Early Detection Of Neurodegenerative Diseases Research Articles

Development and Validation of a Novel Biomarker Panel for Early Detection of Neurodegenerative Diseases

Development and Validation of a Novel Biomarker Panel for Early Detection of Neurodegenerative Diseases

The Promise use of Hybrid PET/MRI

Hybrid PET/MRI is a cutting-edge imaging modality that integrates the molecular imaging capabilities of Positron Emission Tomography (PET) with the superior anatomical and functional imaging strengths of Magnetic Resonance Imaging (MRI). This integration offers significant promise in enhancing diagnostic accuracy, reducing radiation exposure, and providing comprehensive imaging in a single session. Clinically, hybrid PET/MRI has shown particular value in oncology, neurology, and cardiology, offering improved tumor characterization, early detection of neurodegenerative diseases, and detailed cardiac assessments. In the research domain, it provides critical insights into physiological and pathological processes, aiding drug development and the study of disease mechanisms. Despite its high cost, complexity, and limited availability, ongoing technological advancements and the integration of artificial intelligence are expected to expand its clinical applications and improve its practicality. Hybrid PET/MRI thus represents a promising advancement in medical imaging, with the potential to significantly impact personalized and precision medicine.

A Hybrid Protection Scheme for the Gait Analysis in Early Dementia Recognition.

Human activity recognition (HAR) through gait analysis is a very promising research area for early detection of neurodegenerative diseases because gait abnormalities are typical symptoms of some neurodegenerative diseases, such as early dementia. While working with such biometric data, the performance parameters must be considered along with privacy and security issues. In other words, such biometric data should be processed under specific security and privacy requirements. This work proposes an innovative hybrid protection scheme combining a partially homomorphic encryption scheme and a cancelable biometric technique based on random projection to protect gait features, ensuring patient privacy according to ISO/IEC 24745. The proposed hybrid protection scheme has been implemented along a long short-term memory (LSTM) neural network to realize a secure early dementia diagnosis system. The proposed protection scheme is scalable and implementable with any type of neural network because it is independent of the network's architecture. The conducted experiments demonstrate that the proposed protection scheme enables a high trade-off between safety and performance. The accuracy degradation is at most 1.20% compared with the early dementia recognition system without the protection scheme. Moreover, security and computational analyses of the proposed scheme have been conducted and reported.

The Early Detection of Neurodegenerative diseases initiative: emerging insights from the initial implementation of a digital toolkit

AbstractBackgroundThe benefits of the early detection of the neurodegenerative and vascular pathologies which cause dementia are widely acknowledged. These include the opportunity to initiate risk‐reduction strategies, disease‐modifying therapies, and future care planning. However, current pathological biomarkers have several important drawbacks, as they are typically invasive, expensive, impractical, insensitive, or lack real‐world validation. Conversely, the targeted utilization of non‐invasive and inexpensive digital technologies which can be integrated into everyday life raises the possibility of capturing a ‘digital‐fingerprint’ of early disease.MethodThe Early Detection of Neurodegenerative diseases (EDoN) initiative is a meta‐cohort study spearheaded by Alzheimer’s Research UK. EDoN is acquiring high‐dimensional data from a ‘digital toolkit’ comprising a smartwatch (Fitbit Charge 4/5), smartphone software (Mezurio and Longevity), and an electroencephalography headband (Dreem 3). In combination with conventional digital, cognitive and biological markers, these data will inform the development of powerful machine learning models to detect dementia‐causing diseases at the very earliest stages. The initial phase, incorporating four cohorts across three continents, is already underway, and will recruit nearly 900 participants. Cohorts are recruiting healthy individuals, as well as patients with subjective/mild cognitive impairment, or established dementia. Data from version‐1 of the toolkit is being collected over two‐week periods at 3‐monthly intervals for a minimum of 12 months.ResultWe will present early insights from the implementation of the EDoN digital toolkit version‐1. These will include sample characteristics, feasibility and acceptability, and participants’ compliance. PPI data confirmed good acceptability but the potential for inequity due to technical problems and poor digital literacy. Pilot data indicate that six‐month adherence to the EDoN digital tools varies between 82‐89%. The forthcoming AAIC presentation will include important recruitment, toolkit acceptability and analytic updates.ConclusionThe initial implementation of the EDoN digital toolkit suggests that it is feasible and acceptable to collect high‐dimensional digital data over a six‐month period. These ‘digital‐fingerprints’ will form the basis of the initial development of machine learning models to identify dementia‐causing pathologies substantially earlier. The insights gained will also inform the development of version‐2 of the EDoN digital toolkit which will be incorporated into the next meta‐cohort prospective study of around 4,000 participants.

Key considerations when developing and implementing digital technology for early detection of dementia causing diseases: a qualitative study

Abstract Introduction There has been substantial growth in research exploring use of digital health technologies (DHT) to improve global population health. Harnessing the potential of DHT for the early detection of neurodegenerative diseases is one such area.1 According to the National Institute for Health and Care Excellence, DHTs need to be credible for UK Healthcare Professionals (HCPs); HCPs should be involved in the development and implementation process of DHTs to ensure that their needs are met and to promote the successful adoption into healthcare systems.2 Aim To explore HCPs’ perspectives on key considerations of developing and implementing DHT for the early detection of dementia causing diseases, such as Alzheimer’s, within the National Health Service (NHS). Methods Ethical approval was granted from Newcastle University Ethics Committee. HCPs with patient-facing roles within primary or secondary care NHS settings were recruited through various online networks alongside a snowball sampling approach. Participants took part in a semi-structured audio-recorded interview exploring their experience of current diagnostic practices, opinions on the early detection of dementia causing diseases and the use of DHT to aid this approach, and the challenges of implementing such interventions into healthcare. The framework approach was used to thematically analyse the verbatim transcripts to identify core concepts and themes emerging within the interviews. A list of core concepts and themes was applied systematically to the whole data set using QSR N-Vivo (Version 1.6.1). The research team then used the ‘constant comparison’ technique to move backwards and forwards between these data and evolve explanations until a fit was made. Results Eighteen interviews were conducted with 11 primary and seven secondary care HCPs. Our analysis highlighted three core concepts, centring around considerations for healthcare service users, HCPs, and the DHT when developing and implementing DHT for the early detection of dementia causing diseases. HCPs recognised the potential benefits of providing an early detection of dementia causing diseases to healthcare services users, but only if a suitable intervention was available to allow individuals to potentially reduce their risk. Additionally, HCPs were concerned about the impact on health inequity, such as accessibility of this type of service to individuals who have disabilities or could be possibly digitally excluded. Many HCPs were also concerned about the impact on the healthcare system, with fears that it could overburden the NHS if sufficient resources and support were not provided for its implementation. In regard to the DHT, HCPs were positive about the possibility of a DHT to remotely capture real time data and felt it would be important to capture different modalities to help differentiate between dementia causing diseases. Discussion/Conclusion This study highlights the need to design a DHT for the early detection of dementia causing diseases that is accessible to users, to promote health equity. This study also highlights potential barriers for researchers when implementing a DHT into the NHS, although it cannot be assumed that these are the only barriers that may be encountered; developers will need to consider this so that a future DHT meet HCPs’ needs.

Machine Learning for Early Detection of Neurodegenerative Diseases

Machine Learning for Early Detection of Neurodegenerative Diseases

Detection of neuron-derived cfDNA in blood plasma: a new diagnostic approach for neurodegenerative conditions.

Neurodegenerative diseases, such as Alzheimer's disease (AD), pose significant challenges in early diagnosis, leading to irreversible brain damage and cognitive decline. In this study, we present a novel diagnostic approach that utilizes whole molecule analysis of neuron-derived cell-free DNA (cfDNA) as a biomarker for early detection of neurodegenerative diseases. By analyzing Differential Methylation Regions (DMRs) between purified cortical neurons and blood plasma samples, we identified robust biomarkers that accurately distinguish between neuronal and non-neuronal cfDNA. The use of cfDNA offers the advantage of convenient and minimally invasive sample collection compared to traditional cerebrospinal fluid or tissue biopsies, making this approach more accessible and patient friendly. Targeted sequencing at the identified DMR locus demonstrated that a conservative cutoff of 5% of neuron-derived cfDNA in blood plasma accurately identifies 100% of patients diagnosed with AD, showing promising potential for early disease detection. Additionally, this method effectively differentiated between patients with mild cognitive impairment (MCI) who later progressed to AD and those who did not, highlighting its prognostic capabilities. Importantly, the differentiation between patients with neurodegenerative diseases and healthy controls demonstrated the specificity of our approach. Furthermore, this cfDNA-based diagnostic strategy outperforms recently developed protein-based assays, which often lack accuracy and convenience. While our current approach focused on a limited set of loci, future research should explore the development of a more comprehensive model incorporating multiple loci to increase diagnostic accuracy further. Although certain limitations, such as technical variance associated with PCR amplification and bisulfite conversion, need to be addressed, this study emphasizes the potential of cfDNA analysis as a valuable tool for pre-symptomatic detection and monitoring of neurodegenerative diseases. With further development and validation, this innovative diagnostic strategy has the potential to significantly impact the field of neurodegenerative disease research and patient care, offering a promising avenue for early intervention and personalized therapeutic approaches.

Artificial intelligence-powered analysis of medical images for early detection of neurodegenerative diseases

Neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's offer serious health issues to people all over the world, due to their progressive nature and lack of effective therapies. In order to improve patient outcomes and allow for prompt action to limit the progression of the disease, early identification is essential. With a focus on deep learning methods, this study investigates the use of AI-powered analysis of medical images for the early detection of neurodegenerative disorders. The use of several medical imaging modalities, such as PET, CT, and MRI, in identifying disease biomarkers at an early stage is investigated. The usefulness of deep learning techniques to automate feature extraction, categorize illness states, and track disease progression is highlighted. These techniques include convolutional neural networks [CNNs], recurrent neural networks [RNNs], and generative adversarial networks [GANs]. The study also discusses the difficulties in using AI implementation, including data quality, image variability, and the interpretability of AI models. Furthermore, the study explores possible regulatory and ethical considerations in clinical adoption. It also examines AI's growing role in clinical settings and its ability to work with personalized medicine which present promising opportunities for improving the diagnosis and management neurodegenerative disease. The final section of this paper outlines important future directions for increasing the use of AI in clinical care, including multi-modal fusion and transfer learning.

The Early Detection of Neurodegenerative diseases initiative: an international and multidisciplinary effort for transforming the early detection of dementia‐causing diseases

AbstractBackgroundAround 50 million people have dementia worldwide, with nearly 10 million new cases every year. Diagnosis is complex and often relies on expensive and invasive measures, with most patients accessing medical support when they already experience symptoms.MethodThe Early Detection of Neurodegenerative diseases (EDoN) initiative, spearheaded by Alzheimer’s Research UK, brings together over 60 experts from 49 universities, research projects, patient cohorts and technology providers to create machine learning models to detect the earliest stages of dementia‐causing diseases. EDoN has reviewed behavioural and physiological modalities with the strongest association with pre‐clinical disease.ResultOver 140 modalities were identified from the review and were shortlisted to create the version 1 digital toolkit. This first version includes Mezurio and Longevity smartphone apps, a Fitbit charge 4 activity tracker and Dreem 3 sleep headband. This Toolkit was further refined through patient and public involvement studies and collects 26 measures related to 7 aspects of behaviour and physiology (cognition, neural activity, physical activity, heart rate, fine motor movement, sleep, language and speech). The Toolkit is now being used to collect digital data in four international cohorts (Boston University Alzheimer’s Disease Research Center ‐ BU ADRC; The predictors of COgnitive DECline in attenders of memory clinic using digital devices ‐ CODEC‐2; Western Australia Memory Study ‐ WAMS; Healthy Brain Aging ‐ HBA), alongside prospective and retrospective clinical data, to inform the development of machine learning models.ConclusionEDoN will build models with digital markers, validating them against other biomarkers to predict dementia subtypes and individualised disease trajectories. Based on the outputs of the initial models, EDoN will go through a series of iterations of cohort engagement, modality and tool refinement, and data collection. Workstreams are underway to inform data security, privacy, ethics and open policy research, as well as considering the integration of the final EDoN Toolkit into healthcare systems globally. EDoN aims to deliver a cost‐effective, low burden and population‐wide method for early detection of dementia‐causing diseases that will benefit the public, patients, carers, researchers and clinicians, as well as the broader healthcare system and the delivery of new therapies.

Current Understanding of Verbal Fluency in Alzheimer's Disease: Evidence to Date.

Since their development, verbal fluency tests (VFTs) have been used extensively throughout research and in clinical settings to assess a variety of cognitive functions in diverse populations. In Alzheimer's disease (AD), these tasks have proven particularly valuable in identifying the earliest forms of cognitive decline in semantic processing and have been shown to relate specifically to brain regions associated with the initial stages of pathological change. In recent years, researchers have developed more nuanced techniques to evaluate verbal fluency performance, extracting a wide range of cognitive metrics from these simple neuropsychological tests. Such novel techniques allow for a more detailed exploration of the cognitive processes underlying successful task performance beyond the raw test score. The versatility of VFTs and the richness of data they may provide, in light of their low cost and speed of administration, therefore, highlight their potential value both in future research as outcome measures for clinical trials and in a clinical setting as a screening measure for early detection of neurodegenerative diseases.

Towards early detection of neurodegenerative diseases: A gut feeling.

The gastrointestinal tract communicates with the nervous system through a bidirectional network of signaling pathways called the gut-brain axis, which consists of multiple connections, including the enteric nervous system, the vagus nerve, the immune system, endocrine signals, the microbiota, and its metabolites. Alteration of communications in the gut-brain axis is emerging as an overlooked cause of neuroinflammation. Neuroinflammation is a common feature of the pathogenic mechanisms involved in various neurodegenerative diseases (NDs) that are incurable and debilitating conditions resulting in progressive degeneration and death of neurons, such as in Alzheimer and Parkinson diseases. NDs are a leading cause of global death and disability, and the incidences are expected to increase in the following decades if prevention strategies and successful treatment remain elusive. To date, the etiology of NDs is unclear due to the complexity of the mechanisms of diseases involving genetic and environmental factors, including diet and microbiota. Emerging evidence suggests that changes in diet, alteration of the microbiota, and deregulation of metabolism in the intestinal epithelium influence the inflammatory status of the neurons linked to disease insurgence and progression. This review will describe the leading players of the so-called diet-microbiota-gut-brain (DMGB) axis in the context of NDs. We will report recent findings from studies in model organisms such as rodents and fruit flies that support the role of diets, commensals, and intestinal epithelial functions as an overlooked primary regulator of brain health. We will finish discussing the pivotal role of metabolisms of cellular organelles such as mitochondria and peroxisomes in maintaining the DMGB axis and how alteration of the latter can be used as early disease makers and novel therapeutic targets.

An Overview of the Latest Developments in the Electrochemical Aptasensing of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are becoming a major global health problem. They constitute an incurable diverse group of disorders characterized by the progressive degeneration of the structure and function of the central or peripheral nervous system. There is an enormous unmet demand worldwide for methods for the early detection of ND biomarkers. The complexity of the molecular mechanisms underlying neuronal degeneration as well as the inhomogeneity of the patient population pose a great challenge for the development of early diagnostic tools. Various analytical technologies have been developed to meet this challenge. Among the various approaches reported so far, biosensors are powerful analytical implements that have been applied to detect biomarkers of NDs. Over the past decade, electrochemical aptasensors have been at the forefront of this development not only thanks to their low cost and simple design but also due to advances in nanomaterials modifying the surface of the transducers involved. The design of electrochemical aptasensors for the detection of ND biomarkers such as α-synuclein, amyloid β peptide, tau protein and human cellular prion protein were summarized and compared. Innovative strategies for increasing their sensitivity and selectivity were also pointed out. Undoubtedly, there is still a need for low-cost, fast and easy-to-use systems for the early detection of NDs.

The relationships between mesopic light sensitivity and macular inner and outer retinal layer thicknesses in older healthy adults

AbstractPurpose: Structural and functional markers are important for the early detection of neurodegenerative diseases of the eye. This study investigates the relationships between macular layer thicknesses and mesopic light sensitivity (MLS) in older healthy eyes.Methods: Measurements were made in 38 eyes of 38 healthy individuals of mean age 67 ± 5.6 years (range 60–80 years) and good visual acuity. MLS was measured with macular integrity assessment (MAIA) microperimetry at the central 10° diameter circle. MLSs at the central subfield (1° radius) and at four regions (temporal, nasal, superior, inferior) of the parafoveal ring (3°–5° radius of eccentricity) were registered. Total, inner, and outer retinal layer (IRL and ORL) thicknesses were measured in the macular region through spectral‐domain optical coherence tomography (SD‐OCT). Relationships between MLS and the retinal thickness in the corresponding macular region were examined using forward stepwise multiple linear regression analyses.Results: ORL and IRL thickness in the inferior parafoveal ring were significant contributing factors to the inferior MLS (ORL: p < 0.0001; IRL: p = 0.0003; R2 model = 51%). Increased ORL thickness and decreased IRL thickness correlated with worse MLS. The total retinal thickness, age, sex, refractive error, axial length, retinal illuminance or visual acuity variables were not found to be significant predictors of MLS.Conclusions: ORL and IRL thicknesses predict inferior parafoveal MLS in healthy older eyes. The measurement of MLS in combination with macular layer thicknesses may be useful for detecting loss of sensitivity caused by early degenerative diseases of the retina.

Extracellular vesicles – upcoming biomarkers in Parkinson's disease's biofluids

The search of a biomarker for an early detection of neurodegenerative diseases is one of the biggest challenges of our times. The second most common neurodegenerative disorder Parkinson's disease (PD) is characterized by misfolded alpha-synuclein (a-syn) aggregates within the central nervous system (CNS). Currently, definitive PD diagnosis still requires post-mortem brain examination. As a result, the misdiagnosis of PD based only on clinical symptoms and delayed diagnosis in advanced stages cannot be excluded. Since a-syn aggregates abnormally, it might be an interesting candidate for a biomarker for PD. Lately, extracellular vesicles (EVs) have emerged as potential biomarker in biofluids since accumulating evidence suggests that their content reflects the pathophysiological alterations occurring in their host cells. Interestingly, EVs can cross the blood-brain barrier (BBB) and thus carry information from the CNS to the periphery and vice versa. EVs seem to play a role in other neurodegenerative disorders such as Alzheimer's and prion disease, where they have also shown certain diagnostic potential. For instance, EV isolation protocols have been described to isolate brain-derived EVs from blood samples, increasing their biomarker potential in neurodegenerative disorders. The results published for PD to date are promising: pathology-associated a-syn forms are found in blood-derived EVs, although the underlying mechanisms of formation and release of a-syn-loaded EVs remain unknown. Interestingly, a-syn level correlate with the disease stage, which underlines the importance of neuronal EVs in disease monitoring. Further research extends to other biofluids, like urine, saliva, and cerebrospinal fluid, where EVs can also be found, opening multiple opportunities for more reliable PD diagnosis.

(Digital Presentation) Bioelectrochemistry of Neuronal Tau Protein

Tau is a neuronal protein and one of the biomarkers of neurodegenerative diseases, such as Alzheimer’s disease. Tau protein undergoes post-translational modifications, aggregation, and is a viable drug target. In addition, this protein is a vital biomarker in biological fluids towards early detection of neurodegenerative diseases. We reported on using electrochemical impedance spectroscopy and cyclic voltammetry for detection of protein-protein interactions (1), protein-ligand interactions (2), enzymatic catalysis and enzyme inhibition (3) while focusing on tau protein and its biochemistry. For example, tau-tau, tau-ferritin, tau-transferrin protein interactions were monitored, and protein kinase-catalyzed phosphorylation of tau protein was detected. The phosphorylation inhibitors, such as antibodies, were screened for their efficacy. Bioelectrochemical methodologies were used to gain insight into various facets of protein biochemistry, and represent promising tools in neuroscience research. References A) Carlin, N., Martic-Milne, S. (2018). Anti-tau antibodies based electrochemical sensor for detection of tau protein. J. Electrochem. Soc.165: G3018-G3025. B) Jahshan, A., Esteves, J.O.V., Martic-Milne, S. (2016). Evaluation of ferritin and transferrin binding to tau protein. J. Inorg. Biochem. 162: 127-134. C) Esteves, J.O.V., Trzeciakiewicz, H., Loeffler, D.A., Martic, S. (2015). Effects of tau domain-specific antibodies and intravenous immunoglobulin on tau aggregation and aggregate degradation. Biochemistry. 54: 15-18.Trzeciakiewicz, H., Esteves, J.O.V., Carlin, N., Martic, S. (2015). Electrochemistry of heparin binding to tau protein on Au surfaces. Electrochim. Acta. 162: 24-30,Esteves, J.O.V., Martic-Milne, S. (2016). Electrochemical detection of anti-tau antibodies binding to tau protein and inhibition of GSK-3-β-catalyzed phosphorylation. Anal. Biochem. 496: 55-62

Effects of healthy aging on internal brainstem structure

AbstractBackgroundThe brainstem is a complex structure and is often affected in the earliest stages of a neurodegenerative diseases. However, internal brainstem structures are not delineated on standard MR sequences complicating the differentiation between healthy aging and an incipient neurodegenerative process in‐vivo. The overall goal of this project was to characterize age‐related atrophy of different brainstem structures using a modification of a previously published multi‐contrast brainstem segmentation approach (1) using standard MR sequences.MethodThe T1 and T2‐weighted images from 677 subjects (age range: 36‐100) of the Lifespan Human Connectome Project Aging were selected. Each subject’s T1w and T2w were co‐registered, re‐scaled and a T1/T2ratio image calculated. After extracting the brainstem/thalamus, a k‐mean cluster analysis was used to identify 4 different brainstem tissue clusters based on the intensities from the 3 images. DARTEL was used to generate a population template from the brainstem tissue‐maps of 100 randomly selected subjects covering the whole age range. Each subjects tissue‐maps were warped onto this template and the Jacobian determinants calculated (Figure 1). The subjects were then divided into 6 groups (4th, 5th, 6th, 7th, 8th, 9th decade). SPM with threshold‐free cluster enhancement (p<0.005, FWE) was used for group comparisons. 58 gray and white matter brainstem regions‐of‐interest were used to extract voxels surpassing this statistical threshold to further characterize the age‐related volume loss.ResultSee Figure 2 for abbreviations and detailed findings. Mesencephalic centers such as NR, CS, CI and PTPT showed age‐related volume loss starting at the age of 50. SN, VTA, and pontine centers such as NRPO, PB, NRPT, NRPC and medullary regions such as OI, NTS, VML, NRMOC and other major white matter tracts became atrophied starting at 60 and after 70 all 58 brainstem centers except for the DR and SC were atrophied.ConclusionAge‐related brainstem atrophy starts in the mesencephalon and progresses to the pons and medulla and also affects white matter tracts. The pattern of age‐related atrophy reflects the timing of typical age‐related brainstem dysfunctions, e.g., age‐related hearing loss. This method might be useful for the early detection of neurodegenerative diseases starting in the brainstem. Reference: Hum Brain Mapp.2020;41:2173‐2186.

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time-Frequency Spectrogram and Deep Learning Neural Network Features.

A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.

T2 heterogeneity as an in vivo marker of microstructural integrity in medial temporal lobe subfields in ageing and mild cognitive impairment

A better understanding of early brain changes that precede loss of independence in diseases like Alzheimer's disease (AD) is critical for development of disease-modifying therapies. Quantitative MRI, such as T2 relaxometry, can identify microstructural changes relevant to early stages of pathology. Recent evidence suggests heterogeneity of T2 may be a more informative MRI measure of early pathology than absolute T2. Here we test whether T2 markers of brain integrity precede the volume changes we know are present in established AD and whether such changes are most marked in medial temporal lobe (MTL) subfields known to be most affected early in AD. We show that T2 heterogeneity was greater in people with mild cognitive impairment (MCI; n = 49) compared to healthy older controls (n = 99) in all MTL subfields, but this increase was greatest in MTL cortices, and smallest in dentate gyrus. This reflects the spatio-temporal progression of neurodegeneration in AD. T2 heterogeneity in CA1-3 and entorhinal cortex and volume of entorhinal cortex showed some ability to predict cognitive decline, where absolute T2 could not, however further studies are required to verify this result. Increases in T2 heterogeneity in MTL cortices may reflect localised pathological change and may present as one of the earliest detectible brain changes prior to atrophy. Finally, we describe a mechanism by which memory, as measured by accuracy and reaction time on a paired associate learning task, deteriorates with age. Age-related memory deficits were explained in part by lower subfield volumes, which in turn were directly associated with greater T2 heterogeneity. We propose that tissue with high T2 heterogeneity represents extant tissue at risk of permanent damage but with the potential for therapeutic rescue. This has implications for early detection of neurodegenerative diseases and the study of brain-behaviour relationships.

Bioelectrochemistry of Neuronal Protein Tauoral

Tau is a neuronal protein and one of the biomarkers of neurodegenerative diseases, such as Alzheimer’s disease. Tau protein undergoes post-translational modifications, aggregation, and is a viable drug target. In addition, this protein is a vital biomarker in biological fluids towards early detection of neurodegenerative diseases. We reported on using electrochemical impedance spectroscopy and cyclic voltammetry for detection of protein-protein interactions (1), protein-ligand interactions (2), enzymatic catalysis and enzyme inhibition (3) while focusing on tau protein and its biochemistry. For example, tau-tau, tau-ferritin, tau-transferring protein interactions were monitored, and protein kinase-catalyzed phosphorylation of tau protein was detected. The phosphorylation inhibitors, such as antibodies, were screened for their efficacies. New research directions in neuroelectrochemistry of tau protein alongside other neuronal biomarkers, such as Amyloid beta and alpha synuclein, will also be described. References A) Carlin, N., Martic-Milne, S. (2018). Anti-tau antibodies based electrochemical sensor for detection of tau protein. J. Electrochem. Soc.165: G3018-G3025. B) Jahshan, A., Esteves, J.O.V., Martic-Milne, S. (2016). Evaluation of ferritin and transferrin binding to tau protein. J. Inorg. Biochem. 162: 127-134. C) Esteves, J.O.V., Trzeciakiewicz, H., Loeffler, D.A., Martic, S. (2015). Effects of tau domain-specific antibodies and intravenous immunoglobulin on tau aggregation and aggregate degradation. Biochemistry. 54: 15-18.Trzeciakiewicz, H., Esteves, J.O.V., Carlin, N., Martic, S. (2015). Electrochemistry of heparin binding to tau protein on Au surfaces. Electrochim. Acta. 162: 24-30,Esteves, J.O.V., Martic-Milne, S. (2016). Electrochemical detection of anti-tau antibodies binding to tau protein and inhibition of GSK-3-β-catalyzed phosphorylation. Anal. Biochem. 496: 55-62

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamics and motor activity. The classification of gait data using machine learning techniques can assist the physicians for early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent.
 Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree).
 Methodology: The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection - subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE.
 Results: In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected features were used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features.
 Conclusion: The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover, the selected features along with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible.