
==== Front
Ann Med Surg (Lond)
Ann Med Surg (Lond)
MS9
Annals of Medicine and Surgery
2049-0801
Lippincott Williams & Wilkins Hagerstown, MD

AMSU-D-24-00905
10.1097/MS9.0000000000002348
00002
3
Editorials
Advancing brain health: harnessing the centilebrain model for enhanced diagnosis of mental health disorders
Muili Abdulbasit O. MBBS ab
Olalekan Abdulrahman A. MBBS acdrerwal@gmail.com

Kuol Piel Panther MBChB ad*jokpiel003@gmail.com

Moradeyo Abdulrahman MBBS ababdulrahmonmoradeyo0@gmail.com

Phiri Emmanuel C. MBBS pschileshe24@gmail.com
ae
Habibat Saka MBBS acsakahabibat@gmail.com

Adekemi Adebayo A. MBBS acaishaadebayor10@gmail.com

Mustapha Mubarak J. MBBS acmustaphamubarakjolayemi@gmail.com

a Mission Brain, University of Ilorin
b Department of Medicine, Ladoke Akintola University of Technology, Ogbomosho
c Department of Medicine and Surgery, University of Ilorin, Ilorin, Nigeria
d Moi University, School of Medicine, Kenya
e Copperbelt University Micheal Chilufya Sata School of Medicine, Ndola, Zambia
* Corresponding author. Address: Moi University School of Medicine, Eldoret, Rift Valley, Kenya. E-mail: jokpiel003@gmail.com (P.P. Kuol).
9 2024
3 7 2024
86 9 49444946
5 5 2024
25 6 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/

OPEN-ACCESSTRUE
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pmc Dear Editor,

In the past, neuroimaging modalities such as computed tomography and MRI scans have been important in the diagnosis of brain diseases and mental health disorders; they are also present in mobile stroke units and ambulances for emergency cases such as stroke and traumatic brain injury1,2. This has provided a fast and efficient method of diagnosis and has brought about improved treatment outcomes.

Recently, emerging technologies such as telemedicine and artificial intelligence have revolutionized the diagnosis of mental health disorders. Through video-teleconferencing, effective diagnosis and management of mental health conditions have been possible in a short period3. Also, using artificial intelligence to analyze diagnostic results from computed tomography and MRI scans has aided in diagnosing mental health disorders.

Though these diagnostic methods on a surface level are important in managing disease, there can be errors in the diagnosis of certain diseases due to the limited specificity of a condition from others causing ‘false positives’. Therefore, intensive identification and understanding of typical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even before onset, and treat them more precisely

Various diseases like diabetes can be detected by measuring fasting blood sugar, coronary heart diseases, hypertension by measuring lipid profiles and blood pressure, and serum creatinine test for kidney diseases. These diagnostic tests have set a standard in the diagnosis of diseases and they are very accurate and precise in detecting these conditions. However, there are no means of measuring certain profiles in the brain for the diagnosis of mental health conditions and brain disorders.

An important step ahead in brain imaging is being made with the innovative CentileBrain project, which fills this crucial gap in brain disorders. The CentileBrain Model was developed by a team of neuroscientists, data scientists, and computer scientists, using brain morphometric data from multiple cohorts provided by the Enhancing Neuroimaging Genetics through meta-analysis (ENIGMA) consortium4. By measuring subcortical volumes, cortical thickness, and cortical surface area from any dataset, the model offers a parameter for producing normative deviation metrics for examining brain structures4.

The model was analyzed in a study by Ge et al.5 offers information on human brain region sizes from more than 37 407 healthy people from all around the world who are between the ages of 3 and 90. With the use of this new resource, illnesses linked to notable structural abnormalities in the brain may be detected earlier. This approach is a paradigm shift in the area that aims to improve the efficiency and accuracy of diagnosing mental health issues.

Unlike traditional diagnostic procedures, the CentileBrain Model includes cutting-edge technology like normative modeling, artificial intelligence, machine learning, and big data analytics6. It outperforms traditional subjective assessments by analyzing large datasets and identifying patterns. This not only speeds up the diagnostic process but also narrows the margin for error. The CentileBrain Model has enormous implications for mental health diagnosis. Its ability to handle large amounts of data quickly enables early detection of illnesses, resulting in timely intervention and personalized treatment strategies. This proactive approach can greatly enhance patient outcomes while reducing the strain on healthcare systems.

One of the model’s distinguishing qualities is its capacity to adapt to varied demographic groups while taking into account individual differences in mental health presentations. This inclusivity ensures that the CentileBrain Model is appropriate for a wide spectrum of patients, taking into consideration the complexities and individuality of each situation. The CentileBrain Model explores the finer points of cognitive patterns, genetic predispositions, and environmental influences rather than focusing only on obvious symptoms7. This all-encompassing method offers a deeper comprehension of mental health, opening the door to more focused and accurate interventions.

Centilebrain model, being a breakthrough innovation in brain imaging, also offers several advantages and bridges critical gaps experienced in this area previously. The Centilebrain model employs normative modeling which is a class of statistical methods to quantify the degree to which an individual-level measure deviates from the pattern observed in a normative reference population5. The employment of this statistical method has established normative ranges for the size of human brain regions based on sex and age, this allows clinicians to have a standard to refer to when faced with multiple individuals’ brain structures which greatly assists in identifying any possible deviations that might be a marker for a mental health disorder.

The study by Ge et al.5 employed data from a lot of healthy individuals worldwide involving 87 datasets from Europe, Australia, the USA, South Africa, and East Asia. This means we now have an existing and easily accessible large bank of datasets giving way for more accurate diagnosis and also the provision of a standardized platform for analysis. The provision of such a platform gives way to early detection of disorders associated with significant deviations in brain structure. Treatment of mental health disorders will especially thrive in timely interventions that curb it at early stages.

There is also potential for a rise in personalized treatments originating from the emergence of this model. This is because of the new provision of more accurate diagnoses from the very diverse datasets available on brain structure and function measure which are classified by sex and age. Clinicians can now tailor treatment plans, leading to better patient care, which improves outcomes and quality of life.

With advancements in the neuroimaging field, the CentileBrain model revolutionizes mental health by providing a normative framework for brain morphometry5,8. This model, available through the CentileBrain website, offers a standardized measure of atypical brain structures, which is crucial for identifying patterns of neuroanatomical variation across neurological and psychiatric disorders. It also allows the quantification of individual deviations from normative trajectories in neuroimaging phenotypes, providing a valuable tool for both research and clinical purposes. The model’s ability to identify neurodevelopmental milestones and its high stability over longitudinal assessments further enhance its utility in understanding and addressing mental health issues9.

Furthermore, an accurate diagnosis using the CentileBrain model can lead to better treatment outcomes for mental health disorders. Allahyari (2022) and Gazzar (2022) both highlight the importance of accurate prediction and diagnosis of mental health, with Allahyari emphasizing the role of various demographic factors and Gazzar presenting a framework for improving the diagnosis of psychiatric disorders9,10. Ge et al.5 further supported this by demonstrating the accuracy and stability of the CentileBrain model in the normative modeling of brain morphometry. Lastly, Scala et al.11 discusses the potential of precision medicine, including the use of multimodal biomarkers, in improving diagnostics and treatment outcomes in psychiatry.

While the initiative holds immense promise for advancing personalized brain structure assessments and early detection of neuropsychiatric disorders, it faces notable challenges12,13. Data quality and diversity pose potential pitfalls, as biases or underrepresentation in the dataset may compromise the model’s accuracy. Despite covering a broad age range, certain age groups or populations may be inadequately represented, impacting the model’s applicability. To transition from research to clinical utility, robust validation is imperative, particularly in the context of populations affected by neuropsychiatric disorders14,15.

Technological challenges in implementing the CentileBrain model could hinder its integration into clinical settings, especially in facilities lacking advanced neuroimaging equipment or computational resources. Ethical considerations surrounding data privacy and consent underscore the need for responsible practices5. Implementing Ethical considerations in CentileBrain Model research involves ensuring data privacy, obtaining informed consent, and addressing the model’s applicability to diverse populations. The issue of data privacy in research and healthcare as a whole cannot be overstated. A case of Data breach can affect the safety of the participants as well as the integrity of the data, which can hamper the trust of participants in the study. Therefore, there is a need for responsible practices such as protecting data from unauthorized individuals or groups.

The absence of longitudinal data also limits the model’s ability to track changes in brain structure over time, crucial for understanding age-related variations and the progression of neuropsychiatric disorders. Interpreting deviations from the norm necessitates further exploration to distinguish between normal variations and pathological indicators5,14. Cultural and societal factors influencing brain structure may not be fully considered, raising questions about the model’s universal applicability12.

Implementing the CentileBrain Model may encounter difficulties in terms of accessibility, necessitating specific technical expertise and a stable internet connection. Affordability issues may arise due to the resource-intensive computational requirements. Continuous validation and updates and the intricate nature of interpreting results also add further complexities. Adequate user training is essential, and the long-term stability of the model over extended periods warrants careful examination. Addressing these challenges is pivotal for using the CentileBrain Model in neuroimaging research and clinical applications5.

There are also several methodological limitations, for instance, the inclusion of young and middle-aged adults and data from longitudinal follow-up over long periods5. In addition, other factors, including childhood adversity, premature birth, or socioeconomic status, are also arguable areas that need to be addressed to extensively marginalize the potential of using this statistical tool and model technology as a potential for groundbreaking investigations on neuropsychiatric disorders.

It is also pertinent to consider how we can implement this model in underserved areas with limited access to neuroimaging equipment. Regions in Africa and other low-income countries and middle-income countries with scarce healthcare resources may struggle to use this model effectively. Therefore, a collaborative effort involving government and organizations like the ENIGMA consortium will be crucial for successfully implementing this model in these countries15.

Overall, the recent groundbreaking research published in the Lancet Digital Health provided an extensive groundbreaking initiative in neuroimaging. In recent years, the focus on neuroimaging has been on brain morphometry through the use of normative modeling provided with the widespread availability of information and data on the use of and provision of data through MRI8. Advancements in studies, technology, and investments in improving the use of normative modeling’s have shown improvements in it being a new approach to investigate the potential development of neuropsychiatric disorders in individuals based on sex, age, and other parameters. However, the robustness of these statistical tools for clinical investigation, and research encourages more research and empirical investigation.

Lastly, it is safe to say that such technology will require more research, funding, and investments. Including large-scale population studies, advancing facilities, and employing more professional techniques through incorporating diverse professional fields will require immense support and dedicated investments.

Ethical approval

Ethics approval was not required for this require.

Consent

Informed consent was not required for this review.

Source of funding

Not applicable.

Author contribution

A.O.M.: conceptualization; A.O.M; A.A.O., P.P.K., A.M., E.C.P., S.H., A.A.A., and M.J.M.: writing – original draft; A.O.M.: writing – review and editing.

Conflicts of interest disclosure

The authors declares no conflicts of interest.

Research registration unique identifying number (UIN)

Not applicable.

Guarantor

Muili Abdulbasit Opeyemi.

Data availability statement

Not applicable.

Provenance and peer review

Not commissioned, externally peer-reviewed.

Assistance with the study

Not applicable.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
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