Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer’s Disease in AIBL Data: Group and Individual Analyses

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data related to 58 AD, 108 mild cognitive impairment (MCI) and 120 healthy elderly (HE) subjects from the Australian imaging, biomarkers and lifestyle (AIBL) dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL) technique, yielding an average of >95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with t-contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (para)hippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by r2 = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with r2 = 0.62 (p < 0.01). Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD

Biosynthesis of dichloroimines in the tropical marine sponge stylotella aurantium

The biosynthetic origin of the dichloroimine carbon in stylotellanes A and B, (1) and (2), is defined by specific incorporation of sodium [C] cyanide. Sodium [C] thiocyanate is also involved in their biosynthesis. A mechanistic scheme is presented for the formation of these bioactive metabolites

Education for sustainability in the built environment: what are students telling us?

There is no doubt that climatic changes are affecting humans and will continue to do so over this century. As the evidence for climate change caused by humans is mounting, there are attendant pressures on how changes can be made to the way humans live and work. Education for sustainability is an important part of the journey to live and work in a sustainable manner

Integrating built heritage and sustainable development: can assessment tools be used to understand the environmental performance of existing buildings with heritage significance?

Attention is turning to the long-term management of the existing building stock, and in particular the adaptation of existing buildings, to achieve more efficient use of resources, reduce energy consumption and greenhouse gas emissions (CSIRO, undated; Kohler and Hasser, 2002; Itard and Klunder, 2007; Empty Homes Agency, 2008; Thomsen and van der Flier, 2009). It is asserted that adaptation of existing buildings can play a key role in sustainable development (Wood, 2005; Douglas, 2006; Kohler and Yang, 2007; Bullen, 2009; Wilkinson, 2009)

Towards a generalization of the MP2RAGE partial volume estimation model to account for B1+inhomogeneities at 7T

International audienceBrain morphometry performed with magnetic resonance (MR) imaging is affected by partial volume (PV) effects when single voxels contain the signal from two different tissues. This paper proposes a generalization of the MP2 RAGE sequence PV estimation model which accounts for transmitted magnetic field (B1 {+}) inhomogeneities at 7T. Our simulation experiments demonstrated that the PV estimation error of the proposed model is significantly lower than the error obtained with the same model neglecting B1 {+} inhomogeneities (p<0.0001). The accuracy and precision of the B1 {+} model (acc=92.0%, prec=89.6%) was significantly increased compared to the non B1 {+} model (acc=69.8%, prec=65.4%). This highlights the importance of accounting for B1 {+} inhomogeneities when computing PV on MP2RAGE data, which would otherwise limit the accuracy of brain morphometry at 7T. © 2021 IEEE

Assessment of the reproducibility of HARDI tractography based cortical connectivity measures suitable for clinical populations using a bootstrap approach

Structural connectivity of the brain using MR diffusion tractography has gained significant interest. A connectivity matrix of cortical connectivity may provide unique insight into brain organisation. We aimed to develop a method to determine the number of seeds required to obtain stable and reproducible connectivity, and to assess reproducibility over time. We employ a bootstrap approach for estimation of these parameters. While connectivity measures of some regions are highly reproducible over time, other connections show poor reproducibility. This study highlights the relationship between seed number and reproducibility of connectivity.Kerstin Pannek, Jane Mathias, James Taylor, Parnesh Raniga, Olivier Salvado, Stephen Rosehttp://www.ismrm.org/meetings-workshops/2010-annual-meeting-3

The use of ultrasound in comparison to radiography in magnetically controlled growth rod lengthening measurement: a prospective study

Purpose This study investigated whether ultrasound (U/ S) is an alternative to radiography when measuring magnetically controlled growth rod (MCGR) length in order to reduce radiation exposure. Distractible spinal growth rods are the gold standard when treating early-onset scoliosis (EOS). Methods This was a prospective series. Patients were already undergoing EOS treatment using MCGRs. Forty-eight data points measured using radiography and U/S were compared. Each U/S data point was measured three times by three observers to assess intra- and inter-observer reliability. The radiation dose of the pre-lengthening and post-lengthening radiographs was recorded. Results The average rod lengths were 1.322 cm with U/S and 1.329 cm with radiography. The ICC (radiography vs. U/S) was 0.992 (95 % confidence interval (CI) 0.976, 1.000). The inter- and intra-rater reliability of U/S had an ICC of 0.987 (95 % CI 0.966, 1.000) and 0.983 (95 % CI 0.956, 1.000), respectively. The mean total effective radiation dose of the pre-lengthening and post-lengthening PA spinal radiographs was 0.26 mSv with a mean attributable lifetime cancer risk of one in 39,686 per lengthening. Conclusion U/S highly agrees with radiography when measuring MCGR length. It has a high inter- and intra-observer reliability and does not require radiation exposure. Although U/S allows accurate MCGR measurement and soft tissue assessment, patients will still need occasional radiographs to assess spine bony elements, overall spinal balance and scoliosis correction. Combining radiography and U/S allows patient monitoring and accurate MCGR measurement whilst decreasing patients’ radiation exposure.Wai Weng Yoon, Angela Christine Chang, Philippa Tyler, Sajid Butt, Sameer Raniga, Hilali Noordee

Manifold drived MR-less PiB SUVR normalisation

Pittsburgh Compound B (PiB) is a C11 PET tracer designed to bind to amyloid plaques, one of the hallmark of Alzheimer\u27s disease. The potential of PiB as an early marker of Alzheimer\u27s disease has lead to an increasing use of PiB and the development of several F18 equivalents. Quantitative analysis of PiB images requires an accurate normalisation, parcellation and estimation of retention in the brain\u27s gray matter. Typically this relies on co-registered MRI to extract the cerebellum, compute the standardized uptake value ratio (SUVR) and provide parcellation and segmentation for quantification of neocortical SUVR. However, not all subjects undergo MRI. In this paper we propose a highly accurate MR-less parcellation, SUVR normalisation and quantification method for PiB images. This involves rigidly registering the raw PiB images to a PiB atlas, computing pair-wise normalised mutual information, and constructing a 2D manifold. Each new scan is mapped on the manifold and its k nearest neighbours are selected as atlases in a segmentation propagation scheme with their associated MRI segmentations and parcellation used as priors to estimate the SUVR normalisation and quantification. Comparison of our MRless approach to an MR-based approach showed a coefficient of correlation of neocortical PiB SUVR of R2=0.94 and an absolute mean error of 5.9%

A hybrid life cycle assessment method for construction

Life cycle assessments (LCAs) are used to evaluate the environmental impacts attributable to products and processes. For construction projects, LCAs can be used to assess the pollution associated with the manufacture of building materials for the construction process. Despite the reliability of traditional LCA data, many upstream processes are excluded, which adversely affects overall reliability. Input-output analysis is systemically complete, but is subject to inherent errors when applied to the LCA of specific products. Analysis of an input-output LCA model provides a basis for more informed decision making regarding processes which can be ignored during the collection of traditional LCA data. This paper proposes a hybrid LCA method for construction in which national input-output data fill those 'gaps' not accounted for by traditional LCA data. Regardless of the level of detail at which data are collected, LCAs can now be performed at similar overall levels of framework completeness.Life Cycle Assessment, Input-output Analysis, Hybrid Lca, Construction,