Brain Aging and Neurodegenerative Diseases
Multi-modal neuroimaging has played a central role in understanding brain changes with aging and prodromal stages of neurodegenerative diseases such as AD. Our group has a long-standing involvement in this area [1-11], with early emphasis on the use of computational neuroanatomy of aging methods:
Three-dimensional views of significant longitudinal tissue loss in specific gray matter regions. Three-dimensional views of the t statistic are shown projected on the outer cortical surface of a representative subject in our sample. The projection on the surface was performed by averaging the value of the t statistic along the normal of each surface vertex; only voxels with t statistic_3.18 were included in this averaging procedure. The color bar shows the colors corresponding to these calculated average t statistics. Bottom, Views of the right and left hemispheres highlight tissue loss in inferior frontal, insular, and posterior temporal regions (right_left) and the inferior parietal (left_right) region. Top, Gray matter volume loss in the insula (right _ left; inferior and coronal views), orbital frontal cortex (inferior and sagittal views), and cingulate cortex (sagittal and coronal views) are highlighted. In the inferior view, the anterior temporal lobe is cut away to expose the surface of the insular cortex.
and later emphasis on the use of machine learning for individualized diagnosis and prediction:
Rate of progression of SPARE-AD score, which captures patterns of AD-like brain atrophy, in cognitively normal individuals predicts subsequent progression to MCI (right).
As of 2016, our work in this area has consolidated around the concept of iSTAGING (Imaging-based coordinate SysTem for AGing and NeurodeGenerative diseases), which integrates multi-modal neuroimaging data from approximately 12,000 individuals from various studies of aging, MCI, AD, epidemiologic, hypertension, and diabetes, and derives a coordinate system that captures the heterogeneity of patterns of brain change occurring with aging and neurodegenerative diseases. The ultimate goal is to relate the position of a new individual in this coordinate system, and relate it to cognitive and clinical variables, including likelihood of clinical progression. Associated is the aim of using such a concise, yet comprehensive coordinate system to better understand inter-relationships between various imaging biomarkers and patterns, thereby elucidating underlying neurodegenerative mechanisms and their longitudinal progression. This is schematically shown below:
Schematic of the iSTAGING coordinate system. Individuals can be placed in this coordinate system based on the imaging patterns they display, potentially revealing relationships between their coordinates and clinical progression, demographic, genetic and other variables. For example, individual A might be someone with early AD, showing amyloid deposition and no atrophy, but with subsequently increasing atrophy (SPARE-AD of his individual would be expected to increase, as AD-like atrophy progresses). Individual B might display a pattern of atrophy associated with SVID. Individual C would present patterns of atrophy and functional connectivity that are consistent with initial functional resilience, ultimately declining functionally. D has concomitant structural and functional deficit patterns.
5. Davatzikos C, Xu F, An Y, Fan Y, Resnick SM: Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain : a journal of neurology 2009, 132(Pt 8):2026-2035.
6. Dotson VM, Davatzikos C, Kraut MA, Resnick SM: Depressive symptoms and brain volumes in older adults: a longitudinal magnetic resonance imaging study. Journal of Psychiatry & Neuroscience 2009, 34(5):367-375.
7. Yotter RA, Doshi J, Clark V, Sojkova J, Zhou Y, Wong DF, Ferrucci L, Resnick SM, Davatzikos C: Memory decline shows stronger associations with estimated spatial patterns of amyloid deposition progression than total amyloid burden. Neurobiol Aging 2013, 34(12):2835-2842.
8. Weintraub D, Doshi J, Koka D, Davatzikos C, Siderowf AD, Duda JE, Wolk DA, Moberg PJ, Xie SX, Clark CM: Neurodegeneration across stages of cognitive decline in Parkinson disease. Archives of neurology 2011, 68(12):1562-1568S.
9. Weintraub D, Dietz N, Duda JE, Wolk DA, Doshi J, Xie SX, Davatzikos C, Clark CM, Siderowf A: Alzheimer's disease pattern of brain atrophy predicts cognitive decline in Parkinson's disease. Brain : a journal of neurology 2012, 135(1):170-180.
10. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 2011, 32(12):2322 e2319-2327.
11. Da X, Toledo JB, Zee J, Wolk DA, Xie SX, Ou Y, Shacklett A, Parmpi P, Shaw L, Trojanowski JQ et al: Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage Clinical 2013, 4:164-173.