Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility.

Facial affect processing inbipolar disorder:a magnetoencephalography study

Background: Identifying biological markers to aid diagnosis of bipolar disorder (BD) is critically important. To be considered a possible biological marker, neural patterns in BD should be discriminant from those in healthy individuals (HI). We examined patterns of neuromagnetic responses revealed by magnetoencephalography (MEG) during implicit emotion-processing using emotional (happy, fearful, sad) and neutral facial expressions, in sixteen BD and sixteen age- and gender-matched healthy individuals. Methods: Neuromagnetic data were recorded using a 306-channel whole-head MEG ELEKTA Neuromag System, and preprocessed using Signal Space Separation as implemented in MaxFilter (ELEKTA). Custom Matlab programs removed EOG and ECG signals from filtered MEG data, and computed means of epoched data (0-250ms, 250-500ms, 500-750ms). A generalized linear model with three factors (individual, emotion intensity and time) compared BD and HI. A principal component analysis of normalized mean channel data in selected brain regions identified principal components that explained 95% of data variation. These components were used in a quadratic support vector machine (SVM) pattern classifier. SVM classifier performance was assessed using the leave-one-out approach. Results: BD and HI showed significantly different patterns of activation for 0-250ms within both left occipital and temporal regions, specifically for neutral facial expressions. PCA analysis revealed significant differences between BD and HI for mild fearful, happy, and sad facial expressions within 250-500ms. SVM quadratic classifier showed greatest accuracy (84%) and sensitivity (92%) for neutral faces, in left occipital regions within 500-750ms. Conclusions: MEG responses may be used in the search for disease specific neural markers.

New biomarkers and risk stratification in atrial fibrillation:simplicity and practicality matter

Advances in our understanding of pathological mechanisms can inform the identification of various biomarkers for risk stratification, monitoring drug efficacy and toxicity; and enabling careful monitoring of polypharmacy. Biomarkers in the broadest sense refer to 'biological markers' and this can be blood-based (eg. fibrin D-dimer, von Willebrand factor, etc) urine-based (eg. thromboxane), or even related to cardiac or cerebral imaging(1). Most biomarkers offer improvements over clinical risk scores in predicting high risk patients - at least statistically - but usually at the loss of simplicity and practicality for easy application in everyday clinical practice. Given the various biomarkers can be informed by different aspects of pathophysiology (e.g. inflammation, clotting, collagen turnover) they can nevertheless contribute to a better understanding of underlying disease processes(2). Indeed, many age-related diseases share common modifiable underpinning mechanisms e.g. inflammation, oxidative stress and visceral adiposity.

Discovering biomarkers for antidepressant response:protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Background: Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. Methods: CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. Discussion: From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research. Trial registration: ClinicalTrials.gov identifier NCT01655706. Registered July 27, 2012.

Mass spectrometric analysis of HOCl- and free-radical-induced damage to lipids and proteins

In inflammatory diseases, release of oxidants leads to oxidative damage to biomolecules. HOCl (hypochlorous acid), released by the myeloperoxidase/H2O2/Cl- system, can cause formation of phospholipid chlorohydrins, or alpha-chloro-fatty aldehydes from plasmalogens. It can attack several amino acid residues in proteins, causing post-translational oxidative modifications of proteins, but the formation of 3-chlorotyrosine is one of the most stable markers of HOCl-induced damage. Soft-ionization MS has proved invaluable for detecting the occurrence of oxidative modifications to both phospholipids and proteins, and characterizing the products generated by HOCl-induced attack. For both phospholipids and proteins, the application of advanced mass spectrometric methods such as product or precursor ion scanning and neutral loss analysis can yield information both about the specific nature of the oxidative modification and the biomolecule modified. The ideal is to be able to apply these methods to complex biological or clinical samples, to determine the site-specific modifications of particular cellular components. This is important for understanding disease mechanisms and offers potential for development of novel biomarkers of inflammatory diseases. In the present paper, we review some of the progress that has been made towards this goal.

To cut a long story short:an analysis of formulaic sequences in short written narratives and their potential as markers of authorship

Previous research into formulaic language has focussed on specialised groups of people (e.g. L1 acquisition by infants and adult L2 acquisition) with ordinary adult native speakers of English receiving less attention. Additionally, whilst some features of formulaic language have been used as evidence of authorship (e.g. the Unabomber’s use of you can’t eat your cake and have it too) there has been no systematic investigation into this as a potential marker of authorship. This thesis reports the first full-scale study into the use of formulaic sequences by individual authors. The theory of formulaic language hypothesises that formulaic sequences contained in the mental lexicon are shaped by experience combined with what each individual has found to be communicatively effective. Each author’s repertoire of formulaic sequences should therefore differ. To test this assertion, three automated approaches to the identification of formulaic sequences are tested on a specially constructed corpus containing 100 short narratives. The first approach explores a limited subset of formulaic sequences using recurrence across a series of texts as the criterion for identification. The second approach focuses on a word which frequently occurs as part of formulaic sequences and also investigates alternative non-formulaic realisations of the same semantic content. Finally, a reference list approach is used. Whilst claiming authority for any reference list can be difficult, the proposed method utilises internet examples derived from lists prepared by others, a procedure which, it is argued, is akin to asking large groups of judges to reach consensus about what is formulaic. The empirical evidence supports the notion that formulaic sequences have potential as a marker of authorship since in some cases a Questioned Document was correctly attributed. Although this marker of authorship is not universally applicable, it does promise to become a viable new tool in the forensic linguist’s tool-kit.

Studies of phospholipid oxidation by electrospray mass spectrometry: from analysis in cells to biological effects

The oxidation of lipids is important in many pathological conditions and lipid peroxidation products such as 4-hydroxynonenal (HNE) and other aldehydes are commonly measured as biomarkers of oxidative stress. However, it is often useful to complement this with analysis of the original oxidized phospholipid. Electrospray mass spectrometry (ESMS) provides an informative method for detecting oxidative alterations to phospholipids, and has been used to investigate oxidative damage to cells, and low-density lipoprotein, as well as for the analysis of oxidized phosphatidylcholines present in atherosclerotic plaque material. There is increasing evidence that intact oxidized phospholipids have biological effects; in particular, oxidation products of 1-palmitoyl-2-arachidonoyl-sn-glycerophosphocholine (PAPC) have been found to cause inflammatory responses, which could be potentially important in the progression of atherosclerosis. The effects of chlorohydrin derivatives of lipids have been much less studied, but it is clear that free fatty acid chlorohydrins and phosphatidylcholine chlorohydrins are toxic to cells at concentrations above 10 micromolar, a range comparable to that of HNE and oxidized PAPC. There is some evidence that chlorohydrins have biological effects that may be relevant to atherosclerosis, but further work is needed to elucidate their pro-inflammatory properties, and to understand the mechanisms and balance of biological effects that could result from oxidation of complex mixtures of lipids in a pathophysiological situation.

Oxidiative lipidomics coming of age:advances in analysis of oxidized phospholipids in physiology and pathology

Significance: Oxidized phospholipids are now well-recognized as markers of biological oxidative stress and bioactive molecules with both pro-inflammatory and anti-inflammatory effects. While analytical methods continue to be developed for studies of generic lipid oxidation, mass spectrometry (MS) has underpinned the advances in knowledge of specific oxidized phospholipids by allowing their identification and characterization, and is responsible for the expansion of oxidative lipidomics. Recent Advances: Studies of oxidized phospholipids in biological samples, both from animal models and clinical samples, have been facilitated by the recent improvements in MS, especially targeted routines that depend on the fragmentation pattern of the parent molecular ion and improved resolution and mass accuracy. MS can be used to identify selectively individual compounds or groups of compounds with common features, which greatly improves the sensitivity and specificity of detection. Application of these methods have enabled important advances in understanding the mechanisms of inflammatory diseases such as atherosclerosis, steatohepatitis, leprosy and cystic fibrosis, and offer potential for developing biomarkers of molecular aspects of the diseases. Critical Issues and Future Directions: The future in this field will depend on development of improved MS technologies, such as ion mobility, novel enrichment methods and databases and software for data analysis, owing to the very large amount of data generated in these experiments. Imaging of oxidized phospholipids in tissue MS is an additional exciting direction emerging that can be expected to advance understanding of physiology and disease.

Interfacing low-energy SAW nebulization with liquid chromatography-mass spectrometry for the analysis of biological samples

Soft ionization methods for the introduction of labile biomolecules into a mass spectrometer are of fundamental importance to biomolecular analysis. Previously, electrospray ionization (ESI) and matrix assisted laser desorption-ionization (MALDI) have been the main ionization methods used. Surface acoustic wave nebulization (SAWN) is a new technique that has been demonstrated to deposit less energy into ions upon ion formation and transfer for detection than other methods for sample introduction into a mass spectrometer (MS). Here we report the optimization and use of SAWN as a nebulization technique for the introduction of samples from a low flow of liquid, and the interfacing of SAWN with liquid chromatographic separation (LC) for the analysis of a protein digest. This demonstrates that SAWN can be a viable, low-energy alternative to ESI for the LC-MS analysis of proteomic samples.

Exploring oxidative modifications of tyrosine:an update on mechanisms of formation, advances in analysis and biological consequences

Protein oxidation is increasingly recognised as an important modulator of biochemical pathways controlling both physiological and pathological processes. While much attention has focused on cysteine modifications in reversible redox signalling, there is increasing evidence that other protein residues are oxidised in vivo with impact on cellular homeostasis and redox signalling pathways. A notable example is tyrosine, which can undergo a number of oxidative post-translational modifications to form 3-hydroxy-tyrosine, tyrosine crosslinks, 3-nitrotyrosine and halogenated tyrosine, with different effects on cellular functions. Tyrosine oxidation has been studied extensively in vitro, and this has generated detailed information about the molecular mechanisms that may occur in vivo. An important aspect of studying tyrosine oxidation both in vitro and in biological systems is the ability to monitor the formation of oxidised derivatives, which depends on a variety of analytical techniques. While antibody-dependent techniques such as ELISAs are commonly used, these have limitations, and more specific assays based on spectroscopic or spectrometric techniques are required to provide information on the exact residues modified and the nature of the modification. These approaches have helped understanding of the consequences of tyrosine oxidation in biological systems, especially its effects on cell signalling and cell dysfunction, linking to roles in disease. There is mounting evidence that tyrosine oxidation processes are important in vivo and can contribute to cellular pathology.