Your competent? doctor should be measuring your brain age on a regular basis to prove that the protocols used actually recover your 5 lost years of brain cognition due to your stroke
The application of convolutional neural networks for brain age prediction: A systematic review
Introduction
Aging is a continuous and inexorable process that gradually reduces physiological functions and exerts negative effects on human functional capacity. The rapid aging of our global population is of great concern, with projections indicating that by 2050, approximately 21% of the world’s population will be over 60 years of age [1]. This significant demographic transition presents considerable challenges to healthcare systems, social care institutions, and the overall economy [2]. Of particular significance in the context of aging is the issue of cognitive decline. This multifaceted challenge encompasses adverse effects on various cognitive domains, including memory, attention, and executive function, consequently affecting the quality of life of older individuals [3]. A comprehensive understanding of the underlying mechanisms of cognitive aging is imperative in the pursuit of effective preventative strategies. Recent advancements in neuroimaging techniques have proven to be indispensable tools in the identification of both anatomical and functional changes that occur in the aging brain. Notably, structural changes represent a hallmark of normal aging and are often characterized by extensive gray matter atrophy [4]. However, these changes are not evenly distributed across the brain, with the frontal and temporal lobes exhibiting the most pronounced degrees of atrophy. Additionally, changes have also been observed in the parietal lobe, while the occipital lobe tends to maintain relative stability [5]. In conjunction with structural changes, aging is also closely associated with disruptions in the anatomical connections of white matter tracts. Specifically, the frontal white matter, anterior cingulum, and genu of the corpus callosum have been identified as regions particularly susceptible to aging processes [6]. Moreover, studies have identified decreased functional connectivity over time within the frontoparietal network and the salience network. These alterations lead to disruptions in the overall functionality of brain networks during the aging process [7].
The traditional measure of aging, chronological age (CA), has long served as a fundamental metric. However, contemporary research underscores the considerable variability in the rates and patterns of aging experienced by individuals. These variations are influenced by a complex interplay of factors, including genotypes, environment, and lifestyles. Consequently, individuals of the same CA can exhibit diverse physiological functions and abilities. For instance, a 60-year-old may exhibit signs of aging similar to those of a person who is 65 years old, depending on factors such as medical history, lifestyle choices, and environmental exposure. Notably, even in monozygotic twins with identical chronologies, the extent and rate of age-related changes can differ significantly [8], [9]. Consequently, relying solely on CA for aging assessment can be misleading. To address the limitations of CA as a comprehensive measure of aging, the concept of biological age (BA) has emerged as a valuable alternative. BA serves as a standardized metric that quantifies an individual’s physiological aging trajectory relative to the average age-matched individual [10]. This nuanced measure provides a more accurate estimate of an individual’s aging process compared to the conventional use of CA [11]. Research in this realm has investigated the potential predictive value of BA regarding age-related diseases, yielding remarkable findings [12], [13], [14]. Concurrently, brain age estimation has emerged as a promising instrument for assessing an individual’s brain age by scrutinizing the intricate dynamics between brain structure and function [10].
Machine learning (ML) has become increasingly popular in recent years as a powerful tool that enables computers to learn and improve from experience. The fusion of ML algorithms with neuroimaging has opened new opportunities for predicting brain age [15]. A burgeoning body of research has demonstrated the promise of ML in accurately predicting brain age using various neuroimaging modalities such as structural magnetic resonance imaging (MRI), functional MRI (fMRI), and diffusion tensor imaging (DTI). For example, an innovative study conducted by Franke et al. skillfully harnessed a combination of structural MRI and ML algorithms to accurately predict brain age [16]. Meanwhile, Baecker et al. and colleagues undertook a comparative analysis, scrutinizing the performance of three commonly employed ML methods in the prediction of brain age [17]. In parallel, Xiong et al. established a multimodal dataset, engaging six distinct ML methods, culminating in a definitive demonstration of the remarkable predictive prowess of MRI and DTI data. Their findings pointed to Lasso as the most precise ML algorithm for predicting brain age [18]. Within the expansive realm of ML, DL assumes a notable role, encompassing neural networks with multiple layers to learn progressively more abstract and hierarchical representations of input data. Specifically, CNNs, a subcategory of DL methods, have demonstrated exceptional performance in the analysis of medical imaging, including neuroimaging data. Neuroimaging datasets are renowned for their complex and substantial variable, making it challenging for traditional ML algorithms in feature extraction. However, CNNs exhibit a unique aptitude for addressing this challenge, as they are adept at learning intricate feature hierarchies that capture pivotal patterns inherent in raw input data. The proficiency of CNNs in mastering complex feature hierarchies bestows upon them a diverse range of valuable neuroimaging applications, including predicting brain age. When compared to traditional ML algorithms, CNNs consistently outperform due to their capability to generalize acquired representations to previously unseen data. This trait is particularly useful in the analysis of extensive volumes of imaging data.
In parallel, a series of comprehensive review articles have illuminated the field of automatic brain age prediction utilizing neuroimaging data. These reviews have predominantly explored the integration of ML methodologies and have substantially contributed to our understanding of neuroimaging-driven brain age estimation as a robust biomarker for a range of brain conditions. For instance, Mishra’s work in 2021 offers an extensive examination of neuroimaging-driven brain age estimation, meticulously categorizing frameworks based on image modality and clinical applications. This review effectively underscores the importance of brain age estimation in the context of disease detection and serves as a catalyst for future research directions [10]. Baecker and colleagues, in their 2021 work, have concentrated on the crucial role of ML in predicting brain age from neuroimaging data. Their work introduces the concept of the “brain-age gap” as a potential marker for brain health, demonstrating its utility in early detection and personalized interventions for age-related disorders [19]. Sajedi et al. have contributed a comprehensive overview of age prediction using brain MRIs. This review encompasses a spectrum of aspects, including preprocessing techniques, tools, and estimation methods. Importantly, it categorizes these approaches based on image processing and ML, emphasizing the remarkable efficacy of DL techniques in enhancing predictive accuracy. The historical landscape of literature reviews in this field has predominantly centered on ML, with relatively limited exploration into the realm of DL [20]. The exhaustive review conducted by Tanveer and his colleagues has marked a significant departure from prior works in the field of brain age prediction spanning from 2017 to March 2022. Their comprehensive assessment of DL models has offered a substantial and consequential contribution to the existing body of knowledge [21]. Building upon this foundation, our current review endeavors to offer a more deliberate and specialized investigation, specifically focusing on CNNs, a subdomain nestled within the expansive domain of DL. Our foremost aim in this comprehensive review is to provide an exhaustive and intricately detailed exposition of the methodologies and strategic foundations that underpin the application of CNNs in the domain of brain age prediction. Central to our examination lies the innovative and meticulously engineered architectural design inherent to these neural networks. Our intention is to methodically dissect the inherent complexities within these models, thereby illuminating the technical intricacies that confer upon them exceptional efficacy, rendering them indispensable tools for the precise prediction of brain age. Moreover, our review not only integrates the latest findings spanning from 2018 to April 2024 but also scrutinizes the practical applications and generalizability of CNNs across various age groups, thus ensuring a comprehensive understanding of their utility in diverse contexts. By focusing on the cutting-edge developments in CNNs for brain age prediction, we provide a detailed and up-to-date perspective poised to inform and guide future research and applications in this dynamic field. This approach contributes to the ongoing evolution of brain age prediction methodologies, fostering advancements that hold significant promise for both scientific inquiry and practical applications.
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