Statistical Model of Dynamic Markers of the Alzheimer’s Pathological Cascade

Balsis, Steve; Geraci, Lisa; Benge, Jared F.; Lowe, Deborah A.; Choudhury, Tabina K.; Tirso, Robert; Doody, Rachelle S.; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1093/geronb/gbx156

Cited by 9 publications

(8 citation statements)

References 35 publications

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“…Therefore, longitudinal approaches are needed to determine the causality. Although the causality order of pulsatility and volume could be alternated, the brain volumetric measure in MRI reveals a relatively early indicator than the severity of cognitive outcomes in AD pathological cascade model ( Balsis et al, 2018 ; Jack et al, 2013 ). Thus, the cerebral pulsatility could also be a sensitive cerebrovascular marker in the relatively early stage of the disease, and therefore helpful to better understanding of early pathophysiological mechanisms of AD progression.…”

Section: Discussionmentioning

confidence: 99%

Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults

Kim

Agarwal

et al. 2021

NeuroImage

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Changes of cardiac-induced regional pulsatility can be associated with specific regions of brain volumetric changes, and these are related with cognitive alterations. Thus, mapping of cardiac pulsatility over the entire brain can be helpful to assess these relationships. A total of 108 subjects (age: 66.5 ± 8.4 years, 68 females, 52 healthy controls, 11 subjective cognitive decline, 17 impaired without complaints, 19 MCI and 9 AD) participated. The pulsatility map was obtained directly from resting-state functional MRI time-series data at 3T. Regional brain volumes were segmented from anatomical MRI. Multidomain neuropsychological battery was performed to test memory, language, attention and visuospatial construction. The Montreal Cognitive Assessment (MoCA) was also administered. The sparse partial least square (SPLS) method, which is desirable for better interpreting high-dimensional variables, was applied for the relationship between the entire brain voxels of pulsatility and 45 segmented brain volumes. A multiple holdout SPLS framework was used to optimize sparsity for assessing the pulsatility-volume relationship model and to test the reliability by fitting the models to 9 different splits of the data. We found statistically significant associations between subsets of pulsatility voxels and subsets of segmented brain volumes by rejecting the omnibus null hypothesis (any of 9 splits has p < 0.0056 (=0.05/9) with the Bonferroni correction). The pulsatility was positively associated with the lateral ventricle, choroid plexus, inferior lateral ventricle, and 3rd ventricle and negatively associated with hippocampus, ventral DC, and thalamus volumes for the first pulsatility-volume relationship. The pulsatility had an additional negative relationship with the amygdala and brain stem volumes for the second pulsatility-volume relationship. The spatial distribution of correlated pulsatility was observed in major feeding arteries to the brain regions, ventricles, and sagittal sinus. The indirect mediating pathways through the volumetric changes were statistically significant between the pulsatility and multiple cognitive measures ( p < 0.01). Thus, the cerebral pulsatility, along with volumetric measurements, could be a potential marker for better understanding of pathophysiology and monitoring disease progression in age-related neurodegenerative disorders.

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Section: Discussionmentioning

confidence: 99%

Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults

Kim

Agarwal

et al. 2021

NeuroImage

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“…Amyloid‐β (Aβ) and tau accumulation occurs in the forms of amyloid plaques and neurofibrillary tangles in the brain of an AD patient, which is accompanied by neural degeneration and loss (Chételat, 2013; Duan et al, 2016; Wang et al, 2020). The brain alternations due to AD may precede memory impairment, which can be detected by neuroimaging methods (Balsis et al, 2018; Habib et al, 2017; Sun et al, 2016). With the progress of AD research, the focus has gradually shifted to the early stage of the disease, for example, preclinical AD (preAD), which refers to the stage without cognitive impairment (CI) but with abnormal AD biomarkers (Aβ+).…”

Section: Introductionmentioning

confidence: 99%

“…accumulation occurs in the forms of amyloid plaques and neurofibrillary tangles in the brain of an AD patient, which is accompanied by neural degeneration and loss (Chételat, 2013;Duan et al, 2016;. The brain alternations due to AD may precede memory impairment, which can be detected by neuroimaging methods (Balsis et al, 2018;Habib et al, 2017;Sun et al, 2016).…”

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confidence: 99%

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

Ding

Zhang

et al. 2021

Human Brain Mapping

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The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET), in the whole-brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN-based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole-brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area.

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“…The ADNI data set has been a rich source of material for those investigating the value of the AT(N) approach. Despite data-driven models of disease progression10,21,23,[55][56][57][58][59] largely recapitulating the classic temporal sequence of events (i.e., amyloidosis preceding pathologic tau aggregation leading to neurodegeneration), other sequences of biomarker abnormality are possible, such as that found in primary tauopathies in which tau aggregation precedes amyloidosis. A study of ADNI CU and MCI participants that followed trajectories of AT(N) biomarkers found that the biomarker for amyloidosis most commonly became pathological first, and subsequently diverged into a faster-progressing A→T→N evolution and the much slower-progressing A→N→T evolution (FigureS7in supporting information) 60.…”

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confidence: 99%

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

Veitch

Weiner

Aisen

et al. 2021

Alzheimer's & Dementia

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Introduction: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. Methods:We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.Results: Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

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Statistical Model of Dynamic Markers of the Alzheimer’s Pathological Cascade

Abstract: Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.

Cited by 9 publications

References 35 publications

Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults

Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

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