Tammie Benzinger^1
1Washington University in Saint Louis, School of Medicine; Mallinckrodt Institute of Radiology

Matteo Mancini¹, Marcel A. de Reus², Laura Serra³, Marco Bozzali³, Martijn van den Heuvel², Mara Cercignani^3,4, and Silvia Conforto^1
1Department of Engineering, University of Rome "Roma Tre", Rome, Italy, ²Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands, ³Neuroimaging Laboratory, IRCSS Santa Lucia Foundation, Rome, Italy, ⁴Brighton & Sussex Medical School, Clinical Imaging Sciences Centre, University of Sussex, Brighton, United Kingdom

In order to identify the white matter impairment that could lead to Alzheimer’s disease (AD), we combined computational simulations with a graph theoretical approach. We reconstructed the structural connectome of AD patients and healthy controls by means of diffusion tensor imaging, and characterized the differences between the two groups using graph theoretical measures. We then simulated neurodegeneration processes in the controls using two different heuristic algorithms. We were able to reproduce the AD disruption pattern in the controls, and we observed a relevant role of the connections between hubs and peripheral regions in the simulated damaging process.

Yong Wang^1,2,3, Qing Wang^2,4, Joshua S Shimony², Anne M Fagan^4,5, John C Morris^5,6, and Tammie L.S. Benzinger^2,6,7
1Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, MO, United States, ²Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States, ³Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States, ⁴Knight Alzheimer's Disease Research Center, St. Louis, MO, United States, ⁵Neurology, Washington University in St. Louis, St. Louis, MO, United States, ⁶Knight Alzheimer’s Disease Research Center, St. Louis, MO, United States, ⁷Neurosurgery, Washington University in St. Louis, St. Louis, MO, United States

The preclinical pathophysiology of Alzheimer’s disease (AD) is not limited to the neuronal compartments. Neuroinflammation characterized by activation of microglia and astrocytes may contribute as much to AD disease pathogenesis as do amyloid plaques and neurofibrillary tangles. We demonstrated that a novel magnetic resonance imaging technique, diffusion basis spectrum imaging (DBSI), can accurately image neuroinflammation changes that occur in preclinical AD patients. DBSI neuroinflammation biomarker can be used to identify asymptomatic subjects at highest risk of developing dementia, and lead to effective new AD disease-modifying therapies targeting neuroinflammation.

Juan Zhou^1
1Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Medical School, Singapore

Each neurodegenerative disease is defined by selectively vulnerable neurons, regions, networks, and functions, as well as genetic risk factors. In the past decade, new network-sensitive neuroimaging methods have made it possible to test the notion of network-based degeneration in living humans. In this talk, the basic theory/preprocessing/data analyses of these methods including structural covariance networks (MRI), functional connectivity (fMRI-BOLD) and structural connectivity (Diffusion MRI) will be introduced. We will focus on applications of these network-sensitive methods on two common causes of dementia, Alzheimer's disease (AD) and frontotemporal dementia. Lastly, important frontiers in the field of network-based neurodegeneration will be reviewed.

Kai Liu¹, Teng Zhang¹, Yanjia Deng¹, Lin Shi^2,3, Defeng Wang^4,5, and ADNI Alzheimer’s Disease Neuroimaging Initiative ^6
1Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ²Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ³Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ⁴Research Center for Medical Image Computing, Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong, ⁵Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China, People's Republic of, ⁶Los Angeles, SC, United States

Apolipoprotein E epsilon 4 allele (APOE-4) is considered as the strongest genetic risk factor for late-onset Alzheimer’s disease, and investigation of its neuropathological effect in normal elderly using advanced neuroimaging connectivity probes has brought much research curiosity. In this study, the underlying abnormal brain connectivity in APOE-4 carriers was analyzed using the functional connectivity strength (FCS), which provides a voxel-wise method to explore the significant connectivity changes at whole-brain level. The results identified APOE-4 related significant connectivity decrease in the bilateral insular and left temporal lobe. We hope these findings could help to shed light on the APOE-4’s neuropathological mechanism.

Letizia Casiraghi^1,2, Fulvia Palesi^2,3, Gloria Castellazzi^2,4, Andrea De Rinaldis^2,4, Elena Sinforiani⁵, Claudia Angela Michela Gandini Wheeler-­Kingshott ^2,6, Egidio D'Angelo^1,2, and Carol Di Perri^2
1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ²Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy, ³Department of Physics, University of Pavia, Pavia, Italy, ⁴Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, ⁵Neurology Unit, C. Mondino National Neurological Institute, Pavia, Italy, ⁶NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom

Mild cognitive impairment (MCI) is considered a transitional state between healthy controls (HC) and Alzheimer’s disease (AD). This study compares the predictive value of neuropsychological evaluation, structural magnetic resonance imaging, diffusion tensor imaging and resting state functional MRI indices able to identify MCI conversion. AD versus HC and converted MCI (cMCI) versus non-converted MCI (ncMCI) presented different features of differentiation. This result suggests adopting advanced MRI techniques to investigate early alterations. Due to the clinical heterogeneity of MCI patients, considering cMCI as AD-like and ncMCI as HC might be inappropriate when attempting to distinguishing between cMCI and non-converted MCI.