Cortical topological network changes following optic neuritis

OBJECTIVE: To differentiate between visual cortical network topology changes following optic neuritis (ON) stemming from different inflammatory disease types, we used mathematical graph theory-based tools to analyze functional imaging data. METHODS: Sixty-two patients were recruited into this cross-sectional study, 23 of whom had neuromyelitis optica spectrum disorder (NMOSD) with ON, 18 with clinically isolated syndrome (CIS)-ON, and 21 with other CIS episodes. Twenty-six healthy controls (HCs) were also recruited. All participants underwent resting-state functional MRI. Visual networks were defined using 50 visual regions of interest. Analysis included graph theory metrics, including degree, density, modularity, and local and global efficiency. RESULTS: Visual network density shows decreased connectivity in all patient groups compared with controls. A higher degree of connections is seen in both ON groups (CIS and NMOSD) compared with the the non-ON group. This pattern is most pronounced in dorsal-lateral regions. Information transfer efficiency and modularity were reduced in both CIS groups, but not in the NMOSD group, compared with the HC group. CONCLUSIONS: Visual network density appears affected by the neurologic deficit sustained (ON), and connectivity changes are more evident in dorsal-lateral regions. Efficiency and modularity appear to be associated with the specific disease type (CIS vs NMOSD). Thus, topological cortical changes in the visual system are associated with the type of neurologic deficit within the limits set on them by the underlying pathophysiology. We suggest that cortical patterns of activity should be considered in the outcome of the patients despite the localized nature of ON

Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns

In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

TEXTURAL CLASSIFICATION OF MULTIPLE SCLEROSISLESIONS IN MULTIMODAL MRI VOLUMES

Background and objectives:Multiple Sclerosis is a common relapsing demyelinating diseasecausing the significant degradation of cognitive and motor skills and contributes towards areduced life expectancy of 5 to 10 years. The identification of Multiple Sclerosis Lesionsat early stages of a patient’s life can play a significant role in the diagnosis, treatment andprognosis for that individual. In recent years the process of disease detection has been aidedthrough the implementation of radiomic pipelines for texture extraction and classificationutilising Computer Vision and Machine Learning techniques. Eight Multiple Sclerosis Patient datasets have been supplied, each containing one standardclinical T2 MRI sequence and four diffusion-weighted sequences (T2, FA, ADC, AD, RD).This work proposes a Multimodal Multiple Sclerosis Lesion segmentation methodology util-ising supervised texture analysis, feature selection and classification. Three Machine Learningmodels were applied to Multimodal MRI data and tested using unseen patient datasets to eval-uate the classification performance of various extracted features, feature selection algorithmsand classifiers to MRI volumes uncommonly applied to MS Lesion detection. Method: First Order Statistics, Haralick Texture Features, Gray-Level Run-Lengths, His-togram of Oriented Gradients and Local Binary Patterns were extracted from MRI volumeswhich were minimally pre-processed using a skull stripping and background removal algorithm.mRMR and LASSO feature selection algorithms were applied to identify a subset of rankingsfor use in Machine Learning using Support Vector Machine, Random Forests and ExtremeLearning Machine classification. Results: ELM achieved a top slice classification accuracy of 85% while SVM achieved 79%and RF 78%. It was found that combining information from all MRI sequences increased theclassification performance when analysing unseen T2 scans in almost all cases. LASSO andmRMR feature selection methods failed to increase accuracy, and the highest-scoring groupof features were Haralick Texture Features, derived from Grey-Level Co-occurrence matrices

Brain network analyses in clinical neuroscience

Network analyses are now considered fundamental for understanding brain function. Nonetheless neuroimaging characterisations of connectivity are just emerging in clinical neuroscience. Here, we briefly outline the concepts underlying structural, functional and effective connectivity, and discuss some cutting-edge approaches to the quantitative assessment of brain architecture and dynamics. As illustrated by recent evidence, comprehensive and integrative network analyses offer the potential for refining pathophysiological concepts and therapeutic strategies in neurological and psychiatric conditions across the lifespan

Understanding cognitive dysfunction in secondary progressive multiple sclerosis using functional and structural MRI

This thesis concerns a 2 year follow-up study of people with secondary progressive multiple sclerosis (SPMS). I investigate: (1) cognitive performance of SPMS and changes over time, (2) the classification of cognitive impairment and predictors of this, (3) mechanisms underlying the SPMS phenotype with and without cognitive impairment using functional and structural MRI. The literature has highlighted the input of executive dysfunction in the cognitive profile of SPMS over and above that seen in other multiple sclerosis (MS) phenotypes. I looked at cognitive performance in SPMS, and predictors of this in this pure SPMS cohort study. I found that being employed, having higher IQ, more premorbid leisure interests, and higher qualifications mitigate against negative cognitive outcomes in SPMS. Additionally, anxiety, even when not reaching clinically diagnostic levels, impacts on tests of information processing speed, verbal working memory, and executive function in SPMS. The symbol digit modality test (SDMT) at baseline is predicted by MS lower limb disability outcome measures; the Expanded Disability Status Scale (EDSS) and timed 25 foot walk (T25FW) which emphasises the role of the SDMT as an adjunctive measure of clinical disability prediction in studies. I show that decline on the SDMT at follow-up is purely predicted by cognitive measures of information processing speed and working memory at either timepoint, supporting, and furthering, the evidence for the SDMT as a sentinel assessment of cognitive performance in SPMS. These findings inform future longitudinal cognitive studies in SPMS, particularly with regards to the importance of tests of executive function, and important associations with clinical outcomes in a highly disabled cohort. I also considered the threshold for classifying cognitive impairment, and its implications. There is marked heterogeneity in these thresholds due to the lack of current consensus on a diagnostic criteria. Using a higher threshold for cognitive impairment in my studies strengthened the associations with clinically relevant outcomes. Additionally, unemployment showed the greatest association with cognitive impairment regardless of criteria used. I found that assessments of information processing speed, verbal memory, and executive function had the greatest input to cognitive impairment in SPMS. These findings indicate the importance of these cognitive domains and demographic factors when evaluating cognitive status in SPMS. These results will guide the international consensus on how best to measure cognitive impairment in SPMS, and in MS more broadly. Posterior and deep resting state networks (RSNs) have been shown to be altered in resting state functional MRI (rs-fMRI) studies of progressive MS phenotypes. I confirm this using functional connectivity (FC) and highlight that this is mainly in terms of cognitive RSNs in SPMS versus healthy controls using a global rs-fMRI analysis technique. Additionally, with cognitive impairment in SPMS, I show that there are key attentional RSN FC reductions. I further highlight the importance of more stringent classification criteria of cognitive impairment to allow for more detailed evaluation of dynamic FC changes, that are missed when using a lenient criteria. Over time, the development of cognitive impairment in SPMS from a preserved state appears to relate to reduced FC in working memory, posterior default mode (DMN) and visual RSNs, and increased FC in the executive control, and more anterior DMN hubs at baseline. Therefore, alterations in posterior cognitive and executive RSNs may inform cognitive status in SPMS. These results provide, to my knowledge, the first longitudinal rs-fMRI study of cognitive status in SPMS. Regional grey matter atrophy has been shown to be greater in SPMS then in other MS phenotypes. I found that SPMS cognitive impairment is associated with grey matter volume, cortical grey matter volume, and deep grey matter and regional deep grey matter atrophy. I also highlighted that proportionally, within the cerebellum, there are greater percentage changes in FC versus volume in those with SPMS with cognitive impairment versus in SPMS overall. These findings therefore show the importance of deeper grey matter atrophy in SPMS underlying cognitive impairment, and indicate the need for a longitudinal study of rs-fMRI and regional grey matter MRI metrics to understand the interplay of underlying mechanisms in more detail

Human Activity Recognition in Healthcare: Challenges, Approaches and Applications

Human activity recognition (HAR) technology that analyzes data acquired from various types of sensing devices, including wearable sensors and vision sensors, is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human activities can be used to provide remote healthcare solutions by identifying particular movements such as falls, gait, and breathing disorders. HAR healthcare system can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. Thanks to the development of machine learning technology, many machine learning methods have been employed in human activity recognition systems in healthcare. However, this field still faces many technical challenges. Some challenges are shared with other pattern recognition fields, such as a limited number of labeled data, while other challenges are unique to sensor-based activity recognition in healthcare and require dedicated methods for real-life healthcare applications, such as data noise of sensor factors in the data collection process. In this dissertation, we start with the challenges of healthcare-oriented HAR systems and summarize the challenge-related machine learning approaches. To overview HAR healthcare applications with wearable sensors, we cover essential components of designing HAR healthcare systems, including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. Next, we present a new healthcare application of HAR, that is, Early Mobility Activity (EMA) recognition for Intensive Care Unit (ICU) patients, to illustrate the system design of HAR applications for healthcare. We identify insensitive wearable sensor orientation features and propose a segment voting process to improve the model accuracy and stability. We further apply the state-of-the-art vision sensor-based HAR approaches in healthcare. We present a healthcare system (BWCNN) to use eye blinks to communicate with the outside world for Amyotrophic Lateral Sclerosis (ALS) patients. The system uses a Convolutional Neural Network (CNN) to predict the eyes' state, which is used to find the blinking pattern. Then, we propose a MASTAF that can quickly learn from a few examples efficiently to solve the limited number of video samples in real-life HAR applications, a common challenge shared with computer vision. MASTAF takes input from a general video spatial and temporal representation,e.g., using 2D CNN, 3D CNN, and video Transformer. Then, to make the most of such representations, we use self- and cross-attention models to highlight the critical spatio-temporal region to increase the inter-class distance and decrease the intra-class distance. Last, MASTAF applies a lightweight fusion network and the nearest neighbor classifier to classify each query video. We demonstrate that MASTAF improves the state-of-the-art performance on three few-shot HAR video benchmarks. Last, we present Multimodal Masked Autoencoders-Based One-Shot Learning (Mu-MAE), which represents a significant advancement in the field of HAR using multimodal sensors. Addressing the challenges posed by labor-intensive data collection and reliance on external pretrained models, MU-MAE introduces a synchronized masking strategy tailored for wearable sensors, coupled with a multimodal masked autoencoder architecture. This innovative approach compels the networks to capture more meaningful spatiotemporal features, facilitating effective self-supervised pretraining without the need for additional data. Furthermore, MU-MAE leverages the representations extracted from multimodal masked autoencoders to enhance cross-attention fusion, which highlights critical spatiotemporal features across different modalities while emphasizing differences between activity classes. Through comprehensive evaluations on MMAct one-shot classification datasets, MU-MAE demonstrates superior performance, achieving up to an 80.17% accuracy for five-way one-shot multimodal classification, thus establishing itself as a state-of-the-art solution in HAR for healthcare applications

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

Multi-modal and multi-dimensional biomedical image data analysis using deep learning

There is a growing need for the development of computational methods and tools for automated, objective, and quantitative analysis of biomedical signal and image data to facilitate disease and treatment monitoring, early diagnosis, and scientific discovery. Recent advances in artificial intelligence and machine learning, particularly in deep learning, have revolutionized computer vision and image analysis for many application areas. While processing of non-biomedical signal, image, and video data using deep learning methods has been very successful, high-stakes biomedical applications present unique challenges such as different image modalities, limited training data, need for explainability and interpretability etc. that need to be addressed. In this dissertation, we developed novel, explainable, and attention-based deep learning frameworks for objective, automated, and quantitative analysis of biomedical signal, image, and video data. The proposed solutions involve multi-scale signal analysis for oraldiadochokinesis studies; ensemble of deep learning cascades using global soft attention mechanisms for segmentation of meningeal vascular networks in confocal microscopy; spatial attention and spatio-temporal data fusion for detection of rare and short-term video events in laryngeal endoscopy videos; and a novel discrete Fourier transform driven class activation map for explainable-AI and weakly-supervised object localization and segmentation for detailed vocal fold motion analysis using laryngeal endoscopy videos. Experiments conducted on the proposed methods showed robust and promising results towards automated, objective, and quantitative analysis of biomedical data, that is of great value for potential early diagnosis and effective disease progress or treatment monitoring.Includes bibliographical references

Fluid Biomarkers in the Frontotemporal Dementia Spectrum

Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disorder, comprising a spectrum of subtypes that are different on the clinical, genetic and pathological level. Clinically, symptoms typically present before the age of 65 and include behavioural and/or language disturbances, yet also motor problems frequently occur. FTD can be heritable, and 10-20% of the patients have an autosomal dominant form, which is most frequently caused by a mutation in granulin (GRN, also known as progranulin), in microtubule-associated protein tau (MAPT), or a repeat expansion in chromosome 9 open reading frame 72 (C9orf72). Pathological examination shows frontotemporal lobar degeneration (FTLD) with inclusions of either TAR DNA-binding protein 43 (FTLD-TDP), tau (FTLD-tau), or FET (fused in sarcoma, Ewing’s sarcoma and TAT-binding protein-associated factor 15). Currently, major advances are being made to develop therapeutic interventions for FTD. However, the heterogeneity of this disorder hampers the diagnostic process, tracking of disease progression, and the appropriate selection of patients for clinical trials. Reliable biomarkers are therefore critically n