Designing a Spatial Navigation Intervention Protocol Informed by Region-specific Brain Activation for Mild Cognitive Impairment

NCT ID: NCT07225400

Last Updated: 2025-11-06

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

30 participants

Study Classification

INTERVENTIONAL

Study Start Date

2026-03-01

Study Completion Date

2027-06-05

Brief Summary

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The goal of this one-arm clinical trial is to determine whether participants with mild cognitive impairment (MCI) can successfully navigate a virtual reality (VR) maze. The VR maze is designed as a training tool aimed at improving participants' spatial navigation abilities.

Main Aims:

1. To determine whether at least 70% of older adults enrolled in the study can complete twenty-four 50-minute training sessions over a 4-month period.
2. To assess whether combining virtual reality with EEG recordings can be used to measure brain activation and changes in brain activation associated with spatial navigation learning.

Participants will:

1. Walk in an open, unobstructed space while wearing VR goggles.
2. Explore up to fifty different virtual mazes in sequence and attempt to find their way through each one.

Detailed Description

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Specific Aims: With no cure available yet, lifestyle interventions to delay onset of Alzheimer's disease and related dementias are essential. Up to a third of dementia cases may be preventable by engaging in protective behaviors, such as staying cognitively active, according to observational data. Yet, cognitive training protocols to delay onset often fall short. The investigator team hypothesizes that designing an intervention informed by specific measurements from brain regions subserving cognition will yield better results.

This pilot project is focused on spatial navigation (SN), the ability to travel familiar/unfamiliar environments, which is particularly suited as a target for early intervention. Difficulties forming new and maintaining old spatial memories is a common and early sign of Alzheimer's disease and can lead to disorientation and dependence in performing daily activities. Tau and amyloid-beta accumulation starts in regions subserving SN, such as mediotemporal and posterior parietal cortex. Even though SN difficulties present an important target, there are few clinical trials aimed at SN. Many trials are desktop- or virtual reality-based with learners using a handheld device to train SN. A major limitation of these training methods is that it deprives learners from movement-related sensory and kinematic information during active navigation.

To overcome this limit, a full-immersive virtual-reality (VR) maze where participants train their navigation abilities has been developed. Importantly, the maze design is informed by specific contributions of mediotemporal, posterior parietal cortex, and retrosplenial complex in SN. To quantify regions-specific contributions, the investigator team applied Mobile Brain Body Imaging (MoBI) to synchronously record body movement with EEG to record and analyze brain activity at the source level during active movement through space. The maze is designed to break down learning into two discrete periods: 1) Stand/Encode the maze from a birds-eyes view to induce allocentric spatial coding (i.e., independent from navigator's position/orientation) and 2) Walk/Navigate the maze with walls raised so that only corridors/intersections are visible during walking to induce egocentric spatial coding (i.e., navigator dependent; turn left at the next intersection). Evidence suggests that the mediotemporal cortex is involved in allocentric and posterior parietal cortex in egocentric spatial coding, with the retrosplenial complex integrating across allo- and egocentric mental frames. Using MoBI, the study team will identify and track intervention-related changes in brain activation during allo- and egocentric spatial coding. 30 community-dwelling non-demented older adults will be enrolled for a personalized supervised one-armed clinical trial SN intervention composed of 24 sessions. VR mazes gradually increase in complexity based on individual learners' progress. Dementia-at-risk status will be determined with cut-scores on validated telephone-based screeners.

Aim 1: Determine feasibility of a personalized Virtual Reality-Spatial Navigation (VR-SN) maze training protocol. The feasibility of the VR-SN maze protocol to train non-demented older adults will be examined. Feasibility metrics, including recruitment sources, implementation (i.e., intervention session completion rates), retention rates, acceptability (post-study interviews) and safety (adverse events monitoring) will be assessed using a mixture of qualitative and quantitative assessment tools.

Aim 2: Explore training-induced neuroplasticity in older adults participating in VR-SN exercises. Validated Neuroplasticity behavioral (Floor Maze test, FMT) and neurophysiological (MoBI) measures are described in the Outcome Measure sections. The FMT is a 7-by-10-foot maze taped on the floor and measures the time needed to navigate the maze, which the study team validated as a robust predictor of cognitive impairment and studies have linked to Alzheimer pathology. Neurophysiological outcomes are based on prior VR-SN MoBI measures obtained in young adults. Hypothesis 2a: VR-SN training will improve performance on the FMT. Hypothesis 2b: VR-SN training will increase modulations of retrosplenial theta power during Stand/Encode and posterior parietal alpha power during Walk/Navigate periods. Hypothesis 2c: Increased modulation in theta/alpha power will be correlated with improvement in the Floor maze test.

VR is a One-Size-Fits-One approach: VR has proven to be effective in helping patients with gross motor and balance difficulties, post-traumatic stress disorder, and cognitive impairment. The versatility of VR allows for generating diverse sets of controlled/safe scenarios designed specifically to a patient's needs. This study's VR SN approach facilitates sustained learning over time through novelty (i.e., new mazes can be created for each session), appropriate challenge (i.e., maze complexity can be matched to learner's progress), and positive feedback (i.e., reaching the maze goal). Participants often report enjoying the game-like maze exercises which is essential to sustain motivation. The investigator team argues that this VR protocol with a focus on spatial navigation may prove clinically significant, much like prior VR interventions that yielded higher gains compared to non-VR interventions. Providing learners the opportunity to actively move and navigate in virtual space (VR SN) while recording related brain activation (VR SN MoBI) will increase scientific rigor and address prior shortcomings (e.g., in-place navigation). Compared to alternative imaging approaches (e.g., MRI, PET), EEG is low-tech, fully portable, and cost-efficient. Ultimately, this may justify an ambulatory EEG protocol commonly used in clinical settings.

Innovation: SN engages spatial (e.g., allo/egocentric spatial coding) and non-spatial cognitive (e.g., attention) abilities. Furthermore, SN requires integrating and flexible switching between allocentric and egocentric mental frames. If the innovative and novel VR-SN MoBI maze protocol is developed and successfully determines region-specific brain activation associated with allo- and egocentric encoding, this will allow the complex interplay of cognitive processes that lead to disorientation and the inability to navigate familiar and unfamiliar environments to be unfolded. Differentiating and recognizing specific contributions, can lead to individualized targeted training protocols to tackle spatial and non-spatial processes, as well as issues of spatial coding related to switching/integrating allocentric and egocentric mental frames. Tracking pre/post intervention changes in region-specific brain activation of SN will provide novel candidate markers of neuroplasticity that can be tested and used as quantifiers (e.g., duration, frequency, complexity) to develop better intervention against cognitive decline and dementia.

Neuroplasticity based on the assumption that engagement in challenging mental exercises (e.g., SN) confers greater protection to respective brain regions (e.g., medial temporal cortex) and/or leads to the recruitment of novel brain regions during task performance, the study team seeks to probe intervention-related changes in EEG brain activation as a means to quantify neuroplasticity. Prior studies have yielded some promising results. For example, in stroke patients recruitment of homologous brain regions is related to recovery of sensorimotor abilities. In contrast, EEG studies probing cognitive/physical interventions and related plasticity have yielded mixed results. EEG spectral power shifts from high to lower frequency bands during dementia have been reported with a reversal in this EEG shift as well as improved cognition after 6 weeks of exercise in patients with MCI. At the same time, studies report no evidence linking cognitive training gains to EEG markers. This proposal builds and advances prior studies by probing for region- and spectral-specific brain activation within a One-Size-Fits-One SN exercise approach to quantify neuroplasticity.

APPROACH Study procedures/assessments

* Telephone Memory Impairment Screen (MIS): The MIS is a brief four-item dementia screen that assesses perceived and recent changes in memory (cut score ≤ 5, sensitivity 85%, specificity 86%).
* 8-item Dementia Screening Interview (AD8): The AD8 is an 8-item dementia screen testing memory, orientation, judgment, and daily function (cut score ≥ 1, sensitivity 74%, specificity 86%).
* Telephone-based Montreal Cognitive Assessment (T-MoCA): The T-MoCA tests different cognitive domains including attention, executive functions, memory, language, and visuospatial/constructional skills and demonstrates sufficient psychometric properties as a screen for mild cognitive impairment (cut score \> 17, sensitivity 72%, specificity 59%).
* Floor Maze Test: FMT is an ecologically valid test of SN developed by the investigator team and has been shown to predict MCI as well as correlate with Alzheimer pathology. Participants will be positioned at the entry point and instructed to find their way to the exit point. A fixed 15-second planning period will be given to plan the route. The time elapsed from the end of the planning period to successful exit will be recorded (immediate maze time \[IMT\], seconds).
* MoBI: navigation-related modulations in region-specific theta and alpha power during Stand/Encode and Walk/Navigate periods using block- and event-related (heel-strike) spectral analysis approaches as detailed in prior MoBI work.

Feasibility domains and benchmarks for success. Aim 1 seeks to test the feasibility of methods and procedures for later use on a larger scale. The study team proposes a 10-day rotational schedule to train thirty older adults participating in 24 VR SN maze sessions over a period of 4 months. A staggered protocol will be applied, with 15 older adults (2 sessions within 10 days) trained in months 6-9 and 15 older adults trained in months 10-14 of this 2-year proposal. Primary criteria for determining success: 1) at least 60% of all eligible older adults can be recruited and 2) 70% of older adults can be trained with the rotational 10-day program (i.e., 378 sessions over 12 months). In addition, data using the classification proposed by Thabane et.al. (see References section) will be collected. Mixed methods using quantitative and qualitative measures to assess feasibility will be applied.

Sample, recruitment, and screening. A random sample of older adults (≥65 of age) will be identified through various means: information materials and presentation at the Fort Washington Senior Center and lists of volunteers that participated in previous studies at Albert Einstein College of Medicine (Division of Cognitive and Motor Aging). Those expressing interest will be screened over the phone using 'dementia-at-risk' cut-scores on either the MIS (\>3 to ≤6) and the AD8 (≥1). Participants enrolled will have a spectrum of cognitive function ranging from normal to Mild Cognitive Impairment (T-MoCA ≤ 17). This will be accounted for in multiple ways. 1. Adjusting for MCI status in analysis; 2. Adjusting for T-MoCA score; 3. Conducting stratified analysis by MCI status.

Intervention Protocol VR maze session 1: Start with an individualized, face-to-face introductory session to describe and answer questions about the protocol. Participants will test the VR goggles, EEG cap, and become familiarized with the VR maze task. VR-SN MoBI session 2 (baseline): Each session lasts 50 minutes. Initial maze complexity will be set at the lowest level (i.e., one turn to reach target). Navigation targets can be dropped anywhere within the maze to manipulate navigation complexity in accordance with learner's needs, resources, and progress. A maze will be repeated until performed without errors after which a new maze is introduced. Many VR mazes are pre-programmed to ensure that exercise is feasible, novel and challenging to incentivize long-term SN training. An up-down transformed rule (UDTR) will be used to adjust complexity based on a participant's performance. A three-up/one-down rule, meaning that for three consecutive error-free mazes will be used where the complexity will be adjusted by introducing an additional turn-to-target and for any error the number of turns reduced to reach the target by one. VR maze sessions 3-23 (no MoBI): Participants take part in two sessions (50 minutes, including breaks) within 10 days over a 4-month period. VR-SN MoBI sessions 12 \& 24: Participants are trained on the VR maze while MoBI is recorded.

Safety Participation in immersive virtual reality can result in nausea and dizziness in some individuals. To ensure safety, the research assistant will be at the participant's side to spot participants as they ambulate through empty space to ensure safety. Breaks after each learned maze are encouraged. Continued exposure to VR will be limited to a maximum of 10 minutes to allow participants to re-engage with the physical world. A recent meta-analysis in patients with MCI/dementia participating in VR interventions reported no significant increases of adverse effects for VR protocols. All interventions are conducted in medical facilities.

EXPERIMENTAL APPROACH

Details regarding Aim 1 are provided for reference only, to ensure a comprehensive account of the study. The registration of this proposal in ClinicalTrials.gov pertains exclusively to Aim 2.

Aim 1: Determine feasibility of the VR-SN maze training Rationale: Gathering preliminary results about the feasibility of VR-SN exercises for non-demented older adults will help optimize the protocol for a future randomized clinical trial.

Aim 2: Explore training-induced neuroplasticity in older adults following a VR-SN maze training Rationale: Hypotheses are VR-SN maze training reduces the time required to navigate the Floor maze and increases modulations of theta/alpha power; Floor maze time is correlated with theta/alpha power change.

Conditions

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Mild Cognitive Impairment (MCI)

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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Full-immersive virtual-reality (VR) maze

Virtual reality maze session 1: Individualized, face-to-face introductory session to describe and answer questions about the protocol. Virtual reality EEG session 2 (baseline): Each session lasts 50 minutes. Initial maze complexity will be set at the lowest level (i.e., one turn to reach target). A maze will be repeated until performed without errors after which a new maze is introduced. The up-down transformed rule will be used to adjust complexity based on a participant's performance. Specifically, a three-up/one-down rule, meaning that for three consecutive error-free mazes the complexity of the maze will be adjusted by introducing an additional turn-to-target and for any error the number of turns to reach the target will be reduced by one. Virtual reality maze sessions 3-23 (no EEG): Participants take part in six sessions within 10 days over a 4-month period. Virtual reality-SN EEG sessions 12 \& 24: Participants are trained on the virtual reality maze while EEG is recorded.

Group Type EXPERIMENTAL

Spatial Navigation training

Intervention Type BEHAVIORAL

A full-immersive virtual-reality environment where participants train ability to navigate and find their way through a maze in virtual reality has been developed. The virtual-reality environment is well-suited to maintain learner motivation throughout the intervention by providing appropriate challenges (i.e., maze complexity can be adjusted to the learner's progress), positive feedback (i.e., reaching the maze goal), and novelty (i.e., new mazes for each session). 50 different VR mazes, varying in difficulty from 1 to 4 intersections, have been built.

Interventions

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Spatial Navigation training

A full-immersive virtual-reality environment where participants train ability to navigate and find their way through a maze in virtual reality has been developed. The virtual-reality environment is well-suited to maintain learner motivation throughout the intervention by providing appropriate challenges (i.e., maze complexity can be adjusted to the learner's progress), positive feedback (i.e., reaching the maze goal), and novelty (i.e., new mazes for each session). 50 different VR mazes, varying in difficulty from 1 to 4 intersections, have been built.

Intervention Type BEHAVIORAL

Eligibility Criteria

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Inclusion Criteria

* Age 65 and older with amnestic mild cognitive impairment (aMCI);
* Can speak English;
* Agrees to MoBI recording;
* Normal or corrected-to-normal vision/audition;
* Able to walk unassisted for 10 minutes;
* Plan to be in the area for next year

Exclusion Criteria

* Dementia (Memory Impairment/AD8 screen);
* Medical conditions that affect participation such as vertigo and neck pain;
* Hospitalization in the past six months or plans for surgery affecting participation in the next four months;
* Mobility limitations solely due to musculoskeletal limitation or pain;
* Terminal illness with life expectancy less than 12 months;
* Presence of clinical disorders that overtly alter attention like delirium;
* Active psychoses or psychiatric symptoms;
* Living in nursing home;
* Participation in intervention trial;
* Standard contraindications to EEG including seizure medication, epilepsy, stroke, traumatic brain injury;
* Pregnant women
Minimum Eligible Age

65 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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National Institute on Aging (NIA)

NIH

Sponsor Role collaborator

Albert Einstein College of Medicine

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Pierfilippo De Sanctis, PhD

Role: PRINCIPAL_INVESTIGATOR

Albert Einstein College of Medicine

Locations

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Albert Einstein College of Medicine

The Bronx, New York, United States

Site Status

Countries

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United States

Central Contacts

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Pierfilippo De Sanctis, PhD

Role: CONTACT

718-862-1828

Maya Hoff, BS

Role: CONTACT

7188397650

Facility Contacts

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Pierfilippo De Sanctis, PhD

Role: primary

718-862-1828

References

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Delaux A, de Saint Aubert JB, Ramanoel S, Becu M, Gehrke L, Klug M, Chavarriaga R, Sahel JA, Gramann K, Arleo A. Mobile brain/body imaging of landmark-based navigation with high-density EEG. Eur J Neurosci. 2021 Dec;54(12):8256-8282. doi: 10.1111/ejn.15190. Epub 2021 May 4.

Reference Type BACKGROUND
PMID: 33738880 (View on PubMed)

Meiner Z, Ayers E, Verghese J. Motoric Cognitive Risk Syndrome: A Risk Factor for Cognitive Impairment and Dementia in Different Populations. Ann Geriatr Med Res. 2020 Mar;24(1):3-14. doi: 10.4235/agmr.20.0001. Epub 2020 Mar 24.

Reference Type BACKGROUND
PMID: 32743316 (View on PubMed)

Coughlan G, Laczo J, Hort J, Minihane AM, Hornberger M. Spatial navigation deficits - overlooked cognitive marker for preclinical Alzheimer disease? Nat Rev Neurol. 2018 Aug;14(8):496-506. doi: 10.1038/s41582-018-0031-x.

Reference Type BACKGROUND
PMID: 29980763 (View on PubMed)

Galvin JE, Roe CM, Xiong C, Morris JC. Validity and reliability of the AD8 informant interview in dementia. Neurology. 2006 Dec 12;67(11):1942-8. doi: 10.1212/01.wnl.0000247042.15547.eb.

Reference Type BACKGROUND
PMID: 17159098 (View on PubMed)

WETHERILL GB, LEVITT H. SEQUENTIAL ESTIMATION OF POINTS ON A PSYCHOMETRIC FUNCTION. Br J Math Stat Psychol. 1965 May;18:1-10. doi: 10.1111/j.2044-8317.1965.tb00689.x. No abstract available.

Reference Type BACKGROUND
PMID: 14324842 (View on PubMed)

Cornwell BR, Salvadore G, Colon-Rosario V, Latov DR, Holroyd T, Carver FW, Coppola R, Manji HK, Zarate CA Jr, Grillon C. Abnormal hippocampal functioning and impaired spatial navigation in depressed individuals: evidence from whole-head magnetoencephalography. Am J Psychiatry. 2010 Jul;167(7):836-44. doi: 10.1176/appi.ajp.2009.09050614. Epub 2010 May 3.

Reference Type BACKGROUND
PMID: 20439387 (View on PubMed)

Lithfous S, Dufour A, Despres O. Spatial navigation in normal aging and the prodromal stage of Alzheimer's disease: insights from imaging and behavioral studies. Ageing Res Rev. 2013 Jan;12(1):201-13. doi: 10.1016/j.arr.2012.04.007. Epub 2012 Jul 5.

Reference Type BACKGROUND
PMID: 22771718 (View on PubMed)

Placido J, de Almeida CAB, Ferreira JV, de Oliveira Silva F, Monteiro-Junior RS, Tangen GG, Laks J, Deslandes AC. Spatial navigation in older adults with mild cognitive impairment and dementia: A systematic review and meta-analysis. Exp Gerontol. 2022 Aug;165:111852. doi: 10.1016/j.exger.2022.111852. Epub 2022 May 27.

Reference Type BACKGROUND
PMID: 35644416 (View on PubMed)

Montero-Odasso M, Ismail Z, Livingston G. One third of dementia cases can be prevented within the next 25 years by tackling risk factors. The case "for" and "against". Alzheimers Res Ther. 2020 Jul 8;12(1):81. doi: 10.1186/s13195-020-00646-x.

Reference Type BACKGROUND
PMID: 32641088 (View on PubMed)

Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, Ballard C, Banerjee S, Burns A, Cohen-Mansfield J, Cooper C, Fox N, Gitlin LN, Howard R, Kales HC, Larson EB, Ritchie K, Rockwood K, Sampson EL, Samus Q, Schneider LS, Selbaek G, Teri L, Mukadam N. Dementia prevention, intervention, and care. Lancet. 2017 Dec 16;390(10113):2673-2734. doi: 10.1016/S0140-6736(17)31363-6. Epub 2017 Jul 20. No abstract available.

Reference Type BACKGROUND
PMID: 28735855 (View on PubMed)

Fricke M, Morawietz C, Wunderlich A, Muehlbauer T, Jansen CP, Gramann K, Wollesen B. Successful wayfinding in age: A scoping review on spatial navigation training in healthy older adults. Front Psychol. 2022 Aug 16;13:867987. doi: 10.3389/fpsyg.2022.867987. eCollection 2022.

Reference Type BACKGROUND
PMID: 36051192 (View on PubMed)

Lovden M, Schaefer S, Noack H, Bodammer NC, Kuhn S, Heinze HJ, Duzel E, Backman L, Lindenberger U. Spatial navigation training protects the hippocampus against age-related changes during early and late adulthood. Neurobiol Aging. 2012 Mar;33(3):620.e9-620.e22. doi: 10.1016/j.neurobiolaging.2011.02.013. Epub 2011 Apr 16.

Reference Type BACKGROUND
PMID: 21497950 (View on PubMed)

Colombo D, Serino S, Tuena C, Pedroli E, Dakanalis A, Cipresso P, Riva G. Egocentric and allocentric spatial reference frames in aging: A systematic review. Neurosci Biobehav Rev. 2017 Sep;80:605-621. doi: 10.1016/j.neubiorev.2017.07.012. Epub 2017 Jul 29.

Reference Type BACKGROUND
PMID: 28760627 (View on PubMed)

Katz MJ, Wang C, Nester CO, Derby CA, Zimmerman ME, Lipton RB, Sliwinski MJ, Rabin LA. T-MoCA: A valid phone screen for cognitive impairment in diverse community samples. Alzheimers Dement (Amst). 2021 Feb 5;13(1):e12144. doi: 10.1002/dad2.12144. eCollection 2021.

Reference Type BACKGROUND
PMID: 33598528 (View on PubMed)

Ralph H.B. Benedict, D. S., Lowell Groninger, Jason Brandt. Hopkins Verbal Learning Test â€" Revised: Normative Data and Analysis of Inter-Form and Test-Retest Reliabilit. The Clinical Neuropsychologist 12, 12 (2010).

Reference Type BACKGROUND

Martelli D, Prado A, Xia B, Verghese J, Agrawal SK. Development of a Virtual Floor Maze Test - Effects of Distal Visual Cues and Correlations With Executive Function in Healthy Adults. IEEE Trans Neural Syst Rehabil Eng. 2019 Oct;27(10):2229-2236. doi: 10.1109/TNSRE.2019.2938103. Epub 2019 Aug 28.

Reference Type BACKGROUND
PMID: 31478863 (View on PubMed)

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 Feb;122:1-11. doi: 10.1016/j.neurobiolaging.2022.11.005. Epub 2022 Nov 9.

Reference Type BACKGROUND
PMID: 36463848 (View on PubMed)

Verghese J, De Sanctis P, Ayers E. Everyday function profiles in prodromal stages of MCI: Prospective cohort study. Alzheimers Dement. 2023 Feb;19(2):498-506. doi: 10.1002/alz.12681. Epub 2022 Apr 26.

Reference Type BACKGROUND
PMID: 35472732 (View on PubMed)

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 Dec;54(12):8075-8080. doi: 10.1111/ejn.15562.

Reference Type BACKGROUND
PMID: 34904290 (View on PubMed)

Galvin JE, Roe CM, Powlishta KK, Coats MA, Muich SJ, Grant E, Miller JP, Storandt M, Morris JC. The AD8: a brief informant interview to detect dementia. Neurology. 2005 Aug 23;65(4):559-64. doi: 10.1212/01.wnl.0000172958.95282.2a.

Reference Type BACKGROUND
PMID: 16116116 (View on PubMed)

Lipton RB, Katz MJ, Kuslansky G, Sliwinski MJ, Stewart WF, Verghese J, Crystal HA, Buschke H. Screening for dementia by telephone using the memory impairment screen. J Am Geriatr Soc. 2003 Oct;51(10):1382-90. doi: 10.1046/j.1532-5415.2003.51455.x.

Reference Type BACKGROUND
PMID: 14511157 (View on PubMed)

Ashendorf L, Jefferson AL, Green RC, Stern RA. Test-retest stability on the WRAT-3 reading subtest in geriatric cognitive evaluations. J Clin Exp Neuropsychol. 2009 Jul;31(5):605-10. doi: 10.1080/13803390802375557. Epub 2008 Sep 27.

Reference Type BACKGROUND
PMID: 18821160 (View on PubMed)

Brink, T. L., Yesavage, J.A.., Lum, O., Heersema, P.H., Adey, M., & Rose, T.L. . Screening tests for depression. Clinical Gerontologist 1, 37-43 (1982).

Reference Type BACKGROUND

Verghese J, Lipton R, Ayers E. Spatial navigation and risk of cognitive impairment: A prospective cohort study. Alzheimers Dement. 2017 Sep;13(9):985-992. doi: 10.1016/j.jalz.2017.01.023. Epub 2017 Mar 3.

Reference Type BACKGROUND
PMID: 28264767 (View on PubMed)

Sanders AE, Holtzer R, Lipton RB, Hall C, Verghese J. Egocentric and exocentric navigation skills in older adults. J Gerontol A Biol Sci Med Sci. 2008 Dec;63(12):1356-63. doi: 10.1093/gerona/63.12.1356.

Reference Type BACKGROUND
PMID: 19126849 (View on PubMed)

Tangen GG, Nilsson MH, Stomrud E, Palmqvist S, Hansson O. Spatial Navigation and Its Association With Biomarkers and Future Dementia in Memory Clinic Patients Without Dementia. Neurology. 2022 Nov 8;99(19):e2081-e2091. doi: 10.1212/WNL.0000000000201106. Epub 2022 Aug 26.

Reference Type BACKGROUND
PMID: 36028328 (View on PubMed)

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 Nov 1;120(5):2246-2259. doi: 10.1152/jn.00079.2018. Epub 2018 Aug 1.

Reference Type BACKGROUND
PMID: 30067106 (View on PubMed)

Miyakoshi M, Gehrke L, Gramann K, Makeig S, Iversen J. The AudioMaze: An EEG and motion capture study of human spatial navigation in sparse augmented reality. Eur J Neurosci. 2021 Dec;54(12):8283-8307. doi: 10.1111/ejn.15131. Epub 2021 Feb 23.

Reference Type BACKGROUND
PMID: 33497490 (View on PubMed)

Do TN, Lin CT, Gramann K. Human brain dynamics in active spatial navigation. Sci Rep. 2021 Jun 22;11(1):13036. doi: 10.1038/s41598-021-92246-4.

Reference Type BACKGROUND
PMID: 34158525 (View on PubMed)

Luna TD, Santamaria V, Agrawal SK. Redistributing Ground Reaction Forces During Squatting Using a Cable-Driven Robotic Device. IEEE Int Conf Rehabil Robot. 2022 Jul;2022:1-6. doi: 10.1109/ICORR55369.2022.9896494.

Reference Type BACKGROUND
PMID: 36176099 (View on PubMed)

Inzitari M, Metti A, Rosano C, Udina C, Perez LM, Carrizo G, Verghese J, Newman AB, Studenski S, Rosso 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 Sep;124:110652. doi: 10.1016/j.exger.2019.110652. Epub 2019 Jul 6.

Reference Type BACKGROUND
PMID: 31288087 (View on PubMed)

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 Jul;15(7):870-877. doi: 10.1016/j.jalz.2019.03.011. Epub 2019 Jun 1.

Reference Type BACKGROUND
PMID: 31164315 (View on PubMed)

Papaioannou T, Voinescu A, Petrini K, Stanton Fraser D. Efficacy and Moderators of Virtual Reality for Cognitive Training in People with Dementia and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis. J Alzheimers Dis. 2022;88(4):1341-1370. doi: 10.3233/JAD-210672.

Reference Type BACKGROUND
PMID: 35811514 (View on PubMed)

Thabane L, Ma J, Chu R, Cheng J, Ismaila A, Rios LP, Robson R, Thabane M, Giangregorio L, Goldsmith CH. A tutorial on pilot studies: the what, why and how. BMC Med Res Methodol. 2010 Jan 6;10:1. doi: 10.1186/1471-2288-10-1.

Reference Type BACKGROUND
PMID: 20053272 (View on PubMed)

Gehrke L, Lopes P, Klug M, Akman S, Gramann K. Neural sources of prediction errors detect unrealistic VR interactions. J Neural Eng. 2022 May 6;19(3). doi: 10.1088/1741-2552/ac69bc.

Reference Type BACKGROUND
PMID: 35462356 (View on PubMed)

Gehrke L, Gramann K. Single-trial regression of spatial exploration behavior indicates posterior EEG alpha modulation to reflect egocentric coding. Eur J Neurosci. 2021 Dec;54(12):8318-8335. doi: 10.1111/ejn.15152. Epub 2021 Mar 11.

Reference Type BACKGROUND
PMID: 33609299 (View on PubMed)

Gramann K, Hohlefeld FU, Gehrke L, Klug M. Human cortical dynamics during full-body heading changes. Sci Rep. 2021 Sep 14;11(1):18186. doi: 10.1038/s41598-021-97749-8.

Reference Type BACKGROUND
PMID: 34521939 (View on PubMed)

Sharma G, Gramann K, Chandra S, Singh V, Mittal AP. Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills. Brain Inform. 2017 Sep;4(3):207-217. doi: 10.1007/s40708-017-0066-6. Epub 2017 May 16.

Reference Type BACKGROUND
PMID: 28510210 (View on PubMed)

Chiu TC, Gramann K, Ko LW, Duann JR, Jung TP, Lin CT. Alpha modulation in parietal and retrosplenial cortex correlates with navigation performance. Psychophysiology. 2012 Jan;49(1):43-55. doi: 10.1111/j.1469-8986.2011.01270.x. Epub 2011 Aug 8.

Reference Type BACKGROUND
PMID: 21824156 (View on PubMed)

Malcolm BR, Foxe JJ, Joshi S, Verghese J, Mahoney JR, Molholm S, De Sanctis P. Aging-related changes in cortical mechanisms supporting postural control during base of support and optic flow manipulations. Eur J Neurosci. 2021 Dec;54(12):8139-8157. doi: 10.1111/ejn.15004. Epub 2020 Oct 27.

Reference Type BACKGROUND
PMID: 33047390 (View on PubMed)

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 Nov;17(3):179-92. doi: 10.1002/hbm.10061.

Reference Type BACKGROUND
PMID: 12391571 (View on PubMed)

Delorme A, Palmer J, Onton J, Oostenveld R, Makeig S. Independent EEG sources are dipolar. PLoS One. 2012;7(2):e30135. doi: 10.1371/journal.pone.0030135. Epub 2012 Feb 15.

Reference Type BACKGROUND
PMID: 22355308 (View on PubMed)

Artoni F, Menicucci D, Delorme A, Makeig S, Micera S. RELICA: a method for estimating the reliability of independent components. Neuroimage. 2014 Dec;103:391-400. doi: 10.1016/j.neuroimage.2014.09.010. Epub 2014 Sep 16.

Reference Type BACKGROUND
PMID: 25234117 (View on PubMed)

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 Dec 1;9:639. doi: 10.3389/fnhum.2015.00639. eCollection 2015.

Reference Type BACKGROUND
PMID: 26648858 (View on PubMed)

Wagner J, Solis-Escalante T, Grieshofer P, Neuper C, Muller-Putz G, Scherer R. Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. Neuroimage. 2012 Nov 15;63(3):1203-11. doi: 10.1016/j.neuroimage.2012.08.019. Epub 2012 Aug 14.

Reference Type BACKGROUND
PMID: 22906791 (View on PubMed)

Gwin JT, Gramann K, Makeig S, Ferris DP. Electrocortical activity is coupled to gait cycle phase during treadmill walking. Neuroimage. 2011 Jan 15;54(2):1289-96. doi: 10.1016/j.neuroimage.2010.08.066. Epub 2010 Sep 9.

Reference Type BACKGROUND
PMID: 20832484 (View on PubMed)

Welch, P. D. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics, 70-73 (1967).

Reference Type BACKGROUND

Makeig S. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr Clin Neurophysiol. 1993 Apr;86(4):283-93. doi: 10.1016/0013-4694(93)90110-h.

Reference Type BACKGROUND
PMID: 7682932 (View on PubMed)

Wollesen B, Fricke M, Jansen CP, Gordt K, Schwenk M, Muehlbauer T, Morawietz C, Kruse A, Gramann K. A three-armed cognitive-motor exercise intervention to increase spatial orientation and life-space mobility in nursing home residents: study protocol of a randomized controlled trial in the PROfit project. BMC Geriatr. 2020 Oct 31;20(1):437. doi: 10.1186/s12877-020-01840-0.

Reference Type BACKGROUND
PMID: 33129261 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

1R21AG091161-01A1

Identifier Type: NIH

Identifier Source: secondary_id

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2024-16312

Identifier Type: -

Identifier Source: org_study_id

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