aß and tau signals was explored. RESULTS: Both the SRTM and graphical analysis methods showed a good fit to the dynamic data for both Aß and tau dynamic PET scans. Mean regional BPND estimates became stable 30 min p.i. for [18F]AV45 and 80 min p.i. for [18F]AV1451. Time stability analysis of static SUVR data showed that the outcome measure starts to become stable for scan windows of 30-50 min p.i. for [18F]AV45 and 80-100 min p.i. for [18F]AV1451. The results from these time windows correlated well with the results from the full dynamic analysis for both tracers (R2 = 0.74 for [18F]AV45 and R2 = 0.88 for [18F]AV1451). There was a high correlation between amyloid uptake estimate using both dynamic analysis methods in thalamus and tau uptake in thalamus, hippocampus and amygdala. CONCLUSIONS: Short static PET scans at appropriate time windows provided SUVR values which were in reasonable agreement with BPND values calculated from dynamic scans using SRTM and reference Logan. These simplified methods may be appropriate for classification and intervention studies, although caution should be employed when considering interventional studies where blood flow and extraction could change.

Effects of mannose-6-phosphate on human fibroblast cells - biomed 2010.

Coordinate expression of CTGF and TGF-ss plays an important role in tissue fibrosis. Expression of these factors is increased under hypoxic conditions. Treatment of cells under hypoxic conditions with fructose-1,6-bisphosphate has been previously shown to decrease production of CTGF [1]. Mannose-6-phosphate (M6P) may function to down regulate fibrosis by bypassing substrate-level phosphorylation in glycolysis or through competitive inhibition of activation of TGF-ss at the M6P/IGFII receptor. Human fibroblast cells were treated for 24, 48, or 72 hours with 0.5 M, 5 M, and 50 M concentrations of M6P. Cell counts, MDA assay, and cell staining were performed after treatment. At 24 hours cell counts for low and medium treatment groups were higher than the control group, but this difference was significant for only the low group. This effect was not seen at 48 or 72 hours. Cell counts for all treatment and control groups dropped significantly after 24 hours. MDA assay showed reduction in cellular damage with low and medium doses of M6P, but with high dose M6P cellular damage approximates that of controls. Additional studies are needed to determine whether M6P dcreases production of CTGF and/or TGF-ss. David A. Black, Alex Whittington, Tamika Taylor, Olga Golanov, Michelle Tucci, and Hamed Benghuzzi.

Changes to open field surfaces typically used to elicit hippocampal remapping elicit graded exploratory responses.

Studies show that changes in environmental context alter the spatial firing patterns ('remapping') and increase immediate early gene activation in hippocampal but not subicular neurons. However, such studies rarely report co-occurring behavioural responses. We examined the behavioural effects of habituating rats to a walled open field, and then of changing the environmental context by altering wall patterns and floor colour. These kinds of cue change are known to elicit spatial remapping in hippocampal regions, but not the subiculum. Relative to controls (no cue alterations), alteration of wall patterns elicited significant increases in exploratory locomotion through the open field, while combined alterations of floor colour and wall patterns elicited an even higher increase in exploratory locomotion. In addition, combined alterations (floor colour and wall patterns) significantly increased rearing frequency, and significantly decreased the time the rats spent immobile. These findings are relevant to how changes in environmental context affect neuronal responses in the hippocampal formation, and may aid in the development of novelty-response tasks where novelty resides in open field surfaces.

Defining the role of hif-1 ? And ctgf in fibrosis - biomed 2009.

Connective tissue growth factor (CTGF) plays a major role in pathways that lead to fibrosis, including fibrosis of major organs, fibroproliferative diseases, and scarring. Tissue hypoxia has been reported to induce the expression and secretion of CTGF in various tissues and organs, such as in renal cortical myofibroblasts, in renal interstitial fibroblasts, and in skin. This implicates CTGF as a mechanism of action for tissue damage caused by hypoxia. Studies have shown that cells deficient in hypoxic inducible factor 1 (HIF-1) were not able to produce CTGF mRNA, suggesting a strong association between the two proteins. In this study fibroblast cells were placed under hypoxic conditions for periods of 2, 24, 48, and 72 hours, and the levels of HIF-1, and CTGF were determined by immunocytochemical techniques. The results show a strong positive response to HIF-1 following hypoxia treatment for periods of 24 hours. By 48 and 72 hours HIF-1 levels are reduced and increases in CTGF cellular staining are noticeable. The results of our study also indicate a strong correlation between HIF-1 and CTGF. Our data also shows that once CTGF is induced, HIF-1 levels decline even though the hypoxic conditions are maintained. Determining the relationship between HIF-1 and CTGF may provide a deeper understanding of the pathogenesis of fibroproliferative disease.