Late-life cognitive impairment and dementia are heterogeneous and multifactorial conditions driven by a combination of genetic, vascular, and lifestyle-related factors. More than 75% of patients with dementia have evidence of cerebrovascular pathology at autopsy. Cerebrovascular disease lesions can be detected on structural MRI and used as biomarkers to determine the extent of cerebrovascular pathology. These biomarkers are associated with cognitive difficulties and increase the risk of dementia for the same level of neurodegenerative pathology. Given that some of the risk factors for cerebrovascular disease are potentially modifiable, identifying the role of cerebrovascular pathology in aging and neurodegenerative disease populations opens a window for prevention of cognitive decline and dementia.

The spatial patterning of each neurodegenerative disease relates closely to a distinct structural and functional network in the human brain. This talk will mainly describe how brain network-sensitive neuroimaging methods such as resting-state fMRI and diffusion MRI can shed light on brain network dysfunctions associated with pathology and cognitive decline from preclinical to clinical dementia. I will first present our findings from two independent datasets on how amyloid and cerebrovascular pathology influence brain functional networks cross-sectionally and longitudinally in individuals with mild cognitive impairment and dementia. Evidence on longitudinal functional network organizational changes in healthy older adults and the influence of APOE genotype will be presented. In the second part, I will describe our work on how different pathology influences brain structural network and white matter microstructure. I will also touch on some new data on how brain network integrity contributes to behavior and disease progression using multivariate or machine learning approaches. These findings underscore the importance of studying selective brain network vulnerability instead of individual region and longitudinal design. Further developed with machine learning approaches, multimodal network-specific imaging signatures will help reveal disease mechanisms and facilitate early detection, prognosis and treatment search of neuropsychiatric disorders.