MCI conversion to dementia and the APOE genotype: A prediction study with FDG-PET

Prognostic model for predicting Alzheimer's disease conversion using functional connectome manifolds

BACKGROUND

Early detection of Alzheimer's disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling.

METHODS

We developed a prognostic model using a large-scale Alzheimer's Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors.

RESULTS

Our prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset.

CONCLUSION

We proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion.

Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that β-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

Accelerated hypometabolism with disease progression associated with faster cognitive decline among amyloid positive patients

Objective

To evaluate the progression of brain glucose metabolism among participants with biological signature of Alzheimer's disease (AD) and its relevance to cognitive decline.

Method

We studied 602 amyloid positive individuals who underwent 18F-fluorodeoxyglucose PET (FDG-PET) scan, 18F-AV-45 amyloid PET (AV45-PET) scan, structural MRI scan and neuropsychological examination, including 116 cognitively normal (CN) participants, 314 participants diagnosed as mild cognitive impairment (MCI), and 172 participants diagnosed as AD dementia. The first FDG-PET scan satisfying the inclusion criteria was considered as the baseline scan. Cross-sectional analysis were conducted with the baseline FDG-PET data to compare the regional differences between diagnostic groups after adjusting confounding factors. Among these participants, 229 participants (55 CN, 139 MCI, and 35 AD dementia) had two-year follow-up FDG-PET data available. Regional glucose metabolism was computed and the progression rates of regional glucose metabolism were derived from longitudinal FDG-PET scans. Then the group differences of regional progression rates were examined to assess whether glucose metabolism deficit accelerates or becomes stable with disease progression. The association of cognitive decline rate with baseline regional glucose metabolism, and progression rate in longitudinal data, were evaluated.

Results

Participants with AD dementia showed substantial glucose metabolism deficit than CN and MCI at left hippocampus, in addition to the traditionally reported frontal and parietal-temporal lobe. More substantial metabolic change was observed with the contrast AD - MCI than the contrast MCI - CN, even after adjusting time duration since cognitive symptom onset. With the longitudinal data, glucose metabolism was observed to decline the most rapidly in the AD dementia group and at a slower rate in MCI. Lower regional glucose metabolism was correlated to faster cognitive decline rate with mild-moderate correlations, and the progression rate was correlated to cognitive decline rate with moderate-large correlations.

Discussion and conclusion

Hippocampus was identified to experience hypometabolism in AD pathology. Hypometabolism accelerates with disease progression toward AD dementia. FDG-PET, particularly longitudinal scans, could potentially help predict how fast cognition declines and assess the impact of treatment in interventional trials.

Decreased Hemodynamic Responses in Left Parietal Lobule and Left Inferior Parietal Lobule in Older Adults with Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study

Background: The brain activation patterns of mild cognitive impairment (MCI) are still unclear and they involve multiple brain regions. Most previous studies have focused on abnormal activation in the frontal and temporal lobes, with few investigating the entire brain. Objective: To identify and compare the changes in cerebral hemodynamics and abnormal activation patterns in the entire brain of MCI patients and healthy older adults. Methods: Patients with MCI (n = 22) and healthy controls (HC, n = 34) matched by age, education levels, sex, and mental state were enrolled. They performed the same letter and category verbal fluency test (VFT) tasks while their behavioral performance and global cerebral hemodynamics were analyzed. Results: The performance during the category VFT task was significantly better than that during the letter VFT task across all participants (HC: correct: p <  0.001; intrusions: p <  0.001; MCI: correct: p <  0.001; intrusions: p <  0.001). The number of correct words during the letter and category VFT tasks was significantly higher in the HC group than in the MCI group (p <  0.001). The deoxygenated-hemoglobin (HbR) concentrations in the left parietal lobule (p = 0.0352) and left inferior parietal lobule (p = 0.0314) were significantly different during the category VFT task. Conclusion: The differences between HC and MCI groups were greater in the category task. The HbR concentration was more sensitive for the category VFT task and concentration changes in the left parietal lobule and left inferior parietal lobule may be useful for clinical screening and application; thus, they deserve more attention.

Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment

Purpose

Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer's disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients' conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient.

Methods

We prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients.

Results

Among the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient.

Conclusion

We were able to propose a predictive algorithm for each aMCI individual's conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.

Endogenous and Exogenous Estrogen Exposures: How Women's Reproductive Health Can Drive Brain Aging and Inform Alzheimer's Prevention

After advanced age, female sex is the major risk factor for late-onset Alzheimer's disease (AD), the most common cause of dementia affecting over 24 million people worldwide. The prevalence of AD is higher in women than in men, with postmenopausal women accounting for over 60% of all those affected. While most research has focused on gender-combined risk, emerging data indicate sex and gender differences in AD pathophysiology, onset, and progression, which may help account for the higher prevalence in women. Notably, AD-related brain changes develop during a 10-20 year prodromal phase originating in midlife, thus proximate with the hormonal transitions of endocrine aging characteristic of the menopause transition in women. Preclinical evidence for neuroprotective effects of gonadal sex steroid hormones, especially 17β-estradiol, strongly argue for associations between female fertility, reproductive history, and AD risk. The level of gonadal hormones to which the female brain is exposed changes considerably across the lifespan, with relevance to AD risk. However, the neurobiological consequences of hormonal fluctuations, as well as that of hormone therapies, are yet to be fully understood. Epidemiological studies have yielded contrasting results of protective, deleterious and null effects of estrogen exposure on dementia risk. In contrast, brain imaging studies provide encouraging evidence for positive associations between greater cumulative lifetime estrogen exposure and lower AD risk in women, whereas estrogen deprivation is associated with negative consequences on brain structure, function, and biochemistry. Herein, we review the existing literature and evaluate the strength of observed associations between female-specific reproductive health factors and AD risk in women, with a focus on the role of endogenous and exogenous estrogen exposures as a key underlying mechanism. Chief among these variables are reproductive lifespan, menopause status, type of menopause (spontaneous vs. induced), number of pregnancies, and exposure to hormonal therapy, including hormonal contraceptives, hormonal therapy for menopause, and anti-estrogen treatment. As aging is the greatest risk factor for AD followed by female sex, understanding sex-specific biological pathways through which reproductive history modulates brain aging is crucial to inform preventative and therapeutic strategies for AD.

A tale of two systems: Lessons learned from female mid-life aging with implications for Alzheimer's prevention & treatment

Neurological aging is frequently viewed as a linear process of decline, whereas in reality, it is a dynamic non-linear process. The dynamic nature of neurological aging is exemplified during midlife in the female brain. To investigate fundamental mechanisms of midlife aging that underlie risk for development of Alzheimer's disease (AD) in late life, we investigated the brain at greatest risk for the disease, the aging female brain. Outcomes of our research indicate that mid-life aging in the female is characterized by the emergence of three phases: early chronological (pre-menopause), endocrinological (peri-menopause) and late chronological (post-menopause) aging. The endocrinological aging program is sandwiched between early and late chronological aging. Throughout the three stages of midlife aging, two systems of biology, metabolic and immune, are tightly integrated through a network of signaling cascades. The network of signaling between these two systems of biology underlie an orchestrated sequence of adaptative starvation responses that shift the brain from near exclusive dependence on a single fuel, glucose, to utilization of an auxiliary fuel derived from lipids, ketone bodies. The dismantling of the estrogen control of glucose metabolism during mid-life aging is a critical contributor to the shift in fuel systems and emergence of dynamic neuroimmune phenotype. The shift in fuel reliance, puts the largest reservoir of local fatty acids, white matter, at risk for catabolism as a source of lipids to generate ketone bodies through astrocytic beta oxidation. APOE4 genotype accelerates the tipping point for emergence of the bioenergetic crisis. While outcomes derived from research conducted in the female brain are not directly translatable to the male brain, the questions addressed in a female centric program of research are directly applicable to investigation of the male brain. Like females, males with AD exhibit deficits in the bioenergetic system of the brain, activation of the immune system and hallmark Alzheimer's pathologies. The drivers and trajectory of mechanisms underlying neurodegeneration in the male brain will undoubtedly share common aspects with the female in addition to factors unique to the male. Preclinical and clinical evidence indicate that midlife endocrine aging can also be a transitional bridge to autoimmune disorders. Collectively, the data indicate that endocrinological aging is a critical period "tipping point" in midlife which can initiate emergence of the prodromal stage of late-onset-Alzheimer's disease. Interventions that target both immune and metabolic shifts that occur during midlife aging have the potential to alter the trajectory of Alzheimer's risk in late life. Further, to achieve precision medicine for AD, chromosomal sex is a critical variable to consider along with APOE genotype, other genetic risk factors and stage of disease.

Cerebral Blood Flow Predicts Conversion of Mild Cognitive Impairment into Alzheimer's Disease and Cognitive Decline: An Arterial Spin Labeling Follow-up Study

BACKGROUND

This is the first longitudinal study to assess regional cerebral blood flow (rCBF) changes during the progression from normal control (NC) through mild cognitive impairment (MCI) and Alzheimer's disease (AD).

OBJECTIVE

We aim to determine if perfusion MRI biomarkers, derived from our prior cross-sectional study, can predict the onset and cognitive decline of AD.

METHODS

Perfusion MRIs using arterial spin labeling (ASL) were acquired in 15 stable-NC, 14 NC-to-MCI, 16 stable-MCI, and 18 MCI/AD-to-AD participants from the Cardiovascular Health Study (CHS) cognition study. Group comparisons, predictions of AD conversion and time to conversion, and Modified Mini-Mental State Examination (3MSE) from rCBF were performed.

RESULTS

Compared to the stable-NC group: 1) the stable-MCI group exhibited rCBF decreases in the right temporoparietal (p = 0.00010) and right inferior frontal and insula (p = 0.0094) regions; and 2) the MCI/AD-to-AD group exhibited rCBF decreases in the bilateral temporoparietal regions (p = 0.00062 and 0.0035). Compared to the NC-to-MCI group, the stable-MCI group exhibited a rCBF decrease in the right hippocampus region (p = 0.0053). The baseline rCBF values in the posterior cingulate cortex (PCC) (p = 0.0043), bilateral superior medial frontal regions (BSMF) (p = 0.012), and left inferior frontal (p = 0.010) regions predicted the 3MSE scores for all the participants at follow-up. The baseline rCBF in the PCC and BSMF regions predicted the conversion and time to conversion from MCI to AD (p < 0.05; not significant after multiple corrections).

CONCLUSION

We demonstrated the feasibility of ASL in detecting rCBF changes in the typical AD-affected regions and the predictive value of baseline rCBF on AD conversion and cognitive decline.

A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks

Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.

Sex-related differences in cognitive trajectories in older individuals with type 2 diabetes and overweight or obesity

INTRODUCTION

It is unknown whether rates of cognitive decline differ between older women and men with type 2 diabetes (T2D) and overweight or obesity.

METHODS

Two to four cognitive assessments were obtained across up to 10 years from 2799 adults (mean age 68 years; 62% women) with T2D who had been enrolled in a clinical trial of weight loss intervention. Sex-related differences in means and rates of decline of cognitive scores were assessed.

RESULTS

Women outperformed men in verbal learning and processing speed (P < 0.001), but not executive function (P = 0.22). The rates of decline over time for women and men were similar (P ≥  0.10); however women, but not men, with apolipoprotein E (APOE) ε4 alleles had steeper declines in verbal learning (P = 0.02) and processing speed (P = 0.007) than those without these alleles.

DISCUSSION

Cognitive advantages for women with T2D and overweight/obesity over men are preserved as they age; however, these are eroded by the APOE ε4 genotype.

Accelerating Structural Degeneration in Temporal Regions and Their Effects on Cognition in Aging of MCI Patients

Age is the major risk factor for Alzheimer's disease (AD) and for mild cognitive impairment (MCI). However, there is limited evidence about MCI-specific aging-related simultaneous changes of the brain structure and their impact on cognition. We analyzed the brain imaging data from 269 subjects (97 MCI patients and 172 cognitively normal [CN] elderly) using voxel-based morphometry and tract-based spatial statistics procedures to explore the special structural pattern during aging. We found that the patients with MCI showed accelerated age-related reductions in gray matter volume in the left planum temporale, thalamus, and posterior cingulate gyrus. The similar age×group interaction effect was found in the fractional anisotropy of the bilateral parahippocampal cingulum white matter tract, which connects the temporal regions. Importantly, the age-related temporal gray matter and white matter alterations were more significantly related to performance in memory and attention tasks in MCI patients. The accelerated degeneration patterns in the brain structure provide evidence for different neural mechanisms underlying aging in MCI patients. Temporal structural degeneration may serve as a potential imaging marker for distinguishing the progression of the preclinical AD stage from normal aging.

Comparison of the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers in patients suspected of Alzheimer's disease

BACKGROUND

The two biomarkers 2-[18F]FDG-PET and cerebrospinal fluid biomarkers are both recommended to support the diagnosis of Alzheimer's disease. However, there is a lack of knowledge for the comparison of the two biomarkers in a routine clinical setting.

OBJECTIVE

The aim was to compare the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers on diagnosis, prognosis, and patient management in patients suspected of Alzheimer's disease.

METHODS

Eighty-one patients clinically suspected of Alzheimer's disease were retrospectively included from the Copenhagen Memory Clinic. As part of the clinical work-up all patients had a standard diagnostic program examination including MRI and ancillary investigations with 2-[18F]FDG-PET and cerebrospinal fluid biomarkers. An incremental study design was used to evaluate the clinical impact of the biomarkers. First, the diagnostic evaluation was based on the standard diagnostic program, then the diagnostic evaluation was revised after addition of either cerebrospinal fluid biomarkers or 2-[18F]FDG-PET. At each diagnostic evaluation, two blinded dementia specialists made a consensus decision on diagnosis, prediction of disease course, and change in patient management. Confidence in the decision was measured on a visual analogue scale (0-100). After 6 months, the diagnostic evaluation was performed with addition of the other biomarker. A clinical follow-up after 12 months was used as reference for diagnosis and disease course.

RESULTS

The two biomarkers had a similar clinical value across all diagnosis when added individually to the standard diagnostic program. However, for the correctly diagnosed patient with Alzheimer's disease cerebrospinal fluid biomarkers had a significantly higher impact on diagnostic confidence (mean scores±SD: 88±11 vs. 82±11, p = 0.046) and a significant reduction in the need for ancillary investigations (23 vs. 18 patients, p = 0.049) compared to 2-[18F]FDG-PET.

CONCLUSION

The two biomarkers had similar clinical impact on diagnosis, but cerebrospinal fluid biomarkers had a more significant value in corroborating the diagnosis of Alzheimer's disease compared to 2-[18F]FDG-PET.

Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death.

INTRODUCTION

In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD.

AIM

In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed.

FINDINGS

Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure.

CONCLUSIONS

Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.

Preliminary Report on the Feasibility and Efficacy of the Modified Atkins Diet for Treatment of Mild Cognitive Impairment and Early Alzheimer's Disease

Ketone bodies, the products of fat metabolism, are a source of energy for the brain and are available even when glucose supplies are inadequate (such as with severe carbohydrate deprivation) or its metabolism is faulty (as it is in Alzheimer's disease). This phase I/II randomized clinical trial examined the feasibility of using a modified Atkins diet (MAD) to induce ketogenesis in persons with mild cognitive impairment (MCI) or early AD, and the effect of this diet on memory and other clinical outcomes. In the first 2.5 years of active recruitment, only 27 eligible and willing patients enrolled. After extensive assessment and education, they and their study partners were randomly assigned for 12 weeks to either the MAD or the National Institute on Aging (NIA) recommended diet for seniors. As of April 2018, 9 patients in the MAD arm and 5 in the NIA arm have completed the trial. In spite of extensive teaching, coaching, and monitoring, adherence to both diets was only fair. Among those in the MAD arm who generated at least trace amounts of urinary ketones, there was a large (effect size = 0.53) and statistically significant (p = 0.03) increase in Memory Composite Score between the baseline and week-6 assessment. MAD participants also reported increased energy between baseline and week-6 assessment. Despite challenges to implementing this trial, resulting in a small sample, our preliminary data suggest that the generation of even trace ketones might enhance episodic memory and patient-reported vitality in very early AD.

Do menopausal status and APOE4 genotype alter the long-term effects of intensive lifestyle intervention on cognitive function in women with type 2 diabetes mellitus?

In the Look AHEAD trial, randomization to Intensive Lifestyle Intervention (ILI) or Diabetes Support and Education (DSE) did not result in differences in cognitive outcomes. However, menopause and APOE genotype are factors that affect the response to this intervention. The effect of this intervention on a single cognitive assessment was examined in 3 groups of women: premenopausal or <5 years postmenopausal (N = 594), within 5-10 years (n = 388), and ≥10 years postmenopausal (n = 963), and as a function of continuous years since menopause. The late postmenopausal group in the ILI had worse composite z-scores compared to those in the DSE, whereas the younger premenopausal or early postmenopausal women in the ILI had better composite z-scores than the DSE. A significant interaction between years since menopause and intervention arm, but not baseline age, was observed on executive function domains. ILI appeared only to benefit cognitive function among non-APOE4 carriers during premenopause or early postmenopause. These findings emphasize the importance of assessing menopause and APOE status to understand how weight loss impacts cognition.

Clinical impact of 18F-FDG-PET among memory clinic patients with uncertain diagnosis

Purpose

To assess the clinical impact and incremental diagnostic value of ¹⁸F-fluorodeoxyglucose (FDG-PET) among memory clinic patients with uncertain diagnosis.

Methods

The study population consisted of 277 patients who, despite extensive baseline cognitive assessment, MRI, and CSF analyses, had an uncertain diagnosis of mild cognitive impairment (MCI) (n = 177) or dementia (n = 100). After baseline diagnosis, each patient underwent an FDG-PET, followed by a post-FDG-PET diagnosis formulation. We evaluated (i) the change in diagnosis (baseline vs. post-FDG-PET), (ii) the change in diagnostic accuracy when comparing each baseline and post-FDG-PET diagnosis to a long-term follow-up (3.6 ± 1.8 years) diagnosis used as reference, and (iii) comparative FDG-PET performance testing in MCI and dementia conditions.

Results

FDG-PET led to a change in diagnosis in 86 of 277 (31%) patients, in particular in 57 of 177 (32%) MCI and in 29 of 100 (29%) dementia patients. Diagnostic change was greater than two-fold in the sub-sample of cases with dementia “of unclear etiology” (change in diagnosis in 20 of 32 (63%) patients). In the dementia group, after results of FDG-PET, diagnostic accuracy improved from 77 to 90% in Alzheimer’s disease (AD) and from 85 to 94% in frontotemporal lobar degeneration (FTLD) patients (p < 0.01). FDG-PET performed better in dementia than in MCI (positive likelihood ratios >5 and < 5, respectively).

Conclusion

Within a selected clinical population, FDG-PET has a significant clinical impact, both in early and differential diagnosis of uncertain dementia. FDG-PET provides significant incremental value to detect AD and FTLD over a clinical diagnosis of uncertain dementia.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04969-7) contains supplementary material, which is available to authorized users.

APOE Alleles and Diet in Brain Aging and Alzheimer's Disease

The APOE gene alleles modify human aging and the response to the diet at many levels with diverse pleotropic effects from gut to brain. To understand the interactions of APOE isoforms and diet, we analyze how cellular trafficking of apoE proteins affects energy metabolism, the immune system, and reproduction. The age-accelerating APOE4 allele alters the endosomal trafficking of cell surface receptors that mediate lipid and glucose metabolism. The APOE4 allele is the ancestral human allele, joined by APOE3 and then APOE2 in the human species. Under conditions of high infection, uncertain food, and shorter life expectancy, APOE4 may be adaptive for reducing mortality. As humans transitioned into modern less-infectious environments and longer life spans, APOE4 increased risks of aging-related diseases, particularly impacting arteries and the brain. The association of APOE4 with glucose dysregulation and body weight promotes many aging-associated diseases. Additionally, the APOE gene locus interacts with adjacent genes on chromosome 19 in haplotypes that modify neurodegeneration and metabolism, for which we anticipate complex gene-environment interactions. We summarize how diet and Alzheimer's disease (AD) risk are altered by APOE genotype in both animal and human studies and identify gaps. Much remains obscure in how APOE alleles modify nutritional factors in human aging. Identifying risk variant haplotypes in the APOE gene complex will clarify homeostatic adaptive responses to environmental conditions.

Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study

BACKGROUND

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Studies on MCI progression are important for Alzheimer's disease (AD) prevention. 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) has been proven to be a powerful tool for measuring cerebral glucose metabolism. In this study, we proposed a classification framework for MCI prediction with both baseline and multiple follow-up FDG-PET scans as well as cognitive scores of 33 progressive MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

METHOD

First, PET images were normalized using the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA). The average metabolic intensities of brain regions were defined as static features. Dynamic features were the intensity variation between baseline and the other three time points and change ratios with the intensity obtained at baseline considered as reference. Mini-mental State Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive section (ADAS-cog) scores of each time point were collected as cognitive features. And F-score was applied for feature selection. Finally, support vector machine (SVM) with radial basis function (RBF) kernel was used for the three above features.

RESULTS

Dynamic features showed the best classification performance in accuracy of 88.61% than static features (accuracy of 78.48%). And the combination of cognitive features and dynamic features improved the classification performance in specificity of 95.65% and Area Under Curve (AUC) of 0.9308.

CONCLUSION

Our results reported that dynamic features are more representative in longitudinal research for MCI prediction work. And dynamic features and cognitive scores complementarily enhance the classification performance in specificity and AUC. These findings may predict the disease course and clinical changes in individuals with mild cognitive impairment.

The "develOpment of metabolic and functional markers of Dementia IN Older people" (ODINO) Study: Rationale, Design and Methods

Mild cognitive impairment (MCI), also termed mild neurocognitive disorder, includes a heterogeneous group of conditions characterized by declines in one or more cognitive domains greater than that expected during "normal" aging but not severe enough to impair functional abilities. MCI has been associated with an increased risk of developing dementia and even considered an early stage of it. Therefore, noninvasively accessible biomarkers of MCI are highly sought after for early identification of the condition. Systemic inflammation, metabolic perturbations, and declining physical performance have been described in people with MCI. However, whether biological and functional parameters differ across MCI neuropsychological subtypes is presently debated. Likewise, the predictive value of existing biomarkers toward MCI conversion into dementia is unclear. The "develOpment of metabolic and functional markers of Dementia IN Older people" (ODINO) study was conceived as a multi-dimensional investigation in which multi-marker discovery will be coupled with innovative statistical approaches to characterize patterns of systemic inflammation, metabolic perturbations, and physical performance in older adults with MCI. The ultimate aim of ODINO is to identify potential biomarkers specific for MCI subtypes and predictive of MCI conversion into Alzheimer's disease or other forms of dementia over a three-year follow-up. Here, we describe the rationale, design, and methods of ODINO.

Two-part hidden Markov models for semicontinuous longitudinal data with nonignorable missing covariates

This study develops a two-part hidden Markov model (HMM) for analyzing semicontinuous longitudinal data in the presence of missing covariates. The proposed model manages a semicontinuous variable by splitting it into two random variables: a binary indicator for determining the occurrence of excess zeros at all occasions and a continuous random variable for examining its actual level. For the continuous longitudinal response, an HMM is proposed to describe the relationship between the observation and unobservable finite-state transition processes. The HMM consists of two major components. The first component is a transition model for investigating how potential covariates influence the probabilities of transitioning from one hidden state to another. The second component is a conditional regression model for examining the state-specific effects of covariates on the response. A shared random effect is introduced to each part of the model to accommodate possible unobservable heterogeneity among observation processes and the nonignorability of missing covariates. A Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure is developed to conduct simultaneous variable selection and estimation. The proposed methodology is applied to a study on the Alzheimer's Disease Neuroimaging Initiative dataset. New insights into the pathology of Alzheimer's disease and its potential risk factors are obtained.

The role of epsilon phenotype in brain glucose consumption in Alzheimer's disease

OBJECTIVE

The aim of our study was to investigate the impact of the epsilon phenotype in brain glucose consumption in a population with Alzheimer's disease.

METHODS

Statistical Parametric Mapping (SPM8) was used to investigate differences in brain glucose consumption (as detectable by means of 18F FDG-PET/CT) in the population examined. A total of 129 patients (72 females and 57 males) with a diagnosis of probable AD according to the NINCDS-ADRDA criteria underwent the PET/CT examination. The mean (SD) age of the patients was 70 (± 7) years; the mean Mini-Mental State Examination was 19(± 5.6). 59 expressed epsilon 4 phenotype (E4) and 70 expressed the epsilon 3 phenotype (E3). Cerebral spinal fluid amyloid, tau, and t-tau have been measured resulting equal to 367.4 (± 149.1), 584.7 (± 312.1), and 79.2(± 45.9) pg/ml, respectively. Patients with confirmed amyloid and Tau changes were classified as AD. Patients with amyloid changes but negative Tau, considered as high risk of AD, were classified as IAD. Age, sex, MMSE, scholarship, and CSF parameters were used as a covariate in the SPM analyses.

RESULTS

We did not find significant differences in age, gender, and MMSE and CSF parameters among groups. In the analysis of the AD group as compared to AD-E3, AD-E4 subjects show a significant reduction of brain glucose consumption in inferior frontal gyrus bilaterally (BA 45, BA 47). In the analysis of the IAD group as compared to IAD-E3, IAD-E4 subjects show a significant reduction of brain glucose consumption in right in medial, middle, and superior frontal gyrus (BA10, BA11), and in left medial and middle frontal gyrus (BA10, BA11). The differences between IAD-E3 and AD-E3 and between IAD-E4 and AD-E4 (and vice versa analysis) resulted not significant.

CONCLUSIONS

APO-e4 is related to a major involvement of the frontal cortex confirming its role of risk factor in AD, while APO-3 seems not related to a specific pattern, supporting the hypothesis of neutral/protective role in AD.

Transitions in metabolic and immune systems from pre-menopause to post-menopause: implications for age-associated neurodegenerative diseases

The brain undergoes two aging programs: chronological and endocrinological. This is particularly evident in the female brain, which undergoes programs of aging associated with reproductive competency. Comprehensive understanding of the dynamic metabolic and neuroinflammatory aging process in the female brain can illuminate windows of opportunities to promote healthy brain aging. Bioenergetic crisis and chronic low-grade inflammation are hallmarks of brain aging and menopause and have been implicated as a unifying factor causally connecting genetic risk factors for Alzheimer's disease and other neurodegenerative diseases. In this review, we discuss metabolic phenotypes of pre-menopausal, peri-menopausal, and post-menopausal aging and their consequent impact on the neuroinflammatory profile during each transition state. A critical aspect of the aging process is the dynamic metabolic neuro-inflammatory profiles that emerge during chronological and endocrinological aging. These dynamic systems of biology are relevant to multiple age-associated neurodegenerative diseases and provide a therapeutic framework for prevention and delay of neurodegenerative diseases of aging. While these findings are based on investigations of the female brain, they have a broader fundamental systems of biology strategy for investigating the aging male brain. Molecular characterization of alterations in fuel utilization and neuroinflammatory mechanisms during these neuro-endocrine transition states can inform therapeutic strategies to mitigate the risk of Alzheimer's disease in women. We further discuss a precision hormone replacement therapy approach to target symptom profiles during endocrine and chronological aging to reduce risk for age-related neurodegenerative diseases.

Evidence in support of chromosomal sex influencing plasma based metabolome vs APOE genotype influencing brain metabolome profile in humanized APOE male and female mice

Late onset Alzheimer’s disease (LOAD) is a progressive neurodegenerative disease with four well-established risk factors: age, APOE4 genotype, female chromosomal sex, and maternal history of AD. Each risk factor impacts multiple systems, making LOAD a complex systems biology challenge. To investigate interactions between LOAD risk factors, we performed multiple scale analyses, including metabolomics, transcriptomics, brain magnetic resonance imaging (MRI), and beta-amyloid assessment, in 16 months old male and female mice with humanized human APOE3 (hAPOE3) or APOE4 (hAPOE4) genes. Metabolomic analyses indicated a sex difference in plasma profile whereas APOE genotype determined brain metabolic profile. Consistent with the brain metabolome, gene and pathway-based RNA-Seq analyses of the hippocampus indicated increased expression of fatty acid/lipid metabolism related genes and pathways in both hAPOE4 males and females. Further, female transcription of fatty acid and amino acids pathways were significantly different from males. MRI based imaging analyses indicated that in multiple white matter tracts, hAPOE4 males and females exhibited lower fractional anisotropy than their hAPOE3 counterparts, suggesting a lower level of white matter integrity in hAPOE4 mice. Consistent with the brain metabolomic and transcriptomic profile of hAPOE4 carriers, beta-amyloid generation was detectable in 16-month-old male and female brains. These data provide therapeutic targets based on chromosomal sex and APOE genotype. Collectively, these data provide a framework for developing precision medicine interventions during the prodromal phase of LOAD, when the potential to reverse, prevent and delay LOAD progression is greatest.

The brain cognitive reserve hypothesis: A review with emphasis on the contribution of nuclear medicine neuroimaging techniques

Neuropathological and clinical evidence indicates that the clinical expression of Alzheimer's disease (AD) occurs as neuropathology exceeds the brain reserve capacity. The brain or cognitive reserve (BCR) hypothesis states that high premorbid intelligence, education, and an active and stimulating lifestyle provide reserve capacity, which acts as a buffer against the cognitive deficits due to accumulating neuropathology. Neuroimaging studies that assessed the BCR hypothesis are critically reviewed with emphasis on study design and statistical analysis. Many studies were performed in the last two decades owing to the increasing availability of positron emission tomography (PET) and PET/computed tomography scanners and to the synthesis of new radiopharmaceuticals, including tracers for amyloid and tau proteins. Studies with different tracers provided complementary consistent results supporting the BCR hypothesis. Many studies were appropriately designed with a measure of reserve, a measure of brain anatomy/function/neuropathology, and a measure of cognitive functions that are necessary. Most of the early studies were performed with PET and [ ¹⁸ F]fluorodeoxyglucose, and occasionally with [ ¹⁵ O]water, reporting a significant association between higher occupation/education and lower glucose metabolism (blood flow) in associative temporo-parietal cortex in patients with AD and also in patients with MCI, after correcting for the degree in the cognitive impairment. On the contrary, performances on several neuropsychological tests increased with increasing education for participants with elevated [ ¹¹ C]PiB uptake. Studies with the tracers specific for tau protein showed that patients with AD with elevated tau deposits had higher cognitive performances compared with patients with similar levels of tau deposits. BCR in AD is also associated with a preserved cholinergic function. The BCR hypothesis has been validated with methodologically sound study designs and sophisticated neuroimaging techniques using different radiotracers and providing an explanation for neuropathological and clinical observations on patients with AD.

The Vascular Hypothesis of Alzheimer's Disease: A Key to Preclinical Prediction of Dementia Using Neuroimaging

The vascular hypothesis of Alzheimer's disease (VHAD) was proposed 24 years ago from observations made in our laboratory using aging rats subjected to chronic brain hypoperfusion. In recent years, VHAD has become a mother-lode to numerous neuroimaging studies targeting cerebral hemodynamic changes, particularly brain hypoperfusion in elderly patients at risk of developing Alzheimer's disease (AD). There is a growing consensus among neuroradiologists that brain hypoperfusion is likely involved in the pathogenesis of AD and that disturbed cerebral blood flow (CBF) can serve as a key biomarker for predicting conversion of mild cognitive impairment to AD. The use of cerebral hypoperfusion as a preclinical predictor of AD is becoming decisive in stratifying low and high risk patients that may develop cognitive decline and for assessing the effectiveness of therapeutic interventions. There is currently an international research drive from neuroimaging groups to seek new perspectives that can broaden our understanding of AD and improve lifestyle. Diverse neuroimaging methods are currently being used to monitor normal and dyscognitive brain activity. Some techniques are very powerful and can detect, diagnose, quantify, prognose, and predict cognitive decline before AD onset, even from a healthy cognitive state. Multimodal imaging offers new insights in the treatment and prevention of cognitive decline during advanced aging and better understanding of the functional and structural organization of the human brain. This review discusses the impact the VHAD and CBF are having on the neuroimaging technology that can usher practical strategies to help prevent AD.

Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies

Polymorphism in the apolipoprotein E (APOE) gene is a major genetic risk determinant of late-onset Alzheimer disease (AD), with the APOE*ε4 allele conferring an increased risk and the APOE*ε2 allele conferring a decreased risk relative to the common APOE*ε3 allele. Strong evidence from clinical and basic research suggests that a major pathway by which APOE4 increases the risk of AD is by driving earlier and more abundant amyloid pathology in the brains of APOE*ε4 carriers. The number of amyloid-β (Aβ)-dependent and Aβ-independent pathways that are known to be differentially modulated by APOE isoforms is increasing. For example, evidence is accumulating that APOE influences tau pathology, tau-mediated neurodegeneration and microglial responses to AD-related pathologies. In addition, APOE4 is either pathogenic or shows reduced efficiency in multiple brain homeostatic pathways, including lipid transport, synaptic integrity and plasticity, glucose metabolism and cerebrovascular function. Here, we review the recent progress in clinical and basic research into the role of APOE in AD pathogenesis. We also discuss how APOE can be targeted for AD therapy using a precision medicine approach.

18F-FDG PET for Prediction of Conversion to Alzheimer's Disease Dementia in People with Mild Cognitive Impairment: An Updated Systematic Review of Test Accuracy

Background:

A previous Cochrane systematic review concluded there is insufficient evidence to support the routine use of ¹⁸F-FDG PET in clinical practice in people with mild cognitive impairment (MCI).

Objectives:

To update the evidence and reassess the accuracy of ¹⁸F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer’s disease (AD) dementia at follow-up.

Methods:

A systematic review including comprehensive search of electronic databases from January 2013 to July 2017, to update original searches (1999 to 2013). All key review steps, including quality assessment using QUADAS 2, were performed independently and blindly by two review authors. Meta-analysis could not be conducted due to heterogeneity across studies.

Results:

When all included studies were examined across all semi-quantitative and quantitative metrics, exploratory analysis for conversion of MCI to AD dementia (n = 24) showed highly variable accuracy; half the studies failed to meet four or more of the seven sets of QUADAS 2 criteria. Variable accuracy for all metrics was also found across eleven newly included studies published in the last 5 years (range: sensitivity 56–100%, specificity 24–100%). The most consistently high sensitivity and specificity values (approximately ≥80%) were reported for the sc-SPM (single case statistical parametric mapping) metric in 6 out of 8 studies.

Conclusion:

Systematic and comprehensive assessment of studies of ¹⁸FDG-PET for prediction of conversion from MCI to AD dementia reveals many studies have methodological limitations according to Cochrane diagnostic test accuracy gold standards, and shows accuracy remains highly variable, including in the most recent studies. There is some evidence, however, of higher and more consistent accuracy in studies using computer aided metrics, such as sc-SPM, in specialized clinical settings. Robust, methodologically sound prospective longitudinal cohort studies with long (≥5 years) follow-up, larger consecutive samples, and defined baseline threshold(s) are needed to test these promising results. Further evidence of the clinical validity and utility of ¹⁸F-FDG PET in people with MCI is needed.

The effect of ApoE ε4 on longitudinal brain region-specific glucose metabolism in patients with mild cognitive impairment: a FDG-PET study

While the ApoE ε4 allele is a known risk factor for mild cognitive impairment (MCI) and Alzheimer's disease, brain region specific effects remain elusive. In this study, we investigate whether the ApoE ε4 allele exhibits brain region specific effects in longitudinal glucose uptake among patients with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed FDG PET images, MRIs, and demographic information were downloaded from the ADNI database. An iterative reblurred Van Cittertiteration method was used for partial volume correction (PVC) on all PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. Longitudinal changes in ROI FDG standardized uptake value ratio (SUVR) relative to cerebellum in 24 ApoE ε4 carriers and 24 age-matched ApoE ε4 non-carriers were measured for up to 84-months (median 72 months, SD = 11.2 months) and compared using a generalized linear mixed effects model controlling for gender, education, baseline age, and follow-up period. Additionally, voxelwise analysis was performed by implementing a paired t-test comparing matched baseline and 72 month FDG SUVR images in ApoE carriers and non-carriers separately. Results with PVC were compared with ones from non-PVC based analysis. After applying PVC, the superior fontal, parietal, lateral temporal, medial temporal, caudate, thalamus, and post-cingulate, and amygdala regions had greater longitudinal decreases in FDG uptake in ApoE ε4 carriers with MCI compared to non-carriers with MCI. Similar forebrain and limbic clusters were found through voxelwise analysis. Compared to the PVC based analysis, fewer significant ApoE-associated regions and clusters were found in the non-PVC based PET analysis. Our findings suggest that the ApoE ε4 genotype is associated with a longitudinal decline in glucose uptake in 8 forebrain and limbic brain regions in the context of MCI. In conclusion, this 84-months longitudinal FDG PET study demonstrates a novel ApoE ε4-associated brain-region specific glucose metabolism pattern in patients with MCI. Partial volume correction improved FDG PET quantification.

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease

Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to prevent or slow the progression of AD, we need to develop objective measures that are able to discriminate the MCI patients who are at risk of AD from those MCI patients who have less risk to develop AD. Here, we present a novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying MCI patients who have a high likelihood of developing AD within 3 years. Our deep learning procedures combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genetic data as input measures. The most novel characteristics of our machine learning model compared to previous ones are the following: 1) our deep learning model is multi-tasking, in the sense that it jointly learns to simultaneously predict both MCI to AD conversion as well as AD vs. healthy controls classification, which facilitates relevant feature extraction for AD prognostication; 2) the neural network classifier employs fewer parameters than other deep learning architectures which significantly limits data-overfitting (we use ∼550,000 network parameters, which is orders of magnitude lower than other network designs); 3) both structural MRI images and their warp field characteristics, which quantify local volumetric changes in relation to the MRI template, were used as separate input streams to extract as much information as possible from the MRI data. All analyses were performed on a subset of the database made publicly available via the Alzheimer's Disease Neuroimaging Initiative (ADNI), (n = 785 participants, n = 192 AD patients, n = 409 MCI patients (including both MCI patients who convert to AD and MCI patients who do not covert to AD), and n = 184 healthy controls). The most predictive combination of inputs were the structural MRI images and the demographic, neuropsychological, and APOe4 data. In contrast, the warp field metrics were of little added predictive value. The algorithm was able to distinguish the MCI patients developing AD within 3 years from those patients with stable MCI over the same time-period with an area under the curve (AUC) of 0.925 and a 10-fold cross-validated accuracy of 86%, a sensitivity of 87.5%, and specificity of 85%. To our knowledge, this is the highest performance achieved so far using similar datasets. The same network provided an AUC of 1 and 100% accuracy, sensitivity, and specificity when classifying patients with AD from healthy controls. Our classification framework was also robust to the use of different co-registration templates and potentially irrelevant features/image portions. Our approach is flexible and can in principle integrate other imaging modalities, such as PET, and diverse other sets of clinical data. The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of several medical conditions and neuropsychiatric disorders via multi-modal imaging and tabular clinical data.

FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis

Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy. We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis. Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD. Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.

Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment

INTRODUCTION

Currently, there are no tools that can accurately predict which patients with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD). Texture analysis uses image processing and statistical methods to identify patterns in voxel intensities that cannot be appreciated by visual inspection. Our main objective was to determine whether MRI texture could be used to predict conversion of MCI to AD.

METHODS

A method of 3-dimensional, whole-brain texture analysis was used to compute texture features from T1-weighted MR images. To assess predictive value, texture changes were compared between MCI converters and nonconverters over a 3-year observation period. A predictive model using texture and clinical factors was used to predict conversion of patients with MCI to AD. This model was then tested on ten randomly selected test groups from the data set.

RESULTS

Texture features were found to be significantly different between normal controls (n = 225), patients with MCI (n = 382), and patients with AD (n = 183). A subset of the patients with MCI were used to compare between MCI converters (n = 98) and nonconverters (n = 106). A composite model including texture features, APOE-ε4 genotype, Mini-Mental Status Examination score, sex, and hippocampal occupancy resulted in an area under curve of 0.905. Application of the composite model to ten randomly selected test groups (nonconverters = 26, converters = 24) predicted MCI conversion with a mean accuracy of 76.2%.

DISCUSSION

Early texture changes are detected in patients with MCI who eventually progress to AD dementia. Therefore, whole-brain 3D texture analysis has the potential to predict progression of patients with MCI to AD.

Biomarkers in Neurodegenerative Diseases

The past decade has seen tremendous efforts in biomarker discovery and validation for neurodegenerative diseases. The source and type of biomarkers has continued to grow for central nervous system diseases, from biofluid-based biomarkers (blood or cerebrospinal fluid (CSF)), to nucleic acids, tissue, and imaging. While DNA remains a predominant biomarker used to identify familial forms of neurodegenerative diseases, various types of RNA have more recently been linked to familial and sporadic forms of neurodegenerative diseases during the past few years. Imaging approaches continue to evolve and are making major contributions to target engagement and early diagnostic biomarkers. Incorporation of biomarkers into drug development and clinical trials for neurodegenerative diseases promises to aid in the development and demonstration of target engagement and drug efficacy for neurologic disorders. This review will focus on recent advancements in developing biomarkers for clinical utility in Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS).

Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers

The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60 + years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functional-structural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis

Brain ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.

Cortical Thickness and Microstructural White Matter Changes Detect Amnestic Mild Cognitive Impairment

Both the apolipoprotein E (APOE) ɛ4 allele and amnestic mild cognitive impairment (aMCI) are considered to be risk factors for Alzheimer's disease (AD). The primary aim of this study was to determine whether the aMCI-related abnormality in gray matter (GM) cortical thickness and white matter (WM) tracts integrity would be modified by the APOE genotype. A total of 146 older adults, including 64 aMCI patients (28 ɛ4 carriers and 36 non-carriers) and 82 healthy controls (39 ɛ4 carriers and 43 non-carriers), underwent a standardized clinical interview, neuropsychological battery assessment, and multi-modal brain magnetic resonance imaging scans. Compared with control subjects, the patients with aMCI showed significantly reduced cortical thickness bilaterally in the parahippocampal gyrus and disrupted WM integrity in the limbic tracts (e.g., increased mean diffusivity in the right parahippocampal cingulum and bilateral uncinate fasciculus). However, no significant main effects of the APOE genotype and diagnosis-by-genotype interaction on GM thickness and WM integrity were observed. Further, diffusivity measures of the limbic WM tracts were significantly correlated with the parahippocampal atrophy in aMCI. Importantly, the parahippocampal thickness and diffusivity measures of the limbic WM tracts were significantly correlated with the cognitive performance (i.e., episodic memory Z score) in patients with aMCI. These results demonstrate that WM microstructural disruptions in the limbic tracts are present at the early stage of AD in an APOE-independent manner; and this degeneration may occur progressively, in parallel with parahippocampal atrophy, and may specifically contribute to early initial impairment in episodic memory.

Constructional apraxia

Since the classic papers of Kleist, Mayer Gross, and Critchley, constructional apraxia (CA) has been considered to be a typical sign of a parietal lobe lesion, and as a precious tool to appreciate the spatial abilities subserved by this lobe. However, the development of more sophisticated neuropsychologic models and methods of investigation has revealed several problematic aspects. It has become increasingly clear that CA is a heterogeneous construct that can be examined with very different tasks, that are only mildly interconnected, and tap various kinds of visuospatial, perceptual, attentional, planning, and motor mechanisms. On the basis of these considerations, the relationships between parietal lobe functions and constructional activities must be considered, taking into account on the one hand the heterogeneity of the tasks and of the cognitive functions requested by different kinds of constructional activities and, on the other hand, the plurality of functions and of processing streams linking different parts of the parietal lobes to the occipital and frontal lobes.

Current and future prospects of nuclear medicine in dementia

In clinical diagnostic imaging of Alzheimer's disease (AD), MRI and nuclear medicine studies such as cerebral blood flow SPECT are positioned as biomarkers expressing pathological conditions. With understanding its usefulness and limitations, it is important to conduct appropriate application and to utilize the correct evaluation of the result in clinical practice. Although FDG-PET and amyloid PET are still not covered for dementia by health insurance, they are extremely useful for differential diagnosis as well as early diagnosis of AD. As image biomarkers, they may have complementary implications. In addition, tau PET under development not only realizes more accurate evaluation of AD but also is expected to be applied in dementia other than AD. In the future, image biomarkers are indispensable for patient selection (early diagnosis) in mild cognitive impairment (MCI) or earlier stages and for judging the therapeutic effect of interventions in cases when early intervention for AD.

The Role of Neuroimaging in the Assessment of the Cognitively Impaired Elderly

This article reviews the current diagnostic tools that are available for structural, functional, and molecular imaging of the brain, summarizing some of the key findings that have been reported in individuals diagnosed with Alzheimer disease, mild cognitive impairment, prodromal AD, or other prevalent dementias. Given recent advances in the development of amyloid PET tracers, current guidelines for the use of amyloid PET imaging in patients with cognitive complaints are reviewed. In addition, data addressing the potential value of amyloid PET imaging in the clinical setting are highlighted.

APOE Genotype Effects on Intrinsic Brain Network Connectivity in Patients with Amnestic Mild Cognitive Impairment

Whether and how the apolipoprotein E (APOE) ε4 genotype specifically modulates brain network connectivity in patients with amnestic mild cognitive impairment (aMCI) remain largely unknown. Here, we employed resting-state (‘task-free’) functional MRI and network centrality approaches to investigate local (degree centrality, DC) and global (eigenvector centrality, EC) functional integrity in the whole-brain connectome in 156 older adults, including 66 aMCI patients (27 ε4-carriers and 39 non-carriers) and 90 healthy controls (45 ε4-carriers and 45 non-carriers). We observed diagnosis-by-genotype interactions on DC in the left superior/middle frontal gyrus, right middle temporal gyrus and cerebellum, with higher values in the ε4-carriers than non-carriers in the aMCI group. We further observed diagnosis-by-genotype interactions on EC, with higher values in the right middle temporal gyrus but lower values in the medial parts of default-mode network in the ε4-carriers than non-carriers in the aMCI group. Notably, these genotype differences in DC or EC were absent in the control group. Finally, the network connectivity DC values were negatively correlated with cognitive performance in the aMCI ε4-carriers. Our findings suggest that the APOE genotype selectively modulates the functional integration of brain networks in patients with aMCI, thus providing important insight into the gene-connectome interaction in this disease.

Longer-Term Investigation of the Value of 18F-FDG-PET and Magnetic Resonance Imaging for Predicting the Conversion of Mild Cognitive Impairment to Alzheimer's Disease: A Multicenter Study

BACKGROUND

The value of fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and magnetic resonance imaging (MRI) for predicting conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) in longer-term is unclear.

OBJECTIVE

To evaluate longer-term prediction of MCI to AD conversion using 18F-FDG-PET and MRI in a multicenter study.

METHODS

One-hundred and fourteen patients with MCI were followed for 5 years. They underwent clinical and neuropsychological examinations, 18F-FDG-PET, and MRI at baseline. PET images were visually classified into predefined dementia patterns. PET scores were calculated as a semi quantitative index. For structural MRI, z-scores in medial temporal area were calculated by automated volume-based morphometry (VBM).

RESULTS

Overall, 72% patients with amnestic MCI progressed to AD during the 5-year follow-up. The diagnostic accuracy of PET scores over 5 years was 60% with 53% sensitivity and 84% specificity. Visual interpretation of PET images predicted conversion to AD with an overall 82% diagnostic accuracy, 94% sensitivity, and 53% specificity. The accuracy of VBM analysis presented little fluctuation through 5 years and it was highest (73%) at the 5-year follow-up, with 79% sensitivity and 63% specificity. The best performance (87.9% diagnostic accuracy, 89.8% sensitivity, and 82.4% specificity) was with a combination identified using multivariate logistic regression analysis that included PET visual interpretation, educational level, and neuropsychological tests as predictors.

CONCLUSION

18F-FDG-PET visual assessment showed high performance for predicting conversion to AD from MCI, particularly in combination with neuropsychological tests. PET scores showed high diagnostic specificity. Structural MRI focused on the medial temporal area showed stable predictive value throughout the 5-year course.

Biomarkers for the Early Detection and Progression of Alzheimer's Disease

The recent failures of potential disease-modifying drugs for Alzheimer's disease (AD) may reflect the fact that the enrolled participants in clinical trials are already too advanced to derive a clinical benefit. Thus, well-validated biomarkers for the early detection and accurate diagnosis of the preclinical stages of AD will be crucial for therapeutic advancement. The combinatorial use of biomarkers derived from biological fluids, such as cerebrospinal fluid (CSF), with advanced molecular imaging and neuropsychological testing may eventually achieve the diagnostic sensitivity and specificity necessary to identify people in the earliest stages of the disease when drug modification is most likely possible. In this regard, positive amyloid or tau tracer retention on positron emission tomography imaging, low CSF concentrations of the amyloid-β 1-42 peptide, high CSF concentrations in total tau and phospho-tau, mesial temporal lobe atrophy on magnetic resonance imaging, and temporoparietal/precuneus hypometabolism or hypoperfusion on 18F-fluorodeoxyglucose positron emission tomography have all emerged as biomarkers for the progression to AD. However, the ultimate AD biomarker panel will likely involve the inclusion of novel CSF and blood biomarkers more precisely associated with confirmed pathophysiologic mechanisms to improve its reliability for detecting preclinical AD. This review highlights advancements in biological fluid and imaging biomarkers that are moving the field towards achieving the goal of a preclinical detection of AD.

Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).