Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Niu, Xin; Taylor, Alexei; Shinohara, Russell T.; Kounios, John; Zhang, Fengqing

doi:10.1093/cercor/bhab530

Cited by 14 publications

(14 citation statements)

References 73 publications

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“…Our findings are consistent with previous studies that found accelerated brain aging in alcohol use (Amen, Egan, Meysami, Raji, & George, 2018;Cole, 2020), cannabis use (Amen et al, 2018), anxiety (Amen et al, 2018), and depression (X. Niu et al, 2022). In addition, accelerated biological aging has been reported in substance use (Bachi, Sierra, Volkow, Goldstein, & Alia-Klein, 2017) and alcohol abuse (Piniewska-Róg et al, 2021).…”

Section: Discussionsupporting

confidence: 93%

“…The negative association between brain age gap and chronological age has been thoroughly investigated in prior work published by our group (Liang et al, 2019;X. Niu et al, 2022;Xin Niu et al, 2020) as well as other studies (James H. Smith et al, 2019).…”

Section: Discussionmentioning

confidence: 82%

“…Brain age estimated by machine learning models and neuroimaging data is another surrogate biomarker on aging (X. Niu, Taylor, Shinohara, Kounios, & Zhang, 2022;Xin Niu, Zhang, Kounios, & Liang, 2020). Similar to biological age, brain age allows us to examine health as an integrative process, not specific to a particular health condition.…”

Section: Introductionmentioning

confidence: 99%

“…Liang, Zhang, & Niu, 2019;X. Niu et al, 2022; Xin Niu et al, 2020;Smith, Vidaurre, Alfaro-Almagro, Nichols, & Miller, 2019).…”

mentioning

confidence: 99%

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Biological age and brain age in midlife: relationship to multimorbidity and mental health

Zhang

Chang

Schaefer

et al. 2022

Preprint

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Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationship between brain age and multimorbidity. To answer these questions, our study investigates whether elevated biological age and accelerated brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States (MIDUS) Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using convolutional neural networks and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity and presents the first effort to test whether sex moderates the relationship between these surrogate aging biomarkers and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of the accumulation of physical and mental health conditions, which may contribute to new strategies to improve the treatment of multimorbidity and detection of at-risk individuals.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

“…Liang, Zhang, & Niu, 2019;X. Niu et al, 2022; Xin Niu et al, 2020;Smith, Vidaurre, Alfaro-Almagro, Nichols, & Miller, 2019).…”

mentioning

confidence: 99%

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Biological age and brain age in midlife: relationship to multimorbidity and mental health

Zhang

Chang

Schaefer

et al. 2022

Preprint

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“…In addition, several recent studies investigated machine-learning based brain age estimation from imaging data in depression. 8 , 9 , 10 Integrating clinical data with multimodal neuroimaging data through machine learning may yield new insights about risk factors for depression and merits future research.…”

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confidence: 99%

Machine learning assessment of risk factors for depression in later adulthood

Zhang

Gou

2022

The Lancet Regional Health - Europe

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Brain development in newborns and infants after ECMO

Yan,

Tang,

Xiao

et al. 2024

World J Pediatr

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Background Extracorporeal membrane oxygenation (ECMO) not only significantly improves survival rates in severely ill neonates but also is associated with long-term neurodevelopmental issues. To systematically review the available literature on the neurodevelopmental outcomes of neonates and infants who have undergone ECMO treatment, with a focus on motor deficits, cognitive impairments, sensory impairments, and developmental delays. This review aims to understand the incidence, prevalence, and risk factors for these problems and to explore current nursing care and management strategies. Data sources A comprehensive literature search was performed across PubMed, EMBASE, and Web of Science using a wide array of keywords and phrases pertaining to ECMO, neonates, infants, and various facets of neurodevelopment. The initial screening involved reviewing titles and abstracts to exclude irrelevant articles, followed by a full-text assessment of potentially relevant literature. The quality of each study was evaluated based on its research methodology and statistical analysis. Moreover, citation searches were conducted to identify potentially overlooked studies. Although the focus was primarily on neonatal ECMO, studies involving children and adults were also included due to the limited availability of neonate-specific literature. Results About 50% of neonates post-ECMO treatment exhibit varying degrees of brain injury, particularly in the frontal and temporoparietal white matter regions, often accompanied by neurological complications. Seizures occur in 18%–23% of neonates within the first 24 hours, and bleeding events occur in 27%–60% of ECMO procedures, with up to 33% potentially experiencing ischemic strokes. Although some studies suggest that ECMO may negatively impact hearing and visual development, other studies have found no significant differences; hence, the influence of ECMO remains unclear. In terms of cognitive, language, and intellectual development, ECMO treatment may be associated with potential developmental delays, including lower composite scores in cognitive and motor functions, as well as potential language and learning difficulties. These studies emphasize the importance of early detection and intervention of potential developmental issues in ECMO survivors, possibly necessitating the implementation of a multidisciplinary follow-up plan that includes regular neuromotor and psychological evaluations. Overall, further multicenter, large-sample, long-term follow-up studies are needed to determine the impact of ECMO on these developmental aspects. Conclusions The impact of ECMO on an infant’s nervous system still requires further investigation with larger sample sizes for validation. Fine-tuned management, comprehensive nursing care, appropriate patient selection, proactive monitoring, nutritional support, and early rehabilitation may potentially contribute to improving the long-term outcomes for these infants.

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Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Cited by 14 publications

References 73 publications

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Machine learning assessment of risk factors for depression in later adulthood

Brain development in newborns and infants after ECMO

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