Multiple markers contribute to risk of progression from normal to mild cognitive impairment

Rabin, Jennifer S.; Neal, Taylor E.; Nierle, Hannah E.; Sikkes, Sietske A.M.; Buckley, Rachel F.; Amariglio, Rebecca E.; Papp, Kathryn V.; Rentz, Dorene M.; Schultz, Aaron P.; Johnson, Keith A.; Sperling, Reisa A.; Hedden, Trey

doi:10.1016/j.nicl.2020.102400

Cited by 9 publications

(10 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kaplan-Meier curves and Cox proportional hazards regression results for lateral orbitofrontal cMD (high/low groups) predicting (A) progression to MCI, (B) progression to CDR = 0.5. cMD cortical mean diffusivity, CTh cortical thickness, PIB_status Aβ+ vs. Aβ−; "High cMD" = top-tertile cMD values ("Low cMD", otherwise). combination, to predict short-term cognitive decline and clinical progression [44,49]. In line with previous observations [8], we found that PIB status was a strong predictor of clinical progression to MCI or CDR = 0.5 during~3.5 years follow-up.…”

Section: Discussionsupporting

confidence: 91%

“…Timeto-event was defined as years from baseline to the first follow-up visit when a participant was diagnosed as mild cognitive impairment (MCI). For comparison, separate survival models were set-up using progression from CDR = 0 to 0.5 as a subtler definition of clinical progression [44]. We applied multivariable Cox proportional hazards regression models to estimate hazard ratios (HRs) with 95% confidence intervals (CI) to investigate whether dichotomous regional cMD ("high cMD" defined as top-tertile cMD; "low cMD" otherwise) predicts subsequent diagnosis of MCI/AD, or progression to CDR = 0.5.…”

Section: Prediction Of Clinical Outcomesmentioning

confidence: 99%

See 1 more Smart Citation

Association of cortical microstructure with amyloid-β and tau: impact on cognitive decline, neurodegeneration, and clinical progression in older adults

et al. 2021

Self Cite

View full text Add to dashboard Cite

Noninvasive biomarkers of early neuronal injury may help identify cognitively normal individuals at risk of developing Alzheimer’s disease (AD). A recent diffusion-weighted imaging (DWI) method allows assessing cortical microstructure via cortical mean diffusivity (cMD), suggested to be more sensitive than macrostructural neurodegeneration. Here, we aimed to investigate the association of cMD with amyloid-β and tau pathology in older adults, and whether cMD predicts longitudinal cognitive decline, neurodegeneration and clinical progression. The study sample comprised n = 196 cognitively normal older adults (mean[SD] 72.5 [9.4] years; 114 women [58.2%]) from the Harvard Aging Brain Study. At baseline, all participants underwent structural MRI, DWI, 11C-Pittsburgh compound-B-PET, 18F-flortaucipir-PET imaging, and cognitive assessments. Longitudinal measures of Preclinical Alzheimer Cognitive Composite-5 were available for n = 186 individuals over 3.72 (1.96)-year follow-up. Prospective clinical follow-up was available for n = 163 individuals over 3.2 (1.7) years. Surface-based image analysis assessed vertex-wise relationships between cMD, global amyloid-β, and entorhinal and inferior-temporal tau. Multivariable regression, mixed effects models and Cox proportional hazards regression assessed longitudinal cognition, brain structural changes and clinical progression. Tau, but not amyloid-β, was positively associated with cMD in AD-vulnerable regions. Correcting for baseline demographics and cognition, increased cMD predicted steeper cognitive decline, which remained significant after correcting for amyloid-β, thickness, and entorhinal tau; there was a synergistic interaction between cMD and both amyloid-β and tau on cognitive slope. Regional cMD predicted hippocampal atrophy rate, independently from amyloid-β, tau, and thickness. Elevated cMD predicted progression to mild cognitive impairment. Cortical microstructure is a noninvasive biomarker that independently predicts subsequent cognitive decline, neurodegeneration and clinical progression, suggesting utility in clinical trials.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Prediction Of Clinical Outcomesmentioning

confidence: 99%

Association of cortical microstructure with amyloid-β and tau: impact on cognitive decline, neurodegeneration, and clinical progression in older adults

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our MRI findings are in agreement with a recent study ( Rabin, 2020 ) that found reduced entorhinal thickness and smaller hippocampal volume predicted conversion to MCI within 5 years. However, we distinguished baseline differences in these markers in a shorter time interval (within a year).…”

Section: Discussionsupporting

confidence: 93%

Neuroimaging and clinical characteristics of cognitive migration in community-dwelling older adults

Duran

Bateman

Williams

et al. 2022

NeuroImage: Clinical

View full text Add to dashboard Cite

“…Twenty-six studies (the first published in 2012) used resting-state MRI (rsMRI): we did not identify any studies using task-based MRI. All but four studies (Hojjati et al 2017;2018;Hojjati, Ebrahimzadeh, and Babajani-Feremi 2019;Rabin et al 2020) focused on diagnosis and the majority (20/26) used ADNI data, either as the primary dataset or as a replication dataset. Graph measures were often used to summarize network characteristics.…”

Section: Functional Mrimentioning

confidence: 99%

“…Around half of studies leveraged structural MRI alone (132/252) and those making use of multiple modalities (49/252) often used sMRI and PET (35/49) together. It is only since 2020 that studies incorporating three or more different modalities have begun to appear (T. R de Vos et al 2020;Rabin et al 2020…”

mentioning

confidence: 99%

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Borchert

Azevedo

Badhwar

et al. 2021

Preprint

View full text Add to dashboard Cite

Introduction Recent developments in artificial intelligence (AI) and neuroimaging offer new opportunities for improving diagnosis and prognosis of dementia. To synthesise the available literature, we performed a systematic review. Methods We systematically reviewed primary research publications up to January 2021, using AI for neuroimaging to predict diagnosis and/or prognosis in cognitive neurodegenerative diseases. After initial screening, data from each study was extracted, including: demographic information, AI methods, neuroimaging features, and results. Results We found 2709 reports, with 252 eligible papers remaining following screening. Most studies relied on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=178) with no other individual dataset used more than 5 times. Algorithmic classifiers, such as support vector machine (SVM), were the most commonly used AI method (47%) followed by discriminative (32%) and generative (11%) classifiers. Structural MRI was used in 71% of studies with a wide range of accuracies for the diagnosis of neurodegenerative diseases and predicting prognosis. Lower accuracy was found in studies using a multi-class classifier or an external cohort as the validation group. There was improvement in accuracy when neuroimaging modalities were combined, e.g. PET and structural MRI. Only 17 papers studied non-Alzheimers disease dementias. Conclusion The use of AI with neuroimaging for diagnosis and prognosis in dementia is a rapidly emerging field. We make a number of recommendations addressing the definition of key clinical questions, heterogeneity of AI methods, and the availability of appropriate and representative data. We anticipate that addressing these issues will enable the field to move towards meaningful clinical translation.

show abstract

Multiple markers contribute to risk of progression from normal to mild cognitive impairment

Cited by 9 publications

References 63 publications

Association of cortical microstructure with amyloid-β and tau: impact on cognitive decline, neurodegeneration, and clinical progression in older adults

Association of cortical microstructure with amyloid-β and tau: impact on cognitive decline, neurodegeneration, and clinical progression in older adults

Neuroimaging and clinical characteristics of cognitive migration in community-dwelling older adults

Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review

Contact Info

Product

Resources

About