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Predicting Age Groups using Brain Imaging Quality Data

the article is written by Kadir Uludag.


Predicting Age Groups using Brain Imaging Quality Data




Kadir Uludag, MS1,2*


1CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, China



2Department of Psychology, University of Chinese Academy of Sciences, China




Brain imaging quality of data is important to confirm their
liability of brain imaging studies. Previous literature confirmed that
some confounding factors such as movement, age, and gender may impact
brain imaging quality. Automatic quality control (QC) applications may
not be able to properly calculate their reliability due to confounding
factors.There are a few studies on brain imaging quality data and
relevant confounding factors such as age or gender.


Methods



Open data from a previous study was used to conduct this
study. In total 26 participants were recruited. Random Forest (RF) and
Neural Networks (NN) machine learning (ML) methods were used to predict
age groups (cut-off age: 16). Patients were grouped by age groups. Then,
the age group was predicted with RF and NN machine learning (ML)
models.


Goal of study



The goal of the study was to predict age groups using brain imaging quality data.


Results



We found that according to NNs, the age group was predicted
with an accuracy of over 60% (accuracy: 64%, sensitivity: 50%,
specificity: 71%, area under curve (AUC): 55%,). Furthermore, the RFML
model found that the age group was predicted with an accuracy of 64%
(sensitivity: 50%, specificity: 71%, AUC: 86.6%).


Conclusion



Our study showed that age groups can be predicted using the
brain imaging quality of the data. Further studies should investigate
the relationship between other brain imaging parameters related to the
quality of data and age.


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