Malek Adjouadi

• Florida International University

This paper presents the theoretical foundation, practical implementation, and empirical evaluation of a glove for interaction with 3-D virtual environments. At the dawn of the “Spatial Computing Era”, where users continuously interact with 3-D Virtual and Augmented Reality environments, the need for a practical and intuitive interaction system that...

Background With the emergence of anti‐amyloid drugs, there is an increased need to determine the presence of brain amyloid in people using non‐invasive, cost‐effective biomarkers. The goal of this study was to determine the added and stand‐alone value of plasma biomarkers for predicting positive amyloid PET in a mixed sample of Hispanic and non‐His...

Background: Attrition is a significant methodological concern in longitudinal studies. Sample loss can limit generalizability and compromise internal validity. Methods: Wave one ( n = 346) and wave two follow-ups ( n = 196) of the 1Florida ADRC clinical core were examined using a 24-month visit window. Results: The sample (59% Hispanic) demonstrate...

Background Semantic intrusion errors (SIEs) are both sensitive and specific to PET amyloid-β (Aβ) burden in older adults with amnestic mild cognitive impairment (aMCI). Objective Plasma Aβ biomarkers including the Aβ42/40 ratio using mass spectrometry are expected to become increasingly valuable in clinical settings. Plasma biomarkers are more cli...

INTRODUCTION Commercially available plasma p‐tau217 biomarker tests are not well studied in ethnically diverse samples. METHODS We evaluated associations between ALZPath plasma p‐tau217 and amyloid‐beta positron emission tomography (Aβ‐PET) in Hispanic/Latino (88% of Cuban or South American ancestry) and non‐Hispanic/Latino older adults. One‐ and...

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade acceleromete...

Introduction This study investigated the role of proactive semantic interference (frPSI) in predicting the progression of amnestic Mild Cognitive Impairment (aMCI) to dementia, taking into account various cognitive and biological factors. Methods The research involved 89 older adults with aMCI who underwent baseline assessments, including amyloid...

Extensive prior work has provided methods for the optimization of routing based on the criteria of travel time and/or the cost of travel and/or the distance traveled. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path...

Background Estimates from the Alzheimer’s Association indicate that approximately one in ten older adults in the US have Alzheimer’s disease (AD) dementia while 15 to 20% have mild cognitive impairment (MCI), projecting that about a third of those will develop dementia within five years. The Cognitive Reserve/ Resilience (CR/R) theory postulates th...

Background Dementia diagnoses are more common among Hispanic (HW) than non‐Hispanic white (NHW) older adults. Alzheimer’s disease (AD) is the most common neuropathological finding in patients with dementia. Plasma AD biomarkers have accelerated efforts towards increasing access to timely diagnosis, but existing data often come from cohorts lacking...

Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer’s disease. For this cross-sectional study, we examined 108 participants...

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and...

INTRODUCTION Alzheimer's disease studies often lack ethnic diversity. METHODS We evaluated associations between plasma biomarkers commonly studied in Alzheimer's (p‐tau181, GFAP, and NfL), clinical diagnosis (clinically normal, amnestic MCI, amnestic dementia, or non‐amnestic MCI/dementia), and Aβ‐PET in Hispanic and non‐Hispanic older adults. His...

Hippocampus segmentation in brain MRI is a critical task for diagnosis, prognosis, and treatment planning of several neurological disorders. However, automated hippocampus segmentation methods have some limitations. More precisely, hippocampus is hard to visualize through MRI due to the low contrast of the surrounding tissue, also it is a relativel...

Introduction Semantic intrusion errors (SI) have distinguished between those with amnestic Mild Cognitive Impairment (aMCI) who are amyloid positive (A+) versus negative (A−) on positron emission tomography (PET). Method This study examines the association between SI and plasma – based biomarkers. One hundred and twenty-eight participants received...

Alzheimer’s disease (AD) is a neurogenerative condition characterized by sharp cognitive decline with no confirmed effective treatment or cure. This makes it critically important to identify the symptoms of Alzheimer’s disease in its early stages before significant cognitive deterioration has taken hold and even before any brain morphology and neur...

There is increasing interest in using low-cost and lightweight Micro Electro-Mechanical System (MEMS) modules containing tri-axial accelerometers, gyroscopes and magnetometers for tracking the motion of segments of the human body. We are specifically interested in using these devices, called “Magnetic, Angular-Rate and Gravity” (“MARG”) modules, to...

Objective: The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans). Meth...

Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size...

Geographic datasets are usually accompanied by spatial non-stationarity – a phenomenon that the relationship between features varies across space. Naturally, nonstationarity can be interpreted as the underlying rule that decides how data are generated and alters over space. Therefore, traditional machine learning algorithms are not suitable for han...

resented here is a model objectivizing real estate prices so that prices across time could be compared to understand historical price trends and also to assist in a property evaluation or appraisal, as well as for the analysis of comparables in estimating a reasonable offer for a property on the market. Given a timespan of interest, a locale (e.g.,...

Extensive prior work has provided methods for the optimization of routing based on weights assigned to travel duration, and/or travel cost, and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. A typical method of routing involves building a graph comprised of street...

In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic–angular rate–gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset co...

Purpose: Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mechanism driven by an integrated ML model to predict disease trajectory in a 2-year longitudinal stud...

Extracellular amyloid plaques in gray matter are the earliest pathological marker for Alzheimer's disease (AD), followed by abnormal tau protein accumulation. The link between diffusion changes in gray matter, amyloid and tau pathology, and cognitive decline is not well understood. We first performed cross-sectional analyses on T1-weighted imaging,...

Extensive prior work has provided methods for the optimization of routing based on the criteria of travel time and/or the cost of travel and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. A typical method of routing involves building a graph comprised of street se...

This paper introduces a new algorithm (Gravity & Magnetic North Vector correction – Double SLERP, or “GMV-D”) to estimate the orientation of a MEMS Magnetic/Angular-Rate/Gravity (MARG) sensor module using sensor fusion in the context of a real-time hand tracking application, for human–computer interaction purposes. Integrated MEMS MARG modules are...

Objective: First, to determine whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Sematic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. Second, to correlate LASSI-BC performance to volumetric re...

Cross-cultural differences in the association between neuropsychiatric symptoms and Alzheimer's disease (AD) biomarkers are not well understood. This study aimed to (1) compare depressive symptoms and frequency of reported apathy across diagnostic groups of participants with normal cognition (CN), mild cognitive impairment (MCI), and dementia, as w...

A new approach to correct the orientation estimate for a miniature Magnetic-Angular Rate-Gravity (MARG) module is statistically evaluated in a hand motion tracking system. Thirty human subjects performed an experiment to validate the performance of the proposed orientation correction algorithm in both non-magnetically distorted (MN) and magneticall...

With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance the decision-making process. Since visualization is such an effective tool for human comprehension, memorization, and judgment, we have presented...

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...

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments sensitive enough to measure the early brain-behavior manifestations of AD and correlate with biomarkers of neurodegeneration are needed to identify and monitor indiv...

Background One of the challenges facing accurate diagnosis and prognosis of Alzheimer’s disease, beyond identifying the subtle changes that define its early onset, is the scarcity of sufficient data compounded by the missing data challenge. Although there are many participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, many...

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 furthe...

Background: Understanding functional connectivity (FC) patterns of epileptic brain networks as they relate to the presence or absence of interictal epileptiform discharges (IEDs) can enhance machine learning (ML) algorithms identifying them. Methods: Changes in brain dynamics induced by the presence of IEDs are demonstrated by constructing FC maps...

Objective Determine the relationship between diffusion microstructure and early changes in Alzheimer’s disease (AD) severity as assessed by clinical diagnosis, cognitive performance, dementia severity, and plasma concentrations of neurofilament light chain. Methods Diffusion MRI scans were collected on cognitively normal participants (CN), patient...

We examined the association between bilingualism, executive function (EF), and brain volume in older monolinguals and bilinguals who spoke English, Spanish, or both, and were cognitively normal (CN) or diagnosed with Mild Cognitive Impairment (MCI) or dementia. Gray matter volume (GMV) was higher in language and EF brain regions among bilinguals, b...

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...

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 distin...

This study proposes a novel Long Short-Term Memory Neural Network (LSTM) architecture for the diagnosis of myocardial infarctions from individual heartbeats of single-lead electrocardiograms (ECGs). The proposed model is trained using an unbiased patient split approach and validated using 10-fold cross-validation over 148 myocardial infarction and...

Neuroimaging is essential in brain studies for the diagnosis and identification of disease, structure, and function of the brain in its healthy and disease states. Literature shows that there are advantages of multitasking with some deep learning (DL) schemes in challenging neuroimaging applications. This study examines the feasibility of using mul...

This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and peppe...

This study utilises a deep convolutional neural network (CNN) implementing regularisation and batch normalisation for the removal of mixed, random, impulse, and Gaussian noise of various levels from digital images. This deep CNN achieves minimal loss of detail and yet yields an optimal estimation of structural metrics when dealing with both known a...

Objective: This study demonstrates how functional connectivity (FC) patterns are affected in direct relation to the lobe that is mostly affected by seizures. Methods: The novel idea of penalized FC (pFC) maps is compared against standard FC maps in the four fundamental EEG frequency sub-bands. The FC measure between any two specific electrodes is s...

Background Plasma NfL (pNfL) levels are elevated in many neurological disorders. However, the utility of pNfL in a clinical setting has not been established. Objective In a cohort of diverse older participants, we examined: 1) the association of pNfL to age, sex, Hispanic ethnicity, diagnosis, and structural and amyloid imaging biomarkers; and 2)...

Objective: To investigate the association between the functional activities questionnaire (FAQ) and brain biomarkers (bilateral hippocampal volume [HV], bilateral entorhinal volume [ERV], and entorhinal cortical thickness [ERT]) in cognitively normal (CN) individuals, mild cognitive impairment (MCI), or dementia. Method: In total, 226 participan...

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 augme...

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segme...

Background: The development and validation of clinical outcome measures to detect early cognitive decline associated with Alzheimer's disease (AD) biomarkers is imperative. Semantic intrusions on the Loewenstein Acevedo Scales of Semantic Interference and Learning (LASSI-L) has outperformed widely used cognitive measures as an early correlate of e...

Background: Diagnosis of early mild cognitive impairment (EMCI) as a prodromal stage of Alzheimer's disease (AD) and its delineation from the cognitively normal (CN), 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...

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, multitas...

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 impairmen...

Validating sensitive markers of hippocampal degeneration is fundamental for understanding neurodegenerative conditions such as Alzheimer's disease. In this paper, we test the hypothesis that free-water in the hippocampus will be more sensitive to early stages of cognitive decline than hippocampal volume, and that free-water in hippocampus will incr...

This paper outlines the statistical evaluation of novel and traditional orientation estimates from an IMU-instrumented glove. Thirty human subjects participated in the experiment by performing the instructed hand movements in order to compare the performance of the proposed orientation correction algorithm with Kalman-based orientation filtering. T...

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for the diagnosis, screening and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ se...

Objective: Connectivity patterns of interictal epileptiform discharges (IED) are all subtle indicators of where the 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 affec...

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...

The threshold for amyloid positivity by visual assessment on PET has been validated by comparison to amyloid load measured histopathologically and biochemically at post mortem. As such, it is now feasible to use qualitative visual assessment of amyloid positivity as an in-vivo gold standard to determine those factors which can modify the quantitati...

Introduction: Yttrium‐90 (90Y) microsphere post‐treatment imaging reflects the true distribution characteristics of microspheres in the tumor and liver compartments. However, due to its decay spectra profile lacking a pronounced photopeak, the bremsstrahlung imaging for 90Y has inherent limitations. The absorbed dose calculations for 90Y microspher...

This study introduces an image denoising method in the presence of combined Speckle and Gaussian noise. The dual-tree complex wavelet transform (DT-CWT) is applied to the image in order to obtain specific coefficients characterizing these types of noise. Then, these extracted coefficients are removed by thresholding and an inverse wavelet transform...

This review article provides a comprehensive survey on state-of-the-art impulse and Gaussian denoising filters applied to images and summarizes the progress that has been made over the years in all applications involving image processing. The random noise model in this survey is assumed to be comprised of impulse (salt and pepper) and Gaussian nois...

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...