MRI and cognitive scores complement each other to accurately predict Alzheimer's dementia 2 to 7 years before clinical onset

Zandifar, Azar; Fonov, Vladimir; Ducharme, Simon; Belleville, Sylvie; Collins, D. Louis

doi:10.1016/j.nicl.2019.102121

Cited by 19 publications

(22 citation statements)

References 27 publications

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“…This is documented by several previous studies relating early changes in cognitive function to changes in specific regions and structures of the brain, including an expansion of the ventricles and volume loss in the hippocampus and entorhinal cortex 13 , 14 . A more precise prediction of AD is therefore expected if information from results on cognitive tests are combined with information from magnetic resonance imaging (MRI) of the brain 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

“…In a first set of analyses we defined features characterising longitudinal changes in memory function (Rey Auditory Learning Test (RAVLT)) 11 and in a more global measure of cognitive function (ADAS-Cog-13 (ADAS13)) 9 , 17 . Expecting more precise predictions by including information from MRI examinations 15 , 16 , we investigated the add-on effect of including morphometric brain measures associated with memory function (entorhinal cortex and hippocampus 14 ) and a global measure of cognitive function (the volume of the ventricles as a proxy for a global tissue loss 18 ). More specifically, we used a pipeline proposed by Mofrad et al 19 based on a combination of mixed effects and machine learning models for analysis of longitudinal data.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Mofrad

Lundervold

Vik

et al. 2021

Sci Rep

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The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in $$F_1$$ F 1 -score from 60 to 77%. The $$F_1$$ F 1 -scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer’s disease is well-established in the brain.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Mofrad

Lundervold

Vik

et al. 2021

Sci Rep

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“…In our previous work, we showed that when predicting onset of dementia in subjects with mild cognitive impairment, MRI-based features (SNIPE) are more sensitive compared to cognitive features, and even more so with longer follow-up periods, while cognitive features contribute more to the specificity of the prediction [ 15 ]. Here, we also show that cognitive features lose sensitivity when it comes to predicting functional and cognitive decline at 36 months compared to that at 24 months.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous work, we showed that baseline SNIPE scores could differentiate patients with MCI that remain stable versus those that progress to AD [ 13 ], and that baseline SNIPE scores enable AD prediction in a group of cognitively intact subjects seven years before the clinical diagnosis of AD dementia [ 14 ]. More recently, we demonstrated that combining MRI features and neurocognitive test results at baseline could yield 78%accuracy in prediction of conversion from MCI to AD at 2 and 3 years before diagnosis of AD (and up to 87%accuracy, five years before diagnosis) [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer’s Disease

Shafiee

Dadar

Ducharme

et al. 2021

JAD

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Background: While both cognitive and magnetic resonance imaging (MRI) data has been used to predict progression in Alzheimer’s disease, heterogeneity between patients makes it challenging to predict the rate of cognitive and functional decline for individual subjects. Objective: To investigate prognostic power of MRI-based biomarkers of medial temporal lobe atrophy and macroscopic tissue change to predict cognitive decline in individual patients in clinical trials of early Alzheimer’s disease. Methods: Data used in this study included 312 patients with mild cognitive impairment from the ADNI dataset with baseline MRI, cerebrospinal fluid amyloid-β, cognitive test scores, and a minimum of two-year follow-up information available. We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects remain stable and which functionally decline over 2 and 3-year follow-up periods. Results: Combining both sets of features yields 77%accuracy (81%sensitivity and 75%specificity) to predict cognitive decline at 2 years (74%accuracy at 3 years with 75%sensitivity and 73%specificity). When used to select trial participants, this tool yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25%treatment effect to reduce cognitive decline. Conclusion: When used in clinical trials for cohort enrichment, this tool could accelerate development of new treatments by significantly increasing statistical power to detect differences in cognitive decline between arms. In addition, detection of future decline can help clinicians improve patient management strategies that will slow or delay symptom progression.

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“…These processing methods have previously shown patterns of atrophy in cognitively normal, MCI, dementia, and neurodegenerative disease populations, including ADNI (37)(38)(39)(40). These techniques thus have the required sensitivity to reveal group differences between SCD-and SCD+.…”

Section: Discussionmentioning

confidence: 99%

Regional atrophy and cognitive decline depend on definition of subjective cognitive decline

Morrison

Dadar

Shafiee

et al. 2021

Preprint

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BackgroundPeople with subjective cognitive decline (SCD) may be at increased risk for Alzheimer’s disease (AD). However, not all studies have observed this increased risk. Inconsistencies may be related to different methods used to define SCD. The current project examined whether four methods of defining SCD (applied to the same sample) results in different patterns of atrophy and future cognitive decline between cognitively normal older adults with (SCD+) and without SCD (SCD-).MethodsMRI scans and questionnaire data for 273 cognitively normal older adults from Alzheimer’s Disease Neuroimaging Initiative were examined. To operationalize SCD we used four common methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog + Worry, and Worry only. A previously validated MRI analysis method (SNIPE) was used to measure hippocampal volume and grading. Deformation-based morphometry was performed to examine volume at regions known to be vulnerable to AD. Logistic regressions were completed to determine whether diagnostic method was associated with volume differences between SCD- and SCD+. Linear mixed effects models were performed to examine the relationship between the definitions of SCD and future cognitive decline.ResultsResults varied between the four methods of defining SCD. Left hippocampal grading was lower in SCD+ than SCD-when using the CCI (p=.041) and Worry (p=.021) definitions. The right (p=.008) and left (p=.003) superior temporal regions were smaller in SCD+ than SCD-, but only with the ECog. SCD+ was associated with greater future cognitive decline measured by Alzheimer’s Disease Assessment Scale, but only with the CCI definition. In contrast, only the ECog definition of SCD was associated with future decline on the Montreal Cognitive Assessment.ConclusionThe current findings suggest that the various methods used to differentiate between SCD- and SCD+ influence whether volume differences and findings of cognitive decline are observed between groups in this retrospective analysis.

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MRI and cognitive scores complement each other to accurately predict Alzheimer's dementia 2 to 7 years before clinical onset

Cited by 19 publications

References 27 publications

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer’s Disease

Regional atrophy and cognitive decline depend on definition of subjective cognitive decline

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