Artefactual structure from least squares multidimensional scaling

We consider the problem of illusory or artefactual structure from the visualisation of high-dimensional structureless data. In particular we examine the role of the distance metric in the use of topographic mappings based on the statistical field of multidimensional scaling. We show that the use of a squared Euclidean metric (i.e. the SSTRESs measure) gives rise to an annular structure when the input data is drawn from a high-dimensional isotropic distribution, and we provide a theoretical justification for this observation

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms

The biodiversity benefit of native forests and mixed-species plantations over monoculture plantations

Aim: China's Grain for Green Program (GFGP) is the largest reforestation programme in the world and has been operating since 1999. The GFGP has promoted the establishment of tree plantations over the restoration of diverse native forests. In a previous study, we showed that native forests support a higher species richness and abundance of birds and bees than do GFGP plantations and that mixed-species GFGP plantations support a higher level of bird (but not bee) diversity than do any individual GFGP monocultures (although still below that of native forests). Here, we use metabarcoding of arthropod diversity to test the generality of these results. Location: Sichuan, China. Methods: We sampled arthropod communities using pan traps in the land cover types concerned under the GFGP. These land use types include croplands (the land cover being reforested under the GFGP), native forests (the reference ecosystem as the benchmark for the GFGP’s biodiversity effects) and the dominant GFGP reforestation outcomes: monoculture and mixed-species plantations. We used COI-amplicon sequencing (“metabarcoding”) of the arthropod samples to quantify and assess the arthropod community profiles associated with each land cover type. Results: Native forests support the highest overall levels of arthropod species diversity, followed by mixed-species plantations, followed by bamboo and other monocultures. Also, the arthropod community in native forests shares more species with mixed-species plantations than it does with any of the monocultures. Together, these results broadly corroborate our previous conclusions on birds and bees but show a higher arthropod biodiversity value of mixed-species plantations than previously indicated by bees alone. Main conclusion: In our previous study, we recommended that GFGP should prioritize the conservation and restoration of native forests. Also, where plantations are to be used, we recommended that the GFGP should promote mixed-species arrangements over monocultures. Both these recommendations should result in more effective protection of terrestrial biodiversity, which is an important objective of China's land-sustainability spending. The results of this study strengthen these recommendations because our policy prescriptions are now also based on a dataset that includes over 500 species-resolution taxa, ranging across the Arthropoda

Comparison of seven modelling algorithms for γ-aminobutyric acid–edited proton magnetic resonance spectroscopy

Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.publishedVersio

A Modernized View of Coherence Pathways Applied to Magnetic Resonance Experiments in Unstable, Inhomogeneous Fields

Over recent decades, the value of conducting experiments at lower frequencies and in inhomogeneous and/or time-variable fields has grown. For example, an interest in the nanoscale heterogeneities of hydration dynamics demands increasingly sophisticated and automated measurements deploying Overhauser Dynamic Nuclear Polarization (ODNP) at low field. The development of these methods poses various challenges that drove us to develop a standardized alternative to the traditional schema for acquiring and analyzing coherence pathway information employed by the overwhelming majority of contemporary Nuclear Magnetic Resonance (NMR) research. Specifically, on well-tested, stable NMR systems running well-tested pulse sequences in highly optimized, homogeneous magnetic fields, traditional hardware and software quickly isolate a meaningful subset of data by averaging and discarding between 3/4 and 127/128 of the digitized data. In contrast, spurred by recent advances in the capabilities of open-source libraries, the domain colored coherence transfer (DCCT) schema implemented here builds on the long-extant concept of Fourier transformation along the pulse phase cycle domain to enable data visualization that more fully reflects the rich physics underlying these NMR experiments. In addition to discussing the outline and implementation of the general DCCT schema and associated plotting methods, this manuscript presents a collection of algorithms that provide robust phasing, avoidance of baseline distortion, and the ability to realize relatively weak signals amidst background noise through signal-averaged correlation alignment. The methods for visualizing the raw data, together with the processing routines whose development they guide should apply directly to or extend easily to other techniques facing similar challenges.Comment: 32 pages, 18 figure

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience

Dopaminergic organization of striatum is linked to cortical activity and brain expression of genes associated with psychiatric illness

Dopamine signaling is constrained to discrete tracts yet has brain-wide effects on neural activity. The nature of this relationship between local dopamine signaling and brain-wide neuronal activity is not clearly defined and has relevance for neuropsychiatric illnesses where abnormalities of cortical activity and dopamine signaling coexist. Using simultaneous PET-MRI in healthy volunteers, we find strong evidence that patterns of striatal dopamine signaling and cortical blood flow (an index of local neural activity) contain shared information. This shared information links amphetamine-induced changes in gradients of striatal dopamine receptor availability to changes in brain-wide blood flow and is informed by spatial patterns of gene expression enriched for genes implicated in schizophrenia, bipolar disorder, and autism spectrum disorder. These results advance our knowledge of the relationship between cortical function and striatal dopamine, with relevance for understanding pathophysiology and treatment of diseases in which simultaneous aberrations of these systems exist

Multi-component MRI transverse-relaxation parameter estimation to detect and monitor neuromuscular disease

We aimed to optimise the estimation of skeletal muscle-water spin-spin relaxation time (T2m), and fat fraction estimated from multi-echo MRI, as potential biomarkers, by accounting for instrumental factors such as B1 errors, non-Gaussian noise and non-ideal echo train evolution. A multi-component slice-profile-compensated extended phase graph (sEPG) model for multi-echo Carr-Purcell-Meiboom-Gill (CPMG) spin-echo sequence signals was implemented, modelling the fat signal as two empirically calibrated sEPG components with fixed parameters, and the remaining unknown parameters (B1 field factor, T2m, fat fraction (ffa), global amplitude and Rician noise SD) determined by maximum likelihood estimation. After validation using a calibrated test object the algorithm was used to analyse clinical muscle study data from patient groups with amyotrophic lateral sclerosis (ALS), Kennedy’s disease (KD) and Duchenne muscular dystrophy (DMD) and matched healthy controls. Parameter maps were generated using quality control steps to reject pixels failing fit quality or physical meaningfulness criteria. Muscle fat-fraction was also determined independently by 3-point Dixon MRI (ffd). In ALS and KD median T2m were significantly elevated compared with healthy controls in varied patterns and time courses, whereas it was decreased in DMD; other T2m distribution histogram metrics such as the skewness and full width at quarter maximum also differed significantly between patients and healthy volunteers. Quantitative comparison of ffa and ffd in the same muscles revealed a monotonic relationship deviating from linearity due to differing deviations from the assumed ideal signal behaviour in each method. Finally, the effects upon estimation accuracy and precision of practically realisable pulse sequence parameter choices were explored in simulations and with real data. Recommendations are presented for optimal choices. Clinically practical conventional CPMG sequences, combined with an appropriate signal model and parameter estimation method can provide robust T2m and ffa measures which change in disease and may sensitively reflect different aspects of neuromuscular pathology