Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Discriminative patterns of white matter changes in Alzheimer's

Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres.  The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.

Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20-33, n = 51 and age 60-79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.

Connection between microstructural alterations detected by diffusion MRI and cognitive dysfunction in MS: A model-free analysis approach

BACKGROUND

Cognitive decline is a prominent symptom of MS. Clear connection between cognitive status and white matter microstructural changes has not been unequivocally observed to date.

OBJECTIVE

To characterise the relationship between white matter microstructure and cognitive performance a partial least squares (PLS) approach was used.

METHODS

53 RR MS patients' T1 and DTI images and BICAMS subtests were used in our analysis. Standard FSL pipeline was used to obtain diffusion parameters. A PLS approach was applied to reveal the diffusion parameter patterns responsible for the cognitive dysfunction.

RESULTS

The first latent variable (LV) was mainly associated with demyelination, while the second and third explained axonal damage. While the first two LV represented mainly Brief Visuospatial Memory Test (BVMT) and Single Digit Modality Test (SDMT), the third LV depicted diffusion alterations mainly the verbal subtest. The first LVs spatial map showed demyelination in the corpus callosum. The second LVs spatial map showed the diffusion alterations in the thalamus. The third LV depicted diffusion alterations in the putative left superior longitudinal fascicle.

CONCLUSION

Visual memory demanding tasks versus language functions depend on distinct patterns of diffusion parameters and the spatial organisation. Axial diffusivity alterations, a putative marker of irreversible axonal loss explained around 20% of variability in the cognitive functions.

Alzheimer-related altered white matter microstructural integrity in Down syndrome: A model for sporadic AD?

INTRODUCTION

Virtually all adults with Down syndrome (DS) develop Alzheimer's disease (AD)-associated neuropathology by the age of 40, with risk for dementia increasing from the early 50s. White matter (WM) pathology has been reported in sporadic AD, including early demyelination, microglial activation, loss of oligodendrocytes and reactive astrocytes but has not been extensively studied in the at-risk DS population.

METHODS

Fifty-six adults with DS (35 cognitively stable adults, 11 with mild cognitive impairment, 10 with dementia) underwent diffusion-weighted magnetic resonance imaging (MRI), amyloid imaging, and had assessments of cognition and functional abilities using tasks appropriate for persons with intellectual disability.

RESULTS

Early changes in late-myelinating and relative sparing of early-myelinating pathways, consistent with the retrogenesis model proposed for sporadic AD, were associated with AD-related cognitive deficits and with regional amyloid deposition.

DISCUSSION

Our findings suggest that quantification of WM changes in DS could provide a promising and clinically relevant biomarker for AD clinical onset and progression.

Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.

White Matter Microstructural Damage as an Early Sign of Subjective Cognitive Decline

Background and Objective: Subjective cognitive decline (SCD) is considered a preclinical state of Alzheimer's disease (AD) and may represent a more advanced preclinical status than amnestic mild cognitive impairment (aMCI). Our aim was to explore changes in the white matter (WM) microstructure and their correlation with cognitive function in these AD-spectrum patients. Methods: Diffusion tensor images from 43 individuals with normal cognition (NC), 38 SCD patients, and 36 aMCI patients were compared using an atlas-based segmentation strategy. The correlation between diffusion parameters and cognitive function was further analyzed. Results: The anatomical pattern of WM impairment was generally similar between SCD and aMCI patients. However, aMCI patients showed significantly lower fractional anisotropy (i.e., corpus callosum forceps major and forceps minor) and increased mean diffusivity [i.e., bilateral anterior thalamic radiation (ATR), left corticospinal tract (CST), forceps minor, left cingulum (cingulate gyrus), left cingulum hippocampus, and left inferior fronto-occipital fasciculus (IFO)] in some tracts than did SCD subjects, indicating a disruption in WM microstructural integrity in the aMCI. Individuals with microstructural disruption in forceps minor, left cingulum (cingulate gyrus), and left cingulum hippocampus tracts performed worse in general cognition and memory function tests, as indicated by line regression analysis. Conclusion: SCD individuals had extensive WM microstructural damage in a pattern similar to that seen in aMCI, although presenting a cognitive performance comparable with that of cognitively healthy individuals. Our results suggest that WM integrity might precede objectively measurable memory decline and may be a potential early biomarker for AD.

No relationship between fornix and cingulum degradation and within-network decreases in functional connectivity in prodromal Alzheimer's disease

INTRODUCTION

The earliest changes in the brain due to Alzheimer's disease are associated with the neural networks related to memory function. We investigated changes in functional and structural connectivity among regions that support memory function in prodromal Alzheimer's disease, i.e., during the mild cognitive impairment (MCI) stage.

METHODS

Twenty-three older healthy controls and 25 adults with MCI underwent multimodal MRI scanning. Limbic white matter tracts-the fornix, parahippocampal cingulum, retrosplenial cingulum, subgenual cingulum and uncinate fasciculus-were reconstructed in ExploreDTI using constrained spherical deconvolution-based tractography. Using a network-of-interest approach, resting-state functional connectivity time-series correlations among sub-parcellations of the default mode and limbic networks, the hippocampus and the thalamus were calculated in Conn.

ANALYSIS

Controlling for age, education, and gender between group linear regressions of five diffusion-weighted measures and of resting state connectivity measures were performed per hemisphere. FDR-corrections were performed within each class of measures. Correlations of within-network Fisher Z-transformed correlation coefficients and the mean diffusivity per tract were performed. Whole-brain graph theory measures of cluster coefficient and average path length were inspecting using the resting state data.

RESULTS & CONCLUSION

MCI-related changes in white matter structure were found in the fornix, left parahippocampal cingulum, left retrosplenial cingulum and left subgenual cingulum. Functional connectivity decreases were observed in the MCI group within the DMN-a sub-network, between the hippocampus and sub-areas -a and -c of the DMN, between DMN-c and DMN-a, and, in the right hemisphere only between DMN-c and both the thalamus and limbic-a. No relationships between white matter tract 'integrity' (mean diffusivity) and within sub-network functional connectivity were found. Graph theory revealed that changes in the MCI group was mostly restricted to diminished between-neighbour connections of the hippocampi and of nodes within DMN-a and DMN-b.

Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease: A Predictive Study

Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g.: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows us to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e., the capacity of predicting biomarker values) is assessed in a cross-validation framework.

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis

OBJECTIVE

Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) analysis is a promising non-linear regression model, characterized by its high flexibility and utility in clinical diagnosis. ANN simulates neuron learning procedures and it could provide good diagnostic performances in this complex and heterogeneous disease compared with linear regression analysis.

DESIGN AND METHODS

In our study, a new set of lipid peroxidation compounds were determined in urine and plasma samples from patients diagnosed with early Alzheimer Disease (n = 70) and healthy controls (n = 26) by means of ultra-performance liquid chromatography coupled with tandem mass-spectrometry. Then, a model based on ANN was developed to classify groups of participants.

RESULTS

The diagnostic performances obtained using an ANN model for each biological matrix were compared with the corresponding linear regression model based on partial least squares (PLS), and with the non-linear (radial and polynomial) support vector machine (SVM) models. Better accuracy, in terms of receiver operating characteristic-area under curve (ROC-AUC), was obtained for the ANN models (ROC-AUC 0.882 in plasma and 0.839 in urine) than for PLS and SVM models.

CONCLUSION

Lipid peroxidation and ANN constitute a useful approach to establish a reliable diagnosis when the prognosis is complex, multidimensional and non-linear.

A promising structural magnetic resonance imaging assessment in patients with preclinical cognitive decline and diabetes mellitus

Subjective cognitive decline (SCD) is frequently reported in diabetic patients. Diabetes mellitus (DM) is associated with changes in the microstructure of the brain arise in diabetic patients, including changes in gray matter volume (GMV). However, the underlying mechanisms of changes in GMV in DM patients with cognitive impairment remain uncertain. Here, we present an overview of amyloid-β-dependent cognitive impairment in DM patients with SCD. Moreover, we review the evolving insights from studies on the GMV changes in GMV and cognitive dysfunction to which provide the mechanisms of cognitive impairment in T2DM. Ultimately, the novel structural magnetic resonance imaging (MRI) protocol was used for detecting neuroimaging biomarkers that can predict the clinical outcomes in diabetic patients with SCD. A reliable MRI protocol would be helpful to detect neurobiomarkers, and to understand the pathological mechanisms of preclinical cognitive impairment in diabetic patients.

Profiling heterogeneity of Alzheimer's disease using white-matter impairment factors

The clinical presentation of Alzheimer's disease (AD) is not unitary as heterogeneity exists in the disease's clinical and anatomical characteristics. MRI studies have revealed that heterogeneous gray matter atrophy patterns are associated with specific traits of cognitive decline. Although white matter (WM) impairment also contributes to AD pathology, its heterogeneity remains unclear. The Latent Dirichlet Allocation (LDA) method is a suitable framework to study heterogeneity and allows to identify latent impairment factors of AD instead of simply mapping an overall disease effect. By exploring whole brain WM skeleton images by using LDA, three latent factors were revealed in AD: a temporal-frontal impairment factor (temporal and frontal lobes, especially hippocampus and para-hippocampus), a parietal factor (parietal lobe, especially precuneus), and a long fibre bundle factor (corpus callosum and superior longitudinal fasciculus). As revealed by longitudinal analysis, the latent factors have distinct impact on cognitive decline: for executive function (EF), the temporal-frontal factor was more strongly associated with baseline EF compared with the parietal factor, while the long-fibre bundle factor was most associated with decline rate of EF; for memory, the three factors showed almost equal effect on the baseline memory and decline rate. For each participant, LDA estimates his/her composition profile of latent impairment factors, which indicates disease subtype. We also found that the APOE genotype affects the AD subtype. Specifically, APOE ε4 was more associated with the long fibre bundle factor and APOE ε2 was more associated with temporal-frontal factor. By investigating heterogeneity and subtypes of AD through white matter impairment factors, our study could facilitate precision medicine.

•

LDA revealed three latent white matter impairment factors in Alzheimer’s disease.

•

Latent factors associate with executive function and memory decline differently.

•

Individual factor composition indicates disease subtype.

•

The APOE genotype is associated with the factor composition.

Patterns of white matter microstructure in individuals at ultra-high-risk for psychosis: associations to level of functioning and clinical symptoms

BACKGROUND

Individuals at ultra-high-risk (UHR) for psychosis present with emerging symptoms and decline in functioning. Previous univariate analyses have indicated widespread white matter (WM) aberrations in multiple brain regions in UHR individuals and patients with schizophrenia. Using multivariate statistics, we investigated whole brain WM microstructure and associations between WM, clinical symptoms, and level of functioning in UHR individuals.

METHODS

Forty-five UHR individuals and 45 matched healthy controls (HCs) underwent magnetic resonance diffusion tensor imaging (DTI) at 3 Tesla. UHR individuals were assessed with the Comprehensive Assessment of At-Risk Mental States, Scale for the Assessment of Negative Symptoms, and Social and Occupational Functioning Assessment Scale. Partial least-squares correlation analysis (PLSC) was used as statistical method.

RESULTS

PLSC group comparisons revealed one significant latent variable (LV) accounting for 52% of the cross-block covariance. This LV indicated a pattern of lower fractional anisotropy (FA), axial diffusivity (AD), and mode of anisotropy (MO) concomitant with higher radial diffusivity (RD) in widespread brain regions in UHR individuals compared with HCs. Within UHR individuals, PLSC revealed five significant LVs associated with symptoms and level of functioning. The first LV accounted for 31% of the cross-block covariance and indicated a pattern where higher symptom score and lower level of functioning correlated to lower FA, AD, MO, and higher RD.

CONCLUSIONS

UHR individuals demonstrate complex brain patterns of WM abnormalities. Despite the subtle psychopathology of UHR individuals, aberrations in WM appear associated with positive and negative symptoms as well as level of functioning.

White-matter integrity on DTI and the pathologic staging of Alzheimer's disease

Pattern of diffusion tensor MRI (DTI) alterations were investigated in pathologically-staged Alzheimer's disease (AD) patients (n = 46). Patients with antemortem DTI studies and a range of AD pathology at autopsy were included. Patients with a high neurofibrillary tangle (NFT) stage (Braak IV-VI) had significantly elevated mean diffusivity (MD) in the crus of fornix and ventral cingulum tracts, precuneus, and entorhinal white matter on voxel-based analysis after adjusting for age and time from MRI to death (p < 0.001). Higher MD and lower fractional anisotropy in the ventral cingulum tract, entorhinal, and precuneus white matter was associated with higher Braak NFT stage and clinical disease severity. There were no MD and fractional anisotropy differences among the low (none and sparse) and high (moderate and frequent) β-amyloid neuritic plaque groups. The NFT pathology of AD is associated with DTI alterations involving the medial temporal limbic connections and medial parietal white matter. This pattern of diffusion abnormalities is also associated with clinical disease severity.