Corpus callosum shape and size changes in early Alzheimer's disease: a longitudinal MRI study using the OASIS brain database

CCADD: An online webserver for Alzheimer's disease detection from brain MRI

Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.

Cognitively healthy APOE4/4 carriers show white matter impairment associated with serum NfL and amyloid-PET

Except for aging, carrying the APOE ε4 allele (APOE4) is the most important risk factor for sporadic Alzheimer's disease. APOE4 carriers may have reduced capacity to recycle lipids, resulting in white matter microstructural abnormalities. In this study, we evaluated whether white matter impairment measured by diffusion tensor imaging (DTI) differs between healthy individuals with a different number of APOE4 alleles, and whether white matter impairment associates with brain beta-amyloid (Aβ) load and serum levels of neurofilament light chain (NfL). We studied 96 participants (APOE3/3, N = 37; APOE3/4, N = 39; APOE4/4, N = 20; mean age 70.7 (SD 5.22) years, 63% females) with a brain MRI including a DTI sequence (N = 96), Aβ-PET (N = 89) and a venous blood sample for the serum NfL concentration measurement (N = 88). Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) in six a priori-selected white matter regions-of-interest (ROIs) were compared between the groups using ANCOVA, with sex and age as covariates. A voxel-weighted average of FA, MD, RD and AxD was calculated for each subject, and correlations with Aβ-PET and NfL levels were evaluated. APOE4/4 carriers exhibited a higher MD and a higher RD in the body of corpus callosum than APOE3/4 (p = 0.0053 and p = 0.0049, respectively) and APOE3/3 (p = 0.026 and p = 0.042). APOE4/4 carriers had a higher AxD than APOE3/4 (p = 0.012) and APOE3/3 (p = 0.040) in the right cingulum adjacent to cingulate cortex. In the total sample, composite MD, RD and AxD positively correlated with the cortical Aβ load (r = 0.26 to 0.33, p < 0.013 for all) and with serum NfL concentrations (r = 0.31 to 0.36, p < 0.0028 for all). In conclusion, increased local diffusivity was detected in cognitively unimpaired APOE4/4 homozygotes compared to APOE3/4 and APOE3/3 carriers, and increased diffusivity correlated with biomarkers of Alzheimer's disease and neurodegeneration. White matter impairment seems to be an early phenomenon in the Alzheimer's disease pathologic process in APOE4/4 homozygotes.

The Associations Between Serum Insulin-like Growth Factor-I, Brain White Matter Volumes, and Cognition in Mild Cognitive Impairment and Alzheimer's Disease

Background

Insulin-like growth factor-I (IGF-I) regulates myelin, but little is known whether IGF-I associates with white matter functions in subjective and objective mild cognitive impairment (SCI/MCI) or Alzheimer's disease (AD).

Objective

To explore whether serum IGF-I is associated with magnetic resonance imaging - estimated brain white matter volumes or cognitive functions.

Methods

In a prospective study of SCI/MCI (n = 106) and AD (n = 59), we evaluated the volumes of the total white matter, corpus callosum (CC), and white matter hyperintensities (WMHs) as well as Mini-Mental State Examination (MMSE), Trail Making Test A and B (TMT-A/B), and Stroop tests I-III at baseline, and after 2 years.

Results

IGF-I was comparable in SCI/MCI and AD (113 versus 118 ng/mL, p = 0.44). In SCI/MCI patients, the correlations between higher baseline IGF-I and greater baseline and 2-year volumes of the total white matter and total CC lost statistical significance after adjustment for intracranial volume and other covariates. However, after adjustment for covariates, higher baseline IGF-I correlated with better baseline scores of MMSE and Stroop test II in SCI/MCI and with better baseline results of TMT-B and Stroop test I in AD. IGF-I did not correlate with WMH volumes or changes in any of the variables.

Conclusions

Both in SCI/MCI and AD, higher IGF-I was associated with better attention/executive functions at baseline after adjustment for covariates. Furthermore, the baseline associations between IGF-I and neuropsychological test results in AD may argue against significant IGF-I resistance in the AD brain.

Corpus callosum anatomical changes in Alzheimer patients and the effect of acetylcholinesterase inhibitors on corpus callosum morphometry

The Corpus Callosum (CC) is an important structure that includes the majority of fibers connecting the two brain hemispheres. Several neurodegenerative diseases may alter CC size and morphology leading to its atrophy and malfunction which may play a role in the pathological manifestations found in these diseases. The purpose of the current study is to determine any possible changes in CC size in patients suffering from Alzheimer’s disease. The Study also investigated the effect of acetylcholinesterase inhibitors (AChEIs) on the size of CC and its association with improvement in the Alzheimer disease severity scores. Midsagittal size of CC were recorded prospectively from 439 routine T1-weighted MRI brain images in normal individuals. The internal skull surface was measured to calculate CC/ internal skull surface ratio. Two groups of patients were studied: 300 (150 male / 150 female) were healthy subjects and 130 (55 males / 75 females) had Alzheimer disease. Out of the 130 Alzheimer disease pateints, 70 patients were treated with Donepezil or Rivastigmine or both. The size of the CC was measured based on T1-weighted MRI images after the treatment to investigate any possible improvement in CC size. The mean surface area of CC in controls was 6.53±1.105 cm². There was no significant difference between males and females (P < 0.627), and CC/ internal skull surface ratio was 4.41±0.77%. Patients with mild or severe Alzheimer disease showed a significant reduction in CC size compared to healthy controls. Treating mild Alzheimer patients with either Donepezil or Rivastigmine exerts a comparable therapeutic effect in improving the CC size. There was more improvement in the size of CC in patients with severe Alzheimer disease by using combined therapy of Donepezil and Rivastigmine than using single a medication. we measured the mean size of the various portions of the corpus callosum in normal individuals and Alzheimer patients before and after taking Donepezil and Rivastigmine. Alzheimer patients have pronounced reduction in CC which is corrected after taking Donepezil and Rivastigmine leading to remarkable improvement in Alzheimer disease severity scores.

Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood: a longitudinal MRI analysis

The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region-dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.

Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment

White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.

Dual feature correlation guided multi-task learning for Alzheimer's disease prediction

Alzheimer's disease (AD) is a gradually progressive neurodegenerative disease affecting cognition functions. Predicting the cognitive scores from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of AD. Despite machine learning algorithms having many successful applications, the prediction model suffers from the so-called curse of dimensionality. Multi-task feature learning (MTFL) has helped tackle this problem incorporating the correlations among multiple clinical cognitive scores. However, MTFL neglects the inherent correlation among brain imaging measures. In order to better predict the cognitive scores and identify stable biomarkers, we first propose a generalized multi-task formulation framework that incorporates the task and feature correlation structures simultaneously. Second, we present a novel feature-aware sparsity-inducing norm (FAS-norm) penalty to incorporate a useful correlation between brain regions by exploiting correlations among features. Three multi-task learning models that incorporate the FAS-norm penalty are proposed following our framework. Finally, the algorithm based on the alternating direction method of multipliers (ADMM) is developed to optimize the non-smooth problems. We comprehensively evaluate the proposed models on the cross-sectional and longitudinal Alzheimer's disease neuroimaging initiative datasets. The inputs are the thickness measures and the volume measures of the cortical regions of interest. Compared with MTFL, our methods achieve an average decrease of 4.28% in overall error in the cross-sectional analysis and an average decrease of 7.97% in the Alzheimer's Disease Assessment Scale cognitive total score longitudinal analysis. Moreover, our methods identify sensitive and stable biomarkers to physicians, such as the hippocampus, lateral ventricle, and corpus callosum.

Analysis of Age-Related White Matter Microstructures Based on Diffusion Tensor Imaging

Population aging has become a serious social problem. Accordingly, many researches are focusing on changes in brains of the elderly. In this study, we used multiple parameters to analyze age-related changes in white matter fibers. A sample cohort of 58 individuals was divided into young and middle-age groups and tract-based spatial statistics (TBSS) were used to analyze the differences in fractional anisotropy (FA), mean diffusion (MD), axial diffusion (AD), and radial diffusion (RD) between the two groups. Deterministic fiber tracking was used to investigate the correlation between fiber number and fiber length with age. The TBSS analysis revealed significant differences in FA, MD, AD, and RD in multiple white matter fibers between the two groups. In the middle-age group FA and AD were lower than in young people, whereas the MD and RD values were higher. Deterministic fiber tracking showed that the fiber length of some fibers correlated positively with age. These fibers were observed in the splenium of corpus callosum (SCC), the posterior limb of internal capsule (PLIC), the right posterior corona radiata (PCR_R), the anterior corona radiata (ACR), the left posterior thalamic radiation (include optic radiation; PTR_L), and the left superior longitudinal fasciculus (SLF_L), among others. The results showed that the SCC, PLIC, PCR_R, ACR, PTR_L, and SLF_L significantly differed between young and middle-age people. Therefore, we believe that these fibers could be used as image markers of age-related white matter changes.

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer's disease

Magnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer’s disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer’s disease.

Shape Profile of Corpus Callosum As a Signature to Phenotype Different Dementia

Background  Phenotyping dementia is always a complex task for a clinician. There is a need for more practical biomarkers to aid clinicians. Objective  The aim of the study is to investigate the shape profile of corpus callosum (CC) in different phenotypes of dementia. Materials and Methods  Our study included patients who underwent neuroimaging in our facility as a part of clinical evaluation for dementia referred from Geriatric Clinic (2017-2018). We have analyzed the shape of CC and interpreted the finding using a seven-segment division. Results  The sample included MPRAGE images of Alzheimer' dementia (AD) ( n = 24), posterior cortical atrophy- Alzheimer' dementia (PCA-AD) ( n = 7), behavioral variant of frontotemporal dementia (Bv-FTD) ( n = 17), semantic variant frontotemporal dementia (Sv-FTD) ( n = 11), progressive nonfluent aphasia (PNFA) ( n = 4), Parkinson's disease dementia (PDD) ( n = 5), diffuse Lewy body dementia ( n = 7), progressive supranuclear palsy (PSP) ( n = 3), and corticobasal degeneration (CBD) ( n = 3). We found in posterior dementias such as AD and PCA-AD that there was predominant atrophy of splenium of CC. In Bv-FTD, the genu and anterior half of the body of CC was atrophied, whereas in PNFA, PSP, PDD, and CBD there was atrophy of the body of CC giving a dumbbell like profile. Conclusion  Our study findings were in agreement with the anatomical cortical regions involved in different phenotypes of dementia. Our preliminary study highlighted potential usefulness of CC in the clinical setting for phenotyping dementia in addition to clinical history and robust biomarkers.

Genetic Association Between Alzheimer's Disease Risk Variant of the PICALM Gene and EEG Functional Connectivity in Non-demented Adults

Genome wide association studies (GWAS) have identified and validated the association of the PICALM genotype with Alzheimer's disease (AD). The PICALM rs3851179 A allele is thought to have a protective effect, whereas the G allele appears to confer risk for AD. The influence of the PICALM genotype on brain functional connectivity in non-demented subjects remains largely unknown. We examined the association of the PICALM rs3851179 genotype with the characteristics of lagged linear connectivity (LLC) of resting EEG sources in 104 non-demented adults younger than 60 years of age. The EEG analysis was performed using exact low-resolution brain electromagnetic tomography (eLORETA) freeware (Pascual-Marqui et al., 2011). We found that the carriers of the A PICALM allele (PICALM AA and AG genotypes) had higher widespread interhemispheric LLC of alpha sources compared to the carriers of the GG PICALM allele. An exploratory correlation analysis showed a moderate positive association between the alpha LLC interhemispheric characteristics and the corpus callosum size and between the alpha interhemispheric LLC characteristics and the Luria word memory scores. These results suggest that the PICALM rs3851179 A allele provides protection against cognitive decline by facilitating neurophysiological reserve capacities in non-demented adults. In contrast, lower functional connectivity in carriers of the AD risk variant, PICALM GG, suggests early functional alterations in alpha rhythm networks.

Classification of Alzheimer's Disease in MRI based on Dictionary Learning and Heavy Tailed Modelling

Diagnosis of brain diseases is considered one of the most challenging medical tasks to perform, even for medical experts who rely on high-resolution anatomical images to identify signs of abnormalities by visual inspection. However, new computational tools which assist to automate this diagnosis have the potential to significantly improve the speed and accuracy of this process. This work presents a model to aid in the task of classification of structural Magnetic Resonance Imaging scans. The classification is performed using a Support Vector Machine, whilst the features to analyze belong to a dictionary space. Such space was mainly built from a dictionary learning perspective, although a predefined one was also assessed. The results indicate that features learnt from the data of interest lead to improved classification performance. The proposed framework was tested on the ADNI dataset stage I.

Unsupervised Method Based on Superpixel Segmentation for Corpus Callosum Parcellation in MRI Scans

In this paper, we introduce an unsupervised method for the parcellation of the Corpus Callosum (CC) from MRI images. Since there are no visible landmarks within the structure that explicit its parcels, non-geometric CC parcellation is a challenging task especially that almost of proposed methods are geometric or data-based. In fact, in order to subdivide the CC from brain sagittal MRI scans, we adopt the probabilistic neural network as a clustering technique. Then, we use a cluster validity measure based on the maximum entropy (Vmep) to obtain the optimal number of classes. After that, we obtain the isolated CC that we parcel automatically using SLIC (Simple Linear Iterative Clustering) as superpixel segmentation technique. The obtained results on two challenging public datasets prove the performance of the proposed method against geometric methods from the state of the art. Indeed, as best as we know, it is the first work that investigates the validation of a CC parcellation method on ground-truth datasets using many objective metrics.

Non-fasting High-Density Lipoprotein Is Associated With White Matter Microstructure in Healthy Older Adults

A growing body of evidence indicates that biomarkers of cardiovascular risk may be related to cerebral health. However, little is known about the role that non-fasting lipoproteins play in assessing age-related declines in a cerebral biomarker sensitive to vascular compromise, white matter (WM) microstructure. High-density lipoprotein cholesterol (HDL-C) is atheroprotective and low-density lipoprotein cholesterol (LDL-C) is a major atherogenic lipoprotein. This study explored the relationships between non-fasting levels of cholesterol and WM microstructure in healthy older adults. A voxelwise and region of interest approach was used to determine the relationship between cholesterol and fractional anisotropy (FA). Participants included 87 older adults between the ages of 59 and 77 (mean age = 65.5 years, SD = 3.9). Results indicated that higher HDL-C was associated with higher FA in diffuse regions of the brain when controlling for age, sex, and body mass index (BMI). HDL-C was also positively associated with FA in the corpus callosum and fornix. No relationship was observed between LDL-C and FA. Findings suggest that a modifiable lifestyle variable associated with cardiovascular health may help to preserve cerebral WM.

Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions

In studying structural inter-connections in the human brain, it is common to first estimate fiber bundles connecting different regions relying on diffusion MRI. These fiber bundles act as highways for neural activity. Current statistical methods reduce the rich information into an adjacency matrix, with the elements containing a count of fibers or a mean diffusion feature along the fibers. The goal of this article is to avoid discarding the rich geometric information of fibers, developing flexible models for characterizing the population distribution of fibers between brain regions of interest within and across different individuals. We start by decomposing each fiber into a rotation matrix, shape and translation from a global reference curve. These components are viewed as data lying on a product space composed of different Euclidean spaces and manifolds. To nonparametrically model the distribution within and across individuals, we rely on a hierarchical mixture of product kernels specific to the component spaces. Taking a Bayesian approach to inference, we develop efficient methods for posterior sampling. The approach automatically produces clusters of fibers within and across individuals. Applying the method to Human Connectome Project data, we find interesting relationships between brain fiber geometry and reading ability. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers

Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.

Correlation of nerve fibers in corpus callosum and number of neurons in cerebral cortex: an innovative mathematical model

Purpose/aim: It is estimated that 10⁹ bits/s information are processed in the human brain. The transmission of this huge amount of information requires all connections in the brain to be highly accurate and have order. The current study attempted to present a new aspect of order and proportion in the ultra-structure of the human brain and to calculate the degree of neural interdependence between the two hemispheres.

MATERIALS AND METHODS

In this model, intensity of interdependence of the brain to hemispheres is estimated to be equal to the mathematical proportion of number of neurons in cerebral cortex divided by 2 (number of hemispheres), divided by number of nerve fibers in the human corpus callosum.

RESULTS

The calculated number is equal to 30-50 and it indicates that for every 30-50 neurons between the two hemispheres, there is a neural interconnecting bridge.

CONCLUSIONS

This connection indicates that the brain's function output follows a mathematical relation.

Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose.

OBJECTIVE

To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm.

METHODS

We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD.

RESULTS

The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI.

CONCLUSION

The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.

The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis

Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.

A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment

RATIONALE AND OBJECTIVES

Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD.

MATERIALS AND METHODS

Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change.

RESULTS

The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%).

CONCLUSION

Short-term longitudinal assessments significantly enhance the performance of AD prediction models.

Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease

In this article, we present a unified statistical pipeline for analyzing the white matter (WM) tracts morphometry and microstructural integrity, both globally and locally within the same WM tract, from diffusion tensor imaging. Morphometry is quantified globally by the volumetric measurement and locally by the vertexwise surface areas. Meanwhile, microstructural integrity is quantified globally by the mean fractional anisotropy (FA) and trace values within the specific WM tract and locally by the FA and trace values defined at each vertex of its bounding surface. The proposed pipeline consists of four steps: (1) fully automated segmentation of WM tracts in a multi-contrast multi-atlas framework; (2) generation of the smooth surface representations for the WM tracts of interest; (3) common template surface generation on which the localized morphometric and microstructural statistics are defined and a variety of statistical analyses can be conducted; (4) multiple comparison correction to determine the significance of the statistical analysis results. Detailed herein, this pipeline has been applied to the corpus callosum in Alzheimer's disease (AD) with significantly decreased FA values and increased trace values, both globally and locally, being detected in patients with AD when compared to normal aging populations. A subdivision of the corpus callosum in both hemispheres revealed that the AD pathology primarily affects the body and splenium of the corpus callosum. Validation analyses and two multiple comparison correction strategies are provided. Hum Brain Mapp 38:1875-1893, 2017. © 2017 Wiley Periodicals, Inc.

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

Alzheimer's disease severity, objectively determined and measured

Introduction

With expansion of clinical trials to individuals across the spectrum of Alzheimer disease (AD) from preclinical to symptomatic phases, it is increasingly important to quantify AD severity using methods that capture underlying pathophysiology.

Methods

We derived an AD severity measure based on biomarkers from brain imaging, neuropathology, and cognitive testing using latent variable modeling. We used data from ADNI-1 (N = 822) and applied findings to BIOCARD study (N = 349). We evaluated criterion validity for distinguishing diagnostic groups and construct validity by evaluating rates of change in AD severity.

Results

The AD severity factor cross-sectionally distinguishes cognitively normal participants from MCI (AUC = 0.87) and AD dementia (AUC = 0.94). Among ADNI MCI subjects, worsening scores predict faster progression to AD dementia (HR = 1.17; 95% CI, 1.13–1.22). In ADNI and BIOCARD, the pace of change in AD severity is steepest among progressors, with persisting differences by baseline diagnosis.

Discussion

Our content-valid latent variable measurement model is a reasonable approach for grading AD severity across a broad spectrum beginning at preclinical stages of AD.

Thalamic Shape and Cognitive Performance in Amnestic Mild Cognitive Impairment

OBJECTIVE

This study aimed to investigate thalamic shape alterations and their relationships with various episodic memory impairments in subjects with amnestic mild cognitive impairment (aMCI).

METHODS

We compared volumes and morphological alterations of the thalamus between aMCI subjects and healthy controls. In addition, we investigated the correlation between thalamic deformations and various memory impairments in aMCI subjects using a comprehensive neuropsychological battery.

RESULTS

The normalized left thalamic volumes of the aMCI group were significantly smaller than those of the healthy control group (p<0.0001). aMCI subjects exhibited significant thalamic deformations in the left thalamic dorso-medial and antero-medial areas compared with healthy individuals. CERAD-K Word List Memory scores were significantly correlated with the left dorso-medial areas in aMCI subjects. There were no significant correlations between verbal fluency, Boston naming test, constructional praxis, Word List Recognition, and Visuospatial Recall scores and thalamic shape in aMCI subjects. Verbal delayed recall scores were also significantly correlated with the left dorso-medial areas in the aMCI group.

CONCLUSION

Structural alterations in the thalamic deformations in the left dorso-medial and antero-medial areas might be core underlying neurobiological mechanisms of thalamic dysfunction related to Word List Memory and delayed verbal recall in individuals with aMCI.

A Novel Form of Compensation in the Tg2576 Amyloid Mouse Model of Alzheimer's Disease

One century after its first description, pathology of Alzheimer’s disease (AD) is still poorly understood. Amyloid-related dendritic atrophy and membrane alterations of susceptible brain neurons in AD, and in animal models of AD are widely recognized. However, little effort has been made to study the potential effects of combined morphological and membrane alterations on signal transfer and synaptic integration in neurons that build up affected neural networks in AD. In this study spatial reconstructions and electrophysiological measurements of layer II/III pyramidal neurons of the somatosensory cortex from wild-type (WT) and transgenic (TG) human amyloid precursor protein (hAPP) overexpressing Tg2576 mice were used to build faithful segmental cable models of these neurons. Local synaptic activities were simulated in various points of the dendritic arbors and properties of subthreshold dendritic impulse propagation and predictors of synaptic input pattern recognition ability were quantified and compared in modeled WT and TG neurons. Despite the widespread dendritic degeneration and membrane alterations in mutant mouse neurons, surprisingly little, or no change was detected in steady-state and 50 Hz sinusoidal voltage transfers, current transfers, and local and propagation delays of PSPs traveling along dendrites of TG neurons. Synaptic input pattern recognition ability was also predicted to be unaltered in TG neurons in two different soma-dendritic membrane models investigated. Our simulations predict the way how subthreshold dendritic signaling and pattern recognition are preserved in TG neurons: amyloid-related membrane alterations compensate for the pathological effects that dendritic atrophy has on subthreshold dendritic signal transfer and integration in layer II/III somatosensory neurons of this hAPP mouse model for AD. Since neither propagation of single PSPs nor integration of multiple PSPs (pattern recognition) changes in TG neurons, we conclude that AD-related neuronal hyperexcitability cannot be accounted for by altered subthreshold dendritic signaling in these neurons but hyperexcitability is related to changes in active membrane properties and network connectivity.

Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy

Introduction

We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Methods

Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (±SD) follow-up of 5.46 (±1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (±1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes.

Results

We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males.

Discussion

Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.

Corpus callosum atrophy rate in mild cognitive impairment and prodromal Alzheimer's disease

BACKGROUND

Corpus callosum (CC) size and shape have been previously studied in Alzheimer's disease (AD) with the majority of studies having been cross-sectional. Due to the large variance in normal CC morphology, cross-sectional studies are limited in statistical power. Determining individual rates of change requires longitudinal data. Physiological changes are particularly relevant in mild cognitive impairment (MCI), in which CC morphology has not been previously studied longitudinally.

OBJECTIVE

To study temporal rates of change in CC morphology in MCI patients over a one-year period, and to determine whether these rates differ between MCI subjects who converted to AD (MCI-C) and those who did not (MCI-NC) over an average (±SD) observation period of 5.4 (±1.6) years.

METHODS

We used a novel multi-atlas based algorithm to segment the mid-sagittal cross-sectional area of the CC in longitudinal MRI scans. Rates of change of CC circularity, total area, and five sub-areas were compared between 57 MCI-NC and 81 MCI-C subjects.

RESULTS

The CC became less circular (-0.89% per year in MCI-NC, -1.85% per year in MCI-C) with time, with faster decline in MCI-C (p = 0.0002). In females, atrophy rates were higher in MCI-C relative to MCI-NC in total CC area (p = 0.0006), genu/rostrum (p = 0.005), and splenium (0.002). In males, these rates did not differ between groups.

CONCLUSION

A greater than normal decline in CC circularity was shown to be an indicator of prodromal AD in MCI subjects. This measure is potentially useful as an imaging biomarker of disease and a therapeutic target in clinical trials.

Corpus callosum area in patients with bipolar disorder with and without psychotic features: an international multicentre study

BACKGROUND

Previous studies have reported MRI abnormalities of the corpus callosum (CC) in patients with bipolar disorder (BD), although only a few studies have directly compared callosal areas in psychotic versus nonpsychotic patients with this disorder. We sought to compare regional callosal areas in a large international multicentre sample of patients with BD and healthy controls.

METHODS

We analyzed anatomic T1 MRI data of patients with BD-I and healthy controls recruited from 4 sites (France, Germany, Ireland and the United States). We obtained the mid-sagittal areas of 7 CC subregions using an automatic CC delineation. Differences in regional callosal areas between patients and controls were compared using linear mixed models (adjusting for age, sex, handedness, brain volume, history of alcohol abuse/dependence, lithium or antipsychotic medication status, symptomatic status and site) and multiple comparisons correction. We also compared regional areas of the CC between patients with BD with and without a history of psychotic features.

RESULTS

We included 172 patients and 146 controls in our study. Patients with BD had smaller adjusted mid-sagittal CC areas than controls along the posterior body, the isthmus and the splenium of the CC. Patients with a positive history of psychotic features had greater adjusted area of the rostral CC region than those without a history of psychotic features.

LIMITATIONS

We found small to medium effect sizes, and there was no calibration technique among the sites.

CONCLUSION

Our results suggest that BD with psychosis is associated with a different pattern of interhemispheric connectivity than BD without psychosis and could be considered a relevant neuroimaging subtype of BD.