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The human brain is a complex organ underlying many cognitive and physiological processes, affected by a wide range of diseases. Genetic associations with macroscopic brain structure are emerging, providing insights into genetic sources of brain variability and risk for functional impairments and disease. However, specific associations with measures of local brain folding, associated with both brain development and decline, remain under-explored. Here we carried out detailed large-scale genome-wide associations of regional brain cortical sulcal measures derived from magnetic resonance imaging data of 40,169 individuals in the UK Biobank. Combining both genotyping and whole-exome sequencing data (∼12 million variants), we discovered 388 regional brain folding associations across 77 genetic loci at p−8, which replicated at pKCNK2 locus with a cortex-specific KCNK2 eQTL. Genetic correlations with neuropsychiatric conditions highlighted emerging patterns across distinct sulcal parameters and related phenotypes. We provide an interactive 3D visualisation of our summary associations, making complex association patterns easier to interpret, and emphasising the added resolution of regional brain analyses compared to global brain measures. Our results offer new insights into the genetic architecture underpinning brain folding and provide a resource to the wider scientific community for studies of pathways driving brain folding and their role in health and disease.

Financial capacity is an important instrumental activity of daily life that comprises those abilities needed for an individual independently to manage financial affairs in a manner consistent with personal self-interest and values. In this chapter, we examine the topic of financial capacity and decision-making in older adults in our aging society. Specifically, we discuss the crucial phenomenon of cognitive aging and diminished financial capacity, the impact on financial skills of cognitive disorders such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and early warning signs of diminished financial capacity. We present a useful clinical conceptual model of financial capacity, describe different approaches to assessing financial capacity, and then discuss empirical studies of financial capacity in older adults with a focus on patients with MCI and AD. This chapter concludes with sections addressing exciting new neuroimaging investigations of financial decision-making and capacity, the importance of psychiatric and other non-cognitive contributions to financial capacity in aging, and directions for future research.

While loss of insight of cognitive deficits, anosognosia, is a common symptom in Alzheimer’s disease dementia, there is a lack of consensus regarding the presence of altered awareness of memory function in the preclinical and prodromal stages of the disease. Paradoxically, very early in the Alzheimer’s disease process, individuals may experience heightened awareness of memory changes before any objective cognitive deficits can be detected, here referred to as hypernosognosia. In contrast, awareness of memory dysfunction shown by individuals with mild cognitive impairment (MCI) is very variable, ranging from marked concern to severe lack of insight. This study aims at improving our mechanistic understanding of how alterations in memory self-awareness are related to pathological changes in clinically normal (CN) adults and MCI patients. 297 CN and MCI patients underwent PiB-PET (Positron Emission Tomography using Pittsburgh Compound B) in vivo amyloid imaging. Amyloid burden was estimated from Alzheimer’s disease vulnerable regions, including the frontal, lateral parietal and lateral temporal, and retrosplenial cortex. Memory self-awareness was assessed using discrepancy scores between subjective and objective measures of memory function. A set of univariate analysis of variance were performed to assess the relationship between self-awareness of memory and amyloid pathology. Whereas CN individuals harboring amyloid pathology demonstrated hypernosognosia, MCI patients with increased amyloid pathology demonstrated anosognosia. In contrast, MCI patients with low amounts of amyloid were observed to have normal insight into their memory functions. Altered self-awareness of memory tracks with amyloid pathology. The findings of variability of awareness may have important implications for the reliability of self-report of dysfunction across the spectrum of preclinical and prodromal Alzheimer’s disease.

We investigated individual differences in longitudinal trajectories of brain aging in cognitively normal healthy adults from the Seattle Longitudinal Study covering 8 years of longitudinal change (across 5 occasions) in cortical thickness in 249 midlife and older adults (52-95 years old). We aimed to understand true brain change; examine the influence of salient risk factors that modify an individual's rate of cortical thinning; and compare cross-sectional age-related differences in cortical thickness to longitudinal within-person cortical thinning. We used Multivariate Multilevel Modeling to simultaneously model dependencies among 5 lobar composites (Frontal, Parietal, Temporal, Occipital, and Cingulate [CING]) and account for the longitudinal nature of the data. Results indicate (1) all 5 lobar composites significantly atrophied across 8 years, showing nonlinear longitudinal rate of cortical thinning decelerated over time, (2) longitudinal thinning was significantly altered by hypertension and Apolipoprotein-E ε4 (APOEε4), varying by location: Frontal and CING thinned more rapidly in APOEε4 carriers. Notably, thinning of parietal and occipital cortex showed synergistic effect of combined risk factors, where individuals who were both APOEε4 carriers and hypertensive had significantly greater 8-year thinning than those with either risk factor alone or neither risk factor, (3) longitudinal thinning was 3 times greater than cross-sectional estimates of age-related differences in thickness in parietal and occipital cortices.

Objective:To define robust resilience metrics by leveraging CSF biomarkers of Alzheimer disease (AD) pathology within a latent variable framework and to demonstrate the ability of such metrics to predict slower rates of cognitive decline and protection against diagnostic conversion.Methods:Participants with normal cognition (n = 297) and mild cognitive impairment (n = 432) were drawn from the Alzheimer’s Disease Neuroimaging Initiative. Resilience metrics were defined at baseline by examining the residuals when regressing brain aging outcomes (hippocampal volume and cognition) on CSF biomarkers. A positive residual reflected better outcomes than expected for a given level of pathology (high resilience). Residuals were integrated into a latent variable model of resilience and validated by testing their ability to independently predict diagnostic conversion, cognitive decline, and the rate of ventricular dilation.Results:Latent variables of resilience predicted a decreased risk of conversion (hazard ratio < 0.54, p < 0.0001), slower cognitive decline (β > 0.02, p < 0.001), and slower rates of ventricular dilation (β < −4.7, p < 2 × 10−15). These results were significant even when analyses were restricted to clinically normal individuals. Furthermore, resilience metrics interacted with biomarker status such that biomarker-positive individuals with low resilience showed the greatest risk of subsequent decline.Conclusions:Robust phenotypes of resilience calculated by leveraging AD biomarkers and baseline brain aging outcomes provide insight into which individuals are at greatest risk of short-term decline. Such comprehensive definitions of resilience are needed to further our understanding of the mechanisms that protect individuals from the clinical manifestation of AD dementia, especially among biomarker-positive individuals.

OBJECTIVE: To evaluate the risk of Alzheimer’s disease-related neuropathology burden at autopsy given older adults’ current cognitive state. METHOD: Participants included 1,303 individuals who enrolled in the Religious Orders Study (ROS) and 1,789 who enrolled in the Rush Memory and Aging Project (MAP). Cognitive status was evaluated via standardized assessments of global cognition and episodic memory. At the time of analyses, about 50% of participants were deceased with the remaining numbers right censored. Using multi-state Cox proportional hazard models, we compared the cognitive status of all subjects alive at a given age and estimated future risk of dying with different AD related neuropathologies. Endpoints considered were Braak Stages (0-2,3-4,5-6), CERAD (0,1,2,3) and TDP-43 (0,1,2,3) level. RESULTS: For all three pathological groupings (Braak, CERAD, TDP-43), we found that a cognitive test score one standard deviation below average put individuals at up to three times the risk for being diagnosed with late stage AD at autopsy according to pathological designations. The effect remained significant after adjusting for sex, APOE-e4 status, smoking status, education level, and vascular health scores. CONCLUSION: Applying multi-state modeling techniques, we were able to identify those at risk of exhibiting specific levels of neuropathology based on current cognitive test performance. This approach presents new and approachable possibilities in clinical settings for diagnosis and treatment development programs.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. The proposed framework is open-source and readily available in the Spinal Cord Toolbox., Comment: 38 pages, 7 figures, 2 tables

International audience; During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced.The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as “OptiC”, from a large dataset involving 20 centers, 4 contrasts (T2-weighted n = 287, T1-weighted n = 120, T2∗-weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions.Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of correlation of the BOLD signal in spatially independent regions of the brain defines the functional connectivity of those regions. During a cognitive fMRI task, a psychophysiological interaction (PPI) analysis can be used to examine changes in the functional connectivity during specific contexts defined by the cognitive task. An example of such a task is one that engages the memory system, asking participants to learn pairs of unrelated words (encoding) and recall the second word in a pair when presented with the first word (retrieval). In the present study, we used this type of associative memory task and a generalized PPI (gPPI) analysis to compare changes in hippocampal connectivity in older adults who are carriers of the Alzheimer's disease (AD) genetic risk factor apolipoprotein-E epsilon-4 (APOEε4). Specifically, we show that the functional connectivity of subregions of the hippocampus changes during encoding and retrieval, the two active phases of the associative memory task. Context-dependent changes in functional connectivity of the hippocampus were significantly different in carriers of APOEε4 compared to non-carriers. PPI analyses make it possible to examine changes in functional connectivity, distinct from univariate main effects, and to compare these changes across groups. Thus, a PPI analysis may reveal complex task effects in specific cohorts that traditional univariate methods do not capture. PPI analyses cannot, however, determine directionality or causality between functionally connected regions. Nevertheless, PPI analyses provide powerful means for generating specific hypotheses regarding functional relationships, which can be tested using causal models. As the brain is increasingly described in terms of connectivity and networks, PPI is an important method for analyzing fMRI task data that is in line with the current conception of the human brain.

We sought to characterize electrophysiological, eye-tracking and behavioral correlates of face-name recognition memory in healthy younger adults using high-density electroencephalography (EEG), infrared eye-tracking (ET), and neuropsychological measures. Twenty-one participants first studied 40 face-name (FN) pairs; 20 were presented four times (4R) and 20 were shown once (1R). Recognition memory was assessed by asking participants to make old/new judgments for 80 FN pairs, of which half were previously studied items and half were novel FN pairs (N). Simultaneous EEG and ET recording were collected during recognition trials. Comparisons of event-related potentials (ERPs) for correctly identified FN pairs were compared across the three item types revealing classic ERP old/new effects including 1) relative positivity (1R > N) bi-frontally from 300 to 500 ms, reflecting enhanced familiarity, 2) relative positivity (4R > 1R and 4R > N) in parietal areas from 500 to 800 ms, reflecting enhanced recollection, and 3) late frontal effects (1R > N) from 1000 to 1800 ms in right frontal areas, reflecting post-retrieval monitoring. ET analysis also revealed significant differences in eye movements across conditions. Exploration of cross-modality relationships suggested associations between memory and executive function measures and the three ERP effects. Executive function measures were associated with several indicators of saccadic eye movements and fixations, which were also associated with all three ERP effects. This novel characterization of face-name recognition memory performance using simultaneous EEG and ET reproduced classic ERP and ET effects, supports the construct validity of the multimodal FN paradigm, and holds promise as an integrative tool to probe brain networks supporting memory and executive functioning.

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,(1) a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.

Functional neuroimaging tools, such as fMRI methods, may elucidate the neural correlates of clinical, behavioral, and cognitive performance. Most functional imaging studies focus on regional task-related activity or resting state connectivity rather than how changes in functional connectivity across conditions and tasks are related to cognitive and behavioral performance. To investigate the promise of characterizing context-dependent connectivity-behavior relationships, this study applies the method of generalized psychophysiological interactions (gPPI) to assess the patterns of associative-memory-related fMRI hippocampal functional connectivity in Alzheimer's disease (AD) associated with performance on memory and other cognitively demanding neuropsychological tests and clinical measures. Twenty-four subjects with mild AD dementia (ages 54-82, nine females) participated in a face-name paired-associate encoding memory study. Generalized PPI analysis was used to estimate the connectivity between the hippocampus and the whole brain during encoding. The difference in hippocampal-whole brain connectivity between encoding novel and encoding repeated face-name pairs was used in multiple-regression analyses as an independent predictor for 10 behavioral, neuropsychological and clinical tests. The analysis revealed connectivity-behavior relationships that were distributed, dynamically overlapping, and task-specific within and across intrinsic networks; hippocampal-whole brain connectivity-behavior relationships were not isolated to single networks, but spanned multiple brain networks. Importantly, these spatially distributed performance patterns were unique for each measure. In general, out-of-network behavioral associations with encoding novel greater than repeated face-name pairs hippocampal-connectivity were observed in the default-mode network, while correlations with encoding repeated greater than novel face-name pairs hippocampal-connectivity were observed in the executive control network (p

International audience; Localizing the center of the spinal cord on MR images is a critical step toward fully automated and robust quantitative analysis, which is essential to achieve clinical utilization. While automatic localization of the spinal cord might appear as a simple task, that has already been addressed extensively, it is much more challenging to achieve this across the large variation in MRI contrasts, field of view, resolutions and pathologies. In this study, we introduce a novel method, called “OptiC”, to automatically and robustly localize the spinal cord on a large variety of MRI data. Starting from a localization map computed by a linear Support Vector Machine trained with Histogram of Oriented Gradient features, the center of the spinal cord is localized by solving an optimization problem, that introduces a trade-off between the localization map and the cord continuity along the superior-inferior axis. The OptiC algorithm features an efficient search (with a linear complexity in the number of voxels) and ensures the global minimum is reached. OptiC was compared to a recently-published method based on the Hough transform using a broad range of MRI data, involving 13 different centers, 3 contrasts (T2-weighted n=278, T1-weighted n=112 and T∗2-weighted n=263), with a total of 441 subjects, including 133 patients with traumatic and neurodegenerative diseases. Overall, OptiC was able to find 98.5% of the gold-standard centerline coverage, with a mean square error of 1.21 mm, suggesting that OptiC could reliably be used for subsequent analyses tasks, such as cord segmentation, opening the door to more robust analysis in patient population.

The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year. (C) 2015 Elsevier Inc. All rights reserved.

Objectives: Although age-related brain changes are becoming better understood, midlife patterns of change are still in need of characterization, and longitudinal studies are lacking. The aim of this study was to determine if baseline fractional anisotropy (FA), obtained from diffusion tensor imaging (DTI) predicts volume change over a 4-year interval. Experimental design: Forty-four cognitively healthy middle-age adults underwent baseline DTI and longitudinal T1-weighted magnetic resonance imaging. Tensor-based morphometry methods were used to evaluate volume change over time. FA values were extracted from regions of interest that included the cingulum, entorhinal white matter, and the genu and splenium of the corpus callosum. Baseline FA was used as a predictor variable, whereas gray and white matter atrophy rates as indexed by Tensor-based morphometry were the dependent variables. Principal observations: Over a 4-year period, participants showed significant contraction of white matter, especially in frontal, temporal, and cerebellar regions (P < 0.05, corrected for multiple comparisons). Baseline FA in entorhinal white matter, genu, and splenium was associated with longitudinal rates of atrophy in regions that included the superior longitudinal fasciculus, anterior corona radiata, temporal stem, and white matter of the inferior temporal gyrus (P < 0.001, uncorrected for multiple comparisons). Conclusions: Brain change with aging is characterized by extensive shrinkage of white matter. Baseline white matter microstructure as indexed by DTI was associated with some of the observed regional volume loss. The findings suggest that both white matter volume loss and microstructural alterations should be considered more prominently in models of aging and neurodegenerative diseases. Hum Brain Mapp 35:2044–2054, 2014. © 2013 Wiley Periodicals, Inc.

fMRI studies have linked the posteromedial cortex to episodic learning (encoding) and remembering (retrieval) processes. The posteromedial cortex is considered part of the default network and tends to deactivate during encoding but activate during retrieval, a pattern known as the encoding/retrieval flip. Yet, the exact relationship between the neural correlates of memory performance (hit/miss) and memory stage (encoding/retrieval) and the extent of overlap with intrinsic cortical networks remains to be elucidated. Using task-based fMRI, we isolated the pattern of activity associated with memory performance, memory stage, and the interaction between both. Using resting-state fMRI, we identified which intrinsic large-scale functional networks overlapped with regions showing task-induced effects. Our results demonstrated an effect of successful memory performance in regions associated with the control network and an effect of unsuccessful memory performance in the ventral attention network. We found an effect of memory retrieval in brain regions that span the default and control networks. Finally, we found an interaction between memory performance and memory stage in brain regions associated with the default network, including the posteromedial cortex, posterior parietal cortex, and parahippocampal cortex. We discuss these findings in relation to the encoding/retrieval flip. In general, the findings demonstrate that task-induced effects cut across intrinsic cortical networks. Furthermore, regions within the default network display functional dissociations, and this may have implications for the neural underpinnings of age-related memory disorders.

Glucose hypometabolism in the pre-clinical stage of Alzheimer’s disease (AD) has been primarily associated with the APOE ɛ4 genotype, rather than fibrillar β-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to β-amyloid burden than APOE ɛ4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as β-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of β-amyloid burden using positron emission tomography scans from the Alzheimer’s Disease Neuroimaging Initiative study. Modeling β-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE ɛ4 genotype and β-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer’s disease. We also determined that the cumulative effects of β-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.

Contains fulltext : 167121.pdf (Publisher’s version ) (Open Access) Brainhack events offer a novel workshop format with participant-generated content that caters to the rapidly growing open neuroscience community. Including components from hackathons and unconferences, as well as parallel educational sessions, Brainhack fosters novel collaborations around the interests of its attendees. Here we provide an overview of its structure, past events, and example projects. Additionally, we outline current innovations such as regional events and post-conference publications. Through introducing Brainhack to the wider neuroscience community, we hope to provide a unique conference format that promotes the features of collaborative, open science.

Neurodegenerative diseases comprise a broad swath of different neurologic diseases characterized by loss of neurons in the central nervous system. The most common neurodegenerative disease is Alzheimer disease, a dementing disease. Dementia is commonly understood as loss of function in at least two cognitive domains that is severe enough to affect daily activities in the social or occupational spheres. Elderly adults represent a significant and rapidly expanding proportion of the population. Some estimates state that by 2030 there will be 72 million individuals over the age of 65 years in the United States, constituting 19% of the population. The World Health Organization estimated in 2010 that there were 36.5 million people worldwide living with dementia, with the global cost for care being $604 billion per year. A new case of dementia is diagnosed approximately every 4 seconds. As the prevalence of many neurologic diseases increases with age, it can be difficult to differentiate between the effects of aging and those of prodromal age-related disease. Advances in neuroimaging have afforded significant insight into progressive neurodegenerative disorders and their mimics. Besides the improvements in structural imaging that have come about through thinner slices, 3-dimensional volumes, and higher spatial resolution, molecular and cellular imaging have made a big impact on how we look at the brain and its function. MR spectroscopy, diffusion tensor imaging, perfusion imaging, functional MRI, and PET scans have further increased our understanding of the pathophysiology of neurodegenerative disorders. Distinguishing between preventable diseases, potentially reversible diseases, and progressive, irreversible diseases is important in planning for a patient’s future medical, social, and economic spheres. Imaging of Neurodegenerative Disorders is a comprehensive, updated reference covering the latest techniques used in neuroimaging as well as the basic structural imaging that facilitates diagnosis of various neurodegenerative disorders. Because there never has been a dedicated textbook on the imaging of neurodegenerative diseases, the aim of this book is to cover each disease as it is currently understood and to show what it might look like using various imaging techniques. Imaging helps enormously in affirming suspicions, differentiating entities that have a clinical overlap, and charting the progression of neurodegenerative diseases. The book is organized into 16 parts with 41 chapters written by 83 contributors who have brought fresh insights and expertise that encompass many disease entities. They attempt to fill the gap of knowledge that exists in the imaging and understanding of neurodegenerative diseases. Chapters are arranged by specific diseases and cover their clinical features, pathologic characteristics, and imaging. Along with a brief discussion of each disease are the images themselves—superb in quality and quite illustrative—in addition to what tests to order, what to look for, what one expects to see for each disorder, and what new clinical and research modalities are available. This book is useful for radiologists, neuroradiologists, neurologists, neurosurgeons, and other internal or family medical physicians, as well as anyone seeking to understand neurodegenerative disorders through the lens of imaging techniques that are becoming increasingly sophisticated.

After traumatic injury, the brain undergoes a prolonged period of degenerative change that is paradoxically accompanied by cognitive recovery. The spatiotemporal pattern of atrophy and the specific relationships of atrophy to cognitive changes are ill understood. The present study used tensor-based morphometry and neuropsychological testing to examine brain volume loss in 17 traumatic brain injury (TBI) patients and 13 controls over a 4-year period. Patients were scanned at 2 months, 1 year, and 4 years post-injury. High-dimensional warping procedures were used to create change maps of each subject's brain for each of the two intervals. TBI patients experienced volume loss in both cortical areas and white matter regions during the first interval. We also observed continuing volume loss in extensive regions of white matter during the second interval. Neuropsychological correlations indicated that cognitive tasks were associated with subsequent volume loss in task-relevant regions. The extensive volume loss in brain white matter observed well beyond the first year post-injury suggests that the injured brain remains malleable for an extended period, and the neuropsychological relationships suggest that this volume loss may be associated with subtle cognitive improvements. (JINS, 2012,18, 1–13)

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a large multi-center study designed to develop optimized methods for acquiring longitudinal neuroimaging, cognitive, and biomarker measures of AD progression in a large cohort of patients with Alzheimer’s disease (AD), patients with mild cognitive impairment, and healthy controls. Detailed neuropsychological testing was conducted on all participants. We examined the factor structure of the ADNI Neuropsychological Battery across older adults with differing levels of clinical AD severity based on the Clinical Dementia Rating Scale (CDR). Confirmatory factor analysis (CFA) of 23 variables from 10 neuropsychological tests resulted in five factors (memory, language, visuospatial functioning, attention, and executive function/processing speed) that were invariant across levels of cognitive impairment. Thus, these five factors can be used as valid indicators of cognitive function in older adults who are participants in ADNI.

The goal of the current study was to examine cognitive change in both healthy controls (n = 229) and individuals with mild cognitive impairment (MCI) (n = 397) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied latent growth modeling to examine baseline and longitudinal change over 36 months in five cognitive factors derived from the ADNI neuropsychological test battery (memory, executive function/processing speed, language, attention and visuospatial). At baseline, MCI patients demonstrated lower performance on all of the five cognitive factors when compared to controls. Both controls and MCI patients declined on memory over 36 months; however, the MCI patients declined at a significantly faster rate than controls. The MCI patients also declined over 36 months on the remaining four cognitive factors. In contrast, the controls did not exhibit significant change over 36 months on the non-memory cognitive factors. Within the MCI group, executive function declined faster than memory, while the other factor scores changed slower than memory over time. These findings suggest different patterns of cognitive change in healthy older adults and MCI patients. The findings also suggest that, when compared with memory, executive function declines faster than other cognitive factors in patients with MCI. Thus, decline in non-memory domains may be an important feature for distinguishing healthy older adults and persons with MCI.

There are several magnetic resonance (MR) imaging techniques that benefit from high-field MR imaging. This article describes a range of novel techniques that are currently being used clinically or will be used in the future for clinical purposes as they gain popularity. These techniques include functional MR imaging, diffusion tensor imaging, cortical thickness assessment, arterial spin labeling perfusion, white matter hyperintensity lesion assessment, and advanced MR angiography.

Higher serum homocysteine (Hcy) levels in humans are associated with vascular pathology and greater risk for dementia, as well as lower global and regional volumes in frontal lobe and hippocampus. Calorie restriction (CR) in rhesus monkeys (Macaca mulatta) may confer neural protection against age- or Hcy-related vascular pathology. Hcy was collected proximal to a magnetic resonance imaging (MRI) acquisition in aged rhesus monkeys and regressed against volumetric and diffusion tensor imaging indexes using voxel-wise analyses. Higher Hcy was associated with lower white matter volume in pons and corpus callosum. Hcy was correlated with lower gray matter volume and density in prefrontal cortices and striatum. CR did not influence Hcy levels. However, control monkeys exhibited a strong negative correlation between Hcy and global gray matter, whereas no relationship was evident for the CR monkeys. Similar group differences were also seen across modalities in the splenium of the corpus callosum, prefrontal cortices, hippocampus, and somatosensory areas. The data suggest that CR may ameliorate the influence of Hcy on several important age-related parameters of parenchymal health.

Background: Rate of decline in 6-L-[18F]fluorodopa (FDOPA) uptake within the striatum has been reported as showing regional differences in Parkinson's disease (PD). Methods: We acquired longitudinal brain FDOPA positron emission tomography (PET) studies in 26 PD subjects and 11 controls over 4.5 years. We analyzed both spatially normalized voxel-wise maps of radiotracer influx (Kocc) and average Kocc values for six non-overlapping volumes of interest (VOIs) encompassing the striatum. Results: The voxel-wise analysis showed that in PD, FDOPA Kocc decline spanned the striatum but was greatest in the posterior putamen ipsilateral and anterior putamen contralateral to initial symptoms. The VOI approached showed that absolute rates of Kocc decline were significantly greater in PD than control subjects, but that the slope of decline did not differ between subregions. In PD, ratios of uptake between subregions did not change during the study with the exception of the ipsilateral putamen/caudate ratio. Decline rates were marginally greater during earlier time segments. Both male gender and advancing age were associated with lower baseline FDOPA uptake, but no difference in decline rates. VOI Kocc values were significantly correlated with disease duration, but only moderately correlated with clinical measures. Discussion: We conclude that FDOPA uptake in subregions of the striatum is strongly correlated with disease duration and age, and declines approximately equally from symptom onset in PD. This implies that in idiopathic PD, relative preservation of uptake in the anterior striatum reflects a delay in pathologic involvement of nigrostriatal projections to this region. © 2011 Movement Disorder Society

Systemic levels of proinflammatory cytokines such as interleukin-6 (IL-6) increase in old age and may contribute to neural atrophy in humans. We investigated IL-6 associations with age in T1-weighted segments and microstructural diffusion indices using MRI in aged rhesus monkeys (Macaca mulatta). Further, we determined if long-term 30% calorie restriction (CR) reduced IL-6 and attenuated its association with lower tissue volume and density. Voxel-based morphometry (VBM) and diffusion-weighted voxelwise analyses were conducted. IL-6 was associated with less global gray and white matter (GM and WM), as well as smaller parietal and temporal GM volumes. Lower fractional anisotropy (FA) was associated with higher IL-6 levels along the corpus callosum and various cortical and subcortical tracts. Higher IL-6 concentrations across subjects were also associated with increased mean diffusivity (MD) throughout many brain regions, particularly in corpus callosum, cingulum, and parietal, frontal, and prefrontal areas. CR monkeys had significantly lower IL-6 and less associated atrophy. An IL-6xCR interaction across modalities also indicated that CR mitigated IL-6 related changes in several brain regions compared to controls. Peripheral IL-6 levels were correlated with atrophy in regions sensitive to aging, and this relationship was decreased by CR.

Caloric restriction (CR) reduces the pathological effects of aging and extends the lifespan in many species, including nonhuman primates, although the effect on the brain is less well characterized. We used two common indicators of aging, motor performance speed and brain iron deposition measuredin vivousing magnetic resonance imaging, to determine the potential effect of CR on elderly rhesus macaques eating restricted (n= 24, 13 males, 11 females) and standard (n= 17, 8 males, 9 females) diets. Both the CR and control monkeys showed age-related increases in iron concentrations in globus pallidus (GP) and substantia nigra (SN), although the CR group had significantly less iron deposition in the GP, SN, red nucleus, and temporal cortex. A Diet × Age interaction revealed that CR modified age-related brain changes, evidenced as attenuation in the rate of iron accumulation in basal ganglia and parietal, temporal, and perirhinal cortex. Additionally, control monkeys had significantly slower fine motor performance on the Movement Assessment Panel, which was negatively correlated with iron accumulation in left SN and parietal lobe, although CR animals did not show this relationship. Our observations suggest that the CR-induced benefit of reduced iron deposition and preserved motor function may indicate neural protection similar to effects described previously in aging rodent and primate species.

Voxel-based morphometry studies have become increasingly common in human neuroimaging over the past several years; however, few studies have utilized this method to study morphometry changes in non-human primates. Here we describe the application of voxel-wise morphometry methods to the rhesus macaque (Macaca mulatta) using the 112RM-SL template and priors (McLaren et al. (2009) [42]) and as an illustrative example we describe age-associated changes in grey matter morphometry. Specifically, we evaluated the unified segmentation routine implemented using Statistical Parametric Mapping (SPM) software and the FMRIB's Automated Segmentation Tool (FAST) in the FMRIB Software Library (FSL); the effect of varying the smoothing kernel; and the effect of the normalization routine. We found that when studying non-human primates, brain images need less smoothing than in human studies, 2-4mm FWHM. Using flow field deformations (DARTEL) improved inter-subject alignment leading to results that were more likely due to morphometry differences as opposed to registration differences.

Although it is established that Alzheimer's disease (AD) leads to cerebral macrostructural atrophy, microstructural diffusion changes have also been observed, but it is not yet known whether these changes offer unique information about the disease pathology. Thus, a multi-modal imaging study was conducted to determine the independent contribution of each modality in moderate to severe AD. Seventeen patients with moderate-severe AD and 13 healthy volunteers underwent diffusion-weighted and T1-weighted MR scanning. Images were processed to obtain measures of macrostructural atrophy (gray and white matter volumes) and microstructural damage (fractional anisotropy and mean diffusivity). Microstructural diffusion changes independent of macrostructural loss were investigated using an ANCOVA where macrostructural maps were used as voxel-wise covariates. The reverse ANCOVA model was also assessed, where macrostructural loss was the dependent variable and microstructural diffusion tensor imaging maps were the imaging covariates. Diffusion differences between patients and controls were observed after controlling for volumetric differences in medial temporal, retrosplenial regions, anterior commissure, corona radiata, internal capsule, thalamus, corticopontine tracts, cerebral peduncle, striatum, and precentral gyrus. Independent volumetric differences were observed in the entorhinal cortex, inferior temporal lobe, posterior cingulate cortex, splenium and cerebellum. While it is well known that AD is associated with pronounced volumetric change, this study suggests that measures of microstructure provide unique information not obtainable with volumetric mapping in regions known to be pivotal in AD and in those thought to be spared. As such this work provides great understanding of the topography of pathological changes in AD that can be captured with imaging.

First-degree family history (FH) of sporadic Alzheimer's disease and the apolipoprotein E epsilon4 allele (APOE4) are risk factors for Alzheimer's disease that may affect brain function prior to onset of clinical symptoms. In this functional MRI (fMRI) study, we used an episodic recognition task that required discrimination of previously viewed (PV) and novel (NV) faces to examine differences in blood oxygen level dependent (BOLD) signal due to risk factors in 74 middle-aged cognitively normal individuals. The group effects on this recognition task were tested with a 2 x 2 ANCOVA factorial design (+FH/-FH and +APOE4/-APOE4). There were significant APOE4 and FH effects in the left dorsal posterior cingulate cortex and precuneus, where decreased risk resulted in greater activity during recollection. Recognition performance was positively correlated with BOLD signal in the left posterior hippocampus, parahippocampal-retrosplenial gyrus and left superior frontal cortex regardless of risk factors. To examine condition-specific group effects, both the PV and NV faces were tested further in separate 2 x 2 ANCOVAs. Both models revealed an APOE effect, with the -APOE4 group showing stronger signal than the +APOE4 group in anterior cingulate cortices, while a FH effect was found in the dorsal cuneus and medial frontal cortices with the -FH group showing stronger signal than the +FH group. Finally, interactions between APOE4 and FH effects were found bilaterally in the fusiform gyrus. These results suggest that risk factors and cognitive performance each influence brain activity during recognition. The findings lend further support to the idea that functional brain changes may begin far in advance of symptomatic Alzheimer's disease.