Neurological Frontiers: Deciphering the Complexities of Brain Structure and Function in Health and Disease

Stephanie Forkel Chair
Stephanie Forkel
Stephanie Forkel
Nijmegen, Gelderland 
Netherlands
 
Valentina Pacella Chair
University School for Advanced Studies (IUSS-Pavia)
Pavia, Pavia 
Italy
 
Wednesday, Jun 26: 10:30 AM - 11:45 AM
Oral Sessions 
COEX 
Room: Grand Ballroom 103 
This session delves into the intricate world of neurology, presenting a spectrum of groundbreaking research that spans from detailed microstructural analysis of white matter to the expansive study of brain connectivity in a variety of neurological conditions, including HIV, Alzheimer's, Parkinson's disease, and stroke. It showcases the innovative application of advanced techniques, such as deep learning, not just to enhance our understanding of these disorders, but also to pioneer new frontiers in predicting their progression. Central to each presentation is a shared focus: a comprehensive exploration of the brain's architecture and its dynamic functions, navigating the complex interplay between healthy states and pathological alterations. Attendees will gain insights into the latest methodologies and findings that are shaping the future of neuroscientific inquiry.

Presentations

A comprehensive exploration of longitudinal white matter microstructure and cognitive trajectories

The primary clinical manifestation of Alzheimer's disease (AD) is cognitive impairment and longitudinal cognitive decline, and several prior diffusion MRI studies have investigated the association between white matter microstructure and cognitive decline in normal aging and AD1–7. Recent work from our group explored the free-water (FW)-corrected associations with longitudinal scores of memory and executive function and found that medial temporal lobe tracts were significantly associated with both domains8. One interesting finding from this prior study is that the FW component, which is a separate 3D map which is created in the FW-correction pipeline, is particularly sensitive to cognitive impairment and decline. This is in line with several prior studies which have demonstrated similar findings in other neurodegenerative diseases. While these studies have been foundational to our understanding of white matter contributions to cognitive impairment and decline, large-scale studies using harmonized scores of cognitive function would drastically enhance our understanding by elucidating which white matter tracts are most vulnerable in individuals with cognitive decline. 

View Abstract 156

Presenter

Derek Archer, PhD, Vanderbilt University Medical Center Nashville, TN 
United States

ENIGMA-HIV: White matter microstructural abnormalities in a global sample of people living with HIV

HIV remains a global public health challenge with an estimated 39 million people living with HIV [1]. Despite widespread access to antiretroviral therapy (ART), neurocognitive impairment is a persistent issue in people living with chronic HIV infection [2]. Persistent HIV viral reservoir instigates an inflammatory cascade that leads to neural dysfunction, often accompanied by white matter (WM) damage. However, clinical and demographic heterogeneity in people with HIV (PwH) worldwide, and variations in MRI acquisition, processing, and analysis methods yielded inconsistencies in reported HIV-related WM differences detected across studies. Here, we pooled diffusion MRI (dMRI) data from ten independent worldwide neuroHIV studies as part of the ENIGMA-HIV consortium [3]; we aimed to identify generalizable WM microstructural associations with infection using standardized data analysis pipelines. 

View Abstract 158

Presenter

Talia Nir, PhD, University of Southern California Keck School of Medicine
Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute
Marina del Rey, CA 
United States

Functional connectivity reorganization over age and Alzheimer’s disease

Cognitive aging is a phenomenon that eventually affects most elderly individuals. This process is accelerated in neurodegenerative diseases like Alzheimer's disease (AD), which involve clinical impairment and decline in functional activities of daily living. Aging is accompanied by changes in brain functional network organization, with one of the hallmarks being decrease in system segregation (1). Similarly, nonlinear alterations to functional networks have been described in AD, posited as early neuronal responses to (and perhaps drivers of) AD pathophysiology (2). Functional changes in aging and AD have, however, mostly been studied in isolation, and the degree to which these phenomena interrelate is not well understood. Further, little is known about how variable such changes are in the population. In this exploratory study, we investigate how the brain's functional networks are reorganized at the individual level in aging and AD independently. 

View Abstract 282

Presenter

Jonathan Rittmo, Lund University Malmö, Scania 
Sweden

Assessing the Sensitivity of Brain-Age to Alzheimer's Disease in different Ethnic Groups

Alzheimer's Disease is the most common neurodegenerative disease and cause of dementia [1,2]. The global burden of dementia is growing, with the number of people living with dementia projected to increase to 152 million by 2050. This growth is estimated to rise particularly in low and middle-income countries [2]. Although there have been advances regarding predicting dementia onset and progression, it is important that the performance of these research outputs are verified in different populations. Additionally, there is a lack of literature examining the potential impact of ethnic and racial factors [3, 4]. Brain-age is an index of the brain's biological age derived from structural imaging. It correlates with an increased risk of dementia in memory clinic patients and has the potential to aid in early dementia diagnosis [5]. However, a significant portion of the brain-age literature uses less diverse cohorts [5, 2]. Thus, this research aims to investigate the sensitivity and generalizability of brain-age in non-white individuals. 

View Abstract 197

Presenter

Zeena Shawa, MRes, University College London
Department of Medical Physics and Biomedical Engineering
London, England 
United Kingdom

Synergic cholinergic and dopaminergic role in motor symptoms of sporadic Parkinson’s disease

Parkinson's Disease (PD) is a dominant neurodegenerative disease, characterized with various motor symptoms.[1] Despite the prevailing dopaminergic treatments for PD motor symptoms,[2] no disease-modifying drugs exist,[3] which implicates the potential involvement of non-dopaminergic neurotransmitter systems. Previous autopsy study indicated that the α-synuclein deposition in nucleus basalis of Meynert (NbM) occurs as early as the Lewy bodies formation and dopaminergic neurons loss in substantia nigra (SN).[4] However, existing works usually explore the association between cholinergic/dopaminergic subcomponents and specific motor symptoms.[5-7] To date, there is a lack of systematic exploration for the relationship between all cholinergic/dopaminergic components and various motor symptoms. Here, using imaging data from Parkinson's Progression Markers Initiative (PPMI), we characterize the cross-sectional and longitudinal role of multimodal cholinergic/dopaminergic regional measurements in motor symptoms. By dividing the PD patients into those present stable (Any) or no/unstable (Never) non-motor symptoms, we also explore the impact of non-motor symptoms on the roles of cholinergic/dopaminergic system. 

View Abstract 277

Presenter

Peng Ren, Fudan University Shanghai, Shanghai 
China

Deep Learning disconnectomes to accelerate & improve long-term predictions for post-stroke symptoms

White matter connections are recognized as fundamental building blocks of behavior and cognition, and their disconnections can be quantified to facilitate personalized prediction. This is particularly relevant in the context of stroke that is going to damage a specific brain region but also disconnect several remote areas. Being able to anticipate the risk of developing motor, cognitive, and emotional impairments following stroke could help to refer the patients to dedicated training to improve their outcomes. With the rise of Artificial Intelligence applications in healthcare, we explored and evaluated the potential of deep-learning models to accurately generate disconnectomes in a population of stroke survivors in order to speed up and accelerate the individualized prediction of neuropsychological scores one year post-stroke. 

View Abstract 2182

Presenter

Anna Matsulevits, University Bordeaux, Institut des Maladies Neurodégénératives CNRS UMR 5293 Université de Bordeaux
Neurofunctional Imaging Group
Bordeaux, Gironde 
France