We investigated the relationship between regional atrophy rates and 2-year cognitive decline in a large cohort of patients with mild cognitive impairment (MCI; n = 103) and healthy controls (n = 90). Longitudinal magnetic resonance image (MRI) scans were analyzed using high-throughput image analysis procedures. Atrophy rates were derived by calculating percent cortical volume loss between baseline and 24 month scans. Stepwise regressions were performed to investigate the contribution of atrophy rates to language, memory, and executive functioning decline, controlling for age, gender, baseline performances, and disease progression. In MCI, left temporal lobe atrophy rates were associated with naming decline, whereas bilateral temporal, left frontal, and left anterior cingulate atrophy rates were associated with semantic fluency decline. Left entorhinal atrophy rate was associated with memory decline and bilateral frontal atrophy rates were associated with executive function decline. These data provide evidence that regional atrophy rates in MCI contribute to domain-specific cognitive decline, which appears to be partially independent of disease progression. MRI measures of regional atrophy can provide valuable information for understanding the neural basis of cognitive impairment in MCI.

Neuropsychiatric symptoms (NPS), such as apathy, irritability and depression, are frequently encountered in patients with Alzheimer's disease (AD). Focal grey matter atrophy has been linked to NPS development. Cerebrovascular disease is common among AD patients and can be detected on MRI as white matter hyperintensities (WMH). In this longitudinal study, the relative contribution of WMH burden and GM atrophy to NPS was evaluated in a cohort of mild cognitive impairment (MCI), AD and normal controls. This study included 121 AD, 315 MCI and 225 normal control subjects from the Alzheimer's Disease Neuroimaging Initiative. NPS were assessed using the Neuropsychiatric Inventory and grouped into hyperactivity, psychosis, affective and apathy subsyndromes. WMH were measured using an automatic segmentation technique and mean deformation-based morphometry (DBM) was used to measure atrophy of grey matter regions. Linear mixed-effects models found focal grey matter atrophy and WMH volume both contributed significantly to NPS subsyndromes in MCI and AD subjects, however, WMH burden played a greater role. This study could provide a better understanding of the pathophysiology of NPS in AD and support the monitoring and control of vascular risk factors.

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.

Little is known about how genetic variation contributes to neuroanatomical variability, and whether particular genomic regions comprising genes or evolutionarily conserved elements are enriched for effects that influence brain morphology. Here, we examine brain imaging and single-nucleotide polymorphisms (SNPs) data from ∼2,700 individuals. We show that a substantial proportion of variation in cortical surface area is explained by additive effects of SNPs dispersed throughout the genome, with a larger heritable effect for visual and auditory sensory and insular cortices (h(2)∼0.45). Genome-wide SNPs collectively account for, on average, about half of twin heritability across cortical regions (N=466 twins). We find enriched genetic effects in or near genes. We also observe that SNPs in evolutionarily more conserved regions contributed significantly to the heritability of cortical surface area, particularly, for medial and temporal cortical regions. SNPs in less conserved regions contributed more to occipital and dorsolateral prefrontal cortices.

Cognitive reserve has been described as offering protection against Alzheimer's disease (AD) and other neurodegenerative conditions, but also against age-associated brain changes. Using data from the Alzheimer's Disease Neuroimaging Initiative, we defined cognitive reserve using the residual reserve index: episodic memory performance residualized for 3T MRI-derived brain volumes and demographics. We examined whether cognitive reserve predicted executive function (EF) decline equally across 2 groups of older adults-AD biomarker-positive (n = 468) and -negative (n = 402)-defined by the tau-to-amyloid ratio in cerebrospinal fluid. A significant interaction between the residual reserve index and biomarker group revealed that the effect of cognitive reserve on EF decline was dependent on pathology status. In the biomarker-positive group, higher cognitive reserve predicted EF decline over five years. However, cognitive reserve did not predict EF decline in the biomarker-negative group. These results suggest a certain level of AD pathology may be needed before cognitive reserve exerts its protective effects on future cognition; however, further research that tracks cognitive reserve longitudinally is needed.

We investigated systematic differences in corpus callosum morphology in periventricular nodular heterotopia (PVNH). Differences in corpus callosum mid-sagittal area and subregional area changes were measured using an automated software-based method. Heterotopic gray matter deposits were automatically labeled and compared with corpus callosum changes. The spatial pattern of corpus callosum changes were interpreted in the context of the characteristic anterior-posterior development of the corpus callosum in healthy individuals. Individuals with periventricular nodular heterotopia were imaged at the Melbourne Brain Center or as part of the multi-site Epilepsy Phenome Genome project. Whole brain T1 weighted MRI was acquired in cases (n=48) and controls (n=663). The corpus callosum was segmented on the mid-sagittal plane using the software "yuki". Heterotopic gray matter and intracranial brain volume was measured using Freesurfer. Differences in corpus callosum area and subregional areas were assessed, as well as the relationship between corpus callosum area and heterotopic GM volume. The anterior-posterior distribution of corpus callosum changes and heterotopic GM nodules were quantified using a novel metric and compared with each other. Corpus callosum area was reduced by 14% in PVNH (p=1.59×10(-9)). The magnitude of the effect was least in the genu (7% reduction) and greatest in the isthmus and splenium (26% reduction). Individuals with higher heterotopic GM volume had a smaller corpus callosum. Heterotopic GM volume was highest in posterior brain regions, however there was no linear relationship between the anterior-posterior position of corpus callosum changes and PVNH nodules. Reduced corpus callosum area is strongly associated with PVNH, and is probably associated with abnormal brain development in this neurological disorder. The primarily posterior corpus callosum changes may inform our understanding of the etiology of PVNH. Our results suggest that interhemispheric pathways are affected in PVNH.

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis.

Typically twin studies are used to investigate the aggregate effects of genetic and environmental influences on brain phenotypic measures. Although some phenotypic measures are highly heritable in twin studies, SNPs (single nucleotide polymorphisms) identified by genome-wide association studies (GWAS) account for only a small fraction of the heritability of these measures. We mapped the genetic variation (the proportion of phenotypic variance explained by variation among SNPs) of volumes of pre-defined regions across the whole brain, as explained by 512,905 SNPs genotyped on 747 adult participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We found that 85% of the variance of intracranial volume (ICV) (p = 0.04) was explained by considering all SNPs simultaneously, and after adjusting for ICV, total grey matter (GM) and white matter (WM) volumes had genetic variation estimates near zero (p = 0.5). We found varying estimates of genetic variation across 93 non-overlapping regions, with asymmetry in estimates between the left and right cerebral hemispheres. Several regions reported in previous studies to be related to Alzheimer's disease progression were estimated to have a large proportion of volumetric variance explained by the SNPs.

Fractal analysis methods are used to quantify the complexity of the human cerebral cortex. Many recent studies have focused on high resolution three-dimensional reconstructions of either the outer (pial) surface of the brain or the junction between the gray and white matter, but ignore the structure between these surfaces. This study uses a new method to incorporate the entire cortical thickness. Data were obtained from the Alzheimer's Disease (AD) Neuroimaging Initiative database (Control N=35, Mild AD N=35). Image segmentation was performed using a semi-automated analysis program. The fractal dimension of three cortical models (the pial surface, gray/white surface and entire cortical ribbon) were calculated using a custom cube-counting triangle-intersection algorithm. The fractal dimension of the cortical ribbon showed highly significant differences between control and AD subjects (p<0.001). The inner surface analysis also found smaller but significant differences (p<0.05). The pial surface dimensionality was not significantly different between the two groups. All three models had a significant positive correlation with the cortical gyrification index (r>0.55, p<0.001). Only the cortical ribbon had a significant correlation with cortical thickness (r=0.832, p<0.001) and the Alzheimer's Disease Assessment Scale cognitive battery (r=-0.513, p=0.002). The cortical ribbon dimensionality showed a larger effect size (d=1.12) in separating control and mild AD subjects than cortical thickness (d=1.01) or gyrification index (d=0.84). The methodological change shown in this paper may allow for further clinical application of cortical fractal dimension as a biomarker for structural changes that accrue with neurodegenerative diseases.

Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative-50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the "rich club" - a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.

The goal was to elucidate the time course of regional brain atrophy rates relative to age in cognitively normal (CN) aging, mild cognitively impairment (MCI), and Alzheimer's disease (AD), without a priori models for atrophy progression. Regional brain volumes from 147 cognitively normal subjects, 164 stable MCI, 93 MCI-to-AD converters and 111 ad patients, between 51 and 91 years old and who had repeated 1.5 T magnetic resonance imaging (MRI) scans over 30 months, were analyzed. Relations between regional brain volume change and age were determined using generalized additive models, an established nonparametric concept for approximating nonlinear relations. Brain atrophy rates varied nonlinearly with age, predominantly in regions of the temporal lobe. Moreover, the atrophy rates of some regions leveled off with increasing age in control and stable MCI subjects whereas those rates progressed further in MCI-to-AD converters and AD patients. The approach has potential uses for early detection of AD and differentiation between stable and progressing MCI.

Noninvasive MRI biomarkers for Alzheimer's disease (AD) may enable earlier clinical diagnosis and the monitoring of therapeutic effectiveness. To assess potential neuroimaging biomarkers, the Alzheimer's Disease Neuroimaging Initiative is following normal controls (NC) and individuals with mild cognitive impairment (MCI) or AD. We applied high-throughput image analyses procedures to these data to demonstrate the feasibility of detecting subtle structural changes in prodromal AD. Raw DICOM scans (139 NC, 175 MCI, and 84 AD) were downloaded for analysis. Volumetric segmentation and cortical surface reconstruction produced continuous cortical surface maps and region-of-interest (ROI) measures. The MCI cohort was subdivided into single- (SMCI) and multiple-domain MCI (MMCI) based on neuropsychological performance. Repeated measures analyses of covariance were used to examine group and hemispheric effects while controlling for age, sex, and, for volumetric measures, intracranial vault. ROI analyses showed group differences for ventricular, temporal, posterior and rostral anterior cingulate, posterior parietal, and frontal regions. SMCI and NC differed within temporal, rostral posterior cingulate, inferior parietal, precuneus, and caudal midfrontal regions. With MMCI and AD, greater differences were evident in these regions and additional frontal and retrosplenial cortices; evidence for non-AD pathology in MMCI also was suggested. Mesial temporal right-dominant asymmetries were evident and did not interact with diagnosis. Our findings demonstrate that high-throughput methods provide numerous measures to detect subtle effects of prodromal AD, suggesting early and later stages of the preclinical state in this cross-sectional sample. These methods will enable a more complete longitudinal characterization and allow us to identify changes that are predictive of conversion to AD.

Insulin resistance and type 2 diabetes are associated with cognitive decline and increased risk for Alzheimer's disease (AD). Relatively few studies have assessed the impact of metabolic dysfunction on conversion to AD in mild cognitive impairment (MCI), and it is unclear whether glycemic status is associated with clinically relevant measures of cognitive decline and brain structure in MCI. This study used the Alzheimer's Disease Neuroimaging Initiative database to examine the relationship of baseline glycemia with conversion to AD and longitudinal clinical, cognitive, and imaging measures of decline. Subjects with MCI (n = 264) with baseline and 2-year Clinical Dementia Rating data available were classified according to American Diabetes Association criteria for fasting glucose at baseline. The groups were normoglycemic (fasting glucose, <100 mg/dL; n = 167) or impaired glycemia (fasting glucose, ≥ 100 mg/dL, n = 97). The impaired glycemia group included individuals with fasting glucose that either reached the American Diabetes Association cut point for impaired fasting glucose or individuals with diagnosed diabetes. Two-year change in Clinical Dementia Rating-Sum of Boxes, cognitive performance testing (global cognition), brain volume (whole-brain and hippocampal volume), fluorodeoxyglucose-positron emission tomography, and conversion to AD were assessed. Subjects with normoglycemia at baseline had less functional (Clinical Dementia Rating-Sum of Boxes) and global cognitive decline over 2 years than subjects with impaired glycemia. Subjects with normoglycemia also lost less whole-brain volume and exhibited lower conversion from MCI to AD. There was no difference in hippocampal volume change or fluorodeoxyglucose-positron emission tomography between groups. These results suggest that baseline glycemia is related to cognitive decline and progression to AD.

Hearing impairment was recently identified as the most prominent risk factor for dementia. However, the mechanisms underlying the link between hearing impairment and dementia are still unclear.

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.

The recent development of tau-specific positron emission tomography (PET) tracers enables in vivo quantification of regional tau pathology, one of the key lesions in Alzheimer's disease (AD). Tau PET imaging may become a useful biomarker for clinical diagnosis and tracking of disease progression but there is no consensus yet on how tau PET signal is best quantified. The goal of the current study was to evaluate multiple whole-brain and region-specific approaches to detect clinically relevant tau PET signal. Two independent cohorts of cognitively normal adults and amyloid-positive (Aβ+) patients with mild cognitive impairment (MCI) or AD-dementia underwent [18F]AV-1451 PET. Methods for tau tracer quantification included: (i) in vivo Braak staging, (ii) regional uptake in Braak composite regions, (iii) several whole-brain measures of tracer uptake, (iv) regional uptake in AD-vulnerable voxels, and (v) uptake in a priori defined regions. Receiver operating curves characterized accuracy in distinguishing Aβ- controls from AD/MCI patients and yielded tau positivity cutoffs. Clinical relevance of tau PET measures was assessed by regressions against cognition and MR imaging measures. Key tracer uptake patterns were identified by a factor analysis and voxel-wise contrasts. Braak staging, global and region-specific tau measures yielded similar diagnostic accuracies, which differed between cohorts. While all tau measures were related to amyloid and global cognition, memory and hippocampal/entorhinal volume/thickness were associated with regional tracer retention in the medial temporal lobe. Key regions of tau accumulation included medial temporal and inferior/middle temporal regions, retrosplenial cortex, and banks of the superior temporal sulcus. Our data indicate that whole-brain tau PET measures might be adequate biomarkers to detect AD-related tau pathology. However, regional measures covering AD-vulnerable regions may increase sensitivity to early tau PET signal, atrophy and memory decline.

Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure from diffusion MRI that allows us to incorporate direct brain connections, as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimer's disease. Our findings suggest that this measure is useful in the study of the normal brain structure, and for quantifying the effects of disease on the brain structure.

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.

Progranulin plays an important role in neuroinflammation in Alzheimer's disease (AD) pathophysiology, being upregulated by activated microglia. This study assessed whether cerebrospinal fluid levels of progranulin correlated with structural neuroimaging measures and cognition in 122 cognitively normal individuals, 81 mild cognitive impairment, and 70 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Cognitively normal subjects were classified into 3 groups using the AT(N) system, whereas all mild cognitive impairment and AD patients were A+/TN+. Correlations between progranulin with neuroanatomical measures and cognitive decline were performed within each group. Progranulin was associated with cortical thickening in parietal, occipital, and frontal regions in cognitively normal individuals with amyloid pathology. These subjects also showed cortical thickening compared with A-/TN- subjects, an effect that was partially mediated by progranulin. In addition, higher progranulin correlated with longitudinal cognitive decline. The association between progranulin and cortical thickening, together with regional "brain swelling" in A+/TN- subjects, suggests progranulin contributes to the neuroinflammatory structural changes in preclinical AD.