Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment

Structural imaging biomarkers of Alzheimer's disease: predicting disease progression

Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.

Meditation as a therapeutic intervention for adults at risk for Alzheimer's disease - potential benefits and underlying mechanisms

Alzheimer's disease (AD) is a chronic, progressive, brain disorder that affects at least 5.3 million Americans at an estimated cost of $148 billion, figures that are expected to rise steeply in coming years. Despite decades of research, there is still no cure for AD, and effective therapies for preventing or slowing progression of cognitive decline in at-risk populations remain elusive. Although the etiology of AD remains uncertain, chronic stress, sleep deficits, and mood disturbance, conditions common in those with cognitive impairment, have been prospectively linked to the development and progression of both chronic illness and memory loss and are significant predictors of AD. Therapies such as meditation that specifically target these risk factors may thus hold promise for slowing and possibly preventing cognitive decline in those at risk. In this study, we briefly review the existing evidence regarding the potential utility of meditation as a therapeutic intervention for those with and at risk for AD, discuss possible mechanisms underlying the observed benefits of meditation, and outline directions for future research.

Memory for the 2008 presidential election in healthy ageing and mild cognitive impairment

The present study examined memory accuracy and confidence for personal and public event details of the 2008 presidential election in healthy older adults and those with mild cognitive impairment (MCI). Participants completed phone interviews within a week after the election and after a 10-month delay. MCI patients and healthy older adults had comparable emotional reactions to learning the outcome of the election, with most people finding it to be a positive experience. After the delay period, details about the election were better remembered by all participants than a less emotionally arousing comparison event. However, MCI patients had more difficulty than healthy older adults correctly recalling details of public information about the election, although often the MCI patients could recognise the correct details. This is the first study to show that MCI patients' memory can benefit from emotionally arousing positive events, complementing the literature demonstrating similar effects for negative events.

ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease

Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.

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Multimodal classifiers have better predictive power than unimodal classifier.

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ApoE4 significantly affects diagnostic discriminability in the MCI and dementia stages.

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Our data supports the hypothesized biomarker trajectory in AD.

Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.

Voxel-wise co-analysis of macro- and microstructural brain alteration in mild cognitive impairment and Alzheimer's disease using anatomical and diffusion MRI

BACKGROUND AND PURPOSE

To determine if a voxel-wise "co-analysis" of structural and diffusion tensor magnetic resonance imaging (MRI) together reveals additional brain regions affected in mild cognitive impairment (MCI) and Alzheimer's disease (AD) than voxel-wise analysis of the individual MRI modalities alone.

METHODS

Twenty-one patients with MCI, 21 patients with AD, and 21 cognitively normal healthy elderly were studied with MRI. Maps of deformation and fractional anisotropy (FA) were computed and used as dependent variables in univariate and multivariate statistical models.

RESULTS

Univariate voxel-wise analysis of macrostructural changes in MCI showed atrophy in the right anterior temporal lobe, left posterior parietal/precuneus region, WM adjacent to the cingulate gyrus, and dorsolateral prefrontal regions, consistent with prior research. Univariate voxel-wise analysis of microstructural changes in MCI showed reduced FA in the left posterior parietal region extending into the corpus callosum, consistent with previous work. The multivariate analysis, which provides more information than univariate tests when structural and FA measures are correlated, revealed additional MCI-related changes in corpus callosum and temporal lobe.

CONCLUSION

These results suggest that in corpus callosum and temporal regions macro- and microstructural variations in MCI can be congruent, providing potentially new insight into the mechanisms of brain tissue degeneration.

Music Making and the Aging Brain

The chapter reviews the evidence in support of the idea that cognitive functions can benefit from listening to music or making music and how this evidence might be used to stabilize cognitive aging and prevent or diminish cognitive decline. The beneficial effects are more or less direct (e. g., for auditory perception) or indirect (e. g., for arousal and motivation). The core functions engaged during music listening or music making are executive functions that include attention, working memory, planning, and motor control. These functions are mainly controlled by neural networks located in the frontal cortex, the brain area that undergoes strongest decline in volume with increasing age. In this paper it is argued that this shrinkage of the frontal cortex or the natural course of the decline in frontal brain volume can be counteracted by engaging frontal executive functions through music listening and making. However, current experimental data supporting beneficial effects of music listening and music making is scarce. Therefore, well controlled randomized control group experiments are urgently needed.

Social Cognition and Emotional Assessment (SEA) is a marker of medial and orbital frontal functions: a voxel-based morphometry study in behavioral variant of frontotemporal degeneration

The aim of this study was to explore the cerebral correlates of functional deficits that occur in behavioral variant frontotemporal dementia (bvFTD). A specific neuropsychological battery, the Social cognition & Emotional Assessment (SEA; Funkiewiez et al., 2012), was used to assess impaired social and emotional functions in 20 bvFTD patients who also underwent structural MRI scanning. The SEA subscores of theory of mind, reversal-learning tests, facial emotion identification, and apathy evaluation were entered as covariates in a voxel-based morphometry analysis. The results revealed that the gray matter volume in the rostral part of the medial prefrontal cortex [mPFC, Brodmann area (BA) 10] was associated with scores on the theory of mind subtest, while gray matter volume within the orbitofrontal (OFC) and ventral mPFC (BA 11 and 47) was related to the scores observed in the reversal-learning subtest. Gray matter volume within BA 9 in the mPFC was correlated with scores on the emotion recognition subtest, and the severity of apathetic symptoms in the Apathy scale covaried with gray matter volume in the lateral PFC (BA 44/45). Among these regions, the mPFC and OFC cortices have been shown to be atrophied in the early stages of bvFTD. In addition, SEA and its abbreviated version (mini-SEA) have been demonstrated to be sensitive to early impairments in bvFTD (Bertoux et al., 2012). Taken together, these results suggest a differential involvement of orbital and medial prefrontal subregions in SEA subscores and support the use of the SEA to evaluate the integrity of these regions in the early stages of bvFTD.

Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning

Predicting Alzheimer's disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and disease progression. Structural magnetic resonance imaging (MRI) has the potential of revealing early signs of neurodegeneration in the human brain and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study we investigated the possibility for using patterns of cortical thickness measurements for predicting AD in subjects with mild cognitive impairment (MCI). We used a novel technique for identifying cortical regions potentially discriminative for separating individuals with MCI who progress to probable AD, from individuals with MCI who do not progress to probable AD. Specific patterns of atrophy were identified at four time periods before diagnosis of probable AD and features were selected as regions of interest within these patterns. The selected regions were used for cortical thickness measurements and applied in a classifier for testing the ability to predict AD at the four stages. In the validation, the test subjects were excluded from the feature selection to obtain unbiased results. The accuracy of the prediction improved as the time to conversion from MCI to AD decreased, from 70% at 3 years before the clinical criteria for AD was met, to 76% at 6 months before AD. By inclusion of test subjects in the feature selection process, the prediction accuracies were artificially inflated to a range of 73% to 81%. Two important results emerge from this study. First, prediction accuracies of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD. Second, the results show that one needs to be careful when designing training, testing and validation schemes to ensure that datasets used to build the predictive models are not used in testing and validation.

Effects of ApoE4 and maternal history of dementia on hippocampal atrophy

We applied an automated hippocampal segmentation technique based on adaptive boosting (AdaBoost) to the 1.5 T magnetic resonance imaging (MRI) baseline and 1-year follow-up data of 243 subjects with mild cognitive impairment (MCI), 96 with Alzheimer's disease (AD), and 145 normal controls (NC) scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). MCI subjects with positive maternal history of dementia had smaller hippocampal volumes at baseline and at follow-up, and greater 12-month atrophy rates than subjects with negative maternal history. Three-dimensional maps and volumetric multiple regression analyses demonstrated a significant effect of positive maternal history of dementia on hippocampal atrophy in MCI and AD after controlling for age, ApoE4 genotype, and paternal history of dementia, respectively. ApoE4 showed an independent effect on hippocampal atrophy in MCI and AD and in the pooled sample.

Sub-Regional Hippocampal Injury is Associated with Fornix Degeneration in Alzheimer's Disease

We examined in vivo evidence of axonal degeneration in association with neuronal pathology in Alzheimer's disease (AD) through analysis of fornix microstructural integrity and measures of hippocampal subfield atrophy. Based on known anatomical topography, we hypothesized that the local thickness of subiculum and CA1 hippocampus fields would be associated with fornix integrity, reflecting an association between AD-related injury to hippocampal neurons and degeneration of associated axon fibers. To test this hypothesis, multi-modal imaging, combining measures of local hippocampal radii with diffusion tensor imaging (DTI), was applied to 44 individuals clinically diagnosed with AD, 44 individuals clinically diagnosed with mild cognitive impairment (MCI), and 96 cognitively normal individuals. Fornix microstructural degradation, as measured by reduced DTI-based fractional anisotropy (FA), was prominent in both MCI and AD, and was associated with reduced hippocampal volumes. Further, reduced fornix FA was associated with reduced anterior CA1 and antero-medial subiculum thickness. Finally, while both lesser fornix FA and lesser hippocampal volume were associated with lesser episodic memory, only the hippocampal measures were significant predictors of episodic memory in models including both hippocampal and fornix predictors. The region-specific association between fornix integrity and hippocampal neuronal death may provide in vivo evidence for degenerative white matter injury in AD: axonal pathology that is closely linked to neuronal injury.

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

Comparing hippocampal atrophy in Alzheimer's dementia and dementia with lewy bodies

BACKGROUND/AIMS

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) are the two most common neurodegenerative dementias. During the early stages, clinical distinction between them is often challenging. Our objective is to compare hippocampal atrophy patterns in mild AD and mild DLB. We hypothesized that DLB subjects have milder hippocampal atrophy relative to AD subjects.

METHODS

We analyzed the T1-weighted magnetic resonance imaging data from 113 subjects: 55 AD, 16 DLB and 42 cognitively normal elderly (normal controls, NC). Using the hippocampal radial distance technique and multiple linear regression, we analyzed the effect of clinical diagnosis on hippocampal radial distance, while adjusting for gender and age. Three-dimensional statistical maps were adjusted for multiple comparisons using permutation-based statistics with a threshold of p < 0.01.

RESULTS

Compared to NC, AD exhibited significantly greater atrophy in the cornu ammonis (CA)1, CA2-3 and subicular regions bilaterally while DLB showed left-predominant atrophy in the CA1 region and subiculum. Compared directly, AD and DLB did not reveal statistically significant differences.

CONCLUSION

Hippocampal atrophy, while present in mildly impaired DLB subjects, is less severe than atrophy seen in mildly impaired AD subjects, when compared to NC. Both groups show predominant atrophy of the CA1 subfield and subiculum.

Genetics of late-onset Alzheimer's disease: update from the alzgene database and analysis of shared pathways

The genetics of late-onset Alzheimer's disease (LOAD) has taken impressive steps forwards in the last few years. To date, more than six-hundred genes have been linked to the disorder. However, only a minority of them are supported by a sufficient level of evidence. This review focused on such genes and analyzed shared biological pathways. Genetic markers were selected from a web-based collection (Alzgene). For each SNP in the database, it was possible to perform a meta-analysis. The quality of studies was assessed using criteria such as size of research samples, heterogeneity across studies, and protection from publication bias. This produced a list of 15 top-rated genes: APOE, CLU, PICALM, EXOC3L2, BIN1, CR1, SORL1, TNK1, IL8, LDLR, CST3, CHRNB2, SORCS1, TNF, and CCR2. A systematic analysis of gene ontology terms associated with each marker showed that most genes were implicated in cholesterol metabolism, intracellular transport of beta-amyloid precursor, and autophagy of damaged organelles. Moreover, the impact of these genes on complement cascade and cytokine production highlights the role of inflammatory response in AD pathogenesis. Gene-gene and gene-environment interactions are prominent issues in AD genetics, but they are not specifically featured in the Alzgene database.

High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization

In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.

Brain activity patterns in stable and progressive mild cognitive impairment during working memory as evidenced by magnetoencephalography

It has been reported that mild cognitive impairment (MCI) patients, when compared with controls, show increased activity in different brain regions within the ventral pathway during memory tasks. A key question is whether this profile of increased activity could be useful to predict which patients will develop dementia. Herein, we present profiles of brain magnetic activity during a memory task recorded with magnetoencephalography from MCI patients (N = 10), Alzheimer's disease (AD) patients (N = 10), and healthy volunteers (N = 17). After 2½ years of follow-up, five of the MCI patients developed AD. Patients who progressed to AD (PMCI) showed higher activity than those who remained stable (SMCI), AD patients and controls. This increased activity in PMCI patients involves regions within the ventral and dorsal pathways. In contrast, SMCI patients showed higher activation than controls only along the ventral pathway. This increase in both the ventral and dorsal pathways in PMCI patients may reflect a compensatory mechanism for the loss in efficiency in memory networks, which would be absent in AD patients as they showed lower activity levels than the rest of the groups.

An overview of biomarkers in Alzheimer's disease

Alzheimer’s disease (AD) is the commonest progressive, dementing neurodegenerative disease in elderly, which affects innumerable people each year, and these numbers are likely to further increase as the population ages. In addition to the financial burden of AD on health care system, the disease has powerful emotional impact on caregivers and families of those afflicted. In this advancing era of AD research, with the availability of new treatment strategies having disease-modifying effects, there is growing need for the early diagnosis in AD, often hampered by paucity of biomarkers of AD. Various candidate biomarkers for AD have been developed that can detect patients with AD at an early stage. In the recent years, the search for an ideal biomarker has undergone a rapid evolution. Novel technologies in proteomics, genomics, and imaging techniques further expand the role of a biomarker not only in early diagnosis, but also in monitoring the response to various treatments. However, the availability of sensitive and specific biomarkers requires the method to be standardized so as to be able to compare the results across studies. Inspite of tremendous advances in this field the quest for an “ideal biomarker” still continues. In this review, we will discuss the various candidate markers in five spheres namely biochemical, neuroanatomical, metabolic, genetic and neuropsychological, and their current status and limitations in AD diagnosis.

Impaired regulation of synaptic actin cytoskeleton in Alzheimer's disease

Representing the most common cause of dementia, Alzheimer's disease (AD) has dramatically impacted the neurological and economic health of our society. AD is a debilitating neurodegenerative disease that produces marked cognitive decline. Much evidence has accumulated over the past decade to suggest soluble oligomers of beta-amyloid (Aβ) have a critical role in mediating AD pathology early in the disease process by perturbing synaptic efficacy. Here we critically review recent research that implicates synapses as key sites of early pathogenesis in AD. Most excitatory synapses in the brain rely on dendritic spines as the sites for excitatory neurotransmission. The structure and function of dendritic spines are dynamically regulated by cellular pathways acting on the actin cytoskeleton. Numerous studies analyzing human postmortem tissue, animal models and cellular paradigms indicate that AD pathology has a deleterious effect on the pathways governing actin cytoskeleton stability. Based on the available evidence, we propose the idea that a contributing factor to synaptic pathology in early AD is an Aβ oligomer-initiated collapse of a "synaptic safety net" in spines, leading to dendritic spine degeneration and synaptic dysfunction. Spine stabilizing pathways may thus represent efficacious therapeutic targets for combating AD pathology.

Associative memory deficits in mild cognitive impairment: the role of hippocampal formation

Neuropathological events featuring early stages of Alzheimer's disease (AD) appear in the entorhinal cortex (EC), subiculum (SB) and cornu ammonis 1 (CA1) of hippocampus, which may account for associative memory deficits in non-demented people with mild cognitive impairment (MCI). To test this hypothesis in vivo, we investigated whether volume changes in these regions are related to failures in associative memory in MCI as compared to cognitively normal (CN) elderly subjects. Volume changes in EC and hippocampal subfields were determined by using deformation-based morphometry techniques applied to probabilistic cytoarchitectonic maps derived from post mortem human brains. CN subjects were distinguished from MCI patients by firstly identifying local volume differences in EC and hippocampus, and then evaluating the way in which these anatomical changes correlated with performance in a non-intentional face-location association task. MCI patients not only performed worse than CN elders in building new associations, but they were further unable to benefit from semantic encoding to improve episodic binding. According to our initial hypothesis, local volume reductions in both EC and hippocampal CA accounted for group differences in associative memory whereas atrophy in CA, but not in EC, accounted for semantic encoding of associations. Two main conclusions can be drawn from the present study: i) access to semantic information during encoding does not reduce the episodic deficit in MCI; and ii) EC and hippocampal CA, two regions early affected by AD neuropathology, are responsible, at least partially, for associative memory deficits observed in MCI patients.

Cognitive intervention for persons with mild cognitive impairment: A meta-analysis

Cognitive training for persons with mild cognitive impairment (MCI) has become a hot topic. However to date it remains controversial whether persons with MCI can really benefit from cognitive intervention. We aim to further investigate this by using meta-analysis of seventeen clinical studies of cognitive intervention for MCI. The results demonstrate that after training, patients with MCI improve significantly both in overall cognition and overall self-ratings. Specifically, persons with MCI obtain moderate benefits in language, self-rated anxiety and functional ability, and receive mild benefits in episodic memory, semantic memory, executive functioning/working memory, visuo-spatial ability, attention/processing speed, MMSE, self-rated memory problem, quality of life, activities of daily life and self-rated depression. The results also suggest that persons with MCI benefit from the cognitive intervention in the follow-up data. The present meta-analysis demonstrates that cognitive intervention can be a potential efficient method to enhance cognitive and functional abilities in persons with MCI, although the improvements may be domain-specific.

Statistical analysis of relative pose information of subcortical nuclei: application on ADNI data

Many brain morphometry studies have been performed in order to characterize the brain atrophy pattern of Alzheimer's disease (AD). The earliest studies focused on the volume of particular brain structures, such as hippocampus and entorhinal cortex. Even though volumetry is a powerful, robust and intuitive technique that has yielded a wealth of findings, more complex shape descriptors have been used to perform statistical shape analysis of particular brain structures. However, in shape analysis studies of brain structures the information of the relative pose between neighbor structures is typically disregarded. This work presents a framework to analyse pose information including the following approaches: similarity transformations with either pseudo-Riemannian or left-invariant Riemannian metric, and centered transformations with a bi-invariant Riemannian metric. As an illustration, an analysis of covariance (ANCOVA) and a discrimination analysis were performed on Alzheimer's Disease Neuroimaging Initiative (ADNI) data.

Music making as a tool for promoting brain plasticity across the life span

Playing a musical instrument is an intense, multisensory, and motor experience that usually commences at an early age and requires the acquisition and maintenance of a range of skills over the course of a musician's lifetime. Thus, musicians offer an excellent human model for studying the brain effects of acquiring specialized sensorimotor skills. For example, musicians learn and repeatedly practice the association of motor actions with specific sound and visual patterns (musical notation) while receiving continuous multisensory feedback. This association learning can strengthen connections between auditory and motor regions (e.g., arcuate fasciculus) while activating multimodal integration regions (e.g., around the intraparietal sulcus). We argue that training of this neural network may produce cross-modal effects on other behavioral or cognitive operations that draw on this network. Plasticity in this network may explain some of the sensorimotor and cognitive enhancements that have been associated with music training. These enhancements suggest the potential for music making as an interactive treatment or intervention for neurological and developmental disorders, as well as those associated with normal aging.

Regional heterogeneity in limbic maturational changes: evidence from integrating cortical thickness, volumetric and diffusion tensor imaging measures

Magnetic resonance imaging (MRI) studies of structural brain development have suggested that the limbic system is relatively preserved in comparison to other brain regions with healthy aging. The goal of this study was to systematically investigate age-related changes of the limbic system using measures of cortical thickness, volumetric and diffusion characteristics. We also investigated if the "relative preservation" concept is consistent across the individual sub-regions of the limbic system. T1 weighted structural MRI and Diffusion Tensor Imaging data from 476 healthy participants from the Brain Resource International Database was used for this study. Age-related changes in grey matter (GM)/white matter (WM) volume, cortical thickness, diffusional characteristics for the pericortical WM and for the fiber tracts associated with the limbic regions were quantified. A regional variability in the aging patterns across the limbic system was present. Four important patterns of age-related changes were highlighted for the limbic sub-regions: 1. early maturation of GM with late loss in the hippocampus and amygdala; 2. an extreme pattern of GM preservation in the entorhinal cortex; 3. a flat pattern of reduced GM loss in the anterior cingulate and the parahippocampus and; 4. accelerated GM loss in the isthmus and posterior cingulate. The GM volumetric data and cortical thickness measures proved to be internally consistent, while the diffusional measures provided complementary data that seem consistent with the GM trends identified. This heterogeneity can be hypothesized to be associated with age-related changes of cognitive function specialized for that region and direct connections to the other brain regions sub-serving these functions.

Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI

Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations mapping a customized template with the observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerequisite for both template estimation and image warping. Subsequent statistical analysis on the spatial transformations is performed to highlight voxel-wise differences. Most of previous TBM studies did not explore the influence of the registration parameters, such as the parameters defining the deformation and the regularization models. In this work performance evaluation of TBM using stationary velocity field (SVF) diffeomorphic registration was performed in a subset of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) study. A wide range of values of the registration parameters that define the transformation smoothness and the balance between image matching and regularization were explored in the evaluation. The proposed methodology provided brain atrophy maps with very detailed anatomical resolution and with a high significance level compared with results recently published on the same data set using a non-linear elastic registration method.

Neuropsychological profiles in MCI and in depression: Differential cognitive dysfunction patterns or similar final common pathway disorder?

The concept of "mild cognitive impairment" (MCI) refers to alterations in cognition in the transition between normal aging and dementia. However, from a neuropsychological point of view the conventional diagnostic criteria appear not sufficiently valid. In particular, it is still difficult to differentiate between subjects with MCI and subjects with depression plus cognitive deficits on the basis of their neuropsychological profiles. The aim of this study is to compare cognitive deficit patterns of subjects with MCI and with depression. 24 subjects with MCI, 50 subjects with depression (DEP) and 20 healthy control subjects were included (age: 55-74years). The neuropsychological assessment consisted of standardized tests to assess attention, memory, and executive functions. Compared to healthy controls both subject groups showed significantly lower performance in all cognitive domains. However, we did not find significant differences in cognitive performance between MCI and DEP subjects, neither at baseline nor at follow-up. In addition, preliminary results of follow-up assessments after 2 (DEP) and 6months (MCI), respectively, revealed no significant changes in cognition in subjects with depression, regardless of whether depressive symptoms had improved. Subjects with MCI also showed no changes in cognition at follow-up. The comparable neuropsychological patterns identified in the two subject groups may be understood as a consequence of similar alterations in cognitive systems, supporting the idea of a final common pathway disorder. Thus, the cognitive deficits present in a subgroup of subjects with depression may possibly better be understood in the context of MCI.

[Neuroimaging in Alzheimer's disease: a synthesis and a contribution to the understanding of physiopathological mechanisms]

Alzheimer's disease has become a major public health issue for occidental societies. Since animal models of Alzheimer's disease currently fail to perfectly mimic pathophysiological mechanisms or the manifestations of the disease, in vivo neuroimaging has a key role in better understanding the pathophysiology of Alzheimer's disease. The diversity of anatomical and functional neuroimaging techniques - anatomical (T1-MRI), functional (fMRI) and diffusion tensor imaging (DTI) via magnetic resonance imaging (MRI) as well as position emission tomography coupled to fluorodeoxyglucose ((18)FDG-PET) - offers a large possibility of investigation of brain alterations in Alzheimer's disease. These techniques have thus provided morphological and functional brain alterations mapping of Alzheimer's disease: on one hand grey matter atrophy first concerns the medial temporal lobe before extending to the temporal neocortex and then other neocortical areas; on the other hand, metabolic alterations are first located within the posterior cingulate cortex and then reach the temporo-parietal area as well as the prefrontal cortex, especially in its medial part. Assessments of white matter alterations with DTI have highlighted a variety of tract alterations including the cingulum bundle, a white matter tract connecting the medial temporal lobe to the posterior cingulate cortex. Finally fMRI activation studies have evidenced compensatory mechanisms through hyperactivations in Alzheimer's disease patients. Altogether these results have led to the hypothesis of two major pathophysiological mechanisms in Alzheimer's disease: on one hand compensatory mechanisms in regions where atrophy exceeds metabolic alterations, on the other disconnection between medial temporal lobe and posterior cingulate cortex through the cingulum bundle, accounting for higher metabolic than structural alterations in the posterior cingulate cortex. Our work has extensively contributed to this disconnection hypothesis thanks to the use of cross-sectional and longitudinal multi-modal neuroimaging approaches. It has underlined the relevance of distant over local mechanisms in the pathophysiology of Alzheimer's disease and offers new perspectives to the exploration of the neural bases of cognitive impairments in this disorder.

Mapping Alzheimer's disease progression in 1309 MRI scans: power estimates for different inter-scan intervals

Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4+/-7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6+/-7.1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (alpha=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.

Joint analysis of structural and perfusion MRI for cognitive assessment and classification of Alzheimer's disease and normal aging

Structural magnetic resonance imaging (MRI) of brain tissue loss and physiological imaging of regional cerebral blood flow (rCBF) can provide complimentary information for the characterization of brain disorders, such as Alzheimer's disease (AD) but studies into gains in classification power for AD using these image modalities jointly have been limited. Our aim in this study was to determine the joint contribution of structural and perfusion-weighted imaging for the classification of AD in a cross-sectional study using an integrated multimodality MRI processing framework and a cortical surface-based analysis approach. We used logistic regression analysis to determine sequentially the value of cortical thickness, rCBF, and cortical thickness and rCBF jointly for classification for diagnosis of AD compared to controls. We further tested the extent to which cortical thinning and reduced rCBF explain individually or together variability in dementia severity. Separate analysis of structural MRI and perfusion-weighted MRI data yielded the well-established pattern of cortical thinning and rCBF reduction in AD, affecting predominantly temporo-parietal brain regions. Using structural MRI and perfusion-weighted MRI jointly indicated that cortical thinning dominated the classification of AD and controls without significant contributions from rCBF. However there was also a positive interaction between reduced rCBF and cortical thinning in the right superior temporal sulcus, implying that structural and physiological brain alterations in AD can be complementary. Compared to reduced rCBF, regional cortical thinning better explained the variability in dementia severity. In conclusion, structural brain alterations compared to physiological variations are the dominant features of MRI in AD.

Mary S. Easton Center of Alzheimer's Disease Research at UCLA: advancing the therapeutic imperative

The Mary S. Easton Center for Alzheimer's Disease Research (UCLA-Easton Alzheimer's Center) is committed to the "therapeutic imperative" and is devoted to finding new treatments for Alzheimer's disease (AD) and to developing technologies (biomarkers) to advance that goal. The UCLA-Easton Alzheimer's Center has a continuum of research and research-related activities including basic/foundational studies of peptide interactions; translational studies in transgenic animals and other animal models of AD; clinical research to define the phenotype of AD, characterize familial AD, develop biomarkers, and advance clinical trials; health services and outcomes research; and active education, dissemination, and recruitment activities. The UCLAEaston Alzheimer's Center is supported by the National Institutes on Aging, the State of California, and generous donors who share our commitment to developing new therapies for AD. The naming donor (Jim Easton) provided substantial funds to endow the center and to support projects in AD drug discovery and biomarker development. The Sidell-Kagan Foundation supports the Katherine and Benjamin Kagan Alzheimer's Treatment Development Program, and the Deane F. Johnson Alzheimer's Research Foundation supports the Deane F. Johnson Center for Neurotherapeutics at UCLA. The John Douglas French Alzheimer's Research Foundation provides grants to junior investigators in critical periods of their academic development. The UCLA-Easton Alzheimer's Center partners with community organizations including the Alzheimer's Association California Southland Chapter and the Leeza Gibbons memory Foundation. Collaboration with pharmaceutical companies, biotechnology companies, and device companies is critical to developing new therapeutics for AD and these collaborations are embraced in the mission of the UCLA-Easton Alzheimer's Center. The Center supports excellent senior 3 investigators and serves as an incubator for new scientists, agents, models, technologies and concepts that will significantly influence the future of AD treatment and AD research.

Brain substrates of learning and retention in mild cognitive impairment diagnosis and progression to Alzheimer's disease

Understanding the underlying qualitative features of memory deficits in mild cognitive impairment (MCI) can provide critical information for early detection of Alzheimer's disease (AD). This study sought to investigate the utility of both learning and retention measures in (a) the diagnosis of MCI, (b) predicting progression to AD, and (c) examining their underlying brain morphometric correlates. A total of 607 participants were assigned to three MCI groups (high learning-low retention; low learning-high retention; low learning-low retention) and one control group (high learning-high retention) based on scores above or below a 1.5 SD cutoff on learning and retention indices of the Rey Auditory Verbal Learning Test. Our results demonstrated that MCI individuals with predominantly a learning deficit showed a widespread pattern of gray matter loss at baseline, whereas individuals with a retention deficit showed more focal gray matter loss. Moreover, either learning or retention measures provided good predictive value for longitudinal clinical outcome over two years, although impaired learning had modestly better predictive power than impaired retention. As expected, impairments in both measures provided the best predictive power. Thus, the conventional practice of relying solely on the use of delayed recall or retention measures in studies of amnestic MCI misses an important subset of older adults at risk of developing AD. Overall, our results highlight the importance of including learning measures in addition to retention measures when making a diagnosis of MCI and for predicting clinical outcome.

The clinical use of structural MRI in Alzheimer disease

Structural imaging based on magnetic resonance is an integral part of the clinical assessment of patients with suspected Alzheimer dementia. Prospective data on the natural history of change in structural markers from preclinical to overt stages of Alzheimer disease are radically changing how the disease is conceptualized, and will influence its future diagnosis and treatment. Atrophy of medial temporal structures is now considered to be a valid diagnostic marker at the mild cognitive impairment stage. Structural imaging is also included in diagnostic criteria for the most prevalent non-Alzheimer dementias, reflecting its value in differential diagnosis. In addition, rates of whole-brain and hippocampal atrophy are sensitive markers of neurodegeneration, and are increasingly used as outcome measures in trials of potentially disease-modifying therapies. Large multicenter studies are currently investigating the value of other imaging and nonimaging markers as adjuncts to clinical assessment in diagnosis and monitoring of progression. The utility of structural imaging and other markers will be increased by standardization of acquisition and analysis methods, and by development of robust algorithms for automated assessment.

Promoting alpha-secretase cleavage of beta-amyloid with engineered proteolytic antibody fragments

Deposition of beta-amyloid (A beta) is considered as an important early event in the pathogenesis of Alzheimer's Disease (AD), and reduction of A beta levels by various therapeutic approaches is actively being pursued. A potentially non-inflammatory approach to facilitate clearance and reduce toxicity is to hydrolyze A beta at its alpha-secretase site. We have previously identified a light chain fragment, mk18, with alpha-secretase-like catalytic activity, producing the 1-16 and 17-40 amino acid fragments of A beta 40 as primary products, although hydrolysis is also observed following other lysine and arginine residues. To improve the specific activity of the recombinant antibody by affinity maturation, we constructed a single chain variable fragment (scFv) library containing a randomized CDR3 heavy chain region. A biotinylated covalently reactive analog mimicking alpha-secretase site cleavage was synthesized, immobilized on streptavidin beads, and used to select yeast surface expressed scFvs with increased specificity for A beta. After two rounds of selection against the analog, yeast cells were individually screened for proteolytic activity towards an internally quenched fluorogenic substrate that contains the alpha-secretase site of A beta. From 750 clones screened, the two clones with the highest increase in proteolytic activity compared to the parent mk18 were selected for further study. Kinetic analyses using purified soluble scFvs showed a 3- and 6-fold increase in catalytic activity (k(cat)/K(M)) toward the synthetic A beta substrate compared to the original scFv primarily due to an expected decrease in K(M) rather than an increase in k(cat). This affinity maturation strategy can be used to select for scFvs with increased catalytic specificity for A beta. These proteolytic scFvs have potential therapeutic applications for AD by decreasing soluble A beta levels in vivo.

Surface feature-guided mapping of cerebral metabolic changes in cognitively normal and mildly impaired elderly

PURPOSE

The aim of this study was to investigate the longitudinal positron emission tomography (PET) metabolic changes in the elderly.

PROCEDURES

Nineteen nondemented subjects (mean Mini-Mental Status Examination 29.4 +/- 0.7 SD) underwent two detailed neuropsychological evaluations and resting 2-deoxy-2-[F-18]fluoro-D: -glucose (FDG)-PET scan (interval 21.7 +/- 3.7 months), baseline structural 3T magnetic resonance (MR) imaging, and apolipoprotein E4 genotyping. Cortical PET metabolic changes were analyzed in 3-D using the cortical pattern matching technique.

RESULTS

Baseline vs. follow-up whole-group comparison revealed significant metabolic decline bilaterally in the posterior temporal, parietal, and occipital lobes and the left lateral frontal cortex. The declining group demonstrated 10-15% decline in bilateral posterior cingulate/precuneus, posterior temporal, parietal, and occipital cortices. The cognitively stable group showed 2.5-5% similarly distributed decline. ApoE4-positive individuals underwent 5-15% metabolic decline in the posterior association cortices.

CONCLUSIONS

Using 3-D surface-based MR-guided FDG-PET mapping, significant metabolic changes were seen in five posterior and the left lateral frontal regions. The changes were more pronounced for the declining relative to the cognitively stable group.

Neuronal networks in Alzheimer's disease

Alzheimer's disease (AD) is a progressive, neurodegenerative disease that can be clinically characterized by impaired memory and many other cognitive functions. Previous studies have demonstrated that the impairment is accompanied by not only regional brain abnormalities but also changes in neuronal connectivity between anatomically distinct brain regions. Specifically, using neurophysiological and neuroimaging techniques as well as advanced graph theory-based computational approaches, several recent studies have suggested that AD patients have disruptive neuronal integrity in large-scale structural and functional brain systems underlying high-level cognition, as demonstrated by a loss of small-world network characteristics. Small world is an attractive model for the description of complex brain networks because it can support both segregated and integrated information processing. The altered small-world organization thus reflects aberrant neuronal connectivity in the AD brain that is most likely to explain cognitive deficits caused by this disease. In this review, we will summarize recent advances in the brain network research on AD, focusing mainly on the large-scale structural and functional descriptions. The literature reviewed here suggests that AD patients are associated with integrative abnormalities in the distributed neuronal networks, which could provide new insights into the disease mechanism in AD and help us to uncover an imaging-based biomarker for the diagnosis and monitoring of the disease.

Biomarkers in geriatric psychiatry: searching for the holy grail?

PURPOSE OF REVIEW

Biomarkers in clinical medicine are used to detect or diagnose specific illnesses, predict disease progression, and predict the response to treatment. As the proportion of adults over 65 years of age rises, there is a growing need to detect neurodegenerative disease at an earlier stage with the goal of improving treatment for highly prevalent illnesses such as late-life depression and dementia.

RECENT FINDINGS

The search for biomarkers of late-life mental disorders includes the exploration of structural neuroimaging, functional neuroimaging, genomics, proteomics, noninvasive neurophysiology, cerebrospinal fluid, and plasma analysis. Novel structural and functional neuroimaging techniques that have recently been developed show promise as biomarkers of both late-life depression and specific dementia syndromes. The fields of proteomics and genomics are advancing our ability to identify genes and aberrant proteins that detect preclinical dementia. As depression is often a harbinger of dementia in late life, recent studies are beginning to elucidate the relationship between different types of late-life depression and the subsequent emergence of dementia.

SUMMARY

Biomarker research in late-life mental disorders is progressing at a rapid pace. The application of current biomarkers to clinical practice may be on the horizon with further research that refines their sensitivity and specificity.