The well-known Alzheimer's Disease Neuroimaging Initiative (ADNI)
Center provides the most advanced, comprehensive, multiparametric
and up-to-date biomarkers for mild cognitive impairment (MCI) and
early Alzheimer's disease (AD) projects, including neuroimaging,
clinical assessments, biospecimens and genetic data. Recent
developments in imaging techniques, including new molecular tracers
for imaging disease burden and systematic multi-modal integration,
have emerged to overcome the limitations of each single modality
and individual-dependent variability. The MRI-based high-resolution
structural and morphological changes in the brain, such as atrophy,
and the abnormal activity/connectivity patterns of the hippocampus
subfields and default mode network (DMN) modulation, together with
the amyloid and tau neuropathological quantification using PET
molecular tracers, could be used to predict brain changes and
cognitive performance declines in early AD, including transitional
MCI. Finally, a generalized and integrative model with multiple
biomarkers could be built to target disease progression and symptom
prediction as well as to optimize patient management. Multiomics
investigates metabolomic, lipidomic, genomic, transcriptomic and
proteomic perspectives by presenting an accurate biochemical
profile of the organism in health and disease. The Alzheimer's
Disease Metabolomics Consortium (ADMC) in partnership with ADNI is
creating a comprehensive biochemical database for patients in the
ADNI1 cohort, consisting of eight metabolomics datasets. The vast
majorities of biospecimen data provide rich biological information
to the human brain at normal and dementia status. One of the
purposes is to reveal the connections between disease and
multiomics such as obesity, hypertension, cholesterol imbalance and
inflammation risks that might lead to neurodegenerative disease.
Multiomic biomarker developments in the dementia field have
provided earlier clues to novel treatments that help correct
metabolic dysfunction and delay disease progression. Furthermore,
the assembling of multiomics-based biomarkers including metabolites
and lipids, cholesterol biosynthesis, purine metabolism,
lipoprotein, bile acids, and genetics as well as their relation to
the pathological amyloid and tau network could improve disease
diagnosis sensitivity and reveal more diverse and complementary
molecular pathways to allow for the advancement of early AD
diagnosis and therapeutic prevention. In this book, we report on
the significant differences of multiple biomarkers from the ADNI
database including neuroimaging, clinical assessments and multiomic
biospecimen/genetic data in MCI and early probable AD (pAD), and
elucidate the interconnections among different metrics at various
domains. Classification results with high accuracies (0.95-1) for
each early dementia subtype including early MCI (EMCI), late MCI
(LMCI) and pAD, and better prediction of clinical symptoms is
achieved with these comprehensive biomarkers. Further longitudinal
changes of imaging and neuropsychological biomarkers, and
inter-correlations with baseline parameters are examined for a
better illustration of disease progression association.
Additionally, an analysis of the post-traumatic stress disorder
biomarkers is performed with high classification accuracy. With
illustrative and rigorous data analyses and confirmative results,
this book provides readers with a full spectrum of biomarker
research for early dementia diagnosis and treatment, and helps
convey the technical development and data evaluation perspectives
in advanced medical imaging and various disease application fields.
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