Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment (MCI) typically precedes Alzheimer's dementia, yet only a fraction (30\%-50\%) of MCI individuals will progress to dementia . Even when a prognosis of dementia is established using machine learning models and biomarkers, the fraction of MCI progressors remain limited (50\%-75\%). Instead of aiming at precise diagnosis in large clinical cohorts known for their heterogeneity, we propose here to identify only a subset of individuals who share a common brain signature highly predictive of oncoming dementia. This signature was  discovered using a machine learning model in a reference public sample (ADNI), where the model was trained to identify patterns of brain atrophy and functional dysconnectivity commonly seen in patients suffering from dementia (N = 24), and not seen in cognitively normal individuals (N = 49) . The model then recognized the same brain signature in 10 MCI individuals, out of N = 56, 90\% of which progressed to dementia within three years. This result is a marked improvement on the state-of-the-art in prognostic precision, while the brain signature still identified 47\% of all MCI progressors (N = 19) . We thus discovered a sizable MCI subpopulation with homogeneous brain abnormalities and highly predictable clinical trajectories, which may represent an excellent recruitment target for clinical trials at the prodromal stage of Alzheimer's disease.
Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment (MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI individuals will progress to dementia , even when screened using biomarkers. We propose here to identify a subset of individuals who share a common brain signature highly predictive of oncoming dementia. This signature was composed of brain atrophy and functional dysconnectivity and discovered using a machine learning model in patients suffering from dementia . The model recognized the same brain signature in MCI individuals, 90\% of which progressed to dementia within three years. This result is a marked improvement on the state-of-the-art in prognostic precision, while the brain signature still identified 47\% of all MCI progressors . We thus discovered a sizable MCI subpopulation which represents an excellent recruitment target for clinical trials at the prodromal stage of Alzheimer's disease.
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(30\%-50\%)
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. Even when a prognosis of dementia is established using machine learning models and biomarkers, the fraction of MCI progressors remain limited (50\%-75\%). Instead of aiming at precise diagnosis in large clinical cohorts known for their heterogeneity, we
, even when screened using biomarkers. We
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only
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composed of brain atrophy and functional dysconnectivity and
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a reference public sample (ADNI), where the model was trained to identify patterns of brain atrophy and functional dysconnectivity commonly seen in
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(N = 24), and not seen in cognitively normal individuals (N = 49)
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then
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10
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out of N = 56,
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(N = 19)
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with homogeneous brain abnormalities and highly predictable clinical trajectories, which may represent
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