Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer's Disease

Relationships among tumor necrosis factor-alpha levels, beta-amyloid accumulation, and hippocampal atrophy in patients with late-life major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is characterized by hippocampal volume reduction, impacting cognitive function. Inflammation, particularly elevated tumor necrosis factor-alpha (TNF-α) levels, is consistently implicated in MDD pathophysiology. This study investigates the relationships between TNF-α levels, hippocampal volume, beta-amyloid (Aβ) burden, and cognitive abilities in MDD patients, aiming to illuminate the complex interplay among inflammatory markers, pathology indicators, structural brain alterations, and cognitive performance in non-demented MDD individuals.

METHOD

Fifty-two non-demented MDD patients, comprising 25 with mild cognitive impairment (MCI), were recruited along with 10 control subjects. Each participant underwent a thorough assessment encompassing TNF-α blood testing, 18F-florbetapir positron emission tomography, magnetic resonance imaging scans, and neuropsychological testing. Statistical analyses, adjusted for age and education, were performed to investigate the associations between TNF-α levels, adjusted hippocampal volume (HVa), global Aβ burden, and cognitive performance.

RESULTS

MCI MDD patients displayed elevated TNF-α levels and reduced HVa relative to controls. Correlation analyses demonstrated inverse relationships between TNF-α level and HVa in MCI MDD, all MDD, and all subjects groups. Both TNF-α level and HVa exhibited significant correlations with processing speed across all MDD and all subjects. Notably, global 18F-florbetapir standardized uptake value ratio did not exhibit significant correlations with TNF-α level, HVa, and cognitive measures.

CONCLUSION

This study highlights elevated TNF-α levels and reduced hippocampal volume in MCI MDD patients, indicating a potential association between peripheral inflammation and structural brain alterations in depression. Furthermore, our results suggest that certain cases of MDD may be affected by non-amyloid-mediated process, which impacts their TNF-α and hippocampal volume. These findings emphasize the importance of further investigating the complex interplay among inflammation, neurodegeneration, and cognitive function in MDD.

Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques

Background

Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques.

Objective

To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio.

Methods

We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score.

Results

Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors.

CONCLUSIONS

Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

The spike-and-slab elastic net as a classification tool in Alzheimer's disease

Alzheimer's disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predictors and reduces model variance, but neither model exploits spatial information in selecting variables. Spatial information can be incorporated into variable selection by placing intrinsic autoregressive priors on the logit probabilities of inclusion within a spike-and-slab elastic net framework. We demonstrate the ability of this framework to improve classification performance by using cortical thickness and tau-PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to classify subjects as cognitively normal or having dementia, and by using a simulation study to examine model performance using finer resolution images.

Comparing Pathological Risk Factors for Dementia between Cognitively Normal Japanese and Americans

The Alzheimer's Disease Neuroimaging Initiative showed that Japanese had significantly lower brain Aβ burden than Americans among a cognitively normal population. This cross-sectional study aimed to compare vascular disease burden, Aβ burden, and neurodegeneration between cognitively normal elderly Japanese and Americans. Japanese and American participants were matched for age (±4-year-old), sex, and Apolipoprotein E (APOE) genotype. Brain vascular disease burden and brain Aβ burden were measured using white matter lesions (WMLs) and 11C-labeled Pittsburgh Compound B (PiB) retention, respectively. Neurodegeneration was measured using hippocampal volumes and cortical thickness. A total of 95 Japanese and 95 Americans were recruited (50.5% men, mean age = 82). Compared to Americans, Japanese participants had larger WMLs, and a similar global Aβ standardized uptake value ratio (SUVR), cortical thickness and hippocampal volumes. Japanese had significantly lower regional Aβ SUVR in the anterior ventral striatum, posterior cingulate cortex, and precuneus. Cognitively normal elderly Japanese and Americans had different profiles regarding vascular disease and Aβ burden. This suggests that multiple risk factors are likely to be involved in the development of dementia. Additionally, Japanese might have a lower risk of dementia due to lower Aβ burden than Americans. Longitudinal follow-up of these cohorts is warranted to ascertain the predictive accuracy of these findings.

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases

Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Maximal aerobic capacity is associated with hippocampal cognitive reserve in older adults with amnestic mild cognitive impairment

Maximal aerobic capacity (MAC) has been associated with preserved neural tissue or brain maintenance (BM) in healthy older adults, including the hippocampus. Amnestic mild cognitive impairment (aMCI) is considered a prodromal stage of Alzheimer's disease. While aMCI is characterized by hippocampal deterioration, the MAC-hippocampal relationship in these patients is not well understood. In contrast to healthy individuals, neurocognitive protective effects in neurodegenerative populations have been associated with mechanisms of cognitive reserve (CR) altering the neuropathology-cognition relationship. We investigated the MAC-hippocampal relationship in aMCI (n = 29) from the perspectives of BM and CR mechanistic models with structural MRI and a memory fMRI paradigm using both group-level (higher-fit patients vs. lower-fit patients) and individual level (continuous correlation) approaches. While MAC was associated with smaller hippocampal volume, contradicting the BM model, higher-fit patients demonstrated statistically significant lower correlation between hippocampal volume and memory performance compared with the lower-fit patients, supporting the model of CR. In addition, while there was no difference in brain activity between the groups during low cognitive demand (encoding of familiar stimuli), higher MAC level was associated with increased cortical and sub-cortical activation during increased cognitive demand (encoding of novel stimuli) and also with bilateral hippocampal activity even when controlling for hippocampal volume, suggesting for an independent effect of MAC. Our results suggest that MAC may be associated with hippocampal-related cognitive reserve in aMCI through altering the relationship between hippocampal-related structural deterioration and cognitive function. In addition, MAC was found to be associated with increased capacity to recruit neural resources during increased cognitive demands.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease

Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.

Moderating effects of sex on the impact of diagnosis and amyloid positivity on verbal memory and hippocampal volume

Background

Alzheimer’s disease (AD) impacts men and women differently, but the effect of sex on predementia stages is unclear. The objective of this study was to examine whether sex moderates the impact of florbetapir positron emission tomography (PET) amyloid positivity (A⁺) on verbal learning and memory performance and hippocampal volume (HV) in normal cognition (NC) and early mild cognitive impairment (eMCI).

Methods

Seven hundred forty-two participants with NC and participants with eMCI from the Alzheimer’s Disease Neuroimaging Initiative (second cohort [ADNI2] and Grand Opportunity Cohort [ADNI-GO]) were included. All had baseline florbetapir PET measured, and 526 had screening visit HV measured. Regression moderation models were used to examine whether A⁺ effects on Rey Auditory Verbal Learning Test learning and delayed recall and right and left HV (adjusted for total intracranial volume) were moderated by diagnosis and sex. Age, cognition at screening, education, and apolipoprotein E ε4 carrier status were controlled.

Results

Women with A⁺, but not those with florbetapir PET amyloid negative (A-),eMCI showed poorer learning. For women with NC, there was no relationship of A⁺ with learning. In contrast, A⁺ men trended toward poorer learning regardless of diagnosis. A similar trend was found for verbal delayed recall: Women with A⁺, but not A-, eMCI trended toward reduced delayed recall; no effects were observed for women with NC or for men. Hippocampal analyses indicated that women with A⁺, but not those with A⁻, eMCI, trended toward smaller right HV; no significant A⁺ effects were observed for women with NC. Men showed similar, though nonsignificant, patterns of smaller right HV in A⁺ eMCI, but not in men with A⁻ eMCI or NC. No interactive effects of sex were noted for left HV.

Conclusions

Women with NC showed verbal learning and memory scores robust to A⁺, and women with A⁺ eMCI lost this advantage. In contrast, A⁺ impacted men’s scores less significantly or not at all, and comparably across those with NC and eMCI. Sex marginally moderated the relationship of A⁺ and diagnosis with right HV, such that women with NC showed no A⁺ effect and women with A⁺ eMCI lost that advantage in neural integrity; the pattern in men was less clear. These findings show that women with A⁺ eMCI (i.e., prodromal AD) have differential neural and cognitive decline, which has implications for considering sex in early detection of AD and development of therapeutics.

Neuroimaging in Alzheimer's disease: preclinical challenges toward clinical efficacy

The scope of this review focuses on recent applications in preclinical and clinical magnetic resonance imaging (MRI) toward accomplishing the goals of early detection and responses to therapy in animal models of Alzheimer's disease (AD). Driven by the outstanding efforts of the Alzheimer's Disease Neuroimaging Initiative (ADNI), a truly invaluable resource, the initial use of MRI in AD imaging has been to assess changes in brain anatomy, specifically assessing brain shrinkage and regional changes in white matter tractography using diffusion tensor imaging. However, advances in MRI have led to multiple efforts toward imaging amyloid beta plaques first without and then with the use of MRI contrast agents. These technological advancements have met with limited success and are not yet appropriate for the clinic. Recent developments in molecular imaging inclusive of high-power liposomal-based MRI contrast agents as well as fluorine 19 ((19)F) MRI and manganese enhanced MRI have begun to propel promising advances toward not only plaque imaging but also using MRI to detect perturbations in subcellular processes occurring within the neuron. This review concludes with a discussion about the necessity for the development of novel preclinical models of AD that better recapitulate human AD for the imaging to truly be meaningful and for substantive progress to be made toward understanding and effectively treating AD. Furthermore, the continued support of outstanding programs such as ADNI as well as the development of novel molecular imaging agents and MRI fast scanning sequences will also be requisite to effectively translate preclinical findings to the clinic.