Relationship between regional atrophy rates and cognitive decline in mild cognitive impairment

Heterogeneity of cerebral atrophic rate in mild cognitive impairment and its interactive association with proteins related to microglia activity on longitudinal cognitive changes

BACKGROUND

Heterogeneity of cerebral atrophic rate commonly exists in mild cognitive impairment (MCI), which may be associated with microglia-involved neuropathology and have an influence on cognitive outcomes.

OBJECTIVE

We aim to explore the heterogeneity of cerebral atrophic rate among MCI and its association with plasma proteins related to microglia activity, with further investigation of their interaction effects on long-term cognition.

SUBJECTS

A total of 630 MCI subjects in the ADNI database were included, of which 260 subjects were available with baseline data on plasma proteins.

METHODS

Group-based multi-trajectory modeling (GBMT) was used to identify the latent classes with heterogeneous cerebral atrophic rates. Associations between latent classes and plasma proteins related to microglia activity were investigated with generalized linear models. Linear mixed effect models (LME) were implemented to explore the interaction effects between proteins related to microglia activity and identified latent classes on longitudinal cognitive changes.

RESULTS

Two latent classes were identified and labeled as the slow-atrophy class and the fast-atrophy class. Associations were found between such heterogeneity of atrophic rates and plasma proteins related to microglia activity, especially AXL receptor tyrosine kinase (AXL), CD40 antigen (CD40), and tumor necrosis factor receptor-like 2 (TNF-R2). Interaction effects on longitudinal cognitive changes showed that higher CD40 was associated with faster cognitive decline in the slow-atrophy class and higher AXL or TNF-R2 was associated with slower cognitive decline in the fast-atrophy class.

CONCLUSIONS

Heterogeneity of atrophic rates at the MCI stage is associated with several plasma proteins related to microglia activity, which show either protective or adverse effects on long-term cognition depending on the variability of atrophic rates.

Exploring cortical morphology biomarkers of amnesic mild cognitive impairment using novel fractal dimension-based structural MRI analysis

Amnestic mild cognitive impairment (aMCI) is considered as an intermediate stage of Alzheimer's disease, but no MRI biomarkers currently distinguish aMCI from healthy individuals effectively. Fractal dimension, a quantitative parameter, provides superior morphological information compared to conventional cortical thickness methods. Few studies have used cortical fractal dimension values to differentiate aMCI from healthy controls. In this study, we aim to build an automated discriminator for accurately distinguishing aMCI using fractal dimension measures of the cerebral cortex. Thirty aMCI patients and 30 health controls underwent structural MRI of the brain. First, the atrophy of participants' cortical sub-regions of Desikan-Killiany cortical atlas was assessed using fractal dimension and cortical thickness. The fractal dimension is more sensitive than cortical thickness in reducing dimensional effects and may accurately reflect morphological changes of the cortex in aMCI. The aMCI group had significantly lower fractal dimension values in the bilateral temporal lobes, right limbic lobe and right parietal lobe, whereas they showed significantly lower cortical thickness values only in the bilateral temporal lobes. Fractal dimension analysis was able to depict most of the significantly different focal regions detected by cortical thickness, but additionally with more regions. Second, applying the measured fractal dimensions (and cortical thickness) of both cerebral hemispheres, an unsupervised discriminator was built for the aMCI and healthy controls. The proposed fractal dimension-based method achieves 80.54% accuracy in discriminating aMCI from healthy controls. The fractal dimension appears to be a promising biomarker for cortical morphology changes that can discriminate patients with aMCI from healthy controls.

Unraveling the complexity of human brain: Structure, function in healthy and disease states

The human brain stands as an intricate organ, embodying a nexus of structure, function, development, and diversity. This review delves into the multifaceted landscape of the brain, spanning its anatomical intricacies, diverse functional capacities, dynamic developmental trajectories, and inherent variability across individuals. The dynamic process of brain development, from early embryonic stages to adulthood, highlights the nuanced changes that occur throughout the lifespan. The brain, a remarkably complex organ, is composed of various anatomical regions, each contributing uniquely to its overall functionality. Through an exploration of neuroanatomy, neurophysiology, and electrophysiology, this review elucidates how different brain structures interact to support a wide array of cognitive processes, sensory perception, motor control, and emotional regulation. Moreover, it addresses the impact of age, sex, and ethnic background on brain structure and function, and gender differences profoundly influence the onset, progression, and manifestation of brain disorders shaped by genetic, hormonal, environmental, and social factors. Delving into the complexities of the human brain, it investigates how variations in anatomical configuration correspond to diverse functional capacities across individuals. Furthermore, it examines the impact of neurodegenerative diseases on the structural and functional integrity of the brain. Specifically, our article explores the pathological processes underlying neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, shedding light on the structural alterations and functional impairments that accompany these conditions. We will also explore the current research trends in neurodegenerative diseases and identify the existing gaps in the literature. Overall, this article deepens our understanding of the fundamental principles governing brain structure and function and paves the way for a deeper understanding of individual differences and tailored approaches in neuroscience and clinical practice-additionally, a comprehensive understanding of structural and functional changes that manifest in neurodegenerative diseases.

Multiple Early Biomarkers to Predict Cognitive Decline in Dementia-Free Older Adults

INTRODUCTION

Baseline olfactory impairment, poor performance on cognitive test, and medial temporal lobe atrophy are considered biomarkers for predicting future cognitive decline in dementia-free older adults. However, the combined effect of these predictors has not been fully investigated.

METHODS

A group of 110 participants without dementia were continuously recruited into this study, and underwent olfactory, cognitive tests and MRI scanning at baseline and 5-year follow-up. Olfactory function was assessed using the University of Pennsylvania Smell Identification Test (UPSIT). Participants were divided into the cognitive decliners and non-decliners.

RESULTS

Among 87 participants who completed the 5-year follow-up, cognitive decline was present in 32 cases and 55 remained stable. Compared with non-decliners, cognitive decliners presented lower scores on both the UPSIT and the Montreal Cognitive Assessment (MoCA), and smaller hippocampal volume at baseline (all P < .001). The logistic regression analysis revealed that lower scores on UPSIT and MoCA, and smaller hippocampal volume were strongly associated with subsequent cognitive decline, respectively (all P < .001). For the prediction of cognitive decline, lower score on UPSIT performed the sensitivity of 63.6% and specificity of 81.2%, lower score on MoCA with the sensitivity of 74.5% and specificity of 65.6%, smaller hippocampal volume with the sensitivity of 70.9% and specificity of 78.1%, respectively. Combining three predictors resulted in the sensitivity of 83.6% and specificity of 93.7%.

CONCLUSIONS

The combination of olfactory test, cognitive test with structural MRI may enhance the predictive ability for future cognitive decline for dementia-free older adults.

Down syndrome and DYRK1A overexpression: relationships and future therapeutic directions

Down syndrome is a genetic-based disorder that results from the triplication of chromosome 21, leading to an overexpression of many triplicated genes, including the gene encoding Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase 1A (DYRK1A). This protein has been observed to regulate numerous cellular processes, including cell proliferation, cell functioning, differentiation, and apoptosis. Consequently, an overexpression of DYRK1A has been reported to result in cognitive impairment, a key phenotype of individuals with Down syndrome. Therefore, downregulating DYRK1A has been explored as a potential therapeutic strategy for Down syndrome, with promising results observed from in vivo mouse models and human clinical trials that administered epigallocatechin gallate. Current DYRK1A inhibitors target the protein function directly, which tends to exhibit low specificity and selectivity, making them unfeasible for clinical or research purposes. On the other hand, antisense oligonucleotides (ASOs) offer a more selective therapeutic strategy to downregulate DYRK1A expression at the gene transcript level. Advances in ASO research have led to the discovery of numerous chemical modifications that increase ASO potency, specificity, and stability. Recently, several ASOs have been approved by the U.S. Food and Drug Administration to address neuromuscular and neurological conditions, laying the foundation for future ASO therapeutics. The limitations of ASOs, including their high production cost and difficulty delivering to target tissues can be overcome by further advances in ASO design. DYRK1A targeted ASOs could be a viable therapeutic approach to improve the quality of life for individuals with Down syndrome and their families.

A review of the application of exercise intervention on improving cognition in patients with Alzheimer's disease: mechanisms and clinical studies

Alzheimer's disease (AD) is the most common form of dementia, leading to sustained cognitive decline. An increasing number of studies suggest that exercise is an effective strategy to promote the improvement of cognition in AD. Mechanisms of the benefits of exercise intervention on cognitive function may include modulation of vascular factors by affecting cardiovascular risk factors, regulating cardiorespiratory health, and enhancing cerebral blood flow. Exercise also promotes neurogenesis by stimulating neurotrophic factors, affecting neuroplasticity in the brain. Additionally, regular exercise improves the neuropathological characteristics of AD by improving mitochondrial function, and the brain redox status. More and more attention has been paid to the effect of Aβ and tau pathology as well as sleep disorders on cognitive function in persons diagnosed with AD. Besides, there are various forms of exercise intervention in cognitive improvement in patients with AD, including aerobic exercise, resistance exercise, and multi-component exercise. Consequently, the purpose of this review is to summarize the findings of the mechanisms of exercise intervention on cognitive function in patients with AD, and also discuss the application of different exercise interventions in cognitive impairment in AD to provide a theoretical basis and reference for the selection of exercise intervention in cognitive rehabilitation in AD.

Alzheimer's Disease Clinical Trials: What Have We Learned From Magnetic Resonance Imaging

Alzheimer's disease (AD) is the leading cause of cognitive impairment and dementia worldwide with rising prevalence, incidence and mortality. Despite many decades of research, there remains an unmet need for disease-modifying treatment that can significantly alter the progression of disease. Recently, with United States Food and Drug Administration (FDA) drug approvals, there have been tremendous advances in this area, with agents demonstrating effects on cognition and biomarkers. Magnetic resonance imaging (MRI) plays an instrumental role in these trials. This review article aims to outline how MRI is used for screening eligibility, monitoring safety and measuring efficacy in clinical trials, leaning on the landscape of past and recent AD clinical trials that have used MRI as examples; further, insight on promising MRI biomarkers for future trials is provided. LEVEL OF EVIDENCE: 1. TECHNICAL EFFICACY: Stage 4.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort

INTRODUCTION

The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort.

METHODS

Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures.

RESULTS

Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone.

CONCLUSION

Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging

BACKGROUND

Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD.

METHODS

445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants.

FINDINGS

We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83).

INTERPRETATION

Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design.

FUNDING

This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.

Meta-prospective memory accuracy in older adults with and without suspected Mild Cognitive Impairment (sMCI)

This study examined if the relationship between generalized and task-specific appraisals of prospective memory (PM) and actual PM performance (i.e., meta-PM accuracy) differed between healthy and suspected Mild Cognitive Impairment (sMCI) older adults. Older adults recruited included 50 healthy and 31 sMCI participants from the community and an outpatient neuropsychology clinic. Data collected consisted of self-reported appraisals and task-specific predictions/postdictions of PM performance, objective PM performance, and executive functioning (EF). The sMCI group had significantly lower scores on objective PM and EF measures related to simple and complex task-switching. Moreover, sMCI participants displayed lower task-specific meta-PM accuracy in the direction of overconfidence, but they displayed relatively equivalent generalized meta-PM accuracy when compared to the healthy group. Notably, the sMCI group's task-specific inaccuracies became non-significant in relation to the healthy group on the final long-term PM tasks after exposure to metacognitive reflection on the first two PM tasks. Despite lower scores on EF measures, EF performance did not explain task-specific meta-PM differences between groups beyond neurocognitive status. Based on these data, sMCI patients may be better assisted by metacognitive calibration strategies, EF protocols, and the implementation of general compensatory memory strategies as targets for early intervention and prevention of neurocognitive decline.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Novel insights into brain lipid metabolism in Alzheimer's disease: Oligodendrocytes and white matter abnormalities

Alzheimer's disease (AD) is the most common cause of dementia. A genome-wide association study has shown that several AD risk genes are involved in lipid metabolism. Additionally, epidemiological studies have indicated that the levels of several lipid species are altered in the AD brain. Therefore, lipid metabolism is likely changed in the AD brain, and these alterations might be associated with an exacerbation of AD pathology. Oligodendrocytes are glial cells that produce the myelin sheath, which is a lipid-rich insulator. Dysfunctions of the myelin sheath have been linked to white matter abnormalities observed in the AD brain. Here, we review the lipid composition and metabolism in the brain and myelin and the association between lipidic alterations and AD pathology. We also present the abnormalities in oligodendrocyte lineage cells and white matter observed in AD. Additionally, we discuss metabolic disorders, including obesity, as AD risk factors and the effects of obesity and dietary intake of lipids on the brain.

Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization

Background

Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy.

Objective

Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker.

Methods

We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2.

Results

The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy.

Conclusions

The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.

Consistency between Treatment Effects on Clinical and Brain Atrophy Outcomes in Alzheimer's Disease Trials

BACKGROUND

Longitudinal changes in volumetric MRI outcome measures have been shown to correlate well with longitudinal changes in clinical instruments and have been widely used as biomarker outcomes in clinical trials for Alzheimer's disease (AD). While instances of discordant findings have been noted in some trials, especially the recent amyloid-removing therapies, the overall relationship between treatment effects on brain atrophy and clinical outcomes, and how it might depend on treatment target or mechanism, clinical instrument or imaging variable is not yet clear.

OBJECTIVE

To systematically assess the consistency and therapeutic class-dependence of treatment effects on clinical outcomes and on brain atrophy in published reports of clinical trials conducted in mild cognitive impairment (MCI) and/or AD.

DESIGN

Quantitative review of the published literature. The consistency of treatment effects on clinical and brain atrophy outcomes was assessed in terms of statistical agreement with hypothesized equal magnitude effects (e.g., 30% slowing of both) and nominal directional concordance, as a function of therapeutic class.

SETTING

Interventional randomized clinical trials.

PARTICIPANTS

MCI or AD trial participants.

INTERVENTION

Treatments included were those that involved ingestion or injection of a putatively active substance into the body, encompassing both pharmacological and controlled dietary interventions.

MEASUREMENTS

Each trial included in the analysis reported at least one of the required clinical outcomes (ADAS-Cog, CDR-SB or MMSE) and at least one of the required imaging outcomes (whole brain, ventricular or hippocampal volume).

RESULTS

Data from 35 trials, comprising 185 pairwise comparisons, were included. Overall, the 95% confidence bounds overlapped with the line of identity for 150/185 (81%) of the imaging-clinical variable pairs. The greatest proportion of outliers was found in trials of anti-amyloid antibodies that have been shown to dramatically reduce the level of PET-detectable amyloid plaques, for which only 13/33 (39%) of observations overlapped the identity line. A Deming regression calculated using all data points yielded a slope of 0.54, whereas if data points from the amyloid remover class were excluded, the Deming regression line had a slope of 0.92. Directional discordance of treatment effects was also most pronounced for the amyloid-removing class, and for comparisons involving ventricular volume.

CONCLUSION

Our results provide a frame of reference for the interpretation of clinical and brain atrophy results from future clinical trials and highlight the importance of mechanism of action in the interpretation of imaging results.

Early Detection of Alzheimer's Disease in Postmenopausal Women Using Thalamic Subnuclear Volumetry

Alzheimer's disease (AD) and aging are intrinsically interconnected with each other and are mediated by molecular, cellular, and biological systems. In particular, a specific pattern of brain volume atrophy is the most profound risk factor for cognitive impairment, including AD, that is directly linked to aging. Thus, this study aimed to investigate knowledge on the early detection of AD in postmenopausal women, focusing on the volume changes of the subcortical regions, including the thalamic subnuclei, in women with AD vs. postmenopausal women. Twenty-one women with AD and twenty-one postmenopausal women without AD underwent magnetic resonance imaging (MRI). Women with AD showed significantly reduced volumes in the hippocampus, thalamus, and amygdala compared with postmenopausal women (p < 0.05, FWE-corrected). After adjustments for age, the right hippocampal volume was found to be significantly lower in the women with AD, but the volumes of the thalamus and amygdala were relatively unaffected. The women with AD exhibited significantly reduced volume in the right laterodorsal nucleus of the thalamus compared with the postmenopausal women (p < 0.05, Bonferroni-corrected). Our findings suggest that the reduced volume of both the right laterodorsal thalamic nucleus and right hippocampus may serve as a potential biomarker for the early detection of AD in postmenopausal women.

A comparison of the five-minute cognitive test with the mini-mental state examination in the elderly for cognitive impairment screening

The five-minute cognitive test (FCT) is a novel cognitive screening method with the quick and reliable merit for detecting cognitive impairment at an early stage. The diagnostic power of FCT in differentiating subjects with cognitive impairment from people with cognition in a normal range was demonstrated effective as that of the Mini-Mental Status Evaluation (MMSE) in a previous cohort study. Here, we analyzed the effect of sociodemographic and health-related factors on FCT performance and further investigated the consistency of FCT. Then, we compared the correlation of subitem scores of FCT or MMSE with a comprehensive battery of neuropsychological tests that focus on specific domains of cognition. Finally, the association of the total FCT scores with the volumes of brain subregions was investigated. There were 360 subjects aged 60 years or above enrolled in this study, including 226 adults with cognitive abilities in normal range, 107 subjects with mild cognitive impairment (MCI) and 27 mild Alzheimer's disease (AD). The results showed that the total FCT scores was negatively associated with increasing age (β = -0.146, p < 0.001), and positively associated with education attainment (β = 0.318, p < 0.001), dwelling condition with family (β = 0.153, p < 0.001) and the Body Mass Index (β = 1.519, p < 0.01). The internal consistency of the FCT (Cronbach's α) was 0.644. The sub-scores of FCT showed a significant correlation with other specific neuropsychological tests. Impressively, the total FCT scores showed a significantly positive association with the volumes of hippocampus related subregions (r = 0.523, p < 0.001) and amygdala (r = 0.479, p < 0.001), but not with cerebellum (r = 0.158, p > 0.05) or subcortical subregions (r = 0.070, p > 0.05). Combining with previous data, FCT is a reliable and valid cognitive screening test for detecting cognitive impairment in a community setting.

Current Understanding of Verbal Fluency in Alzheimer's Disease: Evidence to Date

Since their development, verbal fluency tests (VFTs) have been used extensively throughout research and in clinical settings to assess a variety of cognitive functions in diverse populations. In Alzheimer's disease (AD), these tasks have proven particularly valuable in identifying the earliest forms of cognitive decline in semantic processing and have been shown to relate specifically to brain regions associated with the initial stages of pathological change. In recent years, researchers have developed more nuanced techniques to evaluate verbal fluency performance, extracting a wide range of cognitive metrics from these simple neuropsychological tests. Such novel techniques allow for a more detailed exploration of the cognitive processes underlying successful task performance beyond the raw test score. The versatility of VFTs and the richness of data they may provide, in light of their low cost and speed of administration, therefore, highlight their potential value both in future research as outcome measures for clinical trials and in a clinical setting as a screening measure for early detection of neurodegenerative diseases.

Garcia A, Buetow K, Thomas GS, Finch CE, Stieglitz J, Trumble BC, Gurven MD, Irimia A. Brain volume, energy balance, and cardiovascular health in two nonindustrial South American populations

Little is known about brain aging or dementia in nonindustrialized environments that are similar to how humans lived throughout evolutionary history. This paper examines brain volume (BV) in middle and old age among two indigenous South American populations, the Tsimane and Moseten, whose lifestyles and environments diverge from those in high-income nations. With a sample of 1,165 individuals aged 40 to 94, we analyze population differences in cross-sectional rates of decline in BV with age. We also assess the relationships of BV with energy biomarkers and arterial disease and compare them against findings in industrialized contexts. The analyses test three hypotheses derived from an evolutionary model of brain health, which we call the embarrassment of riches (EOR). The model hypothesizes that food energy was positively associated with late life BV in the physically active, food-limited past, but excess body mass and adiposity are now associated with reduced BV in industrialized societies in middle and older ages. We find that the relationship of BV with both non-HDL cholesterol and body mass index is curvilinear, positive from the lowest values to 1.4 to 1.6 SDs above the mean, and negative from that value to the highest values. The more acculturated Moseten exhibit a steeper decrease in BV with age than Tsimane, but still shallower than US and European populations. Lastly, aortic arteriosclerosis is associated with lower BV. Complemented by findings from the United States and Europe, our results are consistent with the EOR model, with implications for interventions to improve brain health.

Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis

Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.

Neurocognitive considerations in the treatment of meningioma with radiation therapy: applications for quantitative neuroimaging and precision radiation medicine

This article focuses on the role of radiotherapy in the management of meningioma, in the definitive and adjuvant setting and across the spectrum of meningioma grade. Treatment paradigms, informed by clinical evidence, are discussed. Notably, we focus on the impact of radiotherapy on normal brain tissues and neurocognitive function, particularly the dose-dependent changes in white matter and cerebral cortex thickness. Novel imaging techniques have allowed the identification of microstructural changes to eloquent white matter, cortex, and subcortical regions as biomarkers for understanding RT-induced changes in cognitive functioning. Deficits in multiple domains including attention, memory, language and executive function can become more pronounced following radiation. Longitudinal assessment with imaging and neurocognitive testing pre- and post-radiation have allowed correlation between dose to specific regions of the brain and decline in associated domains of neurocognitive function. These findings suggest incorporation of areas at higher risk for neurocognitive sequelae into precision radiation planning. Volumetric arc therapy, advanced planning with cortical sparing, proton therapy and stereotactic radiosurgery are reviewed as options for delivering therapeutic dose to target volumes while minimizing risk to adjacent sensitive regions. The treatment of meningioma is an evolving area, with improving outcomes for higher grade disease in modern trials, where care must be taken to maximize both disease control as well as quality of life for patients.

Comparison of Clinical, Genetic, and Pathologic Features of Limbic and Diffuse Transactive Response DNA-Binding Protein 43 Pathology in Alzheimer's Disease Neuropathologic Spectrum

BACKGROUND

Increasing evidence suggests that TAR DNA-binding protein 43 (TDP-43) pathology in Alzheimer's disease (AD), or AD-TDP, can be diffuse or limbic-predominant. Understanding whether diffuse AD-TDP has genetic, clinical, and pathological features that differ from limbic AD-TDP could have clinical and research implications.

OBJECTIVE

To better characterize the clinical and pathologic features of diffuse AD-TDP and differentiate it from limbic AD-TDP.

METHODS

363 participants from the Mayo Clinic Study of Aging, Alzheimer's Disease Research Center, and Neurodegenerative Research Group with autopsy confirmed AD and TDP-43 pathology were included. All underwent genetic, clinical, neuropsychologic, and neuropathologic evaluations. AD-TDP pathology distribution was assessed using the Josephs 6-stage scale. Stages 1-3 were classified as Limbic, those 4-6 as Diffuse. Multivariable logistic regression was used to identify clinicopathologic features that independently predicted diffuse pathology.

RESULTS

The cohort was 61% female and old at onset (median: 76 years [IQR:70-82]) and death (median: 88 years [IQR:82-92]). Fifty-four percent were Limbic and 46% Diffuse. Clinically, ∼10-20% increases in odds of being Diffuse associated with 5-year increments in age at onset (p = 0.04), 1-year longer disease duration (p = 0.02), and higher Neuropsychiatric Inventory scores (p = 0.03), while 15-second longer Trailmaking Test-B times (p = 0.02) and higher Block Design Test scores (p = 0.02) independently decreased the odds by ~ 10-15%. There was evidence for association of APOEɛ4 allele with limbic AD-TDP and of TMEM106B rs3173615 C allele with diffuse AD-TDP. Pathologically, widespread amyloid-β plaques (Thal phases: 3-5) decreased the odds of diffuse TDP-43 pathology by 80-90%, while hippocampal sclerosis increased it sixfold (p < 0.001).

CONCLUSION

Diffuse AD-TDP shows clinicopathologic and genetic features different from limbic AD-TDP.

Effects of comorbid posttraumatic stress disorder on cognitive dysfunction in Chinese male methamphetamine patients

OBJECTIVES

Cognitive dysfunction and posttraumatic stress disorder (PTSD) are common in methamphetamine patients. However, few studies have investigated the cognitive performance of methamphetamine patients with PTSD. The purpose of this study was to investigate the impact of comorbid PTSD on cognitive function in Chinese male methamphetamine patients.

METHODS

We analyzed 464 methamphetamine patients and 156 healthy volunteers. The PTSD Screening Scale (PCL-5) was used to assess PTSD and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was used to assess cognitive function.

RESULTS

Compared with healthy controls, methamphetamine patients had more cognitive dysfunction in immediate memory, visuospatial/constructional, language, attention and delayed memory. Moreover, methamphetamine patients with PTSD had less cognitive dysfunction in immediate memory, attention, and delayed memory than methamphetamine patients without PTSD. Further stepwise regression analysis showed that PTSD alterations in arousal and reactivity cluster were risk predictors for language, and PTSD negative alteration in cognition and mood cluster were risk predictors for delayed memory.

CONCLUSIONS

Our results indicate that methamphetamine patients without PTSD have poorer cognitive dysfunction than those with PTSD. Some demographic and PTSD symptom clusters are protective or risk factors for cognitive dysfunction in methamphetamine patients.

Axonal degeneration and amyloid pathology predict cognitive decline beyond cortical atrophy

BACKGROUND

Cortical atrophy is associated with cognitive decline, but the association is not perfect. We aimed to identify factors explaining the discrepancy between the degree of cortical atrophy and cognitive decline in cognitively unimpaired elderly.

METHODS

The discrepancy between atrophy and cognitive decline was measured using the residuals from a linear regression analysis between change in whole brain cortical thickness over time and change in a cognitive composite measure over time in 395 cognitively unimpaired participants from the Swedish BioFINDER study. We tested for bivariate associations of this residual measure with demographic, imaging, and fluid biomarker variables using Pearson correlations and independent-samples t-tests, and for multivariate associations using linear regression models. Mediation analyses were performed to explore possible paths between the included variables.

RESULTS

In bivariate analyses, older age (r = -0.11, p = 0.029), male sex (t = -3.00, p = 0.003), larger intracranial volume (r = -0.17, p < 0.001), carrying an APOEe4 allele (t = -2.71, p = 0.007), larger white matter lesion volume (r = -0.16, p = 0.002), lower cerebrospinal fluid (CSF) β-amyloid (Aβ) 42/40 ratio (t = -4.05, p < 0.001), and higher CSF levels of phosphorylated tau (p-tau) 181 (r = -0.22, p < 0.001), glial fibrillary acidic protein (GFAP; r = -0.15, p = 0.003), and neurofilament light (NfL; r = -0.34, p < 0.001) were negatively associated with the residual measure, i.e., associated with worse than expected cognitive trajectory given the level of atrophy. In a multivariate analysis, only lower CSF Aβ42/40 ratio and higher CSF NfL levels explained cognition beyond brain atrophy. Mediation analyses showed that associations between the residual measure and APOEe4 allele, CSF Aβ42/40 ratio, and CSF GFAP and p-tau181 levels were mediated by levels of CSF NfL, as were the associations with the residual measure for age, sex, and WML volume.

CONCLUSIONS

Our results suggest that axonal degeneration and amyloid pathology independently affect the rate of cognitive decline beyond the degree of cortical atrophy. Furthermore, axonal degeneration mediated the negative effects of old age, male sex, and white matter lesions, and in part also amyloid and tau pathology, on cognition over time when accounting for cortical atrophy.

Association between inflammatory biomarkers and cognitive aging

Inflammatory cytokines and chemokines related to the innate and adaptive immune system have been linked to neuroinflammation in Alzheimer's Disease, dementia, and cognitive disorders. We examined the association of 11 plasma proteins (CD14, CD163, CD5L, CD56, CD40L, CXCL16, SDF1, DPP4, SGP130, sRAGE, and MPO) related to immune and inflammatory responses with measures of cognitive function, brain MRI and dementia risk. We identified Framingham Heart Study Offspring participants who underwent neuropsychological testing (n = 2358) or brain MRI (n = 2100) within five years of the seventh examination where a blood sample for quantifying the protein biomarkers was obtained; and who were followed for 10 years for incident all-cause dementia (n = 1616). We investigated the association of inflammatory biomarkers with neuropsychological test performance and brain MRI volumes using linear mixed effect models accounting for family relationships. We further used Cox proportional hazards models to examine the association with incident dementia. False discovery rate p-values were used to account for multiple testing. Participants included in the neuropsychological test and MRI samples were on average 61 years old and 54% female. Participants from the incident dementia sample (average 68 years old at baseline) included 124 participants with incident dementia. In addition to CD14, which has an established association, we found significant associations between higher levels of CD40L and myeloperoxidase (MPO) with executive dysfunction. Higher CD5L levels were significantly associated with smaller total brain volumes (TCBV), whereas higher levels of sRAGE were associated with larger TCBV. Associations persisted after adjustment for APOE ε4 carrier status and additional cardiovascular risk factors. None of the studied inflammatory biomarkers were significantly associated with risk of incident all-cause dementia. Higher circulating levels of soluble CD40L and MPO, markers of immune cell activation, were associated with poorer performance on neuropsychological tests, while higher CD5L, a key regulator of inflammation, was associated with smaller total brain volumes. Higher circulating soluble RAGE, a decoy receptor for the proinflammatory RAGE/AGE pathway, was associated with larger total brain volume. If confirmed in other studies, this data indicates the involvement of an activated immune system in abnormal brain aging.

Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum

INTRODUCTION

Amyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.

METHODS

Within 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.

RESULTS

Sixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.

DISCUSSION

These findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Grey and white matter atrophy one year after stroke aphasia

Dynamic whole-brain changes occur following stroke, and not just in association with recovery. We tested the hypothesis that the presence of a specific behavioural deficit after stroke would be associated with structural decline (atrophy) in the brain regions supporting the affected function, by examining language deficits post-stroke. We quantified whole-brain structural volume changes longitudinally (3–12 months) in stroke participants with (N = 32) and without aphasia (N = 59) as assessed by the Token Test at 3 months post-stroke, compared with a healthy control group (N = 29). While no significant difference in language decline rates (change in Token Test scores from 3 to 12 months) was observed between groups and some participants in the aphasic group improved their scores, stroke participants with aphasia symptoms at 3 months showed significant atrophy (>2%, P = 0.0001) of the left inferior frontal gyrus not observed in either healthy control or non-aphasic groups over the 3–12 months period. We found significant group differences in the inferior frontal gyrus volume, accounting for age, sex, stroke severity at baseline, education and total intracranial volume (Bonferroni-corrected P = 0.0003). In a subset of participants (aphasic N = 14, non-aphasic N = 36, and healthy control N = 25) with available diffusion-weighted imaging data, we found significant atrophy in the corpus callosum and the left superior longitudinal fasciculus in the aphasic compared with the healthy control group. Language deficits at 3 months post-stroke are associated with accelerated structural decline specific to the left inferior frontal gyrus, highlighting that known functional brain reorganization underlying behavioural improvement may occur in parallel with atrophy of brain regions supporting the language function.

Cross-Sectional Association Between Blood Cell Phenotypes, Cognitive Function, and Brain Imaging Measures in the Community-Based Framingham Heart Study

BACKGROUND

Peripheral inflammation is associated with increased risk for dementia. Neutrophil to lymphocyte ratio (NLR), red cell distribution width (RDW), and mean platelet volume (MPV), are easily measured circulating blood cell phenotypes reflecting chronic peripheral inflammation, but their association with dementia status is unclear.

OBJECTIVE

We sought to investigate the cross-sectional association of these inflammatory measures with neuropsychological (NP) test performance, and brain magnetic resonance imaging (MRI) measures in the Framingham Heart Study (FHS) Offspring, Third-generation, and Omni cohorts.

METHODS

We identified FHS participants who attended an exam that included a complete blood cell count (CBC) and underwent NP testing (n = 3,396) or brain MRI (n = 2,770) within five years of blood draw. We investigated the association between NLR, RDW, and MPV and NP test performance and structural MRI-derived volumetric measurements using linear mixed effect models accounting for family relationships and adjusting for potential confounders.

RESULTS

Participants were on average 60 years old, 53% female, and about 80% attended some college. Higher NLR was significantly associated with poorer performance on visual memory, and visuospatial abilities, as well as with larger white matter hyperintensity volume. We also observed associations for higher RDW with poorer executive function and smaller total cerebral brain volume.

CONCLUSION

Chronic peripheral inflammation as measured by NLR and RDW was associated with worse cognitive function, reduced brain volume, and greater microvascular disease in FHS participants. If confirmed in other samples, CBC may provide informative and cost-effective biomarkers of abnormal brain aging in the community.

Regional Brain Volume, Brain Reserve and MMSE Performance in Healthy Aging From the NEUROAGE Cohort: Contributions of Sex, Education, and Depression Symptoms

Objective: The aim of this study was twofold. First, to investigate the relationship between age, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumes, brain reserve (BR), and specific regions of interest (ROIs) with global cognitive function in healthy older adults participating in a longitudinal study on aging in the island country of Cyprus. Second, to assess the contribution of important demographic and psychosocial factors on brain volume. Specifically, the effects of sex and years of education and the association between depression symptoms on brain volume were also explored in this Mediterranean cohort.

Methods: Eighty-seven healthy older adults (males = 37, females = 50) scoring ≥24 on the Mini-Mental State Examination (MMSE) were included, with a mean age of 72.75 years and a mean educational level of 10.48 years. The Geriatric Depression Scale was used to assess depression. T1-weighted magnetic resonance images were used to calculate global and regional volumes.

Results: Age was negatively correlated with GM, WM, BR, MMSE scores, and ROIs, including the hippocampus, amygdala, entorhinal cortex, prefrontal cortex, anterior cingulate gyrus, and positively with CSF. Higher MMSE scores positively correlated with GM volume. Women exhibited greater levels of depression than men. Depression was also negatively correlated with GM volume and MMSE scores. Men had greater ventricular size than women and participants with higher education had greater ventricular expansion than those with fewer years in education.

Conclusions: The reported structural changes provide evidence on the overlap between age-related brain changes and healthy cognitive aging and suggest that these age changes affect certain regions. Furthermore, sex, depressive symptomatology, and education are significant predictors of the aging brain. Brain reserve and higher education accommodate these changes and works against the development of clinical symptoms.

Amyloid and anatomical correlates of executive functioning in middle-aged offspring of patients with late-onset Alzheimer's disease

A traditional hallmark of cognitive impairment associated with late-onset Alzheimer´s disease (LOAD) is episodic memory impairment. However, early alterations have been identified in brain regions associated with executive function in asymptomatic, middle-age offspring of patients with LOAD (O-LOAD) compared to those with no family history. We hypothesized that executive function among O-LOAD would correlate with structural and amyloid brain imaging differently from those without a family history of LOAD (control subjects, CS). Executive function, cortical thickness, and in-vivo Aβ deposits were quantified in 30 O-LOAD and 25 CS. Associations were observed among O-LOAD only. Cortical thickness in the left lateral orbitofrontal cortex was positively associated with Design Fluency. The Stroop Color and Word Test, correlated positively with right rostral mid-frontal cortex thickness. Trails Making Test-B was inversely related to left medial orbitofrontal thickness. Tower of London total time was positively associated with β-amyloid deposition in the right precuneus. These results support previous evidence that early executive dysfunction might reflect subtle, early changes in persons at risk of LOAD and suggests that executive function alterations deserve further exploration in the LOAD literature.

Precision Radiotherapy for Gliomas: Implementing Novel Imaging Biomarkers to Improve Outcomes With Patient-Specific Therapy

ABSTRACT

Gliomas are the most common primary brain cancer, yet are extraordinarily challenging to treat because they can be aggressive and infiltrative, locally recurrent, and resistant to standard treatments. Furthermore, the treatments themselves, including radiation therapy, can affect patients' neurocognitive function and quality of life. Noninvasive imaging is the standard of care for primary brain tumors, including diagnosis, treatment planning, and monitoring for treatment response. This article explores the ways in which advanced imaging has and will continue to transform radiation treatment for patients with gliomas, with a focus on cognitive preservation and novel biomarkers, as well as precision radiotherapy and treatment adaptation. Advances in novel imaging techniques continue to push the field forward, to more precisely guided treatment planning, radiation dose escalation, measurement of therapeutic response, and understanding of radiation-associated injury.

The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders

Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.

Event-related potential and EEG oscillatory predictors of verbal memory in mild cognitive impairment

Reliable biomarkers of memory decline are critical for the early detection of Alzheimer's disease. Previous work has found three EEG measures, namely the event-related brain potential P600, suppression of oscillatory activity in the alpha frequency range (∼10 Hz) and cross-frequency coupling between low theta/high delta and alpha/beta activity, each of which correlates strongly with verbal learning and memory abilities in healthy elderly and patients with mild cognitive impairment or prodromal Alzheimer's disease. In the present study, we address the question of whether event-related or oscillatory measures, or a combination thereof, best predict the decline of verbal memory in mild cognitive impairment and Alzheimer's disease. Single-trial correlation analyses show that despite a similarity in their time courses and sensitivities to word repetition, the P600 and the alpha suppression components are minimally correlated with each other on a trial-by-trial basis (generally |r _s| < 0.10). This suggests that they are unlikely to stem from the same neural mechanism. Furthermore, event-related brain potentials constructed from bandpass filtered (delta, theta, alpha, beta or gamma bands) single-trial data indicate that only delta band activity (1-4 Hz) is strongly correlated (r = 0.94, P < 0.001) with the canonical P600 repetition effect; event-related potentials in higher frequency bands are not. Importantly, stepwise multiple regression analyses reveal that the three event-related brain potential/oscillatory measures are complementary in predicting California Verbal Learning Test scores (overall R ^{2 '} s in 0.45-0.63 range). The present study highlights the importance of combining EEG event-related potential and oscillatory measures to better characterize the multiple mechanisms of memory failure in individuals with mild cognitive impairment or prodromal Alzheimer's disease.

The temporal relationships between white matter hyperintensities, neurodegeneration, amyloid beta, and cognition

Introduction

Cognitive decline in Alzheimer's disease is associated with amyloid beta (Aβ) accumulation, neurodegeneration, and cerebral small vessel disease, but the temporal relationships among these factors is not well established.

Methods

Data included white matter hyperintensity (WMH) load, gray matter (GM) atrophy and Alzheimer's Disease Assessment Scale‐Cognitive‐Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (Aβ1–42) for 461 participants from the Alzheimer's Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships among baseline WMH, GM, and Aβ1–42 to changes in WMH, GM, Aβ1–42, and cognition at 1‐year follow‐up.

Results

Baseline WMHs and Aβ1–42 predicted WMH increase and GM atrophy. Baseline WMHs and Aβ1–42 predicted worsening cognition. Only baseline Aβ1–42 predicted change in Aβ1–42.

Discussion

Baseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline Aβ1–42 predicted WMH increase, suggesting a potential role of amyloid in WM damage.

Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer's disease

BACKGROUND

Compared with single-modal neuroimages classification of AD, multi-modal classification can achieve better performance by fusing different information. Exploring synergy among various multi-modal neuroimages is contributed to identifying the pathological process of neurological disorders. However, it is still problematic to effectively exploit multi-modal information since the lack of an effective fusion method.

NEW METHOD

In this paper, we propose a deep multi-modal fusion network based on the attention mechanism, which can selectively extract features from MRI and PET branches and suppress irrelevant information. In the attention model, the fusion ratio of each modality is assigned automatically according to the importance of the data. A hierarchical fusion method is adopted to ensure the effectiveness of Multi-modal Fusion.

RESULTS

Evaluating the model on the ADNI dataset, the experimental results show that it outperforms the state-of-the-art methods. In particular, the final classification results of the NC/AD, SMCI/PMCI and Four-Class are 95.21 %, 89.79 %, and 86.15 %, respectively.

COMPARISON WITH EXISTING METHODS

Different from the early fusion and the late fusion, the hierarchical fusion method contributes to learning the synergy between the multi-modal data. Compared with some other prominent algorithms, the attention model enables our network to focus on the regions of interest and effectively fuse the multi-modal data.

CONCLUSION

Benefit from the hierarchical structure with attention model, the proposed network is capable of exploiting low-level and high-level features extracted from the multi-modal data and improving the accuracy of AD diagnosis. Results show its promising performance.

Patterns of longitudinal cortical atrophy over 3 years in empirically derived MCI subtypes

OBJECTIVE

We previously identified 4 empirically derived mild cognitive impairment (MCI) subtypes via cluster analysis within the Alzheimer's Disease Neuroimaging Initiative (ADNI) and demonstrated high correspondence between patterns of cortical thinning at baseline and each cognitive subtype. We aimed to determine whether our MCI subtypes demonstrate unique longitudinal atrophy patterns.

METHODS

ADNI participants (295 with MCI and 134 cognitively normal [CN]) underwent annual structural MRI and neuropsychological assessments. General linear modeling compared vertex-wise differences in cortical atrophy rates between each MCI subtype and the CN group. Linear mixed models examined trajectories of cortical atrophy over 3 years within lobar regions of interest.

RESULTS

Compared to the CN group, those with amnestic MCI (memory deficit) initially demonstrated greater atrophy rates within medial temporal lobe regions that became more widespread over time. Those with dysnomic/amnestic MCI (naming/memory deficits) showed greater atrophy rates largely localized to temporal lobe regions. The mixed MCI (impairment in all cognitive domains) group showed greater atrophy rates in widespread regions at all time points. The cluster-derived normal group, who had intact neuropsychological performance and normal cortical thickness at baseline despite their MCI diagnosis via conventional diagnostic criteria, continued to show normal cognition and minimal cortical atrophy over 3 years.

CONCLUSIONS

ADNI's purported amnestic MCI sample produced more refined cognitive subtypes with unique longitudinal cortical atrophy rates. These novel MCI subtypes reliably reflect underlying atrophy, reduce false-positive diagnostic errors, and improve prediction of clinical course. Such improvements have implications for the selection of participants for clinical trials and for providing more precise risk assessment for individuals diagnosed with MCI.

Entorhinal Cortex Volume Is Associated With Dual-Task Gait Cost Among Older Adults With MCI: Results From the Gait and Brain Study

Background

Low dual-task gait performance (the slowing of gait speed while performing a demanding cognitive task) is associated with low cognitive performance and an increased risk of progression to dementia in older adults with mild cognitive impairment. However, the reason for this remains unclear. This study aimed to examine the relationship between dual-task cost and regional brain volume, focusing on the hippocampus, parahippocampal gyrus, entorhinal cortex, and motor and lateral frontal cortices in older adults with mild cognitive impairment.

Methods

Forty older adults with mild cognitive impairment from the “Gait and Brain Study” were included in this study. Gait velocity was measured during single-task (ie, walking alone) and dual-task (ie, counting backwards, subtracting serial sevens, and naming animals, in addition to walking) conditions, using an electronic walkway. Regional brain volumes were derived by automated segmentation, using 3T magnetic resonance imaging.

Results

Partial rank correlation analyses demonstrated that a smaller volume of the left entorhinal cortex was associated with higher dual-task costs in counting backwards and subtracting serial sevens conditions. Subsequent logistic regression analyses demonstrated that a smaller volume of the left entorhinal cortex was independently associated with higher dual-task cost (slowing down >20% when performing cognitive task) in these two conditions. There were no other significant associations.

Conclusions

Our results show that lower dual-task gait performance is associated with volume reduction in the entorhinal cortex. Cognitive and motor dysfunction in older adults with mild cognitive impairment may reflect a shared pathogenic mechanism, and dual-task-related gait changes might be a surrogate motor marker for Alzheimer’s disease pathology.

MRI phenotypes of the brain are related to future stroke and mortality in patients with manifest arterial disease: The SMART-MR study

Neurodegenerative and neurovascular diseases lead to heterogeneous brain abnormalities. A combined analysis of these abnormalities by phenotypes of the brain might give a more accurate representation of the underlying aetiology. We aimed to identify different MRI phenotypes of the brain and assessed the risk of future stroke and mortality within these subgroups. In 1003 patients (59 ± 10 years) from the Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study, different quantitative 1.5T brain MRI markers were used in a hierarchical clustering analysis to identify 11 distinct subgroups with a different distribution in brain MRI markers and cardiovascular risk factors, and a different risk of stroke (Cox regression: from no increased risk compared to the reference group with relatively few brain abnormalities to HR = 10.34; 95% CI 3.80↔28.12 for the multi-burden subgroup) and mortality (from no increased risk compared to the reference group to HR = 4.00; 95% CI 2.50↔6.40 for the multi-burden subgroup). In conclusion, within a group of patients with manifest arterial disease, we showed that different MRI phenotypes of the brain can be identified and that these were associated with different risks of future stroke and mortality. These MRI phenotypes can possibly classify individual patients and assess their risk of future stroke and mortality.

Identifying Morphological Patterns of Hippocampal Atrophy in Patients With Mesial Temporal Lobe Epilepsy and Alzheimer Disease

Purpose: Mesial temporal lobe epilepsy (MTLE) and Alzheimer's disease (AD) are two distinct neurological disorders associated with hippocampal atrophy. Our goal is to analyze the morphologic patterns of hippocampal atrophy to better understand the underlying pathological and clinical characteristics of the two conditions.

Methods: Twenty-five patients with AD and 20 healthy controls with matched age and gender were recruited into the AD group. Twenty-three MTLE patients and 28 healthy controls with matched age and gender were recruited into the MTLE group. All subjects were scanned on 3T-MRI scanner. Automated volumetric analysis was applied to measure and compare the hippocampal volume of the two respective groups. Vertex-based morphologic analysis was applied to characterize the morphologic patterns of hippocampal atrophy within and between groups, and a correlation analysis was performed.

Results: Volumetric analysis revealed significantly decreased hippocampal volume in both AD and MTLE patients compared to the controls. In the patients with AD, the mean total hippocampal volume was 32.70% smaller than that of healthy controls, without a significant difference between the left and the right hippocampus (p < 0.05). In patients with MTLE, a significant reduction in unilateral hippocampal volume was observed, with a mean volume reduction of 28.38% as compared with healthy controls (p < 0.05). Vertex-based morphologic analysis revealed a generalized shrinkage of the hippocampi in AD patients, especially in bilateral medial and lateral regions. In MTLE group, atrophy was seen in the ipsilateral head, ipsilateral lateral body and slightly contralateral tail of the hippocampus (FWE-corrected, p < 0.05).

Conclusions: MTLE and AD have distinctive morphologic patterns of hippocampal atrophy, which provide new insight into the radiology-pathology correlation in these diseases.

Distinct structural correlates of the dominant and nondominant languages in bilinguals with Alzheimer's disease (AD)

Structural adaptations in brain regions involved in domain-general cognitive control are associated with life-long bilingualism and may contribute to the executive function advantage of bilinguals over monolinguals. To the degree that these adaptations support bilingualism, their disruption by Alzheimer's disease (AD) may compromise the ability to maintain proficiency in two languages, particularly in the less proficient, or nondominant, language that has greater control demands. The present study assessed this possibility in Spanish-English bilinguals with AD (n = 21) and cognitively normal controls (n = 30) by examining the brain correlates of dominant versus nondominant language performance on the Multilingual Naming Test (MINT), adjusting for age and education. There were no significant structural correlates of naming performance for either language in controls. In patients with AD, dominant language MINT performance was associated with cortical thickness of the entorhinal cortex and middle temporal gyrus, consistent with previous findings of temporal atrophy and related decline of naming abilities in AD. Nondominant language MINT performance, in contrast, was correlated with thickness of the left caudal anterior cingulate cortex (ACC), a central cognitive control region involved in error monitoring and task switching. The relationship between naming in the nondominant language and ACC in patients with AD but not in controls may reflect increased reliance on the ACC for nondominant language use in the face of atrophy of other control network components. The results are consistent with the possibility that the increased burden nondominant language use places on cognitive control systems compromised in AD may account for faster nondominant than dominant language decline in AD.

Magnetic resonance imaging measures of brain atrophy from the EXPEDITION3 trial in mild Alzheimer's disease

Introduction

Solanezumab is a humanized monoclonal antibody that preferentially binds to soluble amyloid β and promotes its clearance from the brain in preclinical studies. The objective of this study was to assess the effect of solanezumab in slowing global and anatomically localized brain atrophy as measured by volumetric magnetic resonance imaging (MRI).

Methods

In the EXPEDITION3 phase 3 trial, participants with mild Alzheimer's disease were randomized to receive intravenous infusions of either 400 mg of solanezumab or placebo every 4 weeks for 76 weeks. Volumetric MRI scans were acquired at baseline and at 80 weeks from 275 MRI facilities using a standardized imaging protocol. A subset of 1462 patients who completed both MRI and 14-item Alzheimer's Disease Assessment Scale–Cognitive Subscale assessments at both time points were selected for analysis. Longitudinal MRI volume changes were analyzed centrally by tensor-based morphometry with a standard FreeSurfer brain parcellation. Prespecified volumetric measures, including whole brain and ventricles, along with anatomically localized regions in the temporal, parietal, and frontal lobes were evaluated in those participants.

Results

Group-mean differences in brain atrophy rates were directionally consistent across a number of brain regions but small in magnitude (1.3–6.9% slowing) and not statistically significant when corrected for multiple comparisons. The annualized rates of change of the volumetric measures and the correlation of these changes with cognitive changes in placebo-treated subjects were similar to those reported previously.

Discussion

In the EXPEDITION3 trial, solanezumab did not significantly slow down rates of global or anatomically localized brain atrophy. Brain volume changes and their relationship to cognition were consistent with previous reports.

[Study on the improvement of brain cognitive function status by mind-control game training]

This study uses mind-control game training to intervene in patients with mild cognitive impairment to improve their cognitive function. In this study, electroencephalogram (EEG) data of 40 participants were collected before and after two training sessions. The continuous complexity of EEG signals was analyzed to assess the status of cognitive function and explore the effect of mind-control game training on the improvement of cognitive function. The results showed that after two training sessions, the continuous complexity of EEG signal of the subject increased (0.012 44 ± 0.000 29, P < 0.05) and amplitude of curve fluctuation decreased gradually, indicating that with increase of training times, the continuous complexity increased significantly, the cognitive function of brain improved significantly and state was stable. The results of this paper may show that mind-control game training can improve the status of the brain cognitive function, which may provide support and help for the future intervention of cognitive dysfunction.

Lateral ventricle volume trajectories predict response inhibition in older age-A longitudinal brain imaging and machine learning approach

Objective

In a three-wave 6 yrs longitudinal study we investigated if the expansion of lateral ventricle (LV) volumes (regarded as a proxy for brain tissue loss) predicts third wave performance on a test of response inhibition (RI).

Participants and methods

Trajectories of left and right lateral ventricle volumes across the three waves were quantified using the longitudinal stream in Freesurfer. All participants (N = 74;48 females;mean age 66.0 yrs at the third wave) performed the Color-Word Interference Test (CWIT). Response time on the third condition of CWIT, divided into fast, medium and slow, was used as outcome measure in a machine learning framework. Initially, we performed a linear mixed-effect (LME) analysis to describe subject-specific trajectories of the left and right LV volumes (LVV). These features were input to a multinomial logistic regression classification procedure, predicting individual belongings to one of the three RI classes. To obtain results that might generalize, we evaluated the significance of a k-fold cross-validated f1-score with a permutation test, providing a p-value that approximates the probability that the score would be obtained by chance. We also calculated a corresponding confusion matrix.

Results

The LME-model showed an annual ∼ 3.0% LVV increase. Evaluation of a cross-validated score using 500 permutations gave an f1-score of 0.462 that was above chance level (p = 0.014). 56% of the fast performers were successfully classified. All these were females, and typically older than 65 yrs at inclusion. For the true slow performers, those being correctly classified had higher LVVs than those being misclassified, and their ages at inclusion were also higher.

Conclusion

Major contributions were: (i) a longitudinal design, (ii) advanced brain imaging and segmentation procedures with longitudinal data analysis, and (iii) a data driven machine learning approach including cross-validation and permutation testing to predict behaviour, solely from the individual’s brain “signatures” (LVV trajectories).

Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer's disease

Background

Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy.

Methods

We selected 523 individuals from ADNI (100 preclinical AD, 288 prodromal AD, 135 AD dementia) with abnormal baseline amyloid PET/CSF and ≥1 year of MRI follow-up. We calculated total and 90 regional GM volumes for 2281 MRI scans (median [IQR]; 4 [3–5] scans per individual over 2 [1.6–4] years) and used linear mixed models to investigate atrophy as a function of time, aging and decline on MMSE. Analyses included clinical stage as interaction with the predictor and were corrected for baseline age, sex, education, field strength and total intracranial volume. We repeated analyses for a sample of participants with normal amyloid (n = 387) to assess whether associations were specific for amyloid pathology.

Results

Using time or aging as predictors, amyloid abnormal participants annually declined −1.29 ± 0.08 points and − 0.28 ± 0.03 points respectively on the MMSE and −12.23 ± 0.47 cm³ and −8.87 ± 0.34 respectively in total GM volume (p < .001). For the time and age models atrophy was widespread and preclinical and prodromal AD showed similar atrophy patterns. Comparing prodromal AD to AD dementia, AD dementia showed faster atrophy mostly in temporal lobes as modeled with time, while prodromal AD showed faster atrophy in mostly frontoparietal areas as modeled with age (p_FDR < 0.05). Modeling change in GM volume as a function of decline on MMSE, slopes were less steep compared to those based on time and aging (−4.1 ± 0.25 cm³ per MMSE point decline; p < .001) and showed steeper atrophy for prodromal AD compared to preclinical AD in the right inferior temporal gyrus (p < .05) and compared to AD dementia mostly in temporal areas (p_FDR < 0.05). Associations with time, aging and MMSE remained when accounting for these effects in the other models, suggesting that all measures explain part of the variance in GM atrophy. Repeating analyses in amyloid normal individuals, effects for time and aging showed similar widespread anatomical patterns, while associations with MMSE were largely reduced.

Conclusion

Effects of time, aging and MMSE all explained variance in GM atrophy slopes within individuals. Associations with MMSE were weaker than those for time or age, but specific for amyloid pathology. This suggests that at least some of the atrophy observed in time or age models may not be specific to AD.

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Modeling atrophy as a function of time or aging show similar anatomical patterns.

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GM atrophy as a function of time or aging seems nonspecific for amyloid pathology.

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GM atrophy as a function of MMSE shows involvement of different anatomical patterns.

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Atrophy modeled based on time or age was steeper than modeled based on MMSE.

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Atrophy patterns based on MMSE were specific for amyloid pathology.

Effect of sleep quality on amnestic mild cognitive impairment vulnerable brain regions in cognitively normal elderly individuals

STUDY OBJECTIVES

This study aims to evaluate the extent to which sleep quality impacts amnestic mild cognitive impairment (aMCI)-related brain regions in a cognitively normal cohort of individuals.

METHODS

Seventy-four participants were rigorously evaluated using a battery of cognitive tests and a detailed clinical assessment to verify normal cognitive status. We then screened for sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and depressive symptoms using the Geriatric Depression Scale (GDS). Five subjects were excluded due to mild depression. Overall 38 individuals with mean age 70.7 ± 7 were classified as poor sleepers and 31 with mean age of 69.6 ± 6 years as normal sleepers. Structural MRI and Freesurfer brain parcellation were used to measure aMCI-related brain regions.

RESULTS

Relative to normal sleepers, poor sleepers exhibited significant reductions in cortical and subcortical volumes bilaterally in the hippocampi, as well as in the superior parietal lobules and left amygdala. The effects were strongest in the left superior parietal lobule (p < .015), followed by the hippocampi. Diffuse patterns of cortical thinning were observed in the frontal lobes, but significant effects were concentrated in the right mesial frontal cortex. Lower sleep duration was most correlated with cortical volume and thickness reductions among all subjects.

CONCLUSIONS

Atrophy related to poor sleep quality impacted a number of regions implicated in aMCI and Alzheimer's disease (AD). As such, interventions targeted towards improving sleep quality amongst the elderly may prove an effective tool for modulating the course of aMCI and AD.

Examining the relationship between nutrition and cerebral structural integrity in older adults without dementia

The proportion of adults aged 60 years and over is expected to increase over the coming decades. This ageing of the population represents an important health issue, given that marked reductions to cerebral macro- and microstructural integrity are apparent with increasing age. Reduced cerebral structural integrity in older adults appears to predict poorer cognitive performance, even in the absence of clinical disorders such as dementia. As such, it is becoming increasingly important to identify those factors predicting cerebral structural integrity, especially factors that are modifiable. One such factor is nutritional intake. While the literature is limited, data from available cross-sectional studies indicate that increased intake of nutrients such as B vitamins (for example, B6, B12 and folate), choline, n-3 fatty acids and vitamin D, or increased adherence to prudent whole diets (for example, the Mediterranean diet) predicts greater cerebral structural integrity in older adults. There is even greater scarcity of randomised clinical trials investigating the effects of nutritional supplementation on cerebral structure, though it appears that supplementation with B vitamins (B6, B12 and folic acid) or n-3 fatty acids (DHA or EPA) may be beneficial. The current review presents an overview of available research examining the relationship between key nutrients or adherence to select diets and cerebral structural integrity in dementia-free older adults.

Secondary prevention of Alzheimer's dementia: neuroimaging contributions

BACKGROUND

In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations.

MAIN BODY

Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation.

CONCLUSION

We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.

Microstructural brain changes track cognitive decline in mild cognitive impairment

Improved characterization of the microstructural brain changes occurring in the early stages of Alzheimer's disease may permit more timely disease detection. This study examined how longitudinal change in brain microstructure relates to cognitive decline in aging and prodromal Alzheimer's disease. At baseline and two-year follow-up, 29 healthy controls and 21 individuals with mild cognitive impairment or mild Alzheimer's disease underwent neuropsychological evaluation and restriction spectrum imaging (RSI). Microstructural change in the hippocampus, entorhinal cortex, and white matter tracts previously shown to be vulnerable to Alzheimer's disease, was compared between healthy controls and impaired participants. Partial correlations and stepwise linear regressions examined whether baseline RSI metrics predicted subsequent cognitive decline, or change in RSI metrics correlated with cognitive change. In medial temporal gray and white matter, restricted isotropic diffusion and crossing fibers were lower, and free water diffusion was higher, in impaired participants. Restricted isotropic diffusion in the hippocampus declined more rapidly for cognitively impaired participants. Baseline hippocampal restricted isotropic diffusion predicted cognitive decline, and change in hippocampal and entorhinal restricted isotropic diffusion correlated with cognitive decline. Within controls, changes in white matter restricted oriented diffusion and crossing fibers correlated with memory decline. In contrast, there were no correlations between rates of cortical atrophy and cognitive decline in the full sample or within controls. Changes in medial temporal lobe microarchitecture were associated with cognitive decline in prodromal Alzheimer's disease, and these changes were distinct from microstructural changes in normal cognitive aging. RSI metrics of brain microstructure may hold value for predicting cognitive decline in aging and for monitoring the course of Alzheimer's disease.

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Longitudinal diffusion MRI was conducted in individuals with MCI/AD and controls.

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Mean restricted diffusion in hippocampus declined more rapidly in MCI/AD.

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Baseline hippocampal restricted diffusion predicted cognitive decline.

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Change in medial temporal restricted diffusion correlated with cognitive decline.

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In healthy controls, white matter microstructure correlated with memory decline.

Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry

Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.

Convergent Results from Neuropsychology and from Neuroimaging in Patients with Mild Cognitive Impairment

BACKGROUND/AIMS

To investigate the correspondence between neuropsychological single measures and variation in fludeoxyglucose positron emission tomography (FDG PET) glucose metabolism and magnetic resonance imaging (MRI) cortical thickness in mild cognitive impairment (MCI) patients.

METHODS

Forty-two elderly controls and 73 MCI subjects underwent FDG PET and MRI scanning. Backward regression analyses with PET and MRI regions were used as dependent variables, while Rey Auditory Verbal Memory Test (RAVLT) recall, Trail Making Test B (TMT B), and a composite test score (RAVLT learning and immediate recall, TMT A, COWAT, and letter-number sequencing) were used as predictor variables.

RESULTS

The composite score predicted variation in cortical metabolism; supplementary analyses showed that TMT B was significantly correlated with PET metabolism as well. RAVLT and TMT B were significant predictors of variation in MRI cortical thickness.

CONCLUSION

Our results indicate that RAVLT and TMT B are sensitive to variation in Alzheimer disease neuroimaging markers.