Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods

The maternal brain: Region-specific patterns of brain aging are traceable decades after childbirth

Pregnancy involves maternal brain adaptations, but little is known about how parity influences women's brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle- and older-aged women. Using novel applications of brain-age prediction methods, we found that a higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens-a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

INTRODUCTION

Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

METHODS

Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

RESULTS

WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.

DISCUSSION

A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Analysis of MRI Data in Diagnostic Neuroradiology

Magnetic resonance imaging (MRI) is a noninvasive imaging tool for neuroradiological diagnosis. Numerous concepts of automated MRI analysis and the use of machine learning have been proposed to assist diagnosis and prognosis. While these academic innovations have proven effective in principle within controlled environments, their application to clinical practice has faced unmet requirements, such as the ability to perform reliably across a heterogeneous population, to work robustly in the presence of comorbidities, and to be invariant to scanner hardware and image quality. The lack of realistic confidence bounds and the inability to handle missing data have also reduced the application of most of these methods outside of academic studies. Mastering the complex challenges in the diagnostic process may help researchers discover novel biological constructs in multimodal data and improve stratification for clinical trials, paving the way for precision medicine. This review presents the state of the art of computerized brain MRI analysis for diagnostic purposes. We critically evaluate the current clinical usefulness of the methods and highlight challenges and future perspectives of the field.

Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

Reserve in Alzheimer's disease: update on the concept, functional mechanisms and sex differences

PURPOSE OF REVIEW

Epidemiological evidence suggests that higher reserve significantly delays the dementia onset in Alzheimer's disease. Yet, confusion in terminology of reserve and related concepts exists and the lack of quantitative measures and unclear neural substrates of reserve have hampered progress. We review here the latest advances in the concept, measures and functional brain mechanisms of reserve, as well as their moderating factors including sex and gender.

RECENT FINDINGS

The definition of reserve has been revised towards a more simplified concept, and the development of quantitative measurements of a cognitive advantage in disease has been advanced. Functional MRI and FDG-PET studies have provided for the first time converging evidence for the involvement of the cognitive control and salience network and temporal pole in reserve. Women tend to show lower resilience than men at advanced stages of AD.

SUMMARY

Neuroimaging studies have provided substantial evidence for putative brain mechanisms supporting reserve in Alzheimer's disease. However, the findings are still somewhat disparate and call for the development of unifying and testable theory of functional and structural brain properties that subserve reserve. Sex differences emerged as a moderating factor of reserve in Alzheimer's disease and need to be made a major research focus in Alzheimer's disease.

Amide proton transfer-weighted magnetic resonance imaging of human brain aging at 3 Tesla

Background

Amide proton transfer-weighted (APTw) imaging has been revealed to hold great potential in the diagnosis of several brain diseases. The purpose of this proof-of-concept study was to evaluate the feasibility and value of APTw magnetic resonance imaging (MRI) in characterizing normal brain aging.

Methods

A total of 106 healthy subjects were recruited and scanned at 3.0 Tesla, with APTw and conventional magnetization transfer (MT) sequences. Quantitative image analyses were performed in 12 regions of interest (ROIs) for each subject. The APTw or MT ratio (MTR) signal differences among five age groups (young, mature, middle-aged, young-old, and middle-old) were assessed using the one-way analysis of variance, with the Benjamini-Hochberg correction for multiple comparisons. The relationship between APTw and MTR signals and the age dependencies of APTw and MTR signals were assessed using the Pearson correlation and non-linear regression.

Results

There were no significant differences between the APTw or MTR values for males and females in any of the 12 ROIs analyzed. Among the five age groups, there were significant differences in the three white matter regions in the temporal, occipital, and frontal lobes. Overall, the mean APTw values in the older group were higher than those in the younger group. Positive correlations were observed in relation to age in most brain regions, including four with significant positive correlations (r=0.2065-0.4182) and five with increasing trends. As a comparison, the mean MTR values did not appear to be significantly different among the five age groups. In addition, the mean APTw and MTR values revealed significant positive correlations in 10 ROIs (r=0.2214-0.7269) and a significant negative correlation in one ROI (entorhinal cortex, r=-0.2141).

Conclusions

Our early results show that the APTw signal can be used as a promising and complementary imaging biomarker with which normal brain aging can be evaluated at the molecular level.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Precision diagnostics based on machine learning-derived imaging signatures

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.