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

Microstructural white matter injury contributes to cognitive decline: Besides amyloid and tau

BACKGROUND

Cognitive decline and the progression to Alzheimer's disease (AD) are traditionally associated with amyloid-beta (Aβ) and tau pathologies. This study aims to evaluate the relationships between microstructural white matter injury, cognitive decline and AD core biomarkers.

METHODS

We conducted a longitudinal study of 566 participants using peak width of skeletonized mean diffusivity (PSMD) to quantify microstructural white matter injury. The associations of PSMD with changes in cognitive functions, AD pathologies (Aβ, tau, and neurodegeneration), and volumes of AD-signature regions of interest (ROI) or hippocampus were estimated. The associations between PSMD and the incidences of clinical progression were also tested. Covariates included age, sex, education, apolipoprotein E4 status, smoking, and hypertension.

RESULTS

Higher PSMD was associated with greater cognitive decline (β=-0.012, P < 0.001 for Mini-Mental State Examination score; β<0, P < 0.05 for four cognitive domains) and a higher risk of clinical progression from normal cognition to mild cognitive impairment (MCI) or AD (Hazard ratio=2.11 [1.38-3.23], P < 0.001). These associations persisted independently of amyloid status. PSMD did not predict changes in Aβ or tau levels, but predicted changes in volumes of AD-signature ROI (β=-0.003, P < 0.001) or hippocampus (β=-0.002, P = 0.010). Besides, the whole-brain PSMD could predict cognitive decline better than regional PSMDs.

CONCLUSIONS

PSMD may be a valuable biomarker for predicting cognitive decline and clinical progression to MCI and AD, providing insights besides traditional Aβ and tau pathways. Further research could elucidate its role in clinical assessments and therapeutic strategies.

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis

BACKGROUND

To diagnose Alzheimer disease (AD), individuals are classified according to the severity of their cognitive impairment. There are currently no specific causes or conditions for this disease.

OBJECTIVE

The purpose of this systematic review and meta-analysis was to assess AD prevalence across different stages using machine learning (ML) approaches comprehensively.

METHODS

The selection of papers was conducted in 3 phases, as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines: identification, screening, and final inclusion. The final analysis included 24 papers that met the criteria. The selection of ML approaches for AD diagnosis was rigorously based on their relevance to the investigation. The prevalence of patients with AD at 2, 3, 4, and 6 stages was illustrated through the use of forest plots.

RESULTS

The prevalence rate for both cognitively normal (CN) and AD across 6 studies was 49.28% (95% CI 46.12%-52.45%; P=.32). The prevalence estimate for the 3 stages of cognitive impairment (CN, mild cognitive impairment, and AD) is 29.75% (95% CI 25.11%-34.84%, P<.001). Among 5 studies with 14,839 participants, the analysis of 4 stages (nondemented, moderately demented, mildly demented, and AD) found an overall prevalence of 13.13% (95% CI 3.75%-36.66%; P<.001). In addition, 4 studies involving 3819 participants estimated the prevalence of 6 stages (CN, significant memory concern, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, and AD), yielding a prevalence of 23.75% (95% CI 12.22%-41.12%; P<.001).

CONCLUSIONS

The significant heterogeneity observed across studies reveals that demographic and setting characteristics are responsible for the impact on AD prevalence estimates. This study shows how ML approaches can be used to describe AD prevalence across different stages, which provides valuable insights for future research.

Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis

The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Effects of acrobatic training on spatial memory and astrocytic scar in CA1 subfield of hippocampus after chronic cerebral hypoperfusion in male and female rats

Chronic cerebral hypoperfusion leads to neuronal loss in the hippocampus and spatial memory impairments. Physical exercise is known to prevent cognitive deficits in animal models; and there is evidence of sex differences in behavioral neuroprotective approaches. The aim of present study was to investigate the effects of acrobatic training in male and female rats submitted to chronic cerebral hypoperfusion. Males and females rats underwent 2VO (two-vessel occlusion) surgery and were randomly allocated into 4 groups of males and 4 groups of females, as follows: 2VO acrobatic, 2VO sedentary, Sham acrobatic and Sham sedentary. The acrobatic training started 45 days after surgery and lasted 4 weeks; animals were then submitted to object recognition and water maze testing. Brain samples were collected for histological and morphological assessment and flow cytometry. 2VO causes cognitive impairments and acrobatic training prevented spatial memory deficits assessed in the water maze, mainly for females. Morphological analysis showed that 2VO animals had less NeuN labeling and acrobatic training prevented it. Increased number of GFAP positive cells was observerd in females; moreover, males had more branched astrocytes and acrobatic training prevented the branching after 2VO. Flow cytometry showed higher mitochondrial potential in trained animals and more reactive oxygen species production in males. Acrobatic training promoted neuronal survival and improved mitochondrial function in both sexes, and influenced the glial scar in a sex-dependent manner, associated to greater cognitive benefit to females after chronic cerebral hypoperfusion.

Radiomics approach in the neurodegenerative brain

As claimed by Robert Gilles et al., "Images are more than pictures, they are data". This statement refers to the power of imaging to provide large amounts of quantitative features for improving diagnosis, prognosis and therapy response. The conversion of digital medical images into high-dimensional mineable data is called radiomics. Radiomics analysis is based on data-characterisation algorithms which have the potential to uncover disease heterogeneity characteristics that might escape from the expert evaluation. This method has been widely applied in oncology and genetic fields, while the literature on neurodegenerative disorders is in its relative infancy. Here, we provide a preliminary evaluation of the main results reached applying radiomics analyses on well-established MRI features of patients with Alzheimer's Disease and Parkinson's disease.

Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets

BACKGROUND

Early detection of Alzheimer's disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI.

OBJECTIVE

In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals.

METHODS

We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers.

RESULTS

We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement.

CONCLUSION

Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.

A Deformation-Based Shape Study of the Corpus Callosum in First Episode Schizophrenia

Background: Previous first-episode schizophrenia (FES) studies have reported abnormalities in the volume and mid-sagittal size of the corpus callosum (CC), but findings have been inconsistent. Besides, the CC shape has rarely been analyzed in FES. Therefore, in this study, we investigated FES-related CC shape abnormalities using 198 participants [92 FES patients and 106 healthy controls (HCs)]. Methods: We conducted statistical shape analysis of the mid-sagittal CC curve in a large deformation diffeomorphic metric mapping framework. The CC was divided into the genu, body, and splenium (gCC, bCC, and sCC) to target the key CC sub-regions affected by the FES pathology. Gender effects have been investigated. Results: There were significant area differences between FES and HC in the entire CC and gCC but not in bCC nor sCC. In terms of the localized shape morphometrics, significant region-specific shape inward-deformations were detected in the superior portion of gCC and the anterosuperior portion of bCC in FES. These global area and local shape morphometric abnormalities were restricted to female FES but not male FES. Conclusions: gCC was significantly affected in the neuropathology of FES and this finding was specific to female FES. This study suggests that gCC may be a key sub-region that is vulnerable to the neuropathology of FES, specifically in female patients. The morphometrics of gCC may serve as novel and efficient biomarkers for screening female FES patients.

Air Pollution-Related Brain Metal Dyshomeostasis as a Potential Risk Factor for Neurodevelopmental Disorders and Neurodegenerative Diseases

Increasing evidence links air pollution (AP) exposure to effects on the central nervous system structure and function. Particulate matter AP, especially the ultrafine (nanoparticle) components, can carry numerous metal and trace element contaminants that can reach the brain in utero and after birth. Excess brain exposure to either essential or non-essential elements can result in brain dyshomeostasis, which has been implicated in both neurodevelopmental disorders (NDDs; autism spectrum disorder, schizophrenia, and attention deficit hyperactivity disorder) and neurodegenerative diseases (NDGDs; Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis). This review summarizes the current understanding of the extent to which the inhalational or intranasal instillation of metals reproduces in vivo the shared features of NDDs and NDGDs, including enlarged lateral ventricles, alterations in myelination, glutamatergic dysfunction, neuronal cell death, inflammation, microglial activation, oxidative stress, mitochondrial dysfunction, altered social behaviors, cognitive dysfunction, and impulsivity. Although evidence is limited to date, neuronal cell death, oxidative stress, and mitochondrial dysfunction are reproduced by numerous metals. Understanding the specific contribution of metals/trace elements to this neurotoxicity can guide the development of more realistic animal exposure models of human AP exposure and consequently lead to a more meaningful approach to mechanistic studies, potential intervention strategies, and regulatory requirements.

Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease

The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.

Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that causes the decline of some cognitive impairments. The present study aimed to identify the corpus callosum (CC) radiomic features related to the diagnosis of AD and build and evaluate a classification model. Methods: Radiomics analysis was applied to the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images of 78 patients with AD and 44 healthy controls (HC). The CC, in each subject, was segmented manually and 385 features were obtained after calculation. Then, the feature selection were carried out. The logistic regression model was constructed and evaluated according to identified features. Thus, the model can be used for distinguishing the AD from HC subjects. Results: Eleven features were selected from the three-dimensional T1-weighted MPRAGE images using the LASSO model, following which, the logistic regression model was constructed. The area under the receiver operating characteristic curve values (AUC), sensitivity, specificity, accuracy, precision, and positive and negative predictive values were 0.720, 0.792, 0.500, 0.684, 0.731, 0.731, and 0.583, respectively. Conclusion: The results demonstrated the potential of CC texture features as a biomarker for the diagnosis of AD. This is the first study showing that the radiomics model based on machine learning was a valuable method for the diagnosis of AD.

Ventricular and Periventricular Anomalies in the Aging and Cognitively Impaired Brain

Ventriculomegaly (expansion of the brain’s fluid-filled ventricles), a condition commonly found in the aging brain, results in areas of gliosis where the ependymal cells are replaced with dense astrocytic patches. Loss of ependymal cells would compromise trans-ependymal bulk flow mechanisms required for clearance of proteins and metabolites from the brain parenchyma. However, little is known about the interplay between age-related ventricle expansion, the decline in ependymal integrity, altered periventricular fluid homeostasis, abnormal protein accumulation and cognitive impairment. In collaboration with the Baltimore Longitudinal Study of Aging (BLSA) and Alzheimer’s Disease Neuroimaging Initiative (ADNI), we analyzed longitudinal structural magnetic resonance imaging (MRI) and subject-matched fluid-attenuated inversion recovery (FLAIR) MRI and periventricular biospecimens to map spatiotemporally the progression of ventricle expansion and associated periventricular edema and loss of transependymal exchange functions in healthy aging individuals and those with varying degrees of cognitive impairment. We found that the trajectory of ventricle expansion and periventricular edema progression correlated with degree of cognitive impairment in both speed and severity, and confirmed that areas of expansion showed ventricle surface gliosis accompanied by edema and periventricular accumulation of protein aggregates, suggesting impaired clearance mechanisms in these regions. These findings reveal pathophysiological outcomes associated with normal brain aging and cognitive impairment, and indicate that a multifactorial analysis is best suited to predict and monitor cognitive decline.