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Artykuły w czasopismach na temat „Brain-age prediction”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 7 września 2023

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1

Xiong, Min, Lan Lin, Yue Jin, Wenjie Kang, Shuicai Wu, and Shen Sun. "Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults." Sensors 23, no. 7 (2023): 3622. http://dx.doi.org/10.3390/s23073622.

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Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population.

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Zhang, Biao, Shuqin Zhang, Jianfeng Feng, and Shihua Zhang. "Age-level bias correction in brain age prediction." NeuroImage: Clinical 37 (2023): 103319. http://dx.doi.org/10.1016/j.nicl.2023.103319.

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Gómez-Ramírez, Jaime, Miguel A. Fernández-Blázquez, and Javier J. González-Rosa. "Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation." Brain Sciences 12, no. 5 (2022): 579. http://dx.doi.org/10.3390/brainsci12050579.

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Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance

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de Lange, Ann-Marie G., and James H. Cole. "Commentary: Correction procedures in brain-age prediction." NeuroImage: Clinical 26 (2020): 102229. http://dx.doi.org/10.1016/j.nicl.2020.102229.

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Dunås, Tora, Anders Wåhlin, Lars Nyberg, and Carl-Johan Boraxbekk. "Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance." Cerebral Cortex 31, no. 7 (2021): 3393–407. http://dx.doi.org/10.1093/cercor/bhab019.

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Abstract Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to ph

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Cole, James H., Robert Leech, and David J. Sharp. "Prediction of brain age suggests accelerated atrophy after traumatic brain injury." Annals of Neurology 77, no. 4 (2015): 571–81. http://dx.doi.org/10.1002/ana.24367.

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Lombardi, Angela, Nicola Amoroso, Domenico Diacono, Alfonso Monaco, Sabina Tangaro, and Roberto Bellotti. "Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction." Brain Sciences 10, no. 6 (2020): 364. http://dx.doi.org/10.3390/brainsci10060364.

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Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In

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Kassani, Peyman Hosseinzadeh, Alexej Gossmann, and Yu-Ping Wang. "Multimodal Sparse Classifier for Adolescent Brain Age Prediction." IEEE Journal of Biomedical and Health Informatics 24, no. 2 (2020): 336–44. http://dx.doi.org/10.1109/jbhi.2019.2925710.

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Peng, Han, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, and Stephen M. Smith. "Accurate brain age prediction with lightweight deep neural networks." Medical Image Analysis 68 (February 2021): 101871. http://dx.doi.org/10.1016/j.media.2020.101871.

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Lam, Pradeep, Alyssa Zhu, Lauren Salminen, Sophia Thomopoulos, Neda Jahanshad, and Paul Thompson. "Comparison of Deep Learning Methods for Brain Age Prediction." Biological Psychiatry 87, no. 9 (2020): S374—S375. http://dx.doi.org/10.1016/j.biopsych.2020.02.959.

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Niu, Xin, Fengqing Zhang, John Kounios, and Hualou Liang. "Improved prediction of brain age using multimodal neuroimaging data." Human Brain Mapping 41, no. 6 (2020): 1626–43. http://dx.doi.org/10.1002/hbm.24899.

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Valizadeh, S. A., J. Hänggi, S. Mérillat, and L. Jäncke. "Age prediction on the basis of brain anatomical measures." Human Brain Mapping 38, no. 2 (2016): 997–1008. http://dx.doi.org/10.1002/hbm.23434.

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Hussain, Shah, Shahab Haider, Sarmad Maqsood, Robertas Damaševičius, Rytis Maskeliūnas, and Muzammil Khan. "ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction." Diagnostics 13, no. 8 (2023): 1456. http://dx.doi.org/10.3390/diagnostics13081456.

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Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of

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Ly, Maria, Nishita Muppidi, Helmet Karim, et al. "IMPROVING BRAIN AGE PREDICTION MODELS: INCORPORATION OF AMYLOID STATUS IN ALZHEIMER’S DISEASE." Innovation in Aging 3, Supplement_1 (2019): S91. http://dx.doi.org/10.1093/geroni/igz038.347.

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Abstract Brain age prediction may serve as a promising, individualized biomarker of brain health and may help us understand the heterogeneous biological changes that occur in aging. Brain age prediction is a machine learning method that estimates an individual’s chronological age from their neuroimaging scans. If predicted brain age is greater than chronological age, that individual may have an “older” brain than expected, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age

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Jacob, Yael, Gaurav Verma, Sarah Rutter, Laurel Morris, Priti Balchandani, and James Murrough. "P328. Brain Age Prediction Using Functional Brain Network Efficiency in Major Depressive Disorder." Biological Psychiatry 91, no. 9 (2022): S220. http://dx.doi.org/10.1016/j.biopsych.2022.02.564.

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Zhao, Yihong, Arno Klein, F. Xavier Castellanos, and Michael P. Milham. "Brain age prediction: Cortical and subcortical shape covariation in the developing human brain." NeuroImage 202 (November 2019): 116149. http://dx.doi.org/10.1016/j.neuroimage.2019.116149.

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Sun, Jiancheng, Zongqing Tu, Deqi Meng, Yizhou Gong, Mengmeng Zhang, and Jinsong Xu. "Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume." Brain Sciences 12, no. 11 (2022): 1517. http://dx.doi.org/10.3390/brainsci12111517.

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The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and

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Habeck, Christian, Qolamreza Razlighi, and Yaakov Stern. "Predictive utility of task-related functional connectivity vs. voxel activation." PLOS ONE 16, no. 4 (2021): e0249947. http://dx.doi.org/10.1371/journal.pone.0249947.

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Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20–77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during per

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Mazher, Moona, Abdul Qayyum, Domenec Puig, and Mohamed Abdel-Nasser. "Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics." Entropy 24, no. 12 (2022): 1708. http://dx.doi.org/10.3390/e24121708.

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To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the fea

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Lee, Jeyeon, Brian J. Burkett, Hoon-Ki Min, et al. "Deep learning-based brain age prediction in normal aging and dementia." Nature Aging 2, no. 5 (2022): 412–24. http://dx.doi.org/10.1038/s43587-022-00219-7.

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Cai, Huanhuan, Jiajia Zhu, and Yongqiang Yu. "Robust prediction of individual personality from brain functional connectome." Social Cognitive and Affective Neuroscience 15, no. 3 (2020): 359–69. http://dx.doi.org/10.1093/scan/nsaa044.

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Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were use

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Wang, Johnny, Maria J. Knol, Aleksei Tiulpin, et al. "Gray Matter Age Prediction as a Biomarker for Risk of Dementia." Proceedings of the National Academy of Sciences 116, no. 42 (2019): 21213–18. http://dx.doi.org/10.1073/pnas.1902376116.

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The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 6

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Guo, Yingying, Xi Yang, Zilong Yuan, Jianfeng Qiu, and Weizhao Lu. "A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches." Journal of Neural Engineering 19, no. 1 (2022): 016013. http://dx.doi.org/10.1088/1741-2552/ac4bfe.

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Abstract Objective. Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain. Approach. Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of

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Kuo, Chen-Yuan, Pei-Lin Lee, Sheng-Che Hung, et al. "Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker." Cerebral Cortex 30, no. 11 (2020): 5844–62. http://dx.doi.org/10.1093/cercor/bhaa161.

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Abstract The aging process is accompanied by changes in the brain’s cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework’s ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinic

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Deshpande, Prof Deepali, Shravani Bahirat, Vaisnavi Dalvi, Sakshi Darawade, and Shravani Jagtap. "Brain Stroke Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1093–101. http://dx.doi.org/10.22214/ijraset.2023.51431.

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Abstract: A brain stroke, is a saviour disease in which a blood cot or bleeding occur in the brain during a stoke, which can result in long-term harm. This can affects person’s move, think, see, or communicate. The another medical name for the Brain Stroke is cerebrovascular or CVA is when The body part that the blood-starved brain cells regulate ceases to function. Brain Stroke is very critical situation as it directly leads to death or permanent disability. The stroke is classified into two areas ischemic stroke and Haemorrhagic. When blood clots re created in the brain and goes through the

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Wu, Simiao, Ruozhen Yuan, Yanan Wang, et al. "Early Prediction of Malignant Brain Edema After Ischemic Stroke." Stroke 49, no. 12 (2018): 2918–27. http://dx.doi.org/10.1161/strokeaha.118.022001.

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Background and Purpose— Malignant brain edema after ischemic stroke has high mortality but limited treatment. Therefore, early prediction is important, and we systematically reviewed predictors and predictive models to identify reliable markers for the development of malignant edema. Methods— We searched Medline and Embase from inception to March 2018 and included studies assessing predictors or predictive models for malignant brain edema after ischemic stroke. Study quality was assessed by a 17-item tool. Odds ratios, mean differences, or standardized mean differences were pooled in random-ef

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Jusseaume, Kameron, and Iren Valova. "Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects." Sensors 22, no. 21 (2022): 8112. http://dx.doi.org/10.3390/s22218112.

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With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer’s. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer’s and other neurodegenerative diseases later in life. Utilizing recordings of patients’ brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients’ brain ag

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Mao, Lingchao, Jing Li, Todd J. Schwedt, et al. "Questionnaire and structural imaging data accurately predict headache improvement in patients with acute post-traumatic headache attributed to mild traumatic brain injury." Cephalalgia 43, no. 5 (2023): 033310242311727. http://dx.doi.org/10.1177/03331024231172736.

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Background Our prior work demonstrated that questionnaires assessing psychosocial symptoms have utility for predicting improvement in patients with acute post-traumatic headache following mild traumatic brain injury. In this cohort study, we aimed to determine whether prediction accuracy can be refined by adding structural magnetic resonance imaging (MRI) brain measures to the model. Methods Adults with acute post-traumatic headache (enrolled 0–59 days post-mild traumatic brain injury) underwent T1-weighted brain MRI and completed three questionnaires (Sports Concussion Assessment Tool, Pain C

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Baecker, Lea, Rafael Garcia-Dias, Sandra Vieira, Cristina Scarpazza, and Andrea Mechelli. "Machine learning for brain age prediction: Introduction to methods and clinical applications." eBioMedicine 72 (October 2021): 103600. http://dx.doi.org/10.1016/j.ebiom.2021.103600.

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Han, Hongfang, Sheng Ge, and Haixian Wang. "Prediction of brain age based on the community structure of functional networks." Biomedical Signal Processing and Control 79 (January 2023): 104151. http://dx.doi.org/10.1016/j.bspc.2022.104151.

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Ly, Maria, Gary Z. Yu, Helmet T. Karim, et al. "Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease." Neurobiology of Aging 87 (March 2020): 44–48. http://dx.doi.org/10.1016/j.neurobiolaging.2019.11.005.

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Monti, Ricardo Pio, Alex Gibberd, Sandipan Roy, et al. "Interpretable brain age prediction using linear latent variable models of functional connectivity." PLOS ONE 15, no. 6 (2020): e0232296. http://dx.doi.org/10.1371/journal.pone.0232296.

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Richard, Geneviève, Knut Kolskår, Anne-Marthe Sanders, et al. "Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry." PeerJ 6 (November 30, 2018): e5908. http://dx.doi.org/10.7717/peerj.5908.

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Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequate

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Hellstrøm, Torgeir, Nada Andelic, Ann-Marie G. de Lange, Eirik Helseth, Kristin Eiklid та Lars T. Westlye. "Apolipoprotein ɛ4 Status and Brain Structure 12 Months after Mild Traumatic Injury: Brain Age Prediction Using Brain Morphometry and Diffusion Tensor Imaging". Journal of Clinical Medicine 10, № 3 (2021): 418. http://dx.doi.org/10.3390/jcm10030418.

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Background: Apolipoprotein E (APOE) ɛ4 is associated with poor outcome following moderate to severe traumatic brain injury (TBI). There is a lack of studies investigating the influence of APOE ɛ4 on intracranial pathology following mild traumatic brain injury (MTBI). This study explores the association between APOE ɛ4 and MRI measures of brain age prediction, brain morphometry, and diffusion tensor imaging (DTI). Methods: Patients aged 16 to 65 with acute MTBI admitted to the trauma center were included. Multimodal MRI was performed 12 months after injury and associated with APOE ɛ4 status. Co

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Nygate, Yoav, Sam Rusk, Chris Fernandez, et al. "543 EEG-Based Deep Neural Network Model for Brain Age Prediction and Its Association with Patient Health Conditions." Sleep 44, Supplement_2 (2021): A214. http://dx.doi.org/10.1093/sleep/zsab072.541.

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Abstract Introduction Electroencephalogram (EEG) provides clinically relevant information for personalized patient health evaluation and comprehensive assessment of sleep. EEG-based indices have been associated with neurodegenerative conditions, psychiatric disorders, and metabolic and cardiovascular disease, and hold promise as a biomarker for brain health. Methods A deep neural network (DNN) model was trained to predict the age of patients using raw EEG signals recorded during clinical polysomnography (PSG). The DNN was trained on N=126,241 PSGs, validated on N=6,638, and tested on a holdout

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Verscheijden, Laurens F. M., Carlijn H. C. Litjens, Jan B. Koenderink, et al. "Physiologically based pharmacokinetic/pharmacodynamic model for the prediction of morphine brain disposition and analgesia in adults and children." PLOS Computational Biology 17, no. 3 (2021): e1008786. http://dx.doi.org/10.1371/journal.pcbi.1008786.

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Morphine is a widely used opioid analgesic, which shows large differences in clinical response in children, even when aiming for equivalent plasma drug concentrations. Age-dependent brain disposition of morphine could contribute to this variability, as developmental increase in blood-brain barrier (BBB) P-glycoprotein (Pgp) expression has been reported. In addition, age-related pharmacodynamics might also explain the variability in effect. To assess the influence of these processes on morphine effectiveness, a multi-compartment brain physiologically based pharmacokinetic/pharmacodynamic (PB-PK

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Sun, H., K. Dunham, L. Cunningham, Y. Ni, M. Westover, and R. Thomas. "0348 Sleep EEG-Based Brain Age Index is Reduced Under Continuous Positive Airway Pressure Treatment." Sleep 43, Supplement_1 (2020): A132. http://dx.doi.org/10.1093/sleep/zsaa056.345.

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Abstract Introduction Continuous positive airway pressure (CPAP) is a treatment for apnea. With long-term CPAP, changes in electroencephalogram (EEG) include increased delta power (1 - 4Hz) and sigma power (11 - 15Hz, spindle). However, the short-term EEG response to CPAP in a split-night study is less quantified. We recently developed a “brain age” model using sleep EEG features. The brain age index (BAI) is defined as the difference between chronological age and brain age (BA - CA). Here we first quantify how BAI changes during CPAP in the same patient, and then investigate how much brain ag

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Romagnosi, Federico, Adriano Bernini, Filippo Bongiovanni, et al. "Neurological Pupil Index for the Early Prediction of Outcome in Severe Acute Brain Injury Patients." Brain Sciences 12, no. 5 (2022): 609. http://dx.doi.org/10.3390/brainsci12050609.

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In this study, we examined the early value of automated quantitative pupillary examination, using the Neurological Pupil index (NPi), to predict the long-term outcome of acute brain injured (ABI) patients. We performed a single-centre retrospective study (October 2016–March 2019) in ABI patients who underwent NPi measurement during the first 3 days following brain insult. We examined the performance of NPi—alone or in combination with other baseline demographic (age) and radiologic (CT midline shift) predictors—to prognosticate unfavourable 6-month outcome (Glasgow Outcome Scale 1–3). A total

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Simfukwe, Chanda, and Young Chul Youn. "Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI." Dementia and Neurocognitive Disorders 21, no. 4 (2022): 138. http://dx.doi.org/10.12779/dnd.2022.21.4.138.

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Holm, Madelene C., Esten H. Leonardsen, Dani Beck, et al. "Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort." Developmental Cognitive Neuroscience 60 (April 2023): 101220. http://dx.doi.org/10.1016/j.dcn.2023.101220.

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Ballester, Pedro L., Laura Tomaz da Silva, Matheus Marcon, et al. "Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability." Frontiers in Psychiatry 12 (February 25, 2021). http://dx.doi.org/10.3389/fpsyt.2021.598518.

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Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has a

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Kuo, Chen-Yuan, Tsung-Ming Tai, Pei-Lin Lee, et al. "Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework." Frontiers in Psychiatry 12 (March 23, 2021). http://dx.doi.org/10.3389/fpsyt.2021.626677.

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Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assembl

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Gong, Weikang, Christian F. Beckmann, Andrea Vedaldi, Stephen M. Smith, and Han Peng. "Optimising a Simple Fully Convolutional Network for Accurate Brain Age Prediction in the PAC 2019 Challenge." Frontiers in Psychiatry 12 (May 10, 2021). http://dx.doi.org/10.3389/fpsyt.2021.627996.

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Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject's predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject's age in multicentre datasets. We apply a lightweight deep convolutional neural network a

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Niu, Xin, Alexei Taylor, Russell T. Shinohara, John Kounios, and Fengqing Zhang. "Multidimensional Brain-Age Prediction Reveals Altered Brain Developmental Trajectory in Psychiatric Disorders." Cerebral Cortex, January 30, 2022. http://dx.doi.org/10.1093/cercor/bhab530.

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Abstract Brain-age prediction has emerged as a novel approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. This staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. We propose a multidimensional brain-age index (MBAI) that provides regional age p

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Hong, Jinwoo, Hyuk Jin Yun, Gilsoon Park, et al. "Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging." Frontiers in Neuroscience 15 (October 11, 2021). http://dx.doi.org/10.3389/fnins.2021.714252.

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The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the br

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Hsu, Yi-Fang, Florian Waszak, Juho Strömmer, and Jarmo A. Hämäläinen. "Human Brain Ages With Hierarchy-Selective Attenuation of Prediction Errors." Cerebral Cortex, December 1, 2020. http://dx.doi.org/10.1093/cercor/bhaa352.

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Abstract From the perspective of predictive coding, our brain embodies a hierarchical generative model to realize perception, which proactively predicts the statistical structure of sensory inputs. How are these predictive processes modified as we age? Recent research suggested that aging leads to decreased weighting of sensory inputs and increased reliance on predictions. Here we investigated whether this age-related shift from sensorium to predictions occurs at all levels of hierarchical message passing. We recorded the electroencephalography responses with an auditory local–global paradigm

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Ganaie, M. A., M. Tanveer, and Iman Beheshti. "Brain age prediction using improved twin SVR." Neural Computing and Applications, January 7, 2022. http://dx.doi.org/10.1007/s00521-021-06518-1.

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Fang, Keke, Shaoqiang Han, Yuming Li, Jing Ding, Jilian Wu, and Wenzhou Zhang. "The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures." Frontiers in Neuroscience 15 (September 7, 2021). http://dx.doi.org/10.3389/fnins.2021.733316.

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Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion

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Anatürk, Melis, Tobias Kaufmann, James H. Cole, et al. "Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging." Human Brain Mapping, December 14, 2020. http://dx.doi.org/10.1002/hbm.25316.

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Ballester, Pedro L., Jee Su Suh, Natalie C. W. Ho, et al. "Gray matter volume drives the brain age gap in schizophrenia: a SHAP study." Schizophrenia 9, no. 1 (2023). http://dx.doi.org/10.1038/s41537-022-00330-z.

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AbstractNeuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) o

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