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Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56726, first published .
Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study

Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study

Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study

Authors of this article:

Pierfilippo De Sanctis1, 2 Author Orcid Image ;   Jeannette R Mahoney1 Author Orcid Image ;   Johanna Wagner3 Author Orcid Image ;   Helena M Blumen1, 4 Author Orcid Image ;   Wenzhu Mowrey5 Author Orcid Image ;   Emmeline Ayers1 Author Orcid Image ;   Claudia Schneider1 Author Orcid Image ;   Natasha Orellana1 Author Orcid Image ;   Sophie Molholm2 Author Orcid Image ;   Joe Verghese1, 4 Author Orcid Image
Article Authors Cited by Tweetations (4) Metrics

    Protocol

    1Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States

    2Department of Pediatrics, Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, United States

    3Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States

    4Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, United States

    5Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States

    Corresponding Author:

    Pierfilippo De Sanctis, PhD

    Department of Neurology, Division of Cognitive and Motor Aging

    Albert Einstein College of Medicine

    1225 Morris Park Avenue

    Bronx, NY, 10461-0000

    United States

    Phone: 1 7188621828

    Email: pierfilippo.sanctis@einsteinmed.edu


    Related ArticleThis is a corrected version. See correction statement in: https://www.researchprotocols.org/2024/1/e66438/

    Background: Progressive difficulty in performing everyday functional activities is a key diagnostic feature of dementia syndromes. However, not much is known about the neural signature of functional decline, particularly during the very early stages of dementia. Early intervention before overt impairment is observed offers the best hope of reducing the burdens of Alzheimer disease (AD) and other dementias. However, to justify early intervention, those at risk need to be detected earlier and more accurately. The decline in complex daily function (CdF) such as managing medications has been reported to precede impairment in basic activities of daily living (eg, eating and dressing).

    Objective: Our goal is to establish the neural signature of decline in CdF during the preclinical dementia period.

    Methods: Gait is central to many CdF and community-based activities. Hence, to elucidate the neural signature of CdF, we validated a novel electroencephalographic approach to measuring gait-related brain activation while participants perform complex gait-based functional tasks. We hypothesize that dementia-related pathology during the preclinical period activates a unique gait-related electroencephalographic (grEEG) pattern that predicts a subsequent decline in CdF.

    Results: We provide preliminary findings showing that older adults reporting CdF limitations can be characterized by a unique gait-related neural signature: weaker sensorimotor and stronger motor control activation. This subsample also had smaller brain volume and white matter hyperintensities in regions affected early by dementia and engaged in less physical exercise. We propose a prospective observational cohort study in cognitively unimpaired older adults with and without subclinical AD (plasma amyloid-β) and vascular (white matter hyperintensities) pathologies. We aim to (1) establish the unique grEEG activation as the neural signature and predictor of decline in CdF during the preclinical dementia period; (2) determine associations between dementia-related pathologies and incidence of the neural signature of CdF; and (3) establish associations between a dementia risk factor, physical inactivity, and the neural signature of CdF.

    Conclusions: By establishing the clinical relevance and biological basis of the neural signature of CdF decline, we aim to improve prediction during the preclinical stages of ADs and other dementias. Our approach has important research and translational implications because grEEG protocols are relatively inexpensive and portable, and predicting CdF decline may have real-world benefits.

    International Registered Report Identifier (IRRID): DERR1-10.2196/56726

    JMIR Res Protoc 2024;13:e56726

    doi:10.2196/56726

    Keywords

    EEG; electroencephalographic; mobility; preclinical dementia stages 


    Background

    Progressive difficulty in performing everyday functional activities is a key diagnostic feature of dementia. Yet not much is known about the neural underpinnings of functional decline, particularly during the very early stages of dementia. Early intervention before impairment in everyday functions is observed offers the best hope of reducing the burdens of Alzheimer disease (AD) and other dementias. However, to justify early intervention, those at risk need to be detected earlier and more accurately.

    The decline in complex daily function (CdF) such as managing medications and finances has been reported to precede impairment in basic activities of daily living (eg, eating and dressing). Our goal is to establish the neural signature of decline in CdF during the preclinical dementia period. We recently reported that limitations in CdF were more prevalent or declined faster in those who converted to mild cognitive impairment (MCI) [1]. We compared CdF profiles at baseline in 59 community-dwelling older individuals with normal cognitive performance who went on to develop incident MCI (“pre-MCI”) with 284 older individuals who remained cognitively normal over follow-up. While limitations in CdF were reported to be subtle, the mean number of limitations at baseline was 3.1 (SD 3.0) in the pre-MCI cases and 2.0 (SD 2.4) in normal controls (P=.003). This is in line with studies using amyloid-β (Aβ), phosphorylated τ, neurofilament (also referred to as AT(N) classification system) [2] to stratify dementia risk in cognitively normal older adults, showing the presence of AT(N) biomarkers to be associated with more limitations in CdF [3,4].

    While combining behavioral (eg, cognition and CdF assessment) with biological (eg, AT(N)) markers has further improved early risk assessment, it is insufficient to justify intervention during the preclinical dementia stages. For example, up to one-third of older individuals with significant accumulation of AD-related and vascular pathology will not progress to MCI and dementia stages [5,6]. In this proposal, we seek to further improve risk assessment by establishing the neural signature of CdF. We posit that abnormal patterns of neurophysiological activation precedes and can serve as a robust predictor of change in CdF, which in turn increases the risk of conversion to dementia [7]. To test this hypothesis, we designed complex gait tasks. Gait has physical and cognitive contributions. We showed that gait performance (eg, gait speed) during normal and dual-task conditions is associated with scores on the Activities of Daily Living–Prevention Instrument (ADL-PI) [8], which includes questions about abilities to manage money, drive, do the laundry, and use appliances [9,10]. We applied a novel electroencephalographic-based mobile brain-body imaging approach to measure gait-related brain activation while participants perform complex gait-based functional tasks [11-19]. Raising the complexity of the gait task may allow early distinction of individuals with and without the risk of developing limitations in CdF by increasing the cognitive demands of the task. We designed simple (walking-only) and complex (dual-task walking) gait-based tasks to measure differential activation (complex minus simple gait). This proposal builds on and extends 2 decades of work by our team showing that changes in gait (eg, slowing of walking speed) during simple and complex walking conditions are robust predictors of decline in CdF, cognitive decline, and progression to dementia [9,10,20-30].

    Hypothesis

    Our overarching hypothesis illustrated in Figure 1 is that the presence of dementia-related pathology during the preclinical period of dementia is associated with a unique gait-related electroencephalographic (grEEG) pattern that predicts a subsequent decline in CdF. The first aim of this study is to establish the neural signature of CdF. Our preliminary findings show that individuals reporting CdF limitations can be characterized by weaker pre as well as postcentral gyrus and stronger fronto-medial activation during complex gait (eg, dual-task walking). This unique grEEG activation pattern (neural signature) will predict CdF decline longitudinally. Our second aim is to determine the association of dementia-related pathology with the incidence of the neural signature of CdF in individuals without the neural signature at baseline. Specifically, we hypothesize that plasma-based assays of Aβ, T, and white matter hyperintensities (WMH) at baseline will independently and in combination predict the incidence of the neural signature of CdF. We also need to understand if and how behaviors are known to increase dementia risk (eg, physical inactivity) and may modify the neural signature of CdF as a prelude to developing interventions. Our third aim is to determine whether physical inactivity is associated with the prevalence and incidence of the neural signature of CdF in our sample.

    This project is part of a larger effort at the Division of Cognitive and Motor Aging at Albert Einstein College of Medicine (AECOM) to go beyond memory-centered depictions of AD and consider cognitive processes related to motor and sensory abilities as important additional characterization of AD and related dementias. We are partnered with an ongoing investigation (R01AG075679-01) that seeks to establish visual-somatosensory integration (VSI study) as a novel marker of preclinical AD. With Aβ accumulation occurring early in sensory association areas [31], the VSI study (principal investigator [PI] JRM) seeks to define multisensory integration as a preclinical marker of AD and related dementias (see [32] for more details). The VSI study serves as a parent study for cross-enrollment and includes older adults across the cognitive spectrum from cognitively normal to MCI. Participants in the VSI study with confirmed preclinical AD diagnosis (defined as Aβ positive) will undergo positron emission tomography testing. Collectively, these studies will broaden the scope of our investigations in synergistic ways to deepen our understanding of the very early preclinical dementia period.

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    Figure 1. Sequence of events leading to decline in CdF. CdF: complex daily function; Cog.: cognitive; MCI: mild cognitive impairment; Unimp.: unimpaired.

    Study Design and Recruitment

    We propose a prospective observational cohort study of 180 older adults who are cognitively unimpaired. Figure 2 shows the recruitment and timeline of procedures. We will cross-enroll participants from an active study (R01AG075679, PI JRM). We will use telephone-based screening procedures to enroll and follow a community-based cohort. Potential participants aged 65 year and older from the greater New York City area are first contacted by mail and then by telephone to explain the purpose and nature of this study. The telephone interview includes verbal consent, a brief medical history questionnaire, and 2 brief cognitive screens [33,34]. We will ensure that enrollment reflects the race and ethnic diversity of our catchment area. Following the interview, potential participants are invited for further evaluation at our research center. If eligible, written informed consents are obtained at study visits as per institutional review board (IRB) guidelines. This study is approved by the AECOM IRB.

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    Figure 2. Recruitment and procedures conducted under this proposal. Aβ: amyloid beta; CdF: complex daily function; CU: cognitively unimpaired; fun.: function; max.: maximum; MoCA: Montreal Cognitive Assessment; MRI: magnetic resonance imaging; T: phosphorylated τ; WMH: white matter hyperintensities.

    Study Criteria

    Inclusion Criteria

    Adults are included who are aged 65 years and older, can speak and write in English, reside in the New York metropolitan area, plan to be in the area for the next 3 or more years, and are ambulatory.

    Exclusion Criteria

    Exclusion criteria were previous MCI or dementia diagnosis, reported severe sensory impairments, chronic medication use (eg, neuroleptics) that influence cognitive functioning, terminal illness with life expectancy <12 months, existing diagnosis of neurodegenerative diseases (eg, Parkinson disease or amyotrophic lateral sclerosis), presence of clinical disorders that overtly alter attention like delirium, or major psychiatric disorder such as schizophrenia. Additional electroencephalographic and magnetic resonance imaging (MRI)–specific exclusion criteria are seizure medication, stroke, traumatic brain injury, claustrophobia, and MRI contraindications such as pacemakers or any permanent magnetic metal implants.

    Assessments

    Table 1 lists instruments used to assess function and other domains. The ADL-PI will serve as our primary outcome measure. It is a 15-item questionnaire that was developed to target the earliest changes in complex daily activities [8]. A performance-based instrument, the Everyday Problems Test [35], will serve as our secondary CdF outcome measure. It is a 42-item paper-pencil test that examines abilities to understand and execute complex daily activities (eg, write a check).

    Table 1. Measurements conducted under this proposal.
    Domain and measuresTime
    Independent variables

    		Aim 1: neural signature of CdFa (mobile electroencephalographic)2.5 h including rest

    		Aim 2: MRIb white matter hyperintensity and plasma-based Aβ42c/Aβ40/τ1 h

    		Aim 3: Physical Activity Scale for the Elderly [36] and accelerometry5 min and 7-d period
    Outcomes (yearly assessed)

    		Aim 1: Activities of Daily Living–Prevention Instrument [8]—primary5 min

    		Aim 1: Everyday Problem Test—secondary45 min

    		Aims 2 and 3: Incident neural signature of CdF2.5 h including rest
    Covariates

    		Cognition (moderator)Montreal Cognitive Assessment [37] (moderator aim 1) Wechsler Adults Intelligence Scale IV [38]55 min

    		Physical functionQuantitative Gait Assessment (ProtoKinetics Movement Analysis Software) [39-41], hand grip strength [42]15 min

    		PsychosocialGeriatric Depression Scale [43]; Beck Anxiety Inventory [44]15 min

    		Dementia risk factorsApoE, obesity, diet [45], social isolation [46], cognitive engagement [47], depression30 min

    		MedicalMedical history, comorbidities, or medication [30,48]20 min

    		SensoryVisual sensory screen [49], Shoebox auditory testing, Vibratron [50], Michigan Neuropathy Screening Instrument [51,52]15 min

    aCdF: complex daily function.

    bMRI: magnetic resonance imaging.

    cAβ: amyloid-β.

    Neuroimaging and plasma-based blood testing will be conducted during baseline visits for each participant. C2N Diagnostic will quantify plasma τ217 (nonphosphorylated and phosphorylated) and plasma amyloid (Aβ42, Aβ40), and provide plasma ApoE prototyping using liquid chromatography-tandem mass spectrometry platforms (PrecivityAD) [53-55]. Blood samples will be collected and stored in the biorepository at AECOM. Physical activity will be assessed at baseline and during annual in-person visits using the Physical Activity Scale for the Elderly [36] as well as objectively using accelerometry [56] over 7 days (AX3; Axivity Ltd). The Physical Activity Scale for the Elderly [57,58] and AX3 [59] are reliable and valid measures of physical activity.

    Cognitive Diagnoses

    Established clinical consensus case conference procedures [60], where participants’ demographic, neuropsychological, neurological, psychosocial, and functional test results are reviewed by a multidisciplinary clinical team consisting of neurologists and neuropsychologists, will be used to determine normal cognitive status. Cognitively unimpaired individuals testing positive for Aβ are classified as preclinical AD [2]. Cognitive status—normal, MCI, and dementia—will be assigned at baseline and during yearly follow-up visits. Individuals with MCI and dementia at baseline are excluded from this project.

    Outcomes

    For aim 1, the primary outcome is complex daily functional limitations measured with the ADL–PI, as reported in our previous publication [8]. For aims 2 and 3, the outcome is the incidence of the neural signature of CdF (see section below for definition). All outcome measures are assessed annually over a maximal period of 4 years.

    Ethical Considerations

    Protocols for the proposed research project are approved by the AECOM’s IRB (2023-14773). Before enrollment, written consent is obtained from all interested persons who meet the inclusion criteria at the time of the visit. Confidentiality will be preserved by the use of unique ID codes for identification. ID and name associations will be password-protected in an encrypted master file to which only the PI and study coordinator have access. Participant data, including computer data disks, will be kept in a locked room. Participants will receive monetary compensation (US $20 per hour), free lunch, and transportation for participating in this study.

    grEEG Protocol

    We designed simple (walking-only) and complex (dual-task walking) gait tasks to measure differential activation (complex minus simple gait). We provide preliminary findings showing that individuals reporting CdF limitations can be characterized by a unique grEEG pattern: weaker sensorimotor activation, measured by pre or postcentral gyrus 13-28 Hz desynchronization, and stronger motor control activation, measured by fronto-medial 3-7 Hz synchronization. Figure 3 shows an individual performing the visual-perturbed walking task. The participant walking on a treadmill wearing an electroencephalographic cap is immersed into a large-scale visual flow field designed to destabilize posture [61]. We superimpose sideway shifts onto a display of dots radiating outward from a central point of extension to create visual perturbations. We validated the task in prior studies [12,15]. Participants wore harnesses for safety.

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    Figure 3. Participant during the visually perturbed walking task.

    Neural Signatures—Starting With Cognition and Switching to CdF

    In our first attempt to identify the neural signature of CdF, we stratified individuals by their cognitive performance [19]. Cognitively normal individuals (Montreal Cognitive Assessment [MoCA] ≥ 22 [62,63]) were dichotomized into low (score of 22-26; n=10) and high (score 27+; n=16) performing groups. We discovered a unique grEEG activation pattern time-locked to the gait cycle in low-cognitive performers: weaker central gyrus β (13-28 Hz) desynchronization paired with stronger fronto-medial θ (3-7 Hz) synchronization during visually perturbed compared to unperturbed walking. We also found correlations between θ synchronization and worse MoCA scores and between β desynchronization and better MoCA scores. Amplitude suppression (ie, desynchronization) of brain oscillatory activity within 8 to 28 Hz over pre as well as postcentral gyrus during movement is considered a neurophysiological marker of sensorimotor activation related to preparation and execution of movement [64-70]. We suggest that posterior parietal and sensorimotor activation during gait adjustment is related to prioritizing inputs from visual, somatosensory, and vestibular systems based on reliability to build accurate proprioception subserving motor output [19]. On the other hand, increased θ activity (ie, synchronization) over the fronto-medial cortex is thought of as a mechanism by which the need for cognitive or motor control is realized and signaled across brain regions [71,72]. In the context of movement, we and others observed fronto-medial θ in moments of postural instability [12,15,73-78].

    Neural Signature of CdF

    To test whether this unique grEEG activation pattern also associates with the limitation in CdF, we dichotomized our sample in the following way: (1) compute the difference in activation between unperturbed and perturbed walking; (2) use inverse source localization techniques to estimate intracranial sources of scalp-recorded activity; (3) separate individuals with source localized to fronto-medial regions into groups of weak and strong θ synchronizers using the median; (4) separate individuals without sources in fronto-medial and with sources in sensorimotor region into groups of weak and strong β desynchronizers using the median (we chose right sensorimotor β because effects where strongest for that region and frequency: P=.04) [19].

    Figure 4 shows preliminary findings in support of our hypothesis: Individuals characterized by a unique grEEG pattern report limitations in CdF to a higher degree.

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    Figure 4. The neural signature of limitations in CdF and unique gait-related electroencephalographic activation. CdF: complex daily function; med.: medial; pert.: perturbed; PP: posterior parietal; SM: sensorimotor; unpert.: unperturbed; V.: visually.

    Aim 1—Establish the Neural Signature of CdF

    Overview

    Figure 4 shows individuals with and without the unique grEEG pattern and associations with mild complex daily functional limitations measured with ADL-PI [8,79]. A score of 100% in Figure 5 indicates all activities are performed without difficulties. In both walking tasks, individuals with the unique grEEG pattern expressed nominally more difficulties performing complex daily activities.

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    Figure 5. Analysis pipeline for separating brain activity from non–brain related artifacts during MoBI recording. ERSP: event-related spectral perturbation; IC: independent components; ICA: independent component analysis; LH: left heel strike; LTO: left toe off; MoBI: mobile brain body imaging; RH: right heel strike; RTO: right toe off.
    Walking Tasks

    Participants will familiarize themselves with walking on the treadmill and are asked to determine their preferred walking speed before starting the experiment. For the VPW, participants will perform 4 visual perturbed and 4 unperturbed walking blocks randomized within participants. During perturbed stimulation, a large-scale visual field of dots is projected centrally onto a black wall in front of the participant. The stimulation consisted of 200 randomly placed white dots emanating outward from a central area of expansion. Superimposed to the outward motion is a sinusoidal perturbation in the mediolateral direction. A static image of dots placed randomly across the visual field serves as a control condition (ie, unperturbed walking). For the DTW, participants will perform the Go/No-Go task standing (4 blocks) and walking (4 blocks). Blocks are randomized within participants. The Go/No-Go stimuli consist of images from the International Affective Picture System [80]. Participants were instructed to click a wireless computer mouse button with the right hand each time a new image appeared (Go trials) but to withhold their response if the same image was shown twice in a row (No-Go trials). Each block consists of 180 trials and the probability of Go and No-Go trials will be 0.85 and 0.15, respectively. They will also walk wearing a safety harness.

    Kinematics Recordings

    The Optitrack infrared motion capture system with 9 cameras is used to collect kinematic data in the X/Y/Z direction at a sample frequency of 100 Hz (Arena version 1.5 acquisition software, Natural Point).

    Electrophysiological Recordings

    Continuous electroencephalography is recorded with a 64-channel BioSemi ActiveTwo system (digitized at 512 Hz; 0.05 to 100 Hz passband, 24 dB/octave). Data are high-pass filtered at 2 Hz and an automatic channel rejection procedure [81] is applied to exclude noise channels followed by visual inspection. Time-synchronized acquisition of electroencephalographic and motion tracking is conducted with Lab Streaming Layer software (Swartz Center for Computational Neuroscience, University of California San Diego).

    Prevalence of Unique grEEG Activation

    To estimate the prevalence of grEEG abnormalities in aging, we reanalyzed previously published data [16]. Using event-related potentials measured in control participants (ie, young adults), we defined a threshold (1.5 SD from the mean 2.2 µV) to calculate the percentage of unique grEEG (event-related potential, ERPyoung>1.5 SD, mean 2.2 µV) in older participants. The prevalence of grEEG abnormality in older adults was 25% (4 out of 16).

    Data Analysis
    Gait Kinematics

    Spatiotemporal features of gait kinematics are determined by computing the velocity profile for each foot (for a detailed description, see De Sanctis et al [19]).

    Integrated Gait-Electroencephalographic Analysis

    Figure 5 shows the basic steps to identify or correct artifacts and model spatially resolved brain dynamics tied to the gait cycle that we apply in our proposal. We have validated and applied this analysis pipeline in prior work [12,15,61]. Independent component (IC) analysis will be used to parse electroencephalographic signals into ICs. The resulting ICs are source-localized using inverse source modeling [82,83]. Only dipoles located within the brain, with a fit accounting for at least 80% of the variance for a given IC scalp projection, are retained [84,85]. Cortical IC clustering will be used to cluster brain ICs across subjects using feature vectors coding for IC differences in power spectral density, dipole location, and scalp projection [86,87]. Event-related spectral perturbations (ERSPs) are computed from single trial spectrograms for each IC, time-locked to the right heel strike [88]. ERSPs are computed by determining the power spectra over a sliding latency window and normalizing the spectrogram by their respective mean spectra (averaged across the latency window) [88]. To average across all strides taken and compute gait cycle ERSPs, we time-warped single trial spectrograms applying a linear interpolation function to align left toe off, left heel strike, right toe off, and right heel strike across epochs following the methods introduced by Gwin and colleagues [87].

    Statistical Plan Aim 1
    Primary Analyses

    grEEG measures will be compared between individuals with and without limitations in CdF using the 2-sample t tests to assess which grEEG measure is associated with limitations in CdF. Pearson correlation will be computed between grEEG measures and CdF scores.

    Longitudinal Analyses

    The time-to-event analyses including Kaplan-Meier survival curves, log-rank tests, and Cox proportional hazards regression models will be used to assess whether unique grEEG patterns predict a decline in CdF longitudinally.

    Power Analysis

    With the sample size of 180 older adults who are cognitively unimpaired, we estimate that there will be 54 individuals classified with CdF limitations based on our preliminary studies. With a significance level (α) of .05, using a 2-sided 2-sample t test, this study achieves 80% power to detect a minimal difference of 0.5 SD in the grEEG measure between CdF limitation groups. The minimum detectable hazards ratio for CdF decline between older adults with and without unique grEEG pattern ranges from 1.52 to 1.69 (assuming event rate between 0.30 and 0.48) with a power of 0.8 and a 2-sided type I error rate of 5% [89].

    Aim 2—Determine Association of Dementia-Related Pathology With Incidence of Neural Signature of CdF

    Overview

    Figure 6 shows preliminary findings in support of our hypothesis that Aβ burden [90] as well as MRI-regions impacted early during AD and cerebrovascular pathology are nominally associated with the presence of unique grEEG activation. Figure 6 shows the volume and white matter differences in brain regions between participants with and without unique grEEG patterns during DTW (15/28). Further, when grouping participants in tertiles based on amyloid probability score (APS) [90]—APS accounts for Aβ 42/40 ratio, age, and ApoE phenotype - we find that high APS is nominally associated with the increased presence of unique grEEG activation (1st tertile: 1 out of 5; 2nd tertile: 3 out of 6; 3rd tertile: 3 out of 5).

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    Figure 6. Individuals with unique grEEG pattern have smaller cuneus, lingual, ant. cingulate, and increased white matter burden of the fimbra, which connects the hippocampus. Act.: activity; ant.: anterior; Anteri.: anterior; grEEG: gait-related electroencephalographic; MRI: magnetic resonance imaging; WMH: white matter hyperintensities.
    MRI Image Acquisition

    All images will be acquired at the Gruss Magnetic Resonance Research Center at Einstein, using a Philips 3T Elition multinuclear MRI/magnetic resonance spectroscopy system. Our noninvasive MRI imaging procedures are reliable and extensively used in aging and dementia studies. T1-weighted images will be acquired using axial 3D-magnetization-prepared rapid acquisition with gradient echo parameters over a 240 mm field of view; 1.0 mm isotropic resolution, echo time/repetition time=4.6/9.9 ms, α= 8o, and SENSE factor 2.6. 3D-FLAIR images will be acquired with repetition time/inversion time=4800/1650 ms, echo time=335 ms, 240 × 238 acquisition matrix, and 0.46 mm voxel size. Functional (resting-state and task-based), diffusion-weighted, arterial spin labeling, and susceptibility-weighted images will also be acquired as part of the parent study (for additional details see [32]). A radiologist will review each MRI scan and confirm that there are no clinically significant findings for any of the participants. WMH will be quantified from 3D-FLAIR using the lesion segmentation toolbox [91] (implemented with SPM12/MatLab). T1-weighted images will be reconstructed using FreeSurfer (version 7.2; FreeSurfer). FreeSurfer’s subcortical segmentation and cortical parcellation are comparable to manual labeling [92]. Parcellation of the T1-weighted data into anatomical brain regions is important for examining cortical thickness and volume associated with the grEEG pattern during visually perturbed walking or dual-task walking. Gray matter volumes and cortical thicknesses of 68 cortical regions will be extracted from this pipeline and entered into subsequent statistical models.

    Statistical Plan Aim 2
    Primary Analyses

    The time-to-event analyses will be used to assess whether Aβ and WMH predict the incidence of the neural signature of CdF. Prevalent cases—that is, older adults with the unique grEEG pattern at baseline—will be excluded. Time to the neural signature will be defined as the time interval between baseline and the date when the participant demonstrates the unique grEEG pattern during the longitudinal follow-up.

    Power Analysis

    Preclinical Aβ/WMH prevalence is estimated at 24% to 45% among older adults who are cognitively unimpaired [93-95]. With a baseline sample size of 180 and a conservative attrition rate of 20%, the minimum detectable hazards ratio for incident neural signature of CdF between older adults with and without dementia-related pathology is 1.75 (assuming an event rate of 0.25) with a statistical power of 80% and a 2-sided type I error rate of 5%.

    Aim 3: Establish Associations Between Physical Inactivity and Neural Signature of CdF

    Overview

    Figure 7 shows preliminary findings in support of our prediction that less exercise (assessed with the Late Life Function and Disability Instrument [96,97]) is nominally associated with 13-28 Hz less desynchronization during DTW.

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    Figure 7. Less exercise is related to less β desynchronization during DTW. DTW: dual-task walking; W: walking.
    Statistical Plan Aim 3
    Overview

    The time-to-event analysis will be used to assess whether physical activity at baseline predicts the presence of the neural signature of CdF longitudinally.

    Power Analysis

    The minimum detectable hazards ratio for incident neural signature of CdF between older adults with less physical activity and normal level physical activity is 1.78 (assuming an event rate of 0.25) with a power of 0.8 and a 2-sided type I error rate of 5%.


    Principal Findings

    A major effort aimed at curbing the rapid rise of dementia revolves around early intervention. To substantiate early intervention, it is crucial to enhance the accuracy of early detection. We focus on the neural signature of early limitations in CdF to improve detection. Walking is central to many instrumental and complex activities [9,10]. To determine the neural signature of CdF, our team [11-19] and others [73,87,98-104] have established the validity and reliability of neurophysiological recordings while participants are in motion. We designed novel grEEG protocols for this proposal and selected walking as an activity that, compared to other daily activities (eg, preparing a meal), allows for the use of standardized, reproducible, reliable, and well-tolerated study protocols across participants [30,39,105,106].

    Our preliminary findings in 28 older adults without MCI/dementia show that reporting of CdF limitations is associated with a unique grEEG activation pattern: 13-28 Hz central gyrus desynchronization paired with 3-7 Hz fronto-medial synchronization. We term this electroencephalographic activation pattern the neural signature of CdF. This subsample also had higher Aβ burden, smaller brain volume, and WMH in regions affected early by dementia and engaged in less physical exercise.

    Some limitations are important to note. The ceiling effect with a majority reporting no limitations in CdF is a concern (eg, in Marshall et al [79] study only 16% reported limitations). However, in our study, 30% of individuals reported limitations [1]. To optimize detection, we choose 2 validated and reliable instruments (self-report [107] and paper-pencil test [35] with numerous dependent variables such as self-assessment, time-to-complete, and number and type of errors to quantify performance) covering a broad range of complex daily activities. Further, different processes denoted by Aβ deposition and WMH and their respective roles in the etiology of AD and other dementias are matters of ongoing debates. The prevalence of amyloid deposition in normal aging warrants consideration of Aβ in concert with phosphorylated τ and neurofilament light assays to recognize the multitude and temporal order of pathological processes leading up to clinical signs.

    In summary, establishing a noninvasive neurophysiological signature of prevalence and incidence of CdF decline will refine pre-MCI stage characterization. As our recent study shows [1], both individuals who progress and do not progress to MCI may report mild limitations in CdF during the preclinical period, though more limitations are reported in the individuals who go on to develop MCI. By establishing the clinical relevance and biological basis of the neural signature of CdF decline, we aim to improve prediction during preclinical stages of ADs and other dementias.

    Acknowledgments

    This work is supported by grants from the National Institutes of Health (R01AG082188, PI PDS, and R01AG075679, principal investigator JRM).

    Data Availability

    The data set generated in this study is available from the corresponding author upon reasonable request and with the completion of the data user agreement.

    Conflicts of Interest

    JRM has a financial interest in JET Worldwide Enterprises Inc, a digital health startup spun out of research conducted at the Albert Einstein College of Medicine that is not related to this project. All other authors report no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in this paper apart from those disclosed.

    Multimedia Appendix 1

    Peer review summary by NIA study section.

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    1. Verghese J, De Sanctis P, Ayers E. Everyday function profiles in prodromal stages of MCI: prospective cohort study. Alzheimers Dement. 2023;19(2):498-506. [FREE Full text] [CrossRef] [Medline]
    2. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535-562. [FREE Full text] [CrossRef] [Medline]
    3. Dubbelman MA, Sanchez J, Schultz AP, Rentz DM, Amariglio RE, Sikkes SAM, et al. Everyday functioning and entorhinal and inferior temporal tau burden in cognitively normal older adults. J Prev Alzheimers Dis. 2022;9(4):801-808. [FREE Full text] [CrossRef] [Medline]
    4. Dubbelman MA, Jutten RJ, Tomaszewski Farias SE, Amariglio RE, Buckley RF, Visser PJ, et al. Decline in cognitively complex everyday activities accelerates along the Alzheimer's disease continuum. Alzheimers Res Ther. 2020;12(1):138. [FREE Full text] [CrossRef] [Medline]
    5. Nelson PT, Alafuzoff I, Bigio EH, Bouras C, Braak H, Cairns NJ, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71(5):362-381. [FREE Full text] [CrossRef] [Medline]
    6. Montine TJ, Corrada MM, Kawas C, Bukhari SA, White LR, Tian L, et al. Association of cognition and dementia with neuropathologic changes of Alzheimer disease and other conditions in the oldest old. Neurology. 2022;99(10):e1067-e1078. [FREE Full text] [CrossRef] [Medline]
    7. Gouw AA, Alsema AM, Tijms BM, Borta A, Scheltens P, Stam CJ, et al. EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects. Neurobiol Aging. 2017;57:133-142. [CrossRef] [Medline]
    8. Galasko D, Bennett DA, Sano M, Marson D, Kaye J, Edland SD, et al. Alzheimer's Disease Cooperative Study. ADCS prevention instrument project: assessment of instrumental activities of daily living for community-dwelling elderly individuals in dementia prevention clinical trials. Alzheimer Dis Assoc Disord. 2006;20(4 Suppl 3):S152-S169. [CrossRef] [Medline]
    9. Callisaya ML, Verghese J. The association of clinic-based mobility tasks and measures of community performance and risk. PM R. 2018;10(7):704-711.e1. [FREE Full text] [CrossRef] [Medline]
    10. Verghese J, Wang C, Holtzer R. Relationship of clinic-based gait speed measurement to limitations in community-based activities in older adults. Arch Phys Med Rehabil. 2011;92(5):844-846. [FREE Full text] [CrossRef] [Medline]
    11. De Sanctis P, Solis-Escalante T, Seeber M, Wagner J, Ferris DP, Gramann K. Time to move: brain dynamics underlying natural action and cognition. Eur J Neurosci. 2021;54(12):8075-8080. [CrossRef]
    12. Malcolm BR, Foxe JJ, Joshi S, Verghese J, Mahoney JR, Molholm S, et al. Aging-related changes in cortical mechanisms supporting postural control during base of support and optic flow manipulations. Eur J Neurosci. 2020;54(12):8139-8157. [CrossRef]
    13. De Sanctis P, Malcolm BR, Mabie PC, Francisco AA, Mowrey WB, Joshi S, et al. Mobile brain/body imaging of cognitive-motor impairment in multiple sclerosis: deriving EEG-based neuro-markers during a dual-task walking study. Clin Neurophysiol. 2020;131(5):1119-1128. [FREE Full text] [CrossRef] [Medline]
    14. Malcolm BR, Foxe JJ, Butler JS, Mowrey WB, Molholm S, De Sanctis P. Long-term test-retest reliability of event-related potential (ERP) recordings during treadmill walking using the mobile brain/body imaging (MoBI) approach. Brain Res. 2019;1716:62-69. [FREE Full text] [CrossRef] [Medline]
    15. Malcolm BR, Foxe JJ, Butler JS, Molholm S, De Sanctis P. Cognitive load reduces the effects of optic flow on gait and electrocortical dynamics during treadmill walking. J Neurophysiol. 2018;120(5):2246-2259. [FREE Full text] [CrossRef] [Medline]
    16. Malcolm BR, Foxe JJ, Butler JS, De Sanctis P. The aging brain shows less flexible reallocation of cognitive resources during dual-task walking: a mobile brain/body imaging (MoBI) study. Neuroimage. 2015;117:230-242. [FREE Full text] [CrossRef] [Medline]
    17. De Sanctis P, Butler JS, Malcolm BR, Foxe JJ. Recalibration of inhibitory control systems during walking-related dual-task interference: a mobile brain-body imaging (MOBI) study. Neuroimage. 2014;94:55-64. [FREE Full text] [CrossRef] [Medline]
    18. De Sanctis P, Butler JS, Green JM, Snyder AC, Foxe JJ. Mobile Brain/Body Imaging (MoBI): high-density electrical mapping of inhibitory processes during walking. 2012. Presented at: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; August 28-September 01, 2012:1542-1545; San Diego, CA. [CrossRef]
    19. De Sanctis P, Wagner J, Molholm S, Foxe JJ, Blumen HM, Horsthuis DJ. Neural signature of mobility-related everyday function in older adults at-risk of cognitive impairment. Neurobiol Aging. 2023;122:1-11. [FREE Full text] [CrossRef] [Medline]
    20. Inzitari M, Metti A, Rosano C, Udina C, Pérez LM, Carrizo G, et al. Qualitative neurological gait abnormalities, cardiovascular risk factors and functional status in older community-dwellers without neurological diseases: the Healthy Brain Project. Exp Gerontol. 2019;124:110652. [FREE Full text] [CrossRef] [Medline]
    21. Beauchet O, Blumen HM, Callisaya ML, De Cock AM, Kressig RW, Srikanth V, et al. Spatiotemporal gait characteristics associated with cognitive impairment: a multicenter cross-sectional study, the intercontinental "Gait, Cognition & Decline" initiative. Curr Alzheimer Res. Jan 23, 2018;15(3):273-282. [CrossRef] [Medline]
    22. Rosso AL, Verghese J, Metti AL, Boudreau RM, Aizenstein HJ, Kritchevsky S, et al. Slowing gait and risk for cognitive impairment: the hippocampus as a shared neural substrate. Neurology. 2017;89(4):336-342. [CrossRef] [Medline]
    23. Callisaya ML, Launay CP, Srikanth VK, Verghese J, Allali G, Beauchet O. Cognitive status, fast walking speed and walking speed reserve-the Gait and Alzheimer Interactions Tracking (GAIT) study. Geroscience. 2017;39(2):231-239. [FREE Full text] [CrossRef] [Medline]
    24. Cohen JA, Verghese J, Zwerling JL. Cognition and gait in older people. Maturitas. 2016;93:73-77. [CrossRef] [Medline]
    25. Rosso AL, Studenski SA, Chen WG, Aizenstein HJ, Alexander NB, Bennett DA, et al. Aging, the central nervous system, and mobility. J Gerontol A Biol Sci Med Sci. 2013;68(11):1379-1386. [FREE Full text] [CrossRef] [Medline]
    26. Verghese J, Wang C, Bennett DA, Lipton RB, Katz MJ, Ayers E. Motoric cognitive risk syndrome and predictors of transition to dementia: a multicenter study. Alzheimers Dement. 2019;15(7):870-877. [FREE Full text] [CrossRef] [Medline]
    27. Cohen JA, Verghese J. Gait and dementia. Handb Clin Neurol. 2019;167:419-427. [CrossRef] [Medline]
    28. Ceïde ME, Ayers EI, Lipton R, Verghese J. Walking while talking and risk of incident dementia. Am J Geriatr Psychiatry. 2018;26(5):580-588. [FREE Full text] [CrossRef] [Medline]
    29. Doi T, Shimada H, Makizako H, Tsutsumimoto K, Verghese J, Suzuki T. Motoric cognitive risk syndrome: association with incident dementia and disability. J Alzheimers Dis. 2017;59(1):77-84. [CrossRef] [Medline]
    30. Verghese J, Wang C, Lipton RB, Holtzer R, Xue X. Quantitative gait dysfunction and risk of cognitive decline and dementia. J Neurol Neurosurg Psychiatry. 2007;78(9):929-935. [FREE Full text] [CrossRef] [Medline]
    31. Thal DR, Rüb U, Orantes M, Braak H. Phases of a beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002;58(12):1791-1800. [CrossRef] [Medline]
    32. Mahoney JR, Blumen HM, De Sanctis P, Fleysher R, Frankini C, Hoang A, et al. Visual-somatosensory integration (VSI) as a novel marker of Alzheimer's disease: a comprehensive overview of the VSI study. Front Aging Neurosci. 2023;15:1125114. [FREE Full text] [CrossRef] [Medline]
    33. Lipton RB, Katz MJ, Kuslansky G, Sliwinski MJ, Stewart WF, Verghese J, et al. Screening for dementia by telephone using the memory impairment screen. J Am Geriatr Soc. 2003;51(10):1382-1390. [CrossRef] [Medline]
    34. Katz MJ, Wang C, Nester CO, Derby CA, Zimmerman ME, Lipton RB, et al. T-MoCA: a valid phone screen for cognitive impairment in diverse community samples. Alzheimers Dement (Amst). 2021;13(1):e12144. [FREE Full text] [CrossRef] [Medline]
    35. Willis SL, Marsiske M. Everdyday Problems Test. Pennsylvania State University; 1993. URL: https://phhp-marsiske.sites.medinfo.ufl.edu/measures/everyday-problems-test/ [accessed 2024-05-01]
    36. Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46(2):153-162. [CrossRef] [Medline]
    37. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695-699. [CrossRef] [Medline]
    38. Wechsler D. Wechsler Adult Intelligence Scale: WAIS-IV; Technical and Interpretive Manual. San Antonio, TX. Pearson Assessment; 2008.
    39. Verghese J, Holtzer R, Lipton RB, Wang C. Quantitative gait markers and incident fall risk in older adults. J Gerontol A Biol Sci Med Sci. 2009;64(8):896-901. [FREE Full text] [CrossRef] [Medline]
    40. Verghese J, Kuslansky G, Holtzer R, Katz M, Xue X, Buschke H, et al. Walking while talking: effect of task prioritization in the elderly. Arch Phys Med Rehabil. 2007;88(1):50-53. [FREE Full text] [CrossRef] [Medline]
    41. Verghese J, Lipton RB, Hall CB, Kuslansky G, Katz MJ, Buschke H. Abnormality of gait as a predictor of non-Alzheimer's dementia. N Engl J Med. 2002;347(22):1761-1768. [FREE Full text] [CrossRef] [Medline]
    42. Madan SA, Fida N, Barman P, Sims D, Shin J, Verghese J, et al. Frailty assessment in advanced heart failure. J Card Fail. 2016;22(10):840-844. [CrossRef] [Medline]
    43. Brink TL, Yesavage JA, Lum O, Heersema PH, Adey M, Rose TL. Screening tests for geriatric depression. Clinical Gerontologist. 2008;1(1):37-43. [CrossRef]
    44. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988;56(6):893-897. [CrossRef] [Medline]
    45. Morris MC, Tangney CC, Bienias JL, Evans DA, Wilson RS. Validity and reproducibility of a food frequency questionnaire by cognition in an older biracial sample. Am J Epidemiol. 2003;158(12):1213-1217. [FREE Full text] [CrossRef] [Medline]
    46. Russell D, Peplau LA, Cutrona CE. The revised UCLA loneliness scale: concurrent and discriminant validity evidence. J Pers Soc Psychol. 1980;39(3):472-480. [CrossRef] [Medline]
    47. Vemuri P, Lesnick TG, Przybelski SA, Machulda M, Knopman DS, Mielke MM, et al. Association of lifetime intellectual enrichment with cognitive decline in the older population. JAMA Neurol. 2014;71(8):1017-1024. [FREE Full text] [CrossRef] [Medline]
    48. Holtzer R, Verghese J, Xue X, Lipton RB. Cognitive processes related to gait velocity: results from the Einstein Aging Study. Neuropsychology. 2006;20(2):215-223. [CrossRef] [Medline]
    49. Mahoney JR, Li PCC, Oh-Park M, Verghese J, Holtzer R. Multisensory integration across the senses in young and old adults. Brain Res. 2011;1426:43-53. [FREE Full text] [CrossRef] [Medline]
    50. Shy ME, Frohman EM, So YT, Arezzo JC, Cornblath DR, Giuliani MJ, et al. Quantitative sensory testing: report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology. Neurology. 2003;60(6):898-904. [FREE Full text] [CrossRef] [Medline]
    51. Feldman EL, Stevens MJ, Thomas PK, Brown MB, Canal N, Greene DA. A practical two-step quantitative clinical and electrophysiological assessment for the diagnosis and staging of diabetic neuropathy. Diabetes Care. 1994;17(11):1281-1289. [CrossRef] [Medline]
    52. Lunetta M, Le Moli R, Grasso G, Sangiorgio L. A simplified diagnostic test for ambulatory screening of peripheral diabetic neuropathy. Diabetes Res Clin Pract. 1998;39(3):165-172. [CrossRef] [Medline]
    53. West T, Kirmess KM, Meyer MR, Holubasch MS, Knapik SS, Hu Y, et al. A blood-based diagnostic test incorporating plasma Aß42/40 ratio, ApoE proteotype, and age accurately identifies brain amyloid status: findings from a multi cohort validity analysis. Mol Neurodegener. 2021;16(1):30. [FREE Full text] [CrossRef] [Medline]
    54. Verberk IMW, Misdorp EO, Koelewijn J, Ball AJ, Blennow K, Dage JL, et al. Characterization of pre-analytical sample handling effects on a panel of Alzheimer's disease-related blood-based biomarkers: results from the Standardization of Alzheimer's Blood Biomarkers (SABB) working group. Alzheimer’s Dement. 2022;18(8):1484-1497. [FREE Full text]
    55. Kirmess KM, Meyer MR, Holubasch MS, Knapik SS, Hu Y, Jackson EN, et al. The PrecivityAD™ test: accurate and reliable LC-MS/MS assays for quantifying plasma amyloid beta 40 and 42 and apolipoprotein E proteotype for the assessment of brain amyloidosis. Clin Chim Acta. 2021;519:267-275. [FREE Full text] [CrossRef] [Medline]
    56. Bann D, Hire D, Manini T, Cooper R, Botoseneanu A, McDermott MM, et al. Light intensity physical activity and sedentary behavior in relation to body mass index and grip strength in older adults: cross-sectional findings from the Lifestyle Interventions and Independence for Elders (LIFE) study. PLoS One. 2015;10(2):e0116058. [FREE Full text] [CrossRef] [Medline]
    57. Washburn RA, McAuley E, Katula J, Mihalko SL, Boileau RA. The Physical Activity Scale for the Elderly (PASE): evidence for validity. J Clin Epidemiol. 1999;52(7):643-651. [CrossRef] [Medline]
    58. Hagiwara A, Ito N, Sawai K, Kazuma K. Validity and reliability of the Physical Activity Scale for the Elderly (PASE) in Japanese elderly people. Geriatr Gerontol Int. 2008;8(3):143-151. [CrossRef] [Medline]
    59. Feng Y, Wong CK, Janeja V, Kuber R, Mentis HM. Comparison of tri-axial accelerometers step-count accuracy in slow walking conditions. Gait Posture. 2017;53:11-16. [CrossRef] [Medline]
    60. Holtzer R, Verghese J, Wang C, Hall CB, Lipton RB. Within-person across-neuropsychological test variability and incident dementia. JAMA. 2008;300(7):823-830. [FREE Full text] [CrossRef] [Medline]
    61. De Sanctis P, Wagner J, Molholm S, Foxe JJ, Blumen HM, Horsthuis DJ. Neural signature of everyday function in older adults at-risk of cognitive impairment. BioRxiv Preprint. Preprint posted online on December 17, 2021. 2022. [FREE Full text] [CrossRef]
    62. O'Caoimh R, Timmons S, Molloy DW. Screening for mild cognitive impairment: comparison of "MCI Specific" screening instruments. J Alzheimers Dis. 2016;51(2):619-629. [FREE Full text] [CrossRef] [Medline]
    63. Randolph C, Tierney MC, Mohr E, Chase TN. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20(3):310-319. [CrossRef] [Medline]
    64. Pfurtscheller G, Aranibar A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr Clin Neurophysiol. 1979;46(2):138-146. [FREE Full text] [CrossRef] [Medline]
    65. Pfurtscheller G, Neuper C. Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neurosci Lett. 1994;174(1):93-96. [CrossRef] [Medline]
    66. Pfurtscheller G, da Silva FHL. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-1857. [CrossRef] [Medline]
    67. Pfurtscheller G, Neuper C, Andrew C, Edlinger G. Foot and hand area mu rhythms. Int J Psychophysiol. 1997;26(1-3):121-135. [CrossRef] [Medline]
    68. Miller KJ, Schalk G, Fetz EE, den Nijs M, Ojemann JG, Rao RPN. Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc Natl Acad Sci U S A. 2010;107(9):4430-4435. [FREE Full text] [CrossRef] [Medline]
    69. Pfurtscheller G, Neuper C, Ramoser H, Müller-Gerking J. Visually guided motor imagery activates sensorimotor areas in humans. Neurosci Lett. 1999;269(3):153-156. [CrossRef] [Medline]
    70. Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett. 1997;239(2-3):65-68. [CrossRef] [Medline]
    71. Cavanagh JF, Shackman AJ. Frontal midline theta reflects anxiety and cognitive control: meta-analytic evidence. J Physiol Paris. 2015;109(1-3):3-15. [FREE Full text] [CrossRef] [Medline]
    72. Cavanagh JF, Frank MJ. Frontal theta as a mechanism for cognitive control. Trends Cogn Sci. 2014;18(8):414-421. [FREE Full text] [CrossRef] [Medline]
    73. Sipp AR, Gwin JT, Makeig S, Ferris DP. Loss of balance during balance beam walking elicits a multifocal theta band electrocortical response. J Neurophysiol. 2013;110(9):2050-2060. [FREE Full text] [CrossRef] [Medline]
    74. Nordin AD, Hairston WD, Ferris DP. Human electrocortical dynamics while stepping over obstacles. Sci Rep. 2019;9(1):4693. [FREE Full text] [CrossRef] [Medline]
    75. Varghese JP, Beyer KB, Williams L, Miyasike-daSilva V, McIlroy WE. Standing still: is there a role for the cortex? Neurosci Lett. 2015;590:18-23. [CrossRef] [Medline]
    76. Hülsdünker T, Mierau A, Neeb C, Kleinöder H, Strüder HK. Cortical processes associated with continuous balance control as revealed by EEG spectral power. Neurosci Lett. 2015;592:1-5. [CrossRef] [Medline]
    77. Peterson SM, Ferris DP. Differentiation in theta and beta electrocortical activity between visual and physical perturbations to walking and standing balance. eNeuro. 2018;5(4):ENEURO.0207-18.2018. [FREE Full text] [CrossRef] [Medline]
    78. Solis-Escalante T, van der Cruijsen J, de Kam D, van Kordelaar J, Weerdesteyn V, Schouten AC. Cortical dynamics during preparation and execution of reactive balance responses with distinct postural demands. Neuroimage. 2019;188:557-571. [FREE Full text] [CrossRef] [Medline]
    79. Marshall GA, Sikkes SAM, Amariglio RE, Gatchel JR, Rentz DM, Johnson KA, et al. Instrumental activities of daily living, amyloid, and cognition in cognitively normal older adults screening for the A4 Study. Alzheimers Dement (Amst). 2020;12(1):e12118. [FREE Full text] [CrossRef] [Medline]
    80. Lang PJ, Bradley MM, Cuthbert BN. International Affective Picture System (IAPS): affective ratings of pictures and instructional manual. Scientific Research Publishing Inc. 2008. URL: https://www.scirp.org/reference/referencespapers?referenceid=755311 [accessed 2024-05-01] [WebCite Cache]
    81. Kothe CA. The Artifact Subspace Reconstruction Method. Intheon. URL: https://sccn.ucsd.edu/githubwiki/files/asr-final-export.pdf [accessed 2024-05-01]
    82. Oostenveld R, Oostendorp TF. Validating the boundary element method for forward and inverse EEG computations in the presence of a hole in the skull. Hum Brain Mapp. 2002;17(3):179-192. [FREE Full text] [CrossRef] [Medline]
    83. Delorme A, Palmer J, Onton J, Oostenveld R, Makeig S. Independent EEG sources are dipolar. PLoS One. 2012;7(2):e30135. [FREE Full text] [CrossRef] [Medline]
    84. Artoni F, Menicucci D, Delorme A, Makeig S, Micera S. RELICA: a method for estimating the reliability of independent components. Neuroimage. 2014;103:391-400. [FREE Full text] [CrossRef] [Medline]
    85. Snyder KL, Kline JE, Huang HJ, Ferris DP. Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking. Front Hum Neurosci. 2015;9:639. [FREE Full text] [CrossRef] [Medline]
    86. Wagner J, Solis-Escalante T, Grieshofer P, Neuper C, Müller-Putz G, Scherer R. Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. Neuroimage. 2012;63(3):1203-1211. [CrossRef] [Medline]
    87. Gwin JT, Gramann K, Makeig S, Ferris DP. Electrocortical activity is coupled to gait cycle phase during treadmill walking. Neuroimage. 2011;54(2):1289-1296. [CrossRef] [Medline]
    88. Makeig S. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr Clin Neurophysiol. 1993;86(4):283-293. [CrossRef] [Medline]
    89. Rajan KB, Hebert LE, Scherr PA, de Leon CFM, Evans DA. Disability in basic and instrumental activities of daily living is associated with faster rate of decline in cognitive function of older adults. J Gerontol A Biol Sci Med Sci. 2013;68(5):624-630. [FREE Full text] [CrossRef] [Medline]
    90. Hu Y, Kirmess KM, Meyer MR, Rabinovici GD, Gatsonis C, Siegel BA, et al. Assessment of a plasma amyloid probability score to estimate amyloid positron emission tomography findings among adults with cognitive impairment. JAMA Netw Open. 2022;5(4):e228392. [FREE Full text] [CrossRef] [Medline]
    91. Schmidt P, Gaser C, Arsic M, Buck D, Förschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage. 2012;59(4):3774-3783. [CrossRef] [Medline]
    92. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11-22. [FREE Full text] [CrossRef] [Medline]
    93. Wardlaw JM, Valdés Hernández MC, Muñoz-Maniega S. What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc. 2015;4(6):001140. [FREE Full text] [CrossRef] [Medline]
    94. Jansen W, Janssen O, Tijms BM, Vos SJB, Ossenkoppele R, Visser PJ, Amyloid Biomarker Study Group, et al. Prevalence estimates of amyloid abnormality across the Alzheimer disease clinical spectrum. JAMA Neurol. 2022;79(3):228-243. [FREE Full text] [CrossRef] [Medline]
    95. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry. 2001;70(1):9-14. [FREE Full text] [CrossRef] [Medline]
    96. Haley SM, Jette AM, Coster WJ, Kooyoomjian JT, Levenson S, Heeren T, et al. Late life function and disability instrument: II. Development and evaluation of the function component. J Gerontol A Biol Sci Med Sci. 2002;57(4):M217-M222. [FREE Full text] [CrossRef] [Medline]
    97. Jette AM, Haley SM, Coster WJ, Kooyoomjian JT, Levenson S, Heeren T, et al. Late life function and disability instrument: I. Development and evaluation of the disability component. J Gerontol A Biol Sci Med Sci. 2002;57(4):M209-M216. [FREE Full text] [CrossRef] [Medline]
    98. Richer N, Downey RJ, Hairston WD, Ferris DP, Nordin AD. Motion and muscle artifact removal validation using an electrical head phantom, robotic motion platform, and dual layer mobile EEG. IEEE Trans Neural Syst Rehabil Eng. 2020;28(8):1825-1835. [FREE Full text] [CrossRef] [Medline]
    99. Clark DJ, Manini TM, Ferris DP, Hass CJ, Brumback BA, Cruz-Almeida Y, et al. Multimodal imaging of brain activity to investigate walking and mobility decline in older adults (mind in motion study): hypothesis, theory, and methods. Front Aging Neurosci. 2019;11:358. [FREE Full text] [CrossRef] [Medline]
    100. Nordin AD, Hairston WD, Ferris DP. Dual-electrode motion artifact cancellation for mobile electroencephalography. J Neural Eng. 2018;15(5):056024. [FREE Full text] [CrossRef] [Medline]
    101. Symeonidou ER, Nordin AD, Hairston WD, Ferris DP. Effects of cable sway, electrode surface area, and electrode mass on electroencephalography signal quality during motion. Sensors (Basel). 2018;18(4):1073. [FREE Full text] [CrossRef] [Medline]
    102. Oliveira AS, Schlink BR, Hairston WD, König P, Ferris DP. A channel rejection method for attenuating motion-related artifacts in EEG recordings during walking. Front Neurosci. 2017;11:225. [FREE Full text] [CrossRef] [Medline]
    103. Kline JE, Huang HJ, Snyder KL, Ferris DP. Isolating gait-related movement artifacts in electroencephalography during human walking. J Neural Eng. 2015;12(4):046022. [FREE Full text] [CrossRef] [Medline]
    104. Gwin JT, Gramann K, Makeig S, Ferris DP. Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol. 2010;103(6):3526-3534. [FREE Full text] [CrossRef] [Medline]
    105. Verghese J, Robbins M, Holtzer R, Zimmerman M, Wang C, Xue X, et al. Gait dysfunction in mild cognitive impairment syndromes. J Am Geriatr Soc. 2008;56(7):1244-1251. [FREE Full text] [CrossRef] [Medline]
    106. Oh-Park M, Holtzer R, Xue X, Verghese J. Conventional and robust quantitative gait norms in community-dwelling older adults. J Am Geriatr Soc. 2010;58(8):1512-1518. [FREE Full text] [CrossRef] [Medline]
    107. Walsh SP, Raman R, Jones KB, Aisen PS, Alzheimer's Disease Cooperative Study Group. ADCS prevention instrument project: the Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord. 2006;20(4 Suppl 3):S170-S178. [CrossRef] [Medline]

    ‎
    Aβ: amyloid-β
    AD: Alzheimer disease
    ADL-PI: Activities of Daily Living–Prevention Instrument
    AECOM: Albert Einstein College of Medicine
    APS: amyloid probability score
    AT(N): amyloid-β, phosphorylated τ, neurofilament
    CdF: complex daily function
    ERSP: event-related spectral perturbation
    grEEG: gait-related electroencephalographic
    IC: independent component
    IRB: institutional review board
    MCI: mild cognitive impairment
    MoCA: Montreal Cognitive Assessment
    MRI: magnetic resonance imaging
    PI: principal investigator
    VSI: visual-somatosensory integration
    WMH: white matter hyperintensities

    Edited by A Mavragani; The proposal for this study was peer reviewed by ASG - Aging Systems and Geriatrics Study Section - National Institutes of Health (NIH), United States of America (USA). See the Multimedia Appendix for the peer-review report; submitted 30.01.24; accepted 20.02.24; published 06.06.24.

    Copyright

    ©Pierfilippo De Sanctis, Jeannette R Mahoney, Johanna Wagner, Helena M Blumen, Wenzhu Mowrey, Emmeline Ayers, Claudia Schneider, Natasha Orellana, Sophie Molholm, Joe Verghese. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 06.06.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.