Graph neural networks in EEG spike detection.

Objective: This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital.

Methods: Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN.

A transfer learning approach based on gradient boosting machine for diagnosis of Alzheimer's disease.

Early detection of Alzheimer's disease (AD) during the Mild Cognitive Impairment (MCI) stage could enable effective intervention to slow down disease progression. Computer-aided diagnosis of AD relies on a sufficient amount of biomarker data. When this requirement is not fulfilled, transfer learning can be used to transfer knowledge from a source domain with more amount of labeled data than available in the desired target domain.

A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer's Disease.

With the advances in machine learning for the diagnosis of Alzheimer's disease (AD), most studies have focused on either identifying the subject's status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks.

A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and non-converter Alzheimer's disease subjects with consideration for their amyloid beta status.

Introduction: This study aims to determine whether newly introduced biomarkers Visinin-like protein-1 (VILIP-1), chitinase-3-like protein 1 (YKL-40), synaptosomal-associated protein 25 (SNAP-25), and neurogranin (NG) in cerebrospinal fluid are useful in evaluating the asymptomatic and early symptomatic stages of Alzheimer's disease (AD). It further aims to shed new insight into the differences between stable subjects and those who progress to AD by associating cerebrospinal fluid (CSF) biomarkers and specific magnetic resonance imaging (MRI) regions with disease progression, more deeply exploring how such biomarkers relate to AD pathology.

Methods: We examined baseline and longitudinal changes over a 7-year span and the longitudinal interactions between CSF and MRI biomarkers for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Real-time frequency-independent single-Lead and single-beat myocardial infarction detection.

This study proposes a novel real-time frequency-independent myocardial infarction detector for Lead II electrocardiograms. The underlying Deep-LSTM network is trained using the PTB-XL database, the largest to date publicly available electrocardiography dataset, and is tested over the same and the older PTB database. By testing the model over distinct datasets, collected under different conditions and from different patients, a more realistic measure of the performance can be gauged from the deployed system.

PET Imaging of Tau Pathology and Amyloid-β, and MRI for Alzheimer's Disease Feature Fusion and Multimodal Classification.

Background: Machine learning is a promising tool for biomarker-based diagnosis of Alzheimer's disease (AD). Performing multimodal feature selection and studying the interaction between biological and clinical AD can help to improve the performance of the diagnosis models.

Objective: This study aims to formulate a feature ranking metric based on the mutual information index to assess the relevance and redundancy of regional biomarkers and improve the AD classification accuracy.

Gaussian discriminative component analysis for early detection of Alzheimer's disease: A supervised dimensionality reduction algorithm.

Background: Using multiple modalities of biomarkers, several machine leaning-based approaches have been proposed to characterize patterns of structural, functional and metabolic differences discernible from multimodal neuroimaging data for Alzheimer's disease (AD). Current investigations report several studies using binary classification often augmented with local feature selection methods, while fewer other studies address the challenging problem of multiclass classification.

New Method: To assess the merits of each of these research directions, this study introduces a supervised Gaussian discriminative component analysis (GDCA) algorithm, which can effectively delineate subtle changes of early mild cognitive impairment (EMCI) group in relation to the cognitively normal control (CN) group.

A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.

Background: Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) with its delineation from the cognitively normal (CN) group remains a challenging but essential step for the planning of early treatment. Although several studies have focused on the MCI diagnosis, this study introduces the early stage of MCI to assess more thoroughly the earliest signs of disease manifestation and progression.

New Method: We used random forest feature selection model with a Gaussian-based algorithm to perform method evaluation.

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algorithm for predicting the progression of Alzheimer's disease using a distributed multimodal, multitask learning method. More specifically, each individual task is defined as a regression model, which predicts cognitive scores at a single time point.

A cognitive stress test for prodromal Alzheimer's disease: Multiethnic generalizability.

Introduction: Culturally fair cognitive assessments sensitive to detecting changes associated with prodromal Alzheimer's disease are needed.

Methods: Performance of Hispanic and non-Hispanic older adults on the Loewenstein-Acevedo Scale of Semantic Interference and Learning (LASSI-L) was examined in persons with amnestic mild cognitive impairment (aMCI) or normal cognition. The association between a novel cognitive marker, the failure to recover from proactive semantic interference (frPSI), and cortical thinning was explored.

Dynamics and Distant Effects of Frontal/Temporal Epileptogenic Focus Using Functional Connectivity Maps.

Objective: Connectivity patterns of interictal epileptiform discharges are all subtle indicators of where the three-dimensional (3D) source of a seizure could be located. These specific patterns are explored in the recorded electroencephalogram (EEG) signals of 20 individuals diagnosed with focal epilepsy to assess how their functional brain maps could be affected by the 3D onset of a seizure.

Methods: Functional connectivity maps, estimated by phase synchrony among EEG electrodes, were obtained by applying a data-driven recurrence-based method.

Greater Regional Cortical Thickness is Associated with Selective Vulnerability to Atrophy in Alzheimer's Disease, Independent of Amyloid Load and APOE Genotype.

Background: Regional cortical thickness (rCTh) among cognitively normal (CN) adults (rCThCN) varies greatly between brain regions, as does the vulnerability to neurodegeneration.

Objective: The goal of this study was to: 1) rank order rCThCN for various brain regions, and 2) explore their vulnerability to neurodegeneration in Alzheimer's disease (AD) within these brain regions.

Methods: The relationship between rCTh among the CN group (rCThCN) and the percent difference in CTh (% CThDiff) in each region between the CN group and AD patients was examined.

A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer's disease.

Background: Functional magnetic resonance imaging (fMRI) is an MRI-based neuroimaging technique that measures brain activity on the basis of blood oxygenation level. This study reviews the main fMRI methods reported in the literature and their related applications in clinical and preclinical studies, focusing on relating functional brain networks in the prodromal stages of Alzheimer's disease (AD), with a focus on the transition phases from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to AD.

New Method: The purpose of this study is to present and compare different approaches of supervised and unsupervised fMRI analyses and to highlight the different applications of fMRI in the diagnosis of MCI and AD.

A Robust Edge Detection Approach in the Presence of High Impulse Noise Intensity through Switching Adaptive Median and Fixed Weighted Mean Filtering.

This study introduces a robust edge detection method that relies on an integrated process for denoising images in the presence of high impulse noise. This process is shown to be resilient to impulse (or salt and pepper) noise even under high intensity levels. The proposed switching adaptive median and fixed weighted mean filter (SAMFWMF) is shown to yield optimal edge detection and edge detail preservation, an outcome we validate through high correlation, structural similarity index and peak signal to noise ratio measures.

Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer's Disease.

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group.

A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study.

Background: Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values.

The Relationship of Brain Amyloid Load and APOE Status to Regional Cortical Thinning and Cognition in the ADNI Cohort.

Background: Both amyloid (Aβ) load and APOE4 allele are associated with neurodegenerative changes in Alzheimer's disease (AD) prone regions and with risk for cognitive impairment.

Objective: To evaluate the unique and independent contribution of APOE4 allele status (E4+∖E4-), Aβ status (Amy+∖Amy-), and combined APOE4 and Aβ status on regional cortical thickness (CoTh) and cognition among participants diagnosed as cognitively normal (CN, n = 251), early mild cognitive impairment (EMCI, n = 207), late mild cognitive impairment (LMCI, n = 196), and mild AD (n = 162) from the ADNI.

Methods: A series of two-way ANCOVAs with post-hoc Tukey HSD tests, controlling independently for Aβ and APOE4 status and age were examined.

Connectivity maps of different types of epileptogenic patterns.

EEG functional connectivity maps, showing the interactions between brain areas in context to the placement of electrodes, were used for the investigation and comparison of three different types of epileptiform activity defined as single spike, spike followed by slow wave and repetitive spike. A nonlinear data-driven method was used to extract connectivity matrices that helped to identify network synchronization based on the number of connections for all brain regions, as represented by the 10-20 EEG system. This quantification was used to assess these three types of spike patterns in relation to the type of seizure, focal or generalized.

Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient.

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks.

Background: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment.

Methods: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels.

A practical guideline for intracranial volume estimation in patients with Alzheimer's disease.

Background: Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure.

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods.

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure.

Altered small-world anatomical networks in Apolipoprotein-E4 (ApoE4) carriers using MRI.

Apolipoprotein E (ApoE) gene and primarily its allele e4 have been identified as a risk factor for Alzheimer's disease (AD). The prevalence of the gene in 25-30% in the population makes it essential to estimate its role in neuroregulation and its impact on distributed brain networks. In this study, we provide computational neuroanatomy based interpretation of large-scale and small-world cortical networks in cognitive normal (CN) subjects with differing Apolipoprotein-E4 (ApoE4) gene expression.

Multivariate analysis of structural MRI and PET (FDG and 18F-AV-45) for Alzheimer's disease and its prodromal stages.

A multivariate analysis method, orthogonal partial least squares to latent structures (OPLS), was used to discriminate Alzheimer's disease (AD), early and late mild cognitive impairment (EMCI and LMCI) from cognitively normal control (CN) using MRI and PET measures. FreeSurfer 5.1 generated 271 MRI features including 49 subcortical volumes, 68 cortical volumes, 68 cortical thicknesses, 70 surface areas and 16 hippocampus subfields.