Multi-modal techniques for diagnosis and prognosis of Alzheimer’s disease

By R. Perrin, Anne M. Fagan, D. Holtzman. Nature, 2009

692 citations recorded by the source corpus.

Alzheimer's disease affects millions of people around the world. Currently, there are no treatments that prevent or slow the disease. Like other neurodegenerative diseases, Alzheimer's disease is characterized by protein misfolding in the brain. This process and the associated brain damage begin years before the substantial neurodegeneration that accompanies dementia. Studies using new neuroimaging techniques and fluid biomarkers suggest that Alzheimer's disease pathology can be detected preclinically. These advances should allow the design of new clinical trials and early mechanism-based therapeutic intervention.

Subfields: Molecular Biology, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/028b6b5fc492e7233368be6b9270ff4be9b43d08

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Citations in the corpus

New perspectives on the role of melatonin in human sleep, circadian rhythms and their regulation (2018, background): “Classical hallmarks of AD are insoluble extracellular senile plaques composed of the β amyloid (Aβ) peptide and intracellular tangles comprising hyperphosphorylated τ aggregates (Perrin et al., 2009).”
Human apoE isoforms differentially regulate brain amyloid-β peptide clearance (2011, neutral): “Because effective AD treatments will likely depend on intervening during the preclinical (presymptomatic) phase of AD (44, 45), understanding how environmental and genetic risk factors modulate pathological hallmarks of the disease will be critical.”
Cerebrospinal fluid levels of β-amyloid 1-42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia. (2012, background): “Evaluation of the optimal combinations of biomarkers for the identification of prodromal AD in patients with MCI, and of their interaction, needs to be performed in large MCI cohorts with extensive follow-up.(4,7) Note that the incidence of AD dementia in patients with MCI with normal A 42:P-tau rat”
Introduction to Revised Criteria for the Diagnosis of Alzheimer’s Disease: National Institute on Aging and the Alzheimer Association Workgroups (2011, background): “Thus, the set of recommendations presented in this article only refer to other disorders because they relate to differential diagnosis of AD.”
Trusted Multi-View Classification With Dynamic Evidential Fusion (2022, neutral): “For example, in machine-learning-aided multimodal medical diagnosis [19], [20], the algorithm may need to output a confident diagnosis based on a magnetic resonance (MR) image for”
Alzheimer’s Disease: The Challenge of the Second Century (2011, methodology): “On the basis of these and other findings, it is estimated that AD pathology probably begins 10 to 20 years before the onset of clinical symptoms (7, 12).”
The toxic Aβ oligomer and Alzheimer's disease: an emperor in need of clothes (2012, background): “Eisele, Y.S. et al. Peripherally applied Abeta-containing inoculates induce cerebral beta-amyloidosis.”
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis (2013, background): “…including MRI, PET, biological and neurological data for discriminating AD/MCI patients from healthy normal controls (HC) (Fan et al. 2007; Perrin et al. 2009; Kohannim et al. 2010; Walhovd et al. 2010; Cui et al. 2011; Hinrichs et al. 2011; Zhang et al. 2011; Wee et al. 2012; Westman et…”
Drivers of biodiagnostic development (2009, background): “Probes that allow imaging of the brain have been developed and might lead to powerful new biodiagnostic tools, but substantial additional work is needed to identify suitable markers and to correlate their presence and concentration with the state of the diseas”
Regulation of Tau Pathology by the Microglial Fractalkine Receptor (2010, positive): “Given increasing evidence that Ab pathologies precede MAPT pathologies by as much as 10 years in humans (Perrin et al., 2009) our studies strongly suggest that blocking CX3CL1/CX3CR1 signaling has opposing effects at different stages of disease progression.”
Deep Learning-Based Feature Representation for AD/MCI Classification (2013, background): “…such as Magnetic Resonance Imaging (MRI) [3], Positron Emission Tomography (PET) [11], functional MRI (fMRI) [5], etc. Recent research has shown that it’s beneﬁcial to fuse complementary information from diﬀerent modalities in discriminating AD/MCI patients from Healthy normal Controls (HC) [12].”
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014, positive): “…units in the trained DBM.11 Multimodal deep feature fusion There are increasing evidences that biomarkers from different modalities can provide complementary information in AD/MCI diagnosis (Hinrichs et al., 2011; Kohannim et al., 2010; Perrin et al., 2009; Suk and Shen, 2013; Zhang et al., 2011).”