Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. Healthy brain function relies upon efficient connectivity between these areas and, in recent years, neuroimaging has been revolutionised by an ability to estimate this connectivity. In this paper we discuss measurement of network connectivity using magnetoencephalography (MEG), a technique capable of imaging electrophysiological brain activity with good (~5mm) spatial resolution and excellent (~1ms) temporal resolution. The rich information content of MEG facilitates many disparate measures of connectivity between spatially separate regions and in this paper we discuss a single metric known as power envelope correlation. We review in detail the methodology required to measure power envelope correlation including i) projection of MEG data into source space, ii) removing confounds introduced by the MEG inverse problem and iii) estimation of connectivity itself. In this way, we aim to provide researchers with a description of the key steps required to assess envelope based functional networks, which are thought to represent an intrinsic mode of coupling in the human brain. We highlight the principal findings of the techniques discussed, and furthermore, we show evidence that this method can probe how the brain forms and dissolves multiple transient networks on a rapid timescale in order to support current processing demand. Overall, power envelope correlation offers a unique and verifiable means to gain novel insights into network coordination and is proving to be of significant value in elucidating the neural dynamics of the human connectome in health and disease

Brain structure and function in Huntington's disease gene carriers far from predicted disease onset

Whilst there are currently no available disease modifying therapies for Huntington’s Disease (HD), recent progress in huntingtin-lowering strategies hold great promise. Initiating therapies early in the disease course will be important and a complete characterisation of the premanifest period will help inform when to initiate disease modifying therapies and the biomarkers that may be useful in such trials. Previous research has characterised the premanifest period up to approximately 15 years from predicted onset, but even at this early stage the disease process is already underway as evidenced by striatal and white matter atrophy, reductions in structural connectivity within brain networks, rising biofluid biomarkers of neuronal dysfunction, elevations in psychiatric symptoms and emerging subtle cognitive impairments. In order to understand how early neurodegeneration can be detected and which measures are most sensitive to the early disease processes, we need to look even earlier in the disease course. This thesis documents the recruitment and analysis of the HD Young Adult Study: a premanifest cohort further from predicted clinical onset than previously studied with an average of 24 years prior to predicted onset. Differences between gene carriers and controls were examined across a range of imaging, cognitive, neuropsychiatric and biofluid measures. The structural and functional brain connectivity in this cohort is then investigated in further detail. By providing a detailed characterisation of brain structure and function in the early premanifest period along with the most sensitive biomarkers at this stage, this work will inform future treatment strategies that may seek to delay the onset of functional impairments in HD

Relativistic DFT calculation and their effect on the accuracy of results

This study explores the significance of density functional theory (DFT) calculations with relativistic effects for two ethylenediaminetetraacetate (edta) type complexes: trans(O5)-[M(eddadp)]- (M = Rh3+, Co3+). Relativistic effects affect the electronic structure of a molecule and, thus, its chemical and spectroscopic properties. With the use of scalar relativistic corrections (SR-ZORA), as implemented in the ADF package, with the B3LYP functional, the TZP basis set and the COSMO solvation model, structural analyses show improved predictions for the geometries of both complexes. In the case of the Rh3+ complex, the differences in metal-ligand bond lengths with and without the relativistic effects were small. In the case of the Co3+ complex, the changes in metal-ligand bond lengths due to the relativistic effects were slightly more pronounced. Compared to experimental values, excitation energies are better when including relativistic corrections, especially for the Rh3+ complex. These results indicate the importance of relativistic DFT calculations for heavy element compounds

Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

The blood-oxygen label dependent (BOLD) signal obtained from functional magnetic resonance images (fMRI) varies significantly among populations. Yet, there is some agreement among researchers over the pace of the blood flow within several brain regions relative to the subject’s age and cognitive ability. Our analysis further suggested that regional coherence among the BOLD fMRI voxels belonging to the individual region of the brain has some correlation with underlying pathology as well as cognitive performance, which can suggest potential biomarkers to the early onset of the disease. To capitalise on this we propose a method, called Regional Optimum Frequency Analysis (ROFA), which is based on finding the optimum synchrony frequency observed at each brain region for each of the resting-state BOLD frequency bands (Slow 5 (0.01–0.027 Hz), Slow 4 (0.027–0.073 Hz) and slow 3 (0.073 to 0.198 Hz)), and the whole frequency band (0.01–0.167 Hz) respectively. The ROFA is carried out on fMRI data of total 310 scans, i.e., 26, 175 and 109 scans from 21 young-healthy (YH), 69 elderly-healthy (EH) and 33 Alzheimer’s disease (AD) patients respectively, where these scans include repeated scans from some subjects acquired at 3 to 6 months intervals. A 10-fold cross-validation procedure evaluated the performance of ROFA for classification between the YH vs EH, YH vs AD and EH vs AD subjects. Based on the confusion-matrix parameters; accuracy, precision, sensitivity and Matthew’s correlation coefficient (MCC), the proposed ROFA classification outperformed the state-of-the-art Group-independent component analysis (Group-ICA), Functional-connectivity, Graph metrics, Eigen-vector centrality, Amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) based methods with more than 94.99% precision and 95.67% sensitivity for different subject groups. The results demonstrate the effectiveness of the proposed ROFA parameters (frequencies) as adequate biomarkers of Alzheimer’s disease

Regional optimum frequency analysis of resting-state fMRI data for early detection of Alzheimer’s disease biomarkers

The blood-oxygen label dependent (BOLD) signal obtained from functional magnetic resonance images (fMRI) varies significantly among populations. Yet, there is some agreement among researchers over the pace of the blood flow within several brain regions relative to the subject’s age and cognitive ability. Our analysis further suggested that regional coherence among the BOLD fMRI voxels belonging to the individual region of the brain has some correlation with underlying pathology as well as cognitive performance, which can suggest potential biomarkers to the early onset of the disease. To capitalise on this we propose a method, called Regional Optimum Frequency Analysis (ROFA), which is based on finding the optimum synchrony frequency observed at each brain region for each of the resting-state BOLD frequency bands (Slow 5 (0.01–0.027 Hz), Slow 4 (0.027–0.073 Hz) and slow 3 (0.073 to 0.198 Hz)), and the whole frequency band (0.01–0.167 Hz) respectively. The ROFA is carried out on fMRI data of total 310 scans, i.e., 26, 175 and 109 scans from 21 young-healthy (YH), 69 elderly-healthy (EH) and 33 Alzheimer’s disease (AD) patients respectively, where these scans include repeated scans from some subjects acquired at 3 to 6 months intervals. A 10-fold cross-validation procedure evaluated the performance of ROFA for classification between the YH vs EH, YH vs AD and EH vs AD subjects. Based on the confusion-matrix parameters; accuracy, precision, sensitivity and Matthew’s correlation coefficient (MCC), the proposed ROFA classification outperformed the state-of-the-art Group-independent component analysis (Group-ICA), Functional-connectivity, Graph metrics, Eigen-vector centrality, Amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) based methods with more than 94.99% precision and 95.67% sensitivity for different subject groups. The results demonstrate the effectiveness of the proposed ROFA parameters (frequencies) as adequate biomarkers of Alzheimer’s disease

Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease

Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression. The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy. This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant. With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis

Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

In recent years, advanced magnetic resonance imaging (MRI) methods including functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) have indicated an increase in the prevalence of neuropsychiatric disorders such as autism spectrum disorder (ASD), effects one out of six children worldwide. Data driven techniques along with medical image analysis techniques, such as computer-assisted diagnosis (CAD), benefiting from deep learning. With the use of artificial intelligence (AI) and IoT-based intelligent approaches, it would be convenient to support autistic children to adopt the new atmospheres. In this paper, we classify and represent learning tasks of the most powerful deep learning network such as convolution neural network (CNN) and transfer learning algorithm on a combination of data from autism brain imaging data exchange (ABIDE I and ABIDE II) datasets. Due to their four-dimensional nature (three spatial dimensions and one temporal dimension), the resting state-fMRI (rs-fMRI) data can be used to develop diagnostic biomarkers for brain dysfunction. ABIDE is a collaboration of global scientists, where ABIDE-I and ABIDE-II consists of 1112 rs-fMRI datasets from 573 typical control (TC) and 539 autism individuals, and 1114 rs-fMRI from 521 autism and 593 typical control individuals respectively, which were collected from 17 different sites. Our proposed optimized version of CNN achieved 81.56% accuracy. This outperforms prior conventional approaches presented only on the ABIDE I datasets

Rationale and design of a longitudinal study of cerebral small vessel diseases, clinical and imaging outcomes in patients presenting with mild ischaemic stroke: Mild Stroke Study 3

Background: Cerebral small vessel disease is a major cause of dementia and stroke, visible on brain magnetic resonance imaging. Recent data suggest that small vessel disease lesions may be dynamic, damage extends into normal-appearing brain and microvascular dysfunctions include abnormal blood–brain barrier leakage, vasoreactivity and pulsatility, but much remains unknown regarding underlying pathophysiology, symptoms, clinical features and risk factors of small vessel disease. Patients and Methods: The Mild Stroke Study 3 is a prospective observational cohort study to identify risk factors for and clinical implications of small vessel disease progression and regression among up to 300 adults with non-disabling stroke. We perform detailed serial clinical, cognitive, lifestyle, physiological, retinal and brain magnetic resonance imaging assessments over one year; we assess cerebrovascular reactivity, blood flow, pulsatility and blood–brain barrier leakage on magnetic resonance imaging at baseline; we follow up to four years by post and phone. The study is registered ISRCTN 12113543. Summary: Factors which influence direction and rate of change of small vessel disease lesions are poorly understood. We investigate the role of small vessel dysfunction using advanced serial neuroimaging in a deeply phenotyped cohort to increase understanding of the natural history of small vessel disease, identify those at highest risk of early disease progression or regression and uncover novel targets for small vessel disease prevention and therapy