Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Apathy in presymptomatic genetic frontotemporal dementia predicts cognitive decline and is driven by structural brain changes

Abstract: Introduction: Apathy adversely affects prognosis and survival of patients with frontotemporal dementia (FTD). We test whether apathy develops in presymptomatic genetic FTD, and is associated with cognitive decline and brain atrophy. Methods: Presymptomatic carriers of MAPT, GRN or C9orf72 mutations (N = 304), and relatives without mutations (N = 296) underwent clinical assessments and MRI at baseline, and annually for 2 years. Longitudinal changes in apathy, cognition, gray matter volumes, and their relationships were analyzed with latent growth curve modeling. Results: Apathy severity increased over time in presymptomatic carriers, but not in non‐carriers. In presymptomatic carriers, baseline apathy predicted cognitive decline over two years, but not vice versa. Apathy progression was associated with baseline low gray matter volume in frontal and cingulate regions. Discussion: Apathy is an early marker of FTD‐related changes and predicts a subsequent subclinical deterioration of cognition before dementia onset. Apathy may be a modifiable factor in those at risk of FTD

Apathy in presymptomatic genetic frontotemporal dementia predicts cognitive decline and is driven by structural brain changes

Abstract: Introduction: Apathy adversely affects prognosis and survival of patients with frontotemporal dementia (FTD). We test whether apathy develops in presymptomatic genetic FTD, and is associated with cognitive decline and brain atrophy. Methods: Presymptomatic carriers of MAPT, GRN or C9orf72 mutations (N = 304), and relatives without mutations (N = 296) underwent clinical assessments and MRI at baseline, and annually for 2 years. Longitudinal changes in apathy, cognition, gray matter volumes, and their relationships were analyzed with latent growth curve modeling. Results: Apathy severity increased over time in presymptomatic carriers, but not in non‐carriers. In presymptomatic carriers, baseline apathy predicted cognitive decline over two years, but not vice versa. Apathy progression was associated with baseline low gray matter volume in frontal and cingulate regions. Discussion: Apathy is an early marker of FTD‐related changes and predicts a subsequent subclinical deterioration of cognition before dementia onset. Apathy may be a modifiable factor in those at risk of FTD

Apathy in presymptomatic genetic frontotemporal dementia predicts cognitive decline and is driven by structural brain changes

Introduction: Apathy adversely affects prognosis and survival of patients with frontotemporal dementia (FTD). We test whether apathy develops in presymptomatic genetic FTD, and is associated with cognitive decline and brain atrophy. Methods: Presymptomatic carriers of MAPT, GRN or C9orf72 mutations (N = 304), and relatives without mutations (N = 296) underwent clinical assessments and MRI at baseline, and annually for 2 years. Longitudinal changes in apathy, cognition, gray matter volumes, and their relationships were analyzed with latent growth curve modeling. Results: Apathy severity increased over time in presymptomatic carriers, but not in non-carriers. In presymptomatic carriers, baseline apathy predicted cognitive decline over two years, but not vice versa. Apathy progression was associated with baseline low gray matter volume in frontal and cingulate regions. Discussion: Apathy is an early marker of FTD-related changes and predicts a subsequent subclinical deterioration of cognition before dementia onset. Apathy may be a modifiable factor in those at risk of FTD

Amyloid, tau and metabolic PET correlates of cognition in early and late-onset Alzheimer's disease.

Early-onset (age < 65) Alzheimer's disease is associated with greater non-amnestic cognitive symptoms and neuropathological burden than late-onset disease. It is not fully understood whether these groups also differ in the associations between molecular pathology, neurodegeneration and cognitive performance. We studied amyloid-positive patients with early-onset (n = 60, mean age 58 ± 4, MMSE 21 ± 6, 58% female) and late-onset (n = 53, mean age 74 ± 6, MMSE 23 ± 5, 45% female) Alzheimer's disease who underwent neurological evaluation, neuropsychological testing, 11C-Pittsburgh compound B PET (amyloid-PET) and 18F-flortaucipir PET (tau-PET). 18F-fluorodeoxyglucose PET (brain glucose metabolism PET) was also available in 74% (n = 84) of participants. Composite scores for episodic memory, semantic memory, language, executive function and visuospatial domains were calculated based on cognitively unimpaired controls. Voxel-wise regressions evaluated correlations between PET biomarkers and cognitive scores and early-onset versus late-onset differences were tested with a PET × Age group interaction. Mediation analyses estimated direct and indirect (18F-fluorodeoxyglucose mediated) local associations between 18F-flortaucipir binding and cognitive scores in domain-specific regions of interest. We found that early-onset patients had higher 18F-flortaucipir binding in parietal, lateral temporal and lateral frontal cortex; more severe 18F-fluorodeoxyglucose hypometabolism in the precuneus and angular gyrus; and greater 11C-Pittsburgh compound B binding in occipital regions compared to late-onset patients. In our primary analyses, PET-cognition correlations did not meaningfully differ between age groups.18F-flortaucipir and 18F-fluorodeoxyglucose, but not 11C-Pittsburgh compound B, were significantly associated with cognition in expected domain-specific patterns in both age groups (e.g. left perisylvian/language, frontal/executive, occipital/visuospatial). 18F-fluorodeoxyglucose mediated the relationship between 18F-flortaucipir and cognition in both age groups across all domains except episodic memory in late-onset patients. Additional direct effects of 18F-flortaucipir were observed for executive function in all age groups, language in early-onset Alzheimer's disease and in the total sample and visuospatial function in the total sample. In conclusion, tau and neurodegeneration, but not amyloid, were similarly associated with cognition in both early and late-onset Alzheimer's disease. Tau had an association with cognition independent of neurodegeneration in language, executive and visuospatial functions in the total sample. Our findings support tau PET as a biomarker that captures both the clinical severity and molecular pathology specific to Alzheimer's disease across the broad spectrum of ages and clinical phenotypes in Alzheimer's disease

Amyloid, tau and metabolic PET correlates of cognition in early and late-onset Alzheimer's disease.

Early-onset (age &lt; 65) Alzheimer's disease is associated with greater non-amnestic cognitive symptoms and neuropathological burden than late-onset disease. It is not fully understood whether these groups also differ in the associations between molecular pathology, neurodegeneration and cognitive performance. We studied amyloid-positive patients with early-onset (n = 60, mean age 58 ± 4, MMSE 21 ± 6, 58% female) and late-onset (n = 53, mean age 74 ± 6, MMSE 23 ± 5, 45% female) Alzheimer's disease who underwent neurological evaluation, neuropsychological testing, 11C-Pittsburgh compound B PET (amyloid-PET) and 18F-flortaucipir PET (tau-PET). 18F-fluorodeoxyglucose PET (brain glucose metabolism PET) was also available in 74% (n = 84) of participants. Composite scores for episodic memory, semantic memory, language, executive function and visuospatial domains were calculated based on cognitively unimpaired controls. Voxel-wise regressions evaluated correlations between PET biomarkers and cognitive scores and early-onset versus late-onset differences were tested with a PET × Age group interaction. Mediation analyses estimated direct and indirect (18F-fluorodeoxyglucose mediated) local associations between 18F-flortaucipir binding and cognitive scores in domain-specific regions of interest. We found that early-onset patients had higher 18F-flortaucipir binding in parietal, lateral temporal and lateral frontal cortex; more severe 18F-fluorodeoxyglucose hypometabolism in the precuneus and angular gyrus; and greater 11C-Pittsburgh compound B binding in occipital regions compared to late-onset patients. In our primary analyses, PET-cognition correlations did not meaningfully differ between age groups.18F-flortaucipir and 18F-fluorodeoxyglucose, but not 11C-Pittsburgh compound B, were significantly associated with cognition in expected domain-specific patterns in both age groups (e.g. left perisylvian/language, frontal/executive, occipital/visuospatial). 18F-fluorodeoxyglucose mediated the relationship between 18F-flortaucipir and cognition in both age groups across all domains except episodic memory in late-onset patients. Additional direct effects of 18F-flortaucipir were observed for executive function in all age groups, language in early-onset Alzheimer's disease and in the total sample and visuospatial function in the total sample. In conclusion, tau and neurodegeneration, but not amyloid, were similarly associated with cognition in both early and late-onset Alzheimer's disease. Tau had an association with cognition independent of neurodegeneration in language, executive and visuospatial functions in the total sample. Our findings support tau PET as a biomarker that captures both the clinical severity and molecular pathology specific to Alzheimer's disease across the broad spectrum of ages and clinical phenotypes in Alzheimer's disease

Subcortical tau is linked to hypoperfusion in connected cortical regions in 4-repeat tauopathies.

Four-repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R tauopathies are progressive supranuclear palsy (PSP) and corticobasal degeneration characterized by subcortical tau accumulation and cortical neuronal dysfunction, as shown by PET-assessed hypoperfusion and glucose hypometabolism. Yet, there is a spatial mismatch between subcortical tau deposition patterns and cortical neuronal dysfunction, and it is unclear how these two pathological brain changes are interrelated. Here, we hypothesized that subcortical tau pathology induces remote neuronal dysfunction in functionally connected cortical regions to test a pathophysiological model that mechanistically links subcortical tau accumulation to cortical neuronal dysfunction in 4R tauopathies. We included 51 Aβ-negative patients with clinically diagnosed PSP variants (n = 26) or corticobasal syndrome (n = 25) who underwent structural MRI and 18F-PI-2620 tau-PET. 18F-PI-2620 tau-PET was recorded using a dynamic one-stop-shop acquisition protocol to determine an early 0.5-2.5 min post tracer-injection perfusion window for assessing cortical neuronal dysfunction, as well as a 20-40 min post tracer-injection window to determine 4R-tau load. Perfusion-PET (i.e. early window) was assessed in 200 cortical regions, and tau-PET was assessed in 32 subcortical regions of established functional brain atlases. We determined tau epicentres as subcortical regions with the highest 18F-PI-2620 tau-PET signal and assessed the connectivity of tau epicentres to cortical regions of interest using a resting-state functional MRI-based functional connectivity template derived from 69 healthy elderly controls from the ADNI cohort. Using linear regression, we assessed whether: (i) higher subcortical tau-PET was associated with reduced cortical perfusion; and (ii) cortical perfusion reductions were observed preferentially in regions closely connected to subcortical tau epicentres. As hypothesized, higher subcortical tau-PET was associated with overall lower cortical perfusion, which remained consistent when controlling for cortical tau-PET. Using group-average and subject-level PET data, we found that the seed-based connectivity pattern of subcortical tau epicentres aligned with cortical perfusion patterns, where cortical regions that were more closely connected to the tau epicentre showed lower perfusion. Together, subcortical tau-accumulation is associated with remote perfusion reductions indicative of neuronal dysfunction in functionally connected cortical regions in 4R-tauopathies. This suggests that subcortical tau pathology may induce cortical dysfunction, which may contribute to clinical disease manifestation and clinical heterogeneity

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

IntroductionArtificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. MethodsWe systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. ResultsA total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DiscussionThe literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HighlightsThere has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias