Cortical signatures of cognition and their relationship to Alzheimer's disease

Toward a statistical validation of brain signatures as robust measures of behavioral substrates

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.

Pathophysiology of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disease leading to dementia worldwide. While neuritic plaques consisting of aggregated amyloid-beta proteins and neurofibrillary tangles of accumulated tau proteins represent the pathophysiologic hallmarks of AD, numerous processes likely interact with risk and protective factors and one's culture to produce the cognitive loss, neuropsychiatric symptoms, and functional impairments that characterize AD dementia. Recent biomarker and neuroimaging research has revealed how the pathophysiology of AD may lead to symptoms, and as the pathophysiology of AD gains clarity, more potential treatments are emerging that aim to modify the disease and relieve its burden.

Higher motor cortical excitability linked to greater cognitive dysfunction in Alzheimer's disease: results from two independent cohorts

Prior studies have reported increased cortical excitability in people with Alzheimer's disease (AD), but findings have been inconsistent, and how excitability relates to dementia severity remains incompletely understood. The objective of this study was to investigate the association between a transcranial magnetic stimulation (TMS) measure of motor cortical excitability and measures of cognition in AD. A retrospective cross-sectional analysis tested the relationship between resting motor threshold (RMT) and the Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog) across two independent samples of AD participants (a discovery cohort, n=22 and a larger validation cohort, n=129) and a control cohort of cognitively normal adults (n=26). RMT was correlated with ADAS-Cog in the discovery-AD cohort (n=22, β=-.70, p<0.001) but not in the control cohort (n=26, β=-0.13, p=0.513). This relationship was confirmed in the validation-AD cohort (n=129, β=-.35, p<0.001). RMT can be a useful neurophysiological marker of progressive global cognitive dysfunction in AD. Future translational research should focus on the potential of RMT to predict and track individual pathophysiological trajectories of aging.

A robust brain signature region approach for episodic memory performance in older adults

The brain signature concept aims to characterize brain regions most strongly associated with an outcome of interest. Brain signatures derive their power from data-driven searches that select features based solely on performance metrics of prediction or classification. This approach has important potential to delineate biologically relevant brain substrates for prediction or classification of future trajectories. Recent work has used exploratory voxel-wise or atlas-based searches, with some using machine learning techniques to define salient features. These have shown undoubted usefulness, but two issues remain. The preponderance of recent work has been aimed at categorical rather than continuous outcomes, and it is rare for non-atlas reliant voxel-based signatures to be reported that would be useful for modelling and hypothesis testing. We describe a cross-validated signature region model for structural brain components associated with baseline and longitudinal episodic memory across cognitively heterogeneous populations including normal, mild impairment and dementia. We used three non-overlapping cohorts of older participants: from the UC Davis Aging and Diversity cohort (n = 255; mean age 75.3 ± 7.1 years; 128 cognitively normal, 97 mild cognitive impairment, 30 demented and seven unclassified); from Alzheimer's Disease Neuroimaging Initiative (ADNI) 1 (n = 379; mean age 75.1 ± 7.2; 82 cognitively normal, 176 mild cognitive impairment, 121 Alzheimer's dementia); and from ADNI2/GO (n = 680; mean age 72.5 ± 7.1; 220 cognitively normal, 381 mild cognitive impairment and 79 Alzheimer's dementia). We used voxel-wise regression analysis, correcting for multiple comparisons, to generate an array of regional masks corresponding to different association strength levels of cortical grey matter with baseline memory and brain atrophy with memory change. Cognitive measures were episodic memory using Spanish and English Neuropsychological Assessment Scales instruments for UC Davis and ADNI-Mem for ADNI 1 and ADNI2/GO. Performance metric was the adjusted R2 coefficient of determination of each model explaining outcomes in two cohorts other than where it was computed. We compared within-cohort performances of signature models against each other and against other recent signature models of episodic memory. Findings were: (i) two independently generated signature region of interest models performed similarly in a third separate cohort; (ii) a signature region of interest generated in one imaging cohort replicated its performance level when explaining cognitive outcomes in each of other, separate cohorts; and (iii) this approach better explained baseline and longitudinal memory than other recent theory-driven and data-driven models. This suggests our approach can generate signatures that may be easily and robustly applied for modelling and hypothesis testing in mixed cognition cohorts.

A longitudinal study using latent curve models of groups with mild cognitive impairment and Alzheimer's disease

BACKGROUND

This study explores how mild cognitive impairment (MCI) and Alzheimer's disease (AD) develop over time. NEW METHOD: this study involves a new application of latent curve models (LCM) to examine the development trajectory of a healthy, MCI, and AD groups on a series of clinical and neural measures. Multiple-group latent curve models were used to compare the parameters of the trajectories across groups.

RESULTS

LCM results showed that a linear functional form of growth was adequate for all the clinical and neural measures. Positive and significant differences in initial levels were seen across groups on all of the clinical and neural measures. In all groups, the following measures increased slightly, or considerably, over time: Clinical Dementia Rating, Alzheimer's disease Cognitive Assessment, and Montreal Assessment Test for Dementia. In contrast, a slight or a greatly decreasing trajectory was observed on the following measures: Fluorodeoxyglucose, Mini-Mental State Exam, Rey Auditory Verbal Learning Test as well as Hippocampus, Fusiform and Entorhinal Cortex volume measures. However, a constant mean trajectory was seen on Cognition Self Report Memory and languages scores. COMPARISION WITH EXISTING METHODS: there are no prior studies that applied LCM on large AD datasets.

CONCLUSIONS

cognitive decline occurs in the cognitively normal (CN), MCI, and AD groups but at different rates. Further, some important cognitive, neural, and clinical variables that (a) best differentiate between CN, MCI, and AD as well as (b) differentially change over time in MCI and AD, which may explain disease progression.

Patients with Mild Cognitive Impairment May be Stratified by Advanced Diffusion Metrics and Neurocognitive Testing

BACKGROUND AND PURPOSE

Mild cognitive impairment (MCI) is a prevalent disorder, with a subset of patients progressing to dementia each year. Although MCI may be subdivided into amnestic or vascular types as well as into single or multiple cognitive domain involvement, most prior studies using advanced diffusion imaging have not accounted for these categories. The purpose of the current study was to determine if the pattern of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics in patients with amnestic MCI (aMCI) correlate to specific cognitive domain impairments.

METHODS

Nineteen consecutive patients with aMCI referred for brain magnetic resonance imaging (MRI) were included. All subjects underwent neurocognitive testing. A z-score was calculated for each domain and a composite of all four domains. Brain MRI included standard structural imaging and diffusion imaging. Volumetric, DTI, and DKI metrics were calculated and statistical analysis was performed with adjustments for multiple measures and comparisons.

RESULTS

Statistically significant correlations between diffusion metrics and cognitive z-scores were detected: visuospatial-visuoconstructional z-scores only correlated with alterations in the corpus callosum splenium, executive functioning z-scores with the corpus callosum genu, memory testing z-scores with the left hippocampus, and composite z-scores with the anterior centrum semiovale.

CONCLUSION

Neuroimaging studies of patients with aMCI to date have assumed a population with homogeneous cognitive impairment. Our results demonstrate selective patterns of regional diffusion metric alterations correlate to specific cognitive domain impairments. Future studies should account for this heterogeneity, and this may also be useful for prognostication.

Tau PET imaging predicts cognition in atypical variants of Alzheimer's disease

Accumulation of paired helical filament tau contributes to neurodegeneration in Alzheimer's disease (AD). ¹⁸ F-flortaucipir is a positron emission tomography (PET) radioligand sensitive to tau in AD, but its clinical utility will depend in part on its ability to predict cognitive symptoms in diverse dementia phenotypes associated with selective, regional uptake. We examined associations between ¹⁸ F-flortaucipir and cognition in 14 mildly-impaired patients (12 with cerebrospinal fluid analytes consistent with AD pathology) who had amnestic (n = 5) and non-amnestic AD syndromes, including posterior cortical atrophy (PCA, n = 5) and logopenic-variant primary progressive aphasia (lvPPA, n = 4). Amnestic AD patients had deficits in memory; lvPPA in language; and both amnestic AD and PCA patients in visuospatial function. Associations with cognition were tested using sparse regression and compared to associations in anatomical regions-of-interest (ROIs). ¹⁸ F-flortaucipir uptake was expected to show regionally-specific correlations with each domain. In multivariate analyses, uptake was elevated in neocortical areas specifically associated with amnestic and non-amnestic syndromes. Uptake in left anterior superior temporal gyrus accounted for 67% of the variance in language performance. Uptake in right lingual gyrus predicted 85% of the variance in visuospatial performance. Memory was predicted by uptake in right fusiform gyrus and cuneus as well as a cluster comprising right anterior hippocampus and amygdala; this eigenvector explained 57% of the variance in patients' scores. These results provide converging evidence for associations between ¹⁸ F-flortaucipir uptake, tau pathology, and patients' cognitive symptoms.

Independent value added by diffusion MRI for prediction of cognitive function in older adults

The purpose of this study was to determine whether white matter microstructure measured by diffusion magnetic resonance imaging (dMRI) provides independent information about baseline level or change in executive function (EF) or memory (MEM) in older adults with and without cognitive impairment. Longitudinal data was acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study from phases GO and 2 (2009–2015). ADNI participants included were diagnosed as cognitively normal (n = 46), early mild cognitive impairment (MCI) (n = 48), late MCI (n = 29), and dementia (n = 39) at baseline. We modeled the association between dMRI-based global white matter mean diffusivity (MD) and baseline level and change in EF and MEM composite scores, in models controlling for baseline bilateral hippocampal volume, regional cerebral FDG PET metabolism and global cerebral AV45 PET uptake. EF and MEM composite scores were measured at baseline, 6, 12, 24 and 36 months. In the baseline late MCI and dementia groups, greater global MD was associated with lesser baseline EF, but not EF change nor MEM baseline or change. As expected, lesser hippocampal volume and lesser FDG PET metabolism was associated with greater rates of EF and MEM decline. In ADNI-GO/2 participants, white matter integrity provided independent information about current executive function, but was not sensitive to future cognitive change. Since individuals experiencing executive function declines progress to dementia more rapidly than those with only memory impairment, better biomarkers of future executive function decline are needed.

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In the ADNI cohort, MRI and PET predictors of baseline and change in executive function were tested.

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Global mean diffusivity was associated with baseline, but not change in, executive function.

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Diffusion MRI provides independent information on current executive function in older adults.

Thiamine Deficiency and Neurodegeneration: the Interplay Among Oxidative Stress, Endoplasmic Reticulum Stress, and Autophagy

Thiamine (vitamin B1) is an essential nutrient and indispensable for normal growth and development of the organism due to its multilateral participation in key biochemical and physiological processes. Humans must obtain thiamine from their diet since it is synthesized only in bacteria, fungi, and plants. Thiamine deficiency (TD) can result from inadequate intake, increased requirement, excessive deletion, and chronic alcohol consumption. TD affects multiple organ systems, including the cardiovascular, muscular, gastrointestinal, and central and peripheral nervous systems. In the brain, TD causes a cascade of events including mild impairment of oxidative metabolism, neuroinflammation, and neurodegeneration, which are commonly observed in neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD). Thiamine metabolites may serve as promising biomarkers for neurodegenerative diseases, and thiamine supplementations exhibit therapeutic potential for patients of some neurodegenerative diseases. Experimental TD has been used to model aging-related neurodegenerative diseases. However, to date, the cellular and molecular mechanisms underlying TD-induced neurodegeneration are not clear. Recent research evidence indicates that TD causes oxidative stress, endoplasmic reticulum (ER) stress, and autophagy in the brain, which are known to contribute to the pathogenesis of various neurodegenerative diseases. In this review, we discuss the role of oxidative stress, ER stress, and autophagy in TD-mediated neurodegeneration. We propose that it is the interplay of oxidative stress, ER stress, and autophagy that contributes to TD-mediated neurodegeneration.

Suspected non-AD pathology in mild cognitive impairment

We aim to better characterize mild cognitive impairment (MCI) patients with suspected non-Alzheimer's disease (AD) pathology (SNAP) based on their longitudinal outcome, cognition, biofluid, and neuroimaging profile. MCI participants (n = 361) from ADNI-GO/2 were designated "amyloid positive" with abnormal amyloid-beta 42 levels (AMY+) and "neurodegeneration positive" (NEU+) with abnormal hippocampal volume or hypometabolism using fluorodeoxyglucose-positron emission tomography. SNAP was compared with the other MCI groups and with AMY- controls. AMY-NEU+/SNAP, 16.6%, were older than the NEU- groups but not AMY- controls. They had a lower conversion rate to AD after 24 months than AMY+NEU+ MCI participants. SNAP-MCI participants had similar amyloid-beta 42 levels, florbetapir and tau levels, but larger white matter hyperintensity volumes than AMY- controls and AMY-NEU- MCI participants. SNAP participants performed worse on all memory domains and on other cognitive domains, than AMY-NEU- participants but less so than AMY+NEU+ participants. Subthreshold levels of cerebral amyloidosis are unlikely to play a role in SNAP-MCI, but pathologies involving the hippocampus and cerebrovascular disease may underlie the neurodegeneration and cognitive impairment in this group.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.

Multimodality imaging of Alzheimer disease and other neurodegenerative dementias

Neurodegenerative diseases, such as Alzheimer disease, result in cognitive decline and dementia and are a leading cause of mortality in the growing elderly population. These progressive diseases typically have an insidious onset, with overlapping clinical features early in the disease course that make diagnosis challenging. The neurodegenerative diseases are associated with characteristic, although not completely understood, changes in the brain: abnormal protein deposition, synaptic dysfunction, neuronal injury, and neuronal death. Neuroimaging biomarkers-principally regional atrophy on structural MR imaging, patterns of hypometabolism on (18)F-FDG PET, and detection of cerebral amyloid plaque on amyloid PET--are able to evaluate the patterns of these abnormalities in the brain to improve early diagnosis and help predict the disease course. These techniques have unique strengths and synergies in multimodality evaluation of the patient with cognitive decline or dementia. This review discusses the key imaging biomarkers from MR imaging, (18)F-FDG PET, and amyloid PET; the imaging features of the most common neurodegenerative dementias; the role of various neuroimaging studies in differential diagnosis and prognosis; and some promising imaging techniques under development.

A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative

In recent years, numerous laboratories and consortia have used neuroimaging to evaluate the risk for and progression of Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative is a longitudinal, multicenter study that is evaluating a range of biomarkers for use in diagnosis of AD, prediction of patient outcomes, and clinical trials. These biomarkers include brain metrics derived from magnetic resonance imaging (MRI) and positron emission tomography scans as well as metrics derived from blood and cerebrospinal fluid. We focus on Alzheimer's Disease Neuroimaging Initiative studies published between 2011 and March 2013 for which structural MRI was a major outcome measure. Our main goal was to review key articles offering insights into progression of AD and the relationships of structural MRI measures to cognition and to other biomarkers in AD. In Supplement 1, we also discuss genetic and environmental risk factors for AD and exciting new analysis tools for the efficient evaluation of large-scale structural MRI data sets such as the Alzheimer's Disease Neuroimaging Initiative data.

CERAD Neuropsychological Total Scores Reflect Cortical Thinning in Prodromal Alzheimer's Disease

Background

Sensitive cognitive global scores are beneficial in screening and monitoring for prodromal Alzheimer's disease (AD). Early cortical changes provide a novel opportunity for validating established cognitive total scores against the biological disease markers.

Methods

We examined how two different total scores of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery and the Mini-Mental State Examination (MMSE) are associated with cortical thickness (CTH) in mild cognitive impairment (MCI) and prodromal AD. Cognitive and magnetic resonance imaging (MRI) data of 22 progressive MCI, 78 stable MCI, and 98 control subjects, and MRI data of 103 AD patients of the prospective multicenter study were analyzed.

Results

CERAD total scores correlated with mean CTH more strongly (r = 0.34-0.38, p < 0.001) than did MMSE (r = 0.19, p = 0.01). Of those vertex clusters that showed thinning in progressive MCI, 60-75% related to the CERAD total scores and 3% to the MMSE.

Conclusion

CERAD total scores are sensitive to the CTH signature of prodromal AD, which supports their biological validity in detecting early disease-related cognitive changes.

Effect of a computerized brain exercise program on cognitive performance in older adults

OBJECTIVES

Research indicates an association between stimulating mental activities and better memory performance as people age, but studies on computerized mental stimulation programs are limited. We explored whether computerized brain training exercises improved cognitive performance in older adults.

METHODS

In local retirement communities, a convenience sample was randomized into an intervention group (N = 36), who used a computer program 5 days a week for 20-25 minutes each day, or a wait-list control group (N = 33). All were older adults without dementia (mean age: 81.8 years; SD: 6.1; 67% female). Neuropsychological testing was completed at baseline (Time 1), 2 months (Time 2), and 6 months (Time 3). Three cognitive domains (Immediate Memory, Delayed Memory, Language) were compared in the two groups as a function of time using mixed models.

RESULTS

The intervention group used the computerized program (Brain Fitness, Dakim Inc., Santa Monica, CA) for an average of 43 (SD: 4.4) sessions by Time 2 and 81 (SD: 37.5) sessions by Time 3. Mixed models examining cognitive domains as function of time revealed significant group differences in Delayed Memory (F(2,72) = 4.7, p = 0.01) but not Immediate Memory and Language; no significant improvements were noted for the control group. Among all participants, anyone playing at least 40 sessions over the 6 months improved in all three domains (Immediate Memory, Delayed Memory, and Language).

CONCLUSION

Participating in a computerized brain exercise program over 6 months improves cognitive abilities in older adults. These results extend literature indicating the benefit of training exercises, whether in a classroom format or via a computerized self-paced program.

Advanced psychometric analysis and the Alzheimer's Disease Neuroimaging Initiative: reports from the 2011 Friday Harbor conference

This article summarizes a special series of articles from The Advanced Psychometric Methods in Cognitive Aging Research conference, held in June, 2011 at Friday Harbor, Washington. This conference used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to address cognitive change associated with Alzheimer's disease (AD) and how it related to neuroimaging, genetic, and cerebrospinal fluid biomarkers. The 13 articles in this series present innovative approaches to measuring cognition and studying determinants of cognitive decline in AD.