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

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.

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

BACKGROUND

Multiple neuroimaging and clinical biomarkers have been identified to predict cognitive decline and clinical progression to mild cognitive impairment (MCI) or dementia. However, early biomarkers associated with transition to and reversion from cognitive impairment (cognitive migration) require further understanding. We investigated the impacts of baseline neuroimaging and clinical biomarkers on cognitive migration in a community-dwelling older cohort.

METHODS

We studied 391 participants from the Wake Forest Alzheimer's Disease Research Center Clinical Core cohort who underwent neuropsychological assessment and magnetic resonance imaging (MRI). At baseline, each participant was categorized to a functional/cognitive state using global Clinical Dementia Rating (CDR) score: CDR = 0 indicates normal cognitive function; CDR = 0.5 is minimal cognitive impairment. The primary outcome was cognitive migration status determined by CDR change between baseline and follow-up (mean difference = 13.9 months): CDR-0 Stables (no migration; maintained CDR = 0), CDR-0.5 Stables (no migration; maintained CDR = 0.5), Migrants^- (negative migration; CDR 0 to CDR 0.5), and Reverters⁺ (positive migration; CDR 0.5 to CDR 0). Baseline T1-weighted MRI was analyzed for gray matter (GM) volume using voxel-based morphometry (VBM). For VBM, we used a two-sample t-test controlling for age, sex, education years and intracranial volume for group comparisons: CDR-0 Stables vs CDR-0.5 Stables, CDR-0 Stables vs Migrants^-, CDR-0.5 Stables vs Reverters⁺ and Migrants^- vs Reverters⁺ (thresholded at k = 30 voxels, p <.01 uncorrected). Oral Glucose Tolerance Testing (OGTT-2h) assessed blood glucose 120-minute post challenge. Multinomial logistic regression estimated average predicted probabilities of cognitive migration status using OGTT-2h and age range (55-65, 65-75 and 75+) as predictors.

RESULTS

VBM analyses revealed lower GM volume in inferior and middle temporal gyri, hippocampus, parahippocampal gyrus, and superior and inferior frontal regions in Migrants^- and CDR-0.5 Stables. Predicted probabilities indicated that individuals aged 55-65 with normal OGTT-2h levels were more likely to have better cognitive migration status (e.g., CDR-0 Stables or Reverters⁺) than those aged 75+ with high OGTT-2h.

CONCLUSIONS

Lower GM volumes and high OGTT-2h glucose levels may predict worse cognitive migration status in early stages of disease. The opposite is true for better cognitive migration. Validating these biomarkers may guide clinical diagnosis and treatments.

Association of β-Amyloid and Vascular Risk on Longitudinal Patterns of Brain Atrophy

BACKGROUND AND OBJECTIVES

Vascular risk factors and elevated β-amyloid (Aβ) are commonly observed together among older adults. Here, we examined the interactive vs independent effects of systemic vascular risk and Aβ burden on longitudinal gray matter atrophy and how their co-occurrence may be related to cognitive decline in a cohort of clinically normal adults. A secondary goal was to examine whether vascular risk influences gray matter atrophy independently from markers of white matter injury.

METHODS

Participants were 196 adults (age 73.8 ± 6.1 years) from the Harvard Aging Brain Study. Baseline Aβ burden was quantified with Pittsburgh compound B PET. Baseline vascular risk was measured with the Framingham Heart Study cardiovascular disease risk score. Brain atrophy was quantified longitudinally with structural MRI over a median of 4.50 (±1.26) years. Cognition was assessed yearly with the Preclinical Alzheimer Cognitive Composite over a median of 6.25 (±1.40) years. Linear mixed-effects models examined vascular risk and Aβ burden as interactive vs independent predictors of gray matter atrophy, with adjustment for age, sex, years of education, APOE ε4 status, intracranial volume (when appropriate), and their interactions with time. In subsequent models, we adjusted for markers of white matter injury to determine whether vascular risk accelerated brain atrophy independently from diffusion- and fluid-attenuated inversion recovery (FLAIR)-based markers. Mediation analyses examined whether brain atrophy mediated the interactive association of vascular risk and Aβ burden on cognitive decline.

RESULTS

Higher vascular risk and elevated Aβ burden interacted to predict more severe atrophy in frontal and temporal lobes, thalamus, and striatum. Higher Aβ burden, but not vascular risk, was associated with more severe atrophy in parietal and occipital lobes, as well as the hippocampus. Adjusting for diffusion- and FLAIR-based markers of white matter injury had little impact on the above associations. Gray matter atrophy mediated the association between vascular risk and cognitive decline at higher levels of Aβ burden.

DISCUSSION

We observed an interaction between elevated vascular risk and higher Aβ burden with longitudinal brain atrophy, which in turn influenced cognitive decline. These results support vascular risk factor management as a potential intervention to slow neurodegeneration and cognitive decline in preclinical Alzheimer disease.

Association of Lifestyle Activities with Functional Brain Connectivity and Relationship to Cognitive Decline among Older Adults

This study examines the relationship of engagement in different lifestyle activities to connectivity in large-scale functional brain networks, and whether network connectivity modifies cognitive decline, independent of brain amyloid levels. Participants (N = 153, mean age = 69 years, including N = 126 with amyloid imaging) were cognitively normal when they completed resting-state functional magnetic resonance imaging, a lifestyle activity questionnaire, and cognitive testing. They were followed with annual cognitive tests up to 5 years (mean = 3.3 years). Linear regressions showed positive relationships between cognitive activity engagement and connectivity within the dorsal attention network, and between physical activity levels and connectivity within the default-mode, limbic, and frontoparietal control networks, and global within-network connectivity. Additionally, higher cognitive and physical activity levels were independently associated with higher network modularity, a measure of functional network specialization. These associations were largely independent of APOE4 genotype, amyloid burden, global brain atrophy, vascular risk, and level of cognitive reserve. Moreover, higher connectivity in the dorsal attention, default-mode, and limbic networks, and greater global connectivity and modularity were associated with reduced cognitive decline, independent of APOE4 genotype and amyloid burden. These findings suggest that changes in functional brain connectivity may be one mechanism by which lifestyle activity engagement reduces cognitive decline.

Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature

BACKGROUND

Cerebral small vessel disease (CSVD) is a common neurological disease present in the ageing population that is associated with an increased risk of dementia and stroke. Damage to white matter tracts compromises the substrate for interneuronal connectivity. Analysing resting-state functional magnetic resonance imaging (fMRI) can reveal dysfunctional patterns of brain connectivity and contribute to explaining the pathophysiology of clinical phenotypes in CSVD.

MATERIALS AND METHODS

This systematic review provides an overview of methods and results of recent resting-state functional MRI studies in patients with CSVD. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, a systematic search of the literature was performed.

RESULTS

Of 493 studies that were screened, 44 reports were identified that investigated resting-state fMRI connectivity in the context of cerebral small vessel disease. The risk of bias and heterogeneity of results were moderate to high. Patterns associated with CSVD included disturbed connectivity within and between intrinsic brain networks, in particular the default mode, dorsal attention, frontoparietal control, and salience networks; decoupling of neuronal activity along an anterior-posterior axis; and increases in functional connectivity in the early stage of the disease.

CONCLUSION

The recent literature provides further evidence for a functional disconnection model of cognitive impairment in CSVD. We suggest that the salience network might play a hitherto underappreciated role in this model. Low quality of evidence and the lack of preregistered multi-centre studies remain challenges to be overcome in the future.

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

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.