NEUROPSYCHOLOGY COGNITION AND MOBILITY LAB
Current Projects
CENTRAL CONTROL AND NEUROINFLAMMATORY MECHANISMS OF LOCOMOTION IN OLDER ADULTS WITH HIV
(NIH Grant: R01NS127697; Institute: NINDS; PIs: Roee Holtzer & Anjali Sharma)
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Mobility impairments including gait disorders and falls are debilitating and common, yet poorly understood in older persons with HIV (OPWH). The fronto-striatal circuitry is critical for brain control of locomotion and has been shown to be disrupted in HIV. Inflammation persists in HIV despite effective antiretroviral therapy and is a key driver of cognitive impairment among persons with HIV (PWH); the frontal cortex, which supports motivation and learning, is particularly vulnerable to the adverse effects of ongoing inflammation in the context of treated HIV, placing OPWH at risk for resulting mobility disorders. The role of brain circuits and neuroinflammation in gait and falls in OPWH has not been investigated to date, including whether walking performance under attention-demanding conditions could be durably improved with training, and thus amenable to remediation. We propose to use a validated dual-task walking paradigm (predictive of falls in older persons), a burst measurement (i.e., repeated trials) design, and functional-near- infrared spectroscopy (fNIRS) to determine the effect of HIV on brain activation levels and trajectories of walking in 120 OPWH (age 3 50ys) and 120 controls without HIV. We will use multiple MRI methods to determine disruptions in the fronto-striatal circuitry and select markers of neuroinflammation to identify mechanisms of brain control of walking and risk of falls in OPWH. Brain activation patterns and learning trajectories of walking and improvements in their efficiency due to practice may be novel biomarkers to identify OPWH at risk of developing mobility impairments and falls as they survive into older age. Findings from this study will direct physical, cognitive, and pharmacological treatments to improve functional brain control of walking, which in turn will lead to interventions to reduce fall risk in OPWH.
COLLABORATIVE RESEARCH: SCH: ASSESSMENT OF COGNITIVE DECLINE USING MULTIMODAL NEUROIMAGING WITH EMBEDDED ARTIFICIAL INTELLIGENCE
(NIH Grant: R01AG077018; Institute: NIA; PIs: Roee Holtzer, Meltem Izzetoglu, & Xun Jiano)
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Alzheimer’s Disease (AD) and Alzheimer’s Disease-Related Dementias (ADRD) are highly prevalent among older individuals throughout the world. The adverse impact of cognitive decline, ranging from mild cognitive impairment (MCI) to AD and ADRD, presents not only a prohibitive financial cost but also physical, mental, and emotional burden to older adults, their caregivers, and society. MCI is a well-established risk factor for AD. However, traditional diagnostic procedures and biomarkers have limited utility in identifying alterations in brain mechanisms that underlie the cognitive decline observed in MCI. While the literature concerning neuroimaging correlates of MCI, AD and ADRD is considerable, traditional brain imaging methods are expensive, restrictive, and typically conducted separately. Moreover, research using multimodal noninvasive neuroimaging methods that can be utilized in naturalistic settings to detect brain-based signatures of MCI has been limited. Developing tools to extract such signatures can lead to the identification of novel biomarkers that can guide the development of precise, and individualized assessment and treatment of age-related cognitive decline and dementia. In this project, we will develop a toolchain for the assessment of MCI using multimodal neuroimaging and machine learning (ML) methods. We propose three specific aims: (1) to develop a comprehensive cognitive testing battery sensitive to MCI in a mobile software synchronized with multimodal functional near infrared spectroscopy and electroencephalography (fNIRS-EEG) based neuroimaging system that can concurrently provide electrophysiological, hemodynamic and behavioral measures; (2) to extract, select, and validate the multitude of within and across modality biomarkers from fNIRS-EEG data in temporal, spatial, spectral, and complexity domains together with the behavioral ones; (3) to develop a comprehensive multimodal ML approach to detect MCI based on fNIRS-EEG and behavioral features. Developing a mobile application that combines fNIRS and EEG on one platform that could be used in less expensive and restrictive testing environments to determine functional brain alterations in older adults with MCI is very innovative. The findings of this project can lead to a transformation in early detection and monitoring of cognitive decline in older adults at risk of developing AD.
BRAIN PREDICTORS OF MOBILITY AND FALLS IN OLDER ADULTS WITH MULTIPLE SCLEROSIS
(NIH Grant: R01NS109023; Institute: NINDS; PI: Roee Holtzer)
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Mobility impairments are often the most visible symptom of Multiple Sclerosis (MS) and the clinical hallmark of the disease. Falls are also very common in MS. The loss of mobility and notably the frequent falls in MS are associated with a multitude of adverse outcomes including disability and death. Recent epidemiological evidence suggests a shift in the peak prevalence of MS into older age groups, and this will likely coincide with co-occurring aging and MS-related declines in mobility and cognition. Research concerning brain systems of mobility and falls in MS, notably older adults with MS, is scarce. In aim 1 we will determine PFC HbO2 patterns associated with Single-Task-Walk (STW) and Dual-Task-Walk (DTW) in 120 MS patients and 120 controls. Using DTI, we will examine the moderating effect of white matter integrity on PFC HbO2 patterns assessed during active walking. In aim 2 we will use multi-modal neuroimaging methods to establish brain systems controlling STW and DTW in 120 MS patients and 120 healthy controls. In aim 3 we will use PFC HbO2 levels, assessed with fNIRS during DTW, to predict incident falls among 120 MS patients over a longitudinal follow-up (years 1-5). Identifying novel and potentially modifiable biomarkers of falls and mobility impairments in older adults with MS is of paramount epidemiological and clinical significance. Elucidating the mechanistic underpinnings of brain systems controlling mobility in older adults with MS will have a major impact on knowledge and important implications for treatment of mobility impairments and falls.