Comparing 3 T and 1.5 T MRI for tracking Alzheimer's disease progression with tensor-based morphometry

Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI

There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ142 concentration for the former and CSF Aβ1-42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.

The Value of Neuroimaging in Dementia Diagnosis

PURPOSE OF REVIEW

This article discusses neuroimaging in dementia diagnosis, with a focus on new applications of MRI and positron emission tomography (PET).

RECENT FINDINGS

Although the historical use of MRI in dementia diagnosis has been supportive to exclude structural etiologies, recent innovations allow for quantification of atrophy patterns that improve sensitivity for supporting the diagnosis of dementia causes. Neuronuclear approaches allow for localization of specific amyloid and tau neuropathology on PET and are available for clinical use, in addition to dopamine transporter scans in dementia with Lewy bodies and metabolic studies with fludeoxyglucose PET (FDG-PET).

SUMMARY

Using computerized software programs for MRI analysis and cross-sectional and longitudinal evaluations of hippocampal, ventricular, and lobar volumes improves sensitivity in support of the diagnosis of Alzheimer disease and frontotemporal dementia. MRI protocol requirements for such quantification are three-dimensional T1-weighted volumetric imaging protocols, which may need to be specifically requested. Fluid-attenuated inversion recovery (FLAIR) and 3.0T susceptibility-weighted imaging (SWI) sequences are useful for the detection of white matter hyperintensities as well as microhemorrhages in vascular dementia and cerebral amyloid angiopathy. PET studies for amyloid and/or tau pathology can add additional specificity to the diagnosis but currently remain largely inaccessible outside of research settings because of prohibitive cost constraints in most of the world. Dopamine transporter PET scans can help identify Lewy body dementia and are thus of potential clinical value.

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Alzheimer’s disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150–1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878–1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved.

Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

Quantified Brain Magnetic Resonance Imaging Volumes Differentiate Behavioral Variant Frontotemporal Dementia from Early-Onset Alzheimer's Disease

BACKGROUND

The differentiation of behavioral variant frontotemporal dementia (bvFTD) from early-onset Alzheimer's disease (EOAD) by clinical criteria can be inaccurate. The volumetric quantification of clinically available magnetic resonance (MR) brain scans may facilitate early diagnosis of these neurodegenerative dementias.

OBJECTIVE

To determine if volumetric quantification of brain MR imaging can identify persons with bvFTD from EOAD.

METHODS

3D T1 MR brain scans of 20 persons with bvFTD and 45 with EOAD were compared using Neuroreader to measure subcortical, and lobar volumes, and Volbrain for hippocampal subfields. Analyses included: 1) discriminant analysis with leave one out cross-validation; 2) input of predicted probabilities from this process into a receiver operator characteristic (ROC) analysis; and 3) Automated linear regression to identify predictive regions.

RESULTS

Both groups were comparable in age and sex with no statistically significant differences in symptom duration. bvFTD had lower volume percentiles in frontal lobes, thalamus, and putamen. EOAD had lower parietal lobe volumes. ROC analyses showed 99.3% accuracy with Neuroreader percentiles and 80.2% with subfields. The parietal lobe was the most predictive percentile. Although there were differences in hippocampal (particularly left CA2-CA3) subfields, it did not add to the discriminant analysis.

CONCLUSION

Percentiles from an MR based volumetric quantification can help differentiate between bvFTD from EOAD in routine clinical care. Use of hippocampal subfield volumes does not enhance the diagnostic separation of these two early-onset dementias.

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

Structural imaging outcomes in subjective cognitive decline: Community vs. clinical-based samples

Subjective cognitive decline (SCD) has been proposed as a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies have suggested early AD-like structural brain alterations in SCD subjects compared to healthy controls. However, there is substantial heterogeneity in the results, which might depend on whether SCD samples were drawn from the community or from memory clinics. Here we reviewed brain atrophy, assessed through structural magnetic resonance imaging, separately for SCD-community and clinic-based samples. SCD-community samples show a more consistent pattern of atrophy, involving the hippocampus and temporal and parietal cortices. Similarly, in SCD-clinic samples the temporo-parietal cortex showed early vulnerability, however these studies reported a more heterogeneous atrophy pattern. Overall, these studies suggest both commonalities and differences in brain atrophy patterns between SCD clinical and community samples. In SCD-community, the temporal cortex is involved, while SCD-clinical exhibited a more complex pattern of atrophy, which may be related to a more heterogeneous sample reporting neuropsychiatric symptoms along with preclinical AD.

Single-subject gray matter networks predict future cortical atrophy in preclinical Alzheimer's disease

The development of preventive strategies in early-stage Alzheimer's disease (AD) requires measures that can predict future brain atrophy. Gray matter network measures are related to amyloid burden in cognitively normal older individuals and predict clinical progression in preclinical AD. Here, we show that within individuals with preclinical AD, gray matter network measures predict hippocampal atrophy rates, whereas other AD biomarkers (total gray matter volume, cerebrospinal fluid total tau, and Mini-Mental State Examination) do not. Furthermore, in brain areas where amyloid is known to start aggregating (i.e. anterior cingulate and precuneus), disrupted network measures predict faster atrophy in other distant areas, mostly involving temporal regions, which are associated with AD. When repeating analyses in age-matched, cognitively unimpaired individuals without amyloid or tau pathology, we did not find any associations between network measures and hippocampal atrophy, suggesting that the associations are specific for preclinical AD. Our findings suggest that disrupted gray matter networks may indicate a treatment opportunity in preclinical AD individuals but before the onset of irreversible atrophy and cognitive impairment.

Brain Structure Covariance Associated With Gait Control in Aging

BACKGROUND

Structural and functional brain imaging methods have identified age-related changes in brain structures involved in gait control. This cross-sectional study aims to investigate gray matter networks associated with gait control in aging using structural covariance analysis.

METHODS

Walking speed were measured in 326 nondemented older community-dwellers (age 71.3 ± 4.5; 41.7% female) under three different walking conditions: normal walking and two challenging tasks: motor (ie, fast speed) and an attention-demanding dual task (ie, backward counting).

RESULTS

Three main individual gray matter regions were positively correlated with walking speed (ie, slower walking speed was associated with lower brain volumes): right thalamus, right caudate nucleus, and left middle frontal gyrus for normal walking, rapid walking, and dual-task walking condition, respectively. The structural covariance analysis revealed that prefrontal regions were part of the networks associated with every walking condition; the right caudate was associated specifically with the hippocampus, amygdala and insula for the rapid walking condition, and the left middle frontal gyrus with a network involving the cuneus for the dual-task condition.

CONCLUSION

Our results suggest that brain networks associated with gait control vary according to walking speed and depend on each walking condition. Gait control in aging involved a distributed network including regions for emotional control that are recruited in challenging walking conditions.

Effect Modifiers of TDP-43-Associated Hippocampal Atrophy Rates in Patients with Alzheimer's Disease Neuropathological Changes

BACKGROUND

Transactive response DNA-binding protein of 43 kDa (TDP-43) is associated with hippocampal atrophy in Alzheimer's disease (AD), but whether the association is modified by other factors is unknown.

OBJECTIVE

To evaluate whether the associations between TDP-43 and hippocampal volume and atrophy rate are affected by age, gender, apolipoprotein E (APOE) ɛ4, Lewy bodies (LBs), amyloid-β (Aβ), or Braak neurofibrillary tangle (NFT) stage.

METHODS

In this longitudinal neuroimaging-clinicopathological study of 468 cases with AD neuropathological changes (Aβ-positive) that had completed antemortem head MRI, we investigated how age, gender, APOEɛ4, presence of LBs, Aβ, TDP-43, and Braak NFT stages are associated with hippocampal volumes and rates of atrophy over time. We included field strength in the models since our cohort included 1.5T and 3T scans. We then determined whether the associations between hippocampal atrophy and TDP-43 are modified by these factors using mixed effects models.

RESULTS

Older age, female gender, APOEɛ4, higher field strength, higher TDP-43, and Braak NFT stages were associated with smaller hippocampi. Rate of atrophy was greater with higher TDP-43 and Braak NFT stage, but lower in older patients. The association of TDP-43 with greater rate of atrophy was enhanced in APOEɛ4 carriers (p = 0.04).

CONCLUSION

Neurodegenerative effects of TDP-43 seem to be independent of most factors except perhaps APOE in cases with AD neuropathological changes. TDP-43 and tau appear to behave independently of one another.

Default mode network and the timed up and go in MCI: A structural covariance analysis

BACKGROUND

The timed up and go (TUG) is a test used to assess mobility in older adults and patients with neurological conditions. This study aims to compare brain gray matter (GM) correlates and structural covariance networks associated with the TUG time in cognitively healthy individuals (CHI) and in patients with mild cognitive impairment (MCI).

METHODS

The TUG time was measured in 326 non-demented older community-dwellers (age 71.3 ± 4.5; 42% female) - 156 CHI and 170 MCI. GM covariance networks were computed using voxel-based morphometry with the main neural correlates of TUG for each group as seed regions.

RESULTS

Increased TUG time (i.e., poor performance) was associated with distinct brain volume reductions between CHI and MCI. The covariance analysis showed cortical regions involving the default mode network in CHI and bilateral cerebellar regions in MCI.

CONCLUSIONS

GM networks associated with the TUG vary between CHI and MCI, suggesting distinct brain control for locomotion between CHI and MCI patients.

The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review

The Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) was developed in the 1980s to assess the level of cognitive dysfunction in Alzheimer’s disease. Advancements in the research field have shifted focus toward pre-dementia populations, and use of the ADAS-Cog has extended into these pre-dementia studies despite concerns about its ability to detect important changes at these milder stages of disease progression. If the ADAS-Cog cannot detect important changes, our understanding of pre-dementia disease progression may be compromised and trials may incorrectly conclude that a novel treatment approach is not beneficial. The purpose of this review was to assess the performance of the ADAS-Cog in pre-dementia populations, and to review all modifications that have been made to the ADAS-Cog to improve its measurement performance in dementia or pre-dementia populations. The contents of this review are based on bibliographic searches of electronic databases to locate all studies using the ADAS-Cog in pre-dementia samples or subsamples, and to locate all modified versions. Citations from relevant articles were also consulted. Overall, our results suggest the original ADAS-Cog is not an optimal outcome measure for pre-dementia studies; however, given the prominence of the ADAS-Cog, care must be taken when considering the use of alternative outcome measures. Thirty-one modified versions of the ADAS-Cog were found. Modification approaches that appear most beneficial include altering scoring methodology or adding tests of memory, executive function, and/or daily functioning. Although modifications improve the performance of the ADAS-Cog, this is at the cost of introducing heterogeneity that may limit between-study comparison.

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.

Reduced Gray Matter Volume of the Thalamus and Hippocampal Region in Elderly Healthy Adults with no Impact of APOE ɛ4: A Longitudinal Voxel-Based Morphometry Study

BACKGROUND

Many cross-sectional voxel-based morphometry (VBM) investigations have shown significant inverse correlations between chronological age and gray matter (GM) volume in several brain regions in healthy humans. However, few VBM studies have documented GM decrements in the healthy elderly with repeated MRI measurements obtained in the same subjects. Also, the extent to which the APOE ɛ4 allele influences longitudinal findings of GM reduction in the healthy elderly is unclear.

OBJECTIVE

Verify whether regional GM changes are associated with significant decrements in cognitive performance taking in account the presence of the APOE ɛ4 allele.

METHODS

Using structural MRI datasets acquired in 55 cognitively intact elderly subjects at two time-points separated by approximately three years, we searched for voxels showing significant GM reductions taking into account differences in APOE genotype.

RESULTS

We found global GM reductions as well as regional GM decrements in the right thalamus and left parahippocampal gyrus (p < 0.05, family-wise error corrected for multiple comparisons over the whole brain). These findings were not affected by APOE ɛ4.

CONCLUSIONS

Irrespective of APOE ɛ4, longitudinal VBM analyses show that the hippocampal region and thalamus are critical sites where GM shrinkage is greater than the degree of global volume reduction in healthy elderly subjects.

Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis

Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.

Association of hippocampal volume with gait variability in pre-dementia and dementia stages of Alzheimer disease: Results from a cross-sectional study

BACKGROUND

Decreased hippocampal volume is a biomarker of Alzheimer disease (AD). The association of hippocampal volume with gait variability across the spectrum of AD, especially in early stages, has been few studied. The study aims to examine the association of hippocampal volume with the coefficient of variation (CoV) of stride time in individuals with mild and moderate to severe subjective cognitive impairment (SCI), non-amnestic mild cognitive impairment (na-MCI), amnestic mild cognitive impairment (a-MCI), and mild to moderate AD dementia.

METHODS

271 individuals (79 mild SCI, 68 moderate to severe SCI, 47 na-MCI, 42 a-MCI and 35 mild to moderate AD dementia) were included in this cross-sectional study. Hippocampal volume was quantified from a three-dimensional T₁-weighted MRI. CoV of stride time was recorded at self-selected pace with an electronic walkway. Age, sex, body mass index, number of drugs daily taken, history of falls, walking speed, type of MRI scanner, total intracranial volume, and white matter volume abnormality were used as covariates.

RESULTS

Participants with moderate to severe SCI had a higher CoV of stride time compared to those with mild SCI and na-MCI (P < 0.010), and a higher hippocampal volume compared to other groups (P ≤ 0.001). Participants with moderate to severe SCI had increased hippocampal volume associated with increased CoV of stride time (coefficient of regression β = 0.750 with P = 0.041), while the other groups did not show any significant association.

CONCLUSIONS

A positive association between greater hippocampal volume (i.e., better brain morphological structure) and an increased stride time variability (i.e., worse gait performance) in individuals with moderate to severe SCI is reported. This association confirms the key role of the hippocampus in gait control and suggests an inefficient compensatory mechanism in early stages of pathological aging like AD.

Multiplex Networks for Early Diagnosis of Alzheimer's Disease

Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.

Voxel-based Specific Regional Analysis System for Alzheimer's Disease (VSRAD) on 3-tesla Normal Database: Diagnostic Accuracy in Two Independent Cohorts with Early Alzheimer's Disease

Voxel-based specific regional analysis system for Alzheimer’s disease (VSRAD) software is widely used in clinical practice in Alzheimer’s disease (AD). The existing VSRAD is based on the normal database with 1.5-tesla MRI scans (VSRAD-1.5T), and its utility for patients have undergone 3-tesla MRI is still controversial. We recruited 19 patients with early AD and 28 healthy controls who had undergone 3-tesla MRI scans at our institute (Cohort 1). We also used the 3-tesla MRI data of 30 patients with early AD and 13 healthy controls from the Japanese Alzheimer’s Disease Neuroimaging Initiative (Cohort 2). We also created a new VSRAD based on 65 normal subjects’ 3-tesla MRI scans (VSRAD-3T), and compared the detectability of AD between VSRAD-1.5T and VSRAD-3T, using receiver operating characteristic curve and area under the curve (AUC) analyses. As a result, there were no significant differences in the detectability of AD between VSRAD-3T and VSRAD-1.5T, except for the whole white matter atrophy score, which showed significantly better AUC in VSRAD-3T than in VSRAD-1.5T in both Cohort 1 (p=0.04) and 2 (p<0.01). Generally, there were better diagnostic values in Cohort 2 than in Cohort 1. The optimal cutoff values varied but were generally lower than in the previously published data. In conclusion, for patients with 3-tesla MRI, the detectability of early AD by the existing VSRAD was not different from that by the new VSRAD based on 3-tesla database. We should exercise caution when using the existing VSRAD for 3-tesla white matter analyses or for setting cutoff values.

Functional and structural brain networks in posterior cortical atrophy: A two-centre multiparametric MRI study

This study identified structural and functional brain connectivity alterations in two independent samples of patients along the posterior cortical atrophy (PCA) disease course. Twenty-one PCA patients and 44 controls were recruited from two expert centres. Microstructural damage of white matter (WM) tracts was assessed using probabilistic tractography; resting state (RS) functional connectivity of brain networks was explored using a model free approach; grey matter (GM) atrophy was investigated using voxel-based morphometry. Compared with controls, common patterns of damage across PCA patients included: GM atrophy in the occipital-temporal-parietal regions; diffusion tensor (DT) MRI alterations of the corpus callosum and superior (SLF) and inferior longitudinal fasciculi (ILF) bilaterally; and decreased functional connectivity of the occipital gyri within the visual network and the precuneus and posterior cingulum within the default mode network (DMN). In PCA patients with longer disease duration and greater disease severity, WM damage extended to the cingulum and RS functional connectivity alterations spread within the frontal, dorsal attentive and salience networks. In PCA, reduced DMN functional connectivity was associated with SLF and ILF structural alterations. PCA patients showed distributed WM damage. Altered RS functional connectivity extends with disease worsening from occipital to temporo-parietal and frontostriatal regions, and this is likely to occur through WM connections. Future longitudinal studies are needed to establish trajectories of damage spreading in PCA and whether a combined DT MRI/RS functional MRI approach is promising in monitoring the disease progression.

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PCA patients showed distributed WM damage.

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In PCA, WM damage is associated with longer disease duration ad greater severity.

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In PCA, altered RS functional connectivity extends with disease worsening.

Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure

Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. There are parametric and non-parametric approaches for studying the data or sampling distributions, yet few resources are available to provide integrated views of data (observed or simulated), theoretical concepts, computational mechanisms and hands-on utilization via flexible graphical user interfaces. We designed, implemented and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data. The core of this framework is a modern randomization webapp, which may be invoked on any device supporting a JavaScript-enabled web-browser. We demonstrate the use of these resources to analyze proportion, mean, and other statistics using simulated (virtual experiments) and observed (e.g., Acute Myocardial Infarction, Job Rankings) data. Finally, we draw parallels between parametric inference methods and their distribution-free alternatives. The Randomization and Resampling webapp can be used for data analytics, as well as for formal, in-class and informal, out-of-the-classroom learning and teaching of different scientific concepts. Such concepts include sampling, random variation, computational statistical inference and data-driven analytics. The entire scientific community may utilize, test, expand, modify or embed these resources (data, source-code, learning activity, webapp) without any restrictions.

Structural brain abnormalities in patients with type I bipolar disorder and suicidal behavior

Some studies have identified brain morphological changes in the frontolimbic network (FLN) in bipolar subjects who attempt suicide (SA). The present study investigated neuroanatomical abnormalities in the FLN to find a possible neural signature for suicidal behavior in patients with bipolar disorder type I (BD-I). We used voxel-based morphometry to compare euthymic patients with BD-I who had attempted suicide (n=20), who had not attempted suicide (n=19) and healthy controls (HCs) (n=20). We also assessed the highest medical lethality of their previous SA. Compared to the participants who had not attempted suicide, the patients with BD-I who had attempted suicide exhibited significantly increased gray matter volume (GMV) in the right rostral anterior cingulate cortex (ACC), which was more pronounced and extended further to the left ACC in the high-lethality subgroup (p<0.05, with family-wise error (FWE) correction for multiple comparisons using small-volume correction). GMV in the insula and orbitofrontal cortex was also related to suicide lethality (p<0.05, FWE-corrected). The current findings suggest that morphological changes in the FLN could be a signature of previous etiopathogenic processes affecting regions related to suicidality and its severity in BD-I patients.

Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson's disease

We investigated whole-brain atrophy and ventricular enlargement over 18 months in nondemented Parkinson's disease (PD) and examined their associations with clinical measures and baseline CSF markers. PD subjects (n = 100) were classified at baseline into those with mild cognitive impairment (MCI; PD-MCI, n = 36) and no cognitive impairment (PD-NC, n = 64). Percentage of whole-brain volume change (PBVC) and ventricular expansion over 18 months were assessed with FSL-SIENA and ventricular enlargement (VIENA) respectively. PD-MCI showed increased global atrophy (-1.1% ± 0.8%) and ventricular enlargement (6.9 % ± 5.2%) compared with both PD-NC (PBVC: -0.4 ± 0.5, p < 0.01; VIENA: 2.1% ± 4.3%, p < 0.01) and healthy controls. In a subset of 35 PD subjects, CSF levels of tau, and Aβ42/Aβ40 ratio were correlated with PBVC and ventricular enlargement respectively. The sample size required to demonstrate a 20% reduction in PBVC and VIENA was approximately 1/15th of that required to detect equivalent changes in cognitive decline. These findings suggest that longitudinal MRI measurements have potential to serve as surrogate markers to complement clinical assessments for future disease-modifying trials in PD.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

Purpose:

Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O⁶-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes.

Methods:

A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status.

Results:

The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78–0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813.

Conclusions:

Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

Patterns of regional gray matter loss at different stages of schizophrenia: A multisite, cross-sectional VBM study in first-episode and chronic illness

Background: Structural brain abnormalities in schizophrenia have been repeatedly demonstrated in magnetic resonance imaging (MRI) studies, but it remains unclear whether these are static or progressive in nature. While longitudinal MRI studies have been traditionally used to assess the issue of progression of brain abnormalities in schizophrenia, information from cross-sectional neuroimaging studies directly comparing first-episode and chronic schizophrenia patients to healthy controls may also be useful to further clarify this issue. With the recent interest in multisite mega-analyses combining structural MRI data from multiple centers aiming at increased statistical power, the present multisite voxel-based morphometry (VBM) study was carried out to examine patterns of brain structural changes according to the different stages of illness and to ascertain which (if any) of such structural abnormalities would be specifically correlated to potential clinical moderators, including cumulative exposure to antipsychotics, age of onset, illness duration and overall illness severity.

Methods: We gathered a large sample of schizophrenia patients (161, being 99 chronic and 62 first-episode) and controls (151) from four previous morphometric MRI studies (1.5 T) carried out in the same geographical region of Brazil. Image processing and analyses were conducted using Statistical Parametric Mapping (SPM8) software with the diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) algorithm. Group effects on regional gray matter (GM) volumes were investigated through whole-brain voxel-wise comparisons using General Linear Model Analysis of Co-variance (ANCOVA), always including total GM volume, scan protocol, age and gender as nuisance variables. Finally, correlation analyses were performed between the aforementioned clinical moderators and regional and global brain volumes.

Results: First-episode schizophrenia subjects displayed subtle volumetric deficits relative to controls in a circumscribed brain regional network identified only in small volume-corrected (SVC) analyses (p < 0.05, FWE-corrected), including the insula, temporolimbic structures and striatum. Chronic schizophrenia patients, on the other hand, demonstrated an extensive pattern of regional GM volume decreases relative to controls, involving bilateral superior, inferior and orbital frontal cortices, right middle frontal cortex, bilateral anterior cingulate cortices, bilateral insulae and right superior and middle temporal cortices (p < 0.05, FWE-corrected over the whole brain). GM volumes in several of those brain regions were directly correlated with age of disease onset on SVC analyses for conjoined (first-episode and chronic) schizophrenia groups. There were also widespread foci of significant negative correlation between duration of illness and relative GM volumes, but such findings remained significant only for the right dorsolateral prefrontal cortex after accounting for the influence of age of disease onset. Finally, significant negative correlations were detected between life-time cumulative exposure to antipsychotics and total GM and white matter volumes in schizophrenia patients, but no significant relationship was found between indices of antipsychotic usage and relative GM volume in any specific brain region.

Conclusion: The above data indicate that brain changes associated with the diagnosis of schizophrenia are more widespread in chronic schizophrenia compared to first-episode patients. Our findings also suggest that relative GM volume deficits may be greater in (presumably more severe) cases with earlier age of onset, as well as varying as a function of illness duration in specific frontal brain regions. Finally, our results highlight the potentially complex effects of the continued use of antipsychotic drugs on structural brain abnormalities in schizophrenia, as we found that cumulative doses of antipsychotics affected brain volumes globally rather than selectively on frontal-temporal regions.

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Structural brain changes are more widespread in chronic than first-episode schizophrenia.

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Regional GM deficits may be greater in cases with earlier age of onset.

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Illness duration seems to impact in some specific frontal structural brain changes.

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Antipsychotics seem to affect brain volumes globally rather than regionally.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

INTRODUCTION

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.

METHODS

We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3.

RESULTS

Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.

DISCUSSION

Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment

BACKGROUND AND PURPOSE

This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers.

METHODS

Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses.

RESULTS

The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction.

CONCLUSIONS

These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.

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

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

Hippocampal subfield volumes in first episode and chronic schizophrenia

Background

Reduced hippocampal volume in schizophrenia is a well-replicated finding. New imaging techniques allow delineation of hippocampal subfield volumes. Studies including predominantly chronic patients demonstrate differences between subfields in sensitivity to illness, and in associations with clinical features. We carried out a cross-sectional and longitudinal study of first episode, sub-chronic, and chronic patients, using an imaging strategy that allows for the assessment of multiple hippocampal subfields.

Methods

Hippocampal subfield volumes were measured in 34 patients with schizophrenia (19 first episode, 6 sub-chronic, 9 chronic) and 15 healthy comparison participants. A subset of 10 first episode and 12 healthy participants were rescanned after six months.

Results

Total left hippocampal volume was smaller in sub-chronic (p = 0.04, effect size 1.12) and chronic (p = 0.009, effect size 1.42) patients compared with healthy volunteers. The CA2-3 subfield volume of chronic patients was significantly decreased (p = 0.009, effect size 1.42) compared to healthy volunteers. The CA4-DG volume was significantly reduced in all three patient groups compared to healthy group (all p < 0.005). The two affected subfield volumes were inversely correlated with severity of negative symptoms (p < 0.05). There was a small, but statistically significant decline in left CA4-DG volume over the first six months of illness (p = 0.01).

Conclusions

Imaging strategies defining the subfields of the hippocampus may be informative in linking symptoms and structural abnormalities, and in understanding more about progression during the early phases of illness in schizophrenia.

Variation in longevity gene KLOTHO is associated with greater cortical volumes

Objective

Identifying genetic variation associated with brain structures in aging may elucidate new biologic mechanisms underlying resilience to cognitive decline. We investigated whether carrying one copy of the protective haplotype “KL-VS” in longevity gene KLOTHO (KL) is associated with greater gray matter volume in healthy human aging compared to carrying no copies.

Methods

We performed unbiased whole-brain analysis in cognitively normal older adults from two independent cohorts to assess the relationship between KL-VS and gray matter volume using voxel-based morphometry.

Results

We found that KL-VS heterozygosity was associated with greater volume in right dorsolateral prefrontal cortex (rDLPFC). Because rDLPFC is important for executive function, we analyzed working memory and processing speed in individuals. KL-VS heterozygosity was associated with enhanced executive function. Larger rDLPFC volume correlated with better executive function across the lifespan examined. Statistical analysis suggested that volume partially mediates the effect of genotype on cognition.

Interpretation

These results suggest that variation in KL is associated with bigger brain volume and better function.

Comparing 3T and 1.5T MRI for mapping hippocampal atrophy in the Alzheimer's Disease Neuroimaging Initiative

BACKGROUND AND PURPOSE

Prior MR imaging studies, primarily at 1.5T, established hippocampal atrophy as a biomarker for Alzheimer disease. 3T MR imaging offers a higher contrast and signal-to-noise ratio, yet distortions and intensity uniformity are harder to control. We applied our automated hippocampal segmentation technique to 1.5T and 3T MR imaging data, to determine whether hippocampal atrophy detection was enhanced at 3T.

MATERIALS AND METHODS

We analyzed baseline MR imaging data from 166 subjects from the Alzheimer's Disease Neuroimaging Initiative-1 (37 with Alzheimer disease, 76 with mild cognitive impairment, and 53 healthy controls) scanned at 1.5T and 3T. Using multiple linear regression, we analyzed the effect of clinical diagnosis on hippocampal radial distance, while adjusting for sex. 3D statistical maps were adjusted for multiple comparisons by using permutation-based statistics at a threshold of P < .01.

RESULTS

Bilaterally significant radial distance differences in the areas corresponding to the cornu ammonis 1, cornu ammonis 2, and subiculum were detected for Alzheimer disease versus healthy controls and mild cognitive impairment versus healthy controls at 1.5T and more profoundly at 3T. Comparison of Alzheimer disease with mild cognitive impairment did not reveal significant differences at either field strength. Subjects who converted from mild cognitive impairment to Alzheimer disease within 3 years of the baseline scan versus nonconverters showed significant differences in the area corresponding to cornu ammonis 1 of the right hippocampus at 3T but not at 1.5T.

CONCLUSIONS

While hippocampal atrophy patterns in diagnostic comparisons were similar at 1.5T and 3T, 3T showed a superior signal-to-noise ratio and detected atrophy with greater effect size compared with 1.5T.

Pattern of brain atrophy rates in autopsy-confirmed dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is characterized by preserved whole brain and medial temporal lobe volumes compared with Alzheimer's disease dementia (AD) on magnetic resonance imaging. However, frequently coexistent AD-type pathology may influence the pattern of regional brain atrophy rates in DLB patients. We investigated the pattern and magnitude of the atrophy rates from 2 serial MRIs in autopsy-confirmed DLB patients (n = 20) and mixed DLB/AD patients (n = 22), compared with AD (n = 30) and elderly nondemented control subjects (n = 15), followed antemortem. DLB patients without significant AD-type pathology were characterized by lower global and regional rates of atrophy, similar to control subjects. The mixed DLB/AD patients displayed greater atrophy rates in the whole brain, temporoparietal cortices, hippocampus and amygdala, and ventricle expansion, similar to AD patients. In the DLB and DLB/AD patients, the atrophy rates correlated with Braak neurofibrillary tangle stage, cognitive decline, and progression of motor symptoms. Global and regional atrophy rates are associated with AD-type pathology in DLB, and these rates can be used as biomarkers of AD progression in patients with LB pathology.

Automated template-based hippocampal segmentations from MRI: the effects of 1.5T or 3T field strength on accuracy

Hippocampal volumetric measures may be useful for Alzheimer's disease (AD) diagnosis and disease tracking; however, manual segmentation of the hippocampus is labour-intensive. Therefore, automated techniques are necessary for large studies and to make hippocampal measures feasible for clinical use. As large studies and clinical centres are moving from using 1.5 Tesla (T) scanners to higher field strengths it is important to assess whether specific image processing techniques can be used at these field strengths. This study investigated whether an automated hippocampal segmentation technique (HMAPS: hippocampal multi-atlas propagation and segmentation) and volume change measures (BSI: boundary shift integral) were as accurate at 3T as at 1.5T. Eighteen Alzheimer's disease patients and 18 controls with 1.5T and 3T scans at baseline and 12-month follow-up were used from the Alzheimer's Disease Neuroimaging Initiative cohort. Baseline scans were segmented manually and using HMAPS and their similarity was measured by the Jaccard index. BSIs were calculated for serial image pairs. We calculated pair-wise differences between manual and HMAPS rates at 1.5T and 3T and compared the SD of these differences at each field strength. The difference in mean Jaccards (manual and HMAPS) between 1.5T and 3T was small with narrow confidence intervals (CIs) and did not appear to be segmentor dependent. The SDs of the difference between volumes from manual and automated segmentations were similar at 1.5T and 3T, with a relatively narrow CI for their ratios. The SDs of the difference between BSIs from manual and automated segmentations were also similar at 1.5T and 3T but with a wider CI for their ratios. This study supports the use of our automated hippocampal voluming methods, developed using 1.5T images, with 3T images.

Effects of study design in multi-scanner voxel-based morphometry studies

Interest has recently grown in multi-center studies, which have more power than smaller studies in conducting sophisticated evaluations of basic neuroanatomy and neurodegenerative disorders. The large number of subjects that result from pooling multi-center datasets increases sensitivity, but also introduces a between-center variance component. Taking sex differences as an example, we examined the effects of different ratios of cases to controls (males to females) between scanners in multi-scanner morphometric studies, using voxel-based morphometry and data obtained on two scanners of the exact same model. Each subject was scanned twice with both scanners. Using the image obtained on either of the two scanners for each subject, voxel-based analyses were repeated with different ratios of males to females for each scanner. As the ratio of males to females became more imbalanced between the scanners, the differences between the two scanners more strongly affected the results of analyses of sex differences. When the ratio of males to females was balanced, the inclusion of scanner as a covariate in the statistical analysis had almost no influence on the results of analyses of sex differences. When the ratio of males to females was ill-balanced, the inclusion of scanner as a covariate suppressed scanner effects on the results, but made sex differences less likely to become significant. The present results suggest that as long as the ratio of cases to controls is well-balanced across different scanners, it is not always necessary to include scanner as a covariate in the statistical analysis, and that when the ratio of cases to controls is ill-balanced across scanners, the inclusion of scanner as a covariate in the statistical analysis can suppress scanner effects, but may make differences less likely to be detected.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Mapping creatinine- and cystatin C-related white matter brain deficits in the elderly

Poor kidney function is associated with increased risk of cognitive decline and generalized brain atrophy. Chronic kidney disease impairs glomerular filtration rate, and this deterioration is indicated by elevated blood levels of kidney biomarkers such as creatinine and cystatin C. Here we hypothesized that impaired renal function would be associated with brain deficits in regions vulnerable to neurodegeneration. Using tensor-based morphometry, we related patterns of brain volumetric differences to creatinine, cystatin C levels, and glomerular filtration rate in a large cohort of 738 (mean age, 75.5 ± 6.8 years; 438 men, 300 women) elderly Caucasian subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Elevated kidney biomarkers were associated with volume deficits in the white matter region of the brain. All 3 renal parameters in our study showed significant associations consistently with a region that corresponds with the anterior limb of internal capsule, bilaterally. This is the first study to report a marked profile of structural alterations in the brain associated with elevated kidney biomarkers, helping us to explain the cognitive deficits.

Measuring clinical progression in MCI and pre-MCI populations: enrichment and optimizing clinical outcomes over time

Recent biomarker research has improved the identification of individuals with very early stages of Alzheimer's disease (AD) and has demonstrated that biomarkers are sensitive for measuring progression in the pre-dementia or mild cognitive impairment (MCI) stage and even pre-symptomatic or pre-MCI stage of AD. Because there are no validated biomarkers in AD, it is important to seek out clinical outcomes that are also sensitive for measuring progression in these very early stages of disease. Clinical outcomes are more subjective and more affected by measurement error than biomarkers but represent the core aspects of the disease and are critical for validation of biomarkers and for evaluation of clinical relevance. Identification of individuals with pre-MCI stages of AD will need to continue to rely on biomarkers, but the identification of individuals with MCI who will progress to AD can be achieved with biomarkers or clinical criteria. Although standard clinical outcomes have been shown to be less sensitive to progression than biomarker outcomes in MCI and pre-MCI populations, non-standard scoring has improved the performance of the Alzheimer's Disease Assessment Scale cognitive subscale, making it more sensitive to progression. Neuropsychological cognitive testing items are optimal for measuring progression in pre-MCI populations, and current research is exploring the best ways to combine these items into a composite cognitive score with maximum responsiveness. In an MCI stage, cognitive, functional, and global items all change, and the best single composite score for measuring progression may involve all of these aspects of the disease. The best chance of success in demonstrating treatment effects in clinical trials will be achieved in a well-defined pre-MCI or MCI population and with an outcome that tracks well with clinical progression over time and with time. A partial least squares model can be used to identify these optimal weighted combinations.

DIFFUSION IMAGING PROTOCOL EFFECTS ON GENETIC ASSOCIATIONS

Large multi-site image-analysis studies have successfully discovered genetic variants that affect brain structure in tens of thousands of subjects scanned worldwide. Candidate genes have also associated with brain integrity, measured using fractional anisotropy in diffusion tensor images (DTI). To evaluate the heritability and robustness of DTI measures as a target for genetic analysis, we compared 417 twins and siblings scanned on the same day on the same high field scanner (4-Tesla) with two protocols: (1) 94-directions; 2mm-thick slices, (2) 27-directions; 5mm-thickness. Using mean FA in white matter ROIs and FA 'skeletons' derived using FSL, we (1) examined differences in voxelwise means, variances, and correlations among the measures; and (2) assessed heritability with structural equation models, using the classical twin design. FA measures from the genu of the corpus callosum were highly heritable, regardless of protocol. Genome-wide analysis of the genu mean FA revealed differences across protocols in the top associations.

Visual analytics of brain networks

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging.

The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI

Structural changes in neuroanatomical subregions can be measured using serial magnetic resonance imaging scans, and provide powerful biomarkers for detecting and monitoring Alzheimer's disease. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has made a large database of longitudinal scans available, with one of its primary goals being to explore the utility of structural change measures for assessing treatment effects in clinical trials of putative disease-modifying therapies. Several ADNI-funded research laboratories have calculated such measures from the ADNI database and made their results publicly available. Here, using sample size estimates, we present a comparative analysis of the overall results that come from the application of each laboratory's extensive processing stream to the ADNI database. Obtaining accurate measures of change requires correcting for potential bias due to the measurement methods themselves; and obtaining realistic sample size estimates for treatment response, based on longitudinal imaging measures from natural history studies such as ADNI, requires calibrating measured change in patient cohorts with respect to longitudinal anatomical changes inherent to normal aging. We present results showing that significant longitudinal change is present in healthy control subjects who test negative for amyloid-β pathology. Therefore, sample size estimates as commonly reported from power calculations based on total structural change in patients, rather than change in patients relative to change in healthy controls, are likely to be unrealistically low for treatments targeting amyloid-related pathology. Of all the measures publicly available in ADNI, thinning of the entorhinal cortex quantified with the Quarc methodology provides the most powerful change biomarker.

Neurological imaging: statistics behind the pictures

Neurological imaging represents a powerful paradigm for investigation of brain structure, physiology and function across different scales. The diverse phenotypes and significant normal and pathological brain variability demand reliable and efficient statistical methodologies to model, analyze and interpret raw neurological images and derived geometric information from these images. The validity, reproducibility and power of any statistical brain map require appropriate inference on large cohorts, significant community validation, and multidisciplinary collaborations between physicians, engineers and statisticians.

Effect of scanner in longitudinal studies of brain volume changes

PURPOSE

To evaluate the effects of drift in scanner hardware and inter-scanner variability on global and regional brain volume changes using longitudinal data obtained on two scanners of the exact same model.

MATERIALS AND METHODS

A total of 208 subjects (70 females and 138 males, mean age = 56.5 ± 10.1 years) were scanned twice, at an interval of approximately 2 years (mean interval = 1.9 ± 0.15 years), using two 3.0 Tesla (T) scanners of the exact same model. The subjects were divided into four groups according to the combination of scanners used. The effects of scanner drift and inter-scanner variability on global and regional brain volume changes were evaluated using voxel-based morphometry (VBM) and structural image evaluation using normalization of atrophy (SIENA).

RESULTS

The mean whole brain volume calculated using VBM increased by 17.2 ± 29.3 mL, and the changes were significantly different between groups. The mean percentage brain volume change (PBVC) calculated using SIENA was -0.46 ± 0.71 %, and did not differ between groups. VBM showed several regions with a significant increase or decrease in volume; however, SIENA showed that edge displacement values were negative in most regions.

CONCLUSION

Even with scanners of the exact same model, scanner drift, and inter-scanner variability can cancel out actual longitudinal brain volume change. SIENA, which corrects for differences in imaging geometry using the outer skull surface for both time points, can reduce the effects of scanner drift and inter-scanner variability on longitudinal morphometric results.

Voxelwise gene-wide association study (vGeneWAS): multivariate gene-based association testing in 731 elderly subjects

Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.

Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans

We set out to determine factors that influence the rate of brain atrophy in 1-year longitudinal magnetic resonance imaging (MRI) data. With tensor-based morphometry (TBM), we mapped the 3-dimensional profile of progressive atrophy in 144 subjects with probable Alzheimer's disease (AD) (age: 76.5 +/- 7.4 years), 338 with amnestic mild cognitive impairment (MCI; 76.0 +/- 7.2), and 202 healthy controls (77.0 +/- 5.1), scanned twice, 1 year apart. Statistical maps revealed significant age and sex differences in atrophic rates. Brain atrophic rates were about 1%-1.5% faster in women than men. Atrophy was faster in younger than older subjects, most prominently in mild cognitive impairment, with a 1% increase in the rates of atrophy and 2% in ventricular expansion, for every 10-year decrease in age. TBM-derived atrophic rates correlated with reduced beta-amyloid and elevated tau levels (n = 363) at baseline, baseline and progressive deterioration in clinical measures, and increasing numbers of risk alleles for the ApoE4 gene. TBM is a sensitive, high-throughput biomarker for tracking disease progression in large imaging studies; sub-analyses focusing on women or younger subjects gave improved sample size requirements for clinical trials.

Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort

Atrophic changes in early Alzheimer's disease (AD) and amnestic mild cognitive impairment (MCI) have been proposed as biomarkers for detection and monitoring. We analyzed magnetic resonance imaging (MRI) atrophy rate from baseline to 1 year in 4 groups of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI): AD (n = 152), converters from MCI to probable AD (MCI-C, n = 60), stable MCI (MCI-S, n = 261), and healthy controls (HC, n = 200). Scans were analyzed using multiple methods, including voxel-based morphometry (VBM), regions of interest (ROIs), and automated parcellation, permitting comparison of annual percent change (APC) in neurodegeneration markers. Effect sizes and the sample required to detect 25% reduction in atrophy rates were calculated. The influence of APOE genotype on APC was also evaluated. AD patients and converters from MCI to probable AD demonstrated high atrophy APCs across regions compared with minimal change in healthy controls. Stable MCI subjects showed intermediate atrophy rates. APOE genotype was associated with APC in key regions. In sum, APC rates are influenced by APOE genotype, imminent MCI to AD conversion, and AD-related neurodegeneration.

Ventricular maps in 804 ADNI subjects: correlations with CSF biomarkers and clinical decline

Ideal biomarkers of Alzheimer's disease (AD) should correlate with accepted measures of pathology in the cerebrospinal fluid (CSF); they should also correlate with, or predict, future clinical decline, and should be readily measured in hundreds to thousands of subjects. Here we explored the utility of automated 3D maps of the lateral ventricles as a possible biomarker of AD. We used our multi-atlas fluid image alignment (MAFIA) method, to compute ventricular models automatically, without user intervention, from 804 brain MRI scans with 184 AD, 391 mild cognitive impairment (MCI), and 229 healthy elderly controls (446 men, 338 women; age: 75.50 +/- 6.81 [SD] years). Radial expansion of the ventricles, computed pointwise, was strongly correlated with current cognition, depression ratings, Hachinski Ischemic scores, language scores, and with future clinical decline after controlling for any effects of age, gender, and educational level. In statistical maps ranked by effect sizes, ventricular differences were highly correlated with CSF measures of Abeta(1-42), and correlated with ApoE4 genotype. These statistical maps are highly automated, and offer a promising biomarker of AD for large-scale studies.

Effect of scanner in longitudinal diffusion tensor imaging studies

The purpose of this study was to evaluate the effects of longitudinal drift in scanner hardware, inter-scanner variability (bias), and scanner upgrade on longitudinal changes in global and regional diffusion properties using longitudinal data obtained on two scanners of the exact same model at one institution. A total of 224 normal subjects were scanned twice, at an interval of about 1 year, using two 3.0-T scanners of the exact same model. Both scanners were simultaneously upgraded during the study period. The subjects were divided into four groups according to the combination of scanners used. With use of tract-based spatial statistics, we evaluated the effects of scanner drift and inter-scanner variability (bias) on global and regional fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) changes of the white matter. Even with scanners of the exact same model, inter-scanner variability (bias) significantly affected longitudinal results. FA, AD, and RD of the white matter were relatively stable within the same scanner. We also investigated the effect of scanner upgrade on longitudinal FA, AD, and RD changes. The scanner upgrade included only software upgrade, not hardware upgrade; however, there was a significant effect of scanner upgrade on longitudinal results. These results indicate that inter-scanner variability and scanner upgrade can significantly affect the results of longitudinal diffusion tensor imaging studies.