Longitudinal Trajectories in Cortical Thickness and Volume Atrophy: Superior Cognitive Performance Does Not Protect Against Brain Atrophy in Older Adults.

BACKGROUND: Previous research has identified a small subgroup of older adults that maintain a high level of cognitive functioning well into advanced age. Investigation of those with superior cognitive performance (SCP) for their age is important, as age-related decline has previously been thought to be inevitable. OBJECTIVE: Preservation of cortical thickness and volume was evaluated in 76 older adults with SCP and 100 typical older adults (TOAs) assessed up to five times over six years. METHODS: Regions of interest (ROIs) found to have been associated with super-aging status (a construct similar to SCP status) in previous literature were investigated, followed by a discovery phase analyses of additional regions. SCPs were aged 70 + at baseline, scoring at/above normative memory (CVLT-II) levels for demographically similar individuals aged 30-44 years old, and in the unimpaired range for all other cognitive domains over the course of the study. RESULTS: In linear mixed models, following adjustment for multiple comparisons, there were no significant differences between rates of thinning or volume atrophy between SCPs and TOAs in previously identified ROIs, or the discovery phase analyses. With only amyloid-ß negative individuals in the analyses, again there were no significant differences between SCPs and TOAs. CONCLUSION: The increased methodological rigor in classifying groups, together with the influence of cognitive reserve, are discussed as potential factors accounting for our findings as compared to the extant literature on those with superior cognitive performance for their age.

Structural core of the executive control network: A high angular resolution diffusion MRI study.

Executive function (EF) is a set of cognitive capabilities considered essential for successful daily living, and is negatively affected by ageing and neurodegenerative conditions. Underpinning EF performance are functional nodes in the executive control network (ECN), while the structural connectivity underlying this network is not well understood. In this paper, we evaluated the structural white matter tracts that interconnect the ECN and investigated their relationship to the EF performance. Using high-angular resolution diffusion MRI data, we performed tractography analysis of structural connectivity in a cognitively normal cohort (n = 140), specifically targeting the connectivity between ECN nodes. Our data revealed the presence of a strongly-connected "structural core" of the ECN comprising three components: interhemispheric frontal connections, a fronto-parietal subnetwork and fronto-striatal connections between right dorsolateral prefrontal cortex and right caudate. These pathways were strongly correlated with EF performance (p = .003). Post-hoc analysis of subregions within the significant ECN connections showed that these effects were driven by a highly specific subset of interconnected cortical regions. The structural core subnetwork of the functional ECN may be an important feature crucial to a better future understanding of human cognition and behaviour.

Highly oxygenated and structurally diverse diterpenoids from Euphorbia helioscopia.

A phytochemical investigation on the aerial part of Euphorbia helioscopia (Euphorbiaceae) led to the isolation of 22 highly oxygenated diterpenoids with structural types of ent-abietane, ent-kaurane, lathyrane, ent-atisane and ingenane. 17 of them, named euphelionolides A - N, 16-epi-18-hydroxy-abbeokutone, as well as eupheliotriols A and B, were identified to be previously undescribed compounds by extensive analysis of spectroscopic data. The stereostructures of euphelionolides A - K were determined by single crystal X-ray diffraction combined with analysis of substituent effects and comparison of optical characteristics. Eupheliotriol B is the first example of natural occurring lathyrol with 12Z-ene, while ent-atisanes are the first reported from the title plant. Furthermore, euphelionolides F and L exhibited significant cytotoxicity against MCF-7 and PANC-1 cell lines.

Cerebral Glucose Metabolism is Associated with Verbal but not Visual Memory Performance in Community-Dwelling Older Adults.

Increasing evidence suggests that Alzheimer's disease (AD) sufferers show region-specific reductions in cerebral glucose metabolism, as measured by [18F]-fluoro-2-deoxyglucose positron emission tomography (18F-FDG PET). We investigated preclinical disease stage by cross-sectionally examining the association between global cognition, verbal and visual memory, and 18F-FDG PET standardized uptake value ratio (SUVR) in 43 healthy control individuals, subsequently focusing on differences between subjective memory complainers and non-memory complainers. The 18F-FDG PET regions of interest investigated include the hippocampus, amygdala, posterior cingulate, superior parietal, entorhinal cortices, frontal cortex, temporal cortex, and inferior parietal region. In the cohort as a whole, verbal logical memory immediate recall was positively associated with 18F-FDG PET SUVR in both the left hippocampus and right amygdala. There were no associations observed between global cognition, delayed recall in logical memory, or visual reproduction and 18F-FDG PET SUVR. Following stratification of the cohort into subjective memory complainers and non-complainers, verbal logical memory immediate recall was positively associated with 18F-FDG PET SUVR in the right amygdala in those with subjective memory complaints. There were no significant associations observed in non-memory complainers between 18F-FDG PET SUVR in regions of interest and cognitive performance. We observed subjective memory complaint-specific associations between 18F-FDG PET SUVR and immediate verbal memory performance in our cohort, however found no associations between delayed recall of verbal memory performance or visual memory performance. It is here argued that the neural mechanisms underlying verbal and visual memory performance may in fact differ in their pathways, and the characteristic reduction of 18F-FDG PET SUVR observed in this and previous studies likely reflects the pathophysiological changes in specific brain regions that occur in preclinical AD.

Heritability and genetic correlation between the cerebral cortex and associated white matter connections.

The aim of this study is to investigate the genetic influence on the cerebral cortex, based on the analyses of heritability and genetic correlation between grey matter (GM) thickness, derived from structural MR images (sMRI), and associated white matter (WM) connections obtained from diffusion MRI (dMRI). We measured on sMRI the cortical thickness (CT) from a large twin imaging cohort using a surface-based approach (N = 308, average age 22.8 ± 2.3 SD). An ACE model was employed to compute the heritability of CT. WM connections were estimated based on probabilistic tractography using fiber orientation distributions (FOD) from dMRI. We then fitted the ACE model to estimate the heritability of CT and FOD peak measures along WM fiber tracts. The WM fiber tracts where genetic influence was detected were mapped onto the cortical surface. Bivariate genetic modeling was performed to estimate the cross-trait genetic correlation between the CT and the FOD-based connectivity of the tracts associated with the cortical regions. We found some cortical regions displaying heritable and genetically correlated GM thickness and WM connectivity, forming networks under stronger genetic influence. Significant heritability and genetic correlations between the CT and WM connectivity were found in regions including the right postcentral gyrus, left posterior cingulate gyrus, right middle temporal gyri, suggesting common genetic factors influencing both GM and WM. Hum Brain Mapp 37:2331-2347, 2016. © 2016 Wiley Periodicals, Inc.

Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks.

Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.

Investigating brain connectivity heritability in a twin study using diffusion imaging data.

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e.g., to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i.e., their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test-retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

Patient specific prostate segmentation in 3-d magnetic resonance images.

Accurate localization of the prostate and its surrounding tissue is essential in the treatment of prostate cancer. This paper presents a novel approach to fully automatically segment the prostate, including its seminal vesicles, within a few minutes of a magnetic resonance (MR) scan acquired without an endorectal coil. Such MR images are important in external beam radiation therapy, where using an endorectal coil is highly undesirable. The segmentation is obtained using a deformable model that is trained on-the-fly so that it is specific to the patient's scan. This case specific deformable model consists of a patient specific initialized triangulated surface and image feature model that are trained during its initialization. The image feature model is used to deform the initialized surface by template matching image features (via normalized cross-correlation) to the features of the scan. The resulting deformations are regularized over the surface via well established simple surface smoothing algorithms, which is then made anatomically valid via an optimized shape model. Mean and median Dice's similarity coefficients (DSCs) of 0.85 and 0.87 were achieved when segmenting 3T MR clinical scans of 50 patients. The median DSC result was equal to the inter-rater DSC and had a mean absolute surface error of 1.85 mm. The approach is showed to perform well near the apex and seminal vesicles of the prostate.

Antitumor activity of Dioscorea bulbifera L. rhizome in vivo.

Antitumor activities of water extract (fraction A), ethanol extract (fraction B), ethyl acetate extract (fraction C), non-ethyl acetate extract (fraction D) and compound diosbulbin B isolated from Dioscorea bulbifera L. (DB) were investigated in vivo in this present study. The results showed that fractions B and C both decreased tumor weight in S180 and H22 tumor cells bearing mice, while fractions A and D had no such effect. Furthermore, fraction C altered the weight of spleen and thymus, and the amounts of total leukocytes, lymphocytes and neutrophils in tumor-bearing mice. Further results showed that compound diosbulbin B demonstrated anti-tumor effects in the dose-dependent manner at the dosage of 2 to 16 mg/kg without significant toxicity in vivo. Furthermore, on the basis of chemical analysis of the above extracts by high-performance liquid chromatography (HPLC) with a diode array detector (DAD), diosbulbin B was found to be the major antitumor bioactive component of DB. These results suggest that DB has potential anti-tumor effects which may be related to influencing the immune system for the first time, and the compound diosbulbin B is the major antitumor component of DB.

Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models.

The hippocampus is affected at an early stage in the development of Alzheimer's disease (AD). With the use of structural magnetic resonance (MR) imaging, we can investigate the effect of AD on the morphology of the hippocampus. The hippocampal shape variations among a population can be usually described using statistical shape models (SSMs). Conventional SSMs model the modes of variations among the population via principal component analysis (PCA). Although these modes are representative of variations within the training data, they are not necessarily discriminative on labeled data or relevant to the differences between the subpopulations. We use the shape descriptors from SSM as features to classify AD from normal control (NC) cases. In this study, a Hotelling's T2 test is performed to select a subset of landmarks which are used in PCA. The resulting variation modes are used as predictors of AD from NC. The discrimination ability of these predictors is evaluated in terms of their classification performances with bagged support vector machines (SVMs). Restricting the model to landmarks with better separation between AD and NC increases the discrimination power of SSM. The predictors extracted on the subregions also showed stronger correlation with the memory-related measurements such as Logical Memory, Auditory Verbal Learning Test (AVLT) and the memory subscores of Alzheimer Disease Assessment Scale (ADAS).

Increasing power to predict mild cognitive impairment conversion to Alzheimer's disease using hippocampal atrophy rate and statistical shape models.

Identifying mild cognitive impairment (MCI) subjects who will convert to clinical Alzheimer's disease (AD) is important for therapeutic decisions, patient counselling and clinical trials. Hippocampal volume and rate of atrophy predict clinical decline at the MCI stage and progression to AD. In this paper, we create p-maps from the differences in the shape of the hippocampus between 60 normal controls and 60 AD subjects using statistical shape models, and generate different regions of interest (ROI) by thresholding the p-maps at different significance levels. We demonstrate increased statistical power to classify 86 MCI converters and 128 MCI stable subjects using the hippocampal atrophy rates calculated by the boundary shift integral within these ROIs.

Andrographolide inhibits hepatoma cells growth and affects the expression of cell cycle related proteins.

The present study is aimed to investigate the toxic effects of andrographolide (Andro) on hepatoma cells and elucidate its preliminary mechanisms. After cells were treated with different concentrations of Andro (0-50 micromol x L(-1)) for 24 h, cell viability was evaluated with 3-(4,5-dimethylthiazol-2-yl) 2,5-diphenyltetrazolium bromide (MTT) assay. Furthermore, after hepatoma cells (Hep3B and HepG2) were treated with different concentrations of Andro (0-30 micromol x L(-1)) for 14 d, the number of colony formation was accounted under microscope. Cell cycle related proteins such as Cdc-2, phosphorylated-Cdc-2, Cyclin B and Cyclin D1 were detected with Western blotting assay and the cell cycle was analyzed by flow cytometry using propidium iodide staining. MTT results showed that Andro induced growth inhibition of hepatoma cells in a concentration-dependent manner but had no significant effects on human normal liver L-02 cells. Andro dramatically decreased the colony formation of hepatoma cells in the concentration-dependent manner. Moreover, Andro induced a decrease of Hep3B cells at the G0-G1 phase and a concomitant accumulation of cells at G2-M phase. At the molecular level, Western blotting results showed that Andro decreased the expression of Cdc-2, phosphorylated-Cdc-2, Cyclin D1 and Cyclin B proteins in a time-dependent manner, which are all cell cycle related proteins. Taken together, the results demonstrated that Andro specifically inhibited the growth of hepatoma cells and cellular cell cycle related proteins were possibly involved in this process.