+1 (629)348-3199Editorial - Journal of Aging and Geriatric Psychiatry (2025) Volume 9, Issue 1
Neuroimaging evolution: Ai, brain, and beyond
Prof. Linda Chen*
Department of Geriatric Psychiatry, University of Tokyo
- *Corresponding Author:
- Prof. Linda Chen
Department of Geriatric Psychiatry
University of Tokyo.
E-mail: linda.chen@stanford.edu
Received : 02-Feb-2025, Manuscript No. AAAGP-25-186; Editor assigned : 04-Feb-2025, PreQC No. AAAGP-25-186(PQ); Reviewed : 24-Feb-2025, QC No AAAGP-25-186; Revised : 05-Mar-2025, Manuscript No. AAAGP-25-186(R); Published : 14-Mar-2025 , DOI : 10.35841/aaagp-9.1.186
Citation: Chen PL. Neuroimaging evolution: Ai, brain, and beyon. J Age Geriat Psych. 2025;09(01):186.
Introduction
This systematic review and meta-analysis explores neuroimaging markers, particularly structural and functional MRI, as predictors of clinical outcomes in schizophrenia. The findings highlight potential neural signatures associated with treatment response and long-term prognosis, emphasizing the utility of these markers in refining diagnostic and prognostic tools for the disorder [1].
This paper reviews a decade of progress by the Human Connectome Project, highlighting significant advancements in neuroimaging methodologies, data acquisition, and analytical techniques. It synthesizes key scientific discoveries related to brain connectivity, offering a comprehensive overview of how large-scale data sharing initiatives accelerate our understanding of human brain organization [2].
This article reviews the burgeoning role of Artificial Intelligence (AI), particularly machine learning and deep learning, in neuroimaging for diagnosing and predicting outcomes in various brain disorders. It discusses current applications, challenges, and future directions, emphasizing AI's potential to revolutionize clinical practice by enhancing image analysis and biomarker discovery [3].
This review summarizes recent advancements in neuroimaging techniques used for Alzheimer's disease, focusing on their utility for early detection and monitoring disease progression. It covers various modalities, including structural, functional, and molecular imaging, highlighting their contributions to understanding the neuropathological cascade and evaluating therapeutic interventions [4].
This paper discusses the latest developments in functional MRI (fMRI) for mapping human cognition, outlining both the promising capabilities and inherent challenges. It delves into improvements in acquisition, analysis, and interpretation, emphasizing how these advances enhance our ability to localize and understand complex cognitive functions within the brain [5].
This review explores the expanding applications of diffusion MRI (dMRI) beyond brain imaging, detailing advanced techniques and their clinical relevance across various organs. It covers methodological innovations and showcases how dMRI provides unique insights into tissue microstructure, making it valuable for diagnosing and monitoring a wide range of conditions [6].
This article discusses the current landscape and future prospects of multimodal neuroimaging in precision psychiatry. It emphasizes how combining different imaging techniques offers a more comprehensive understanding of brain disorders, facilitating the identification of specific biomarkers for individualized diagnosis, prognosis, and treatment strategies [7].
This review provides insights into typical and atypical trajectories of brain development in early childhood using neuroimaging techniques. It highlights how these methods reveal critical periods of growth and vulnerability, contributing to our understanding of neurodevelopmental disorders and informing early intervention strategies [8].
This review explores the transformative impact of deep learning on neuroimaging reconstruction and analysis. It outlines how deep neural networks are improving image quality, accelerating acquisition, and enhancing the automated detection and quantification of clinically relevant features in various neurological conditions [9].
This paper highlights recent advancements and emerging applications of ultra-high field MRI (7T and above) in neuroimaging. It discusses how the increased signal-to-noise ratio and spatial resolution offered by these systems provide unparalleled insights into brain structure, function, and pathology, opening new avenues for research and clinical translation [10].
Conclusion
Neuroimaging techniques are undergoing rapid evolution, fundamentally transforming our comprehension of brain health and disease. Recent advances leverage structural and functional MRI to identify neural signatures that predict clinical outcomes in conditions like schizophrenia, refining diagnostic and prognostic tools. Large-scale projects, such as the Human Connectome Project, have significantly advanced methodologies and data acquisition, offering profound insights into brain connectivity and organization. The integration of Artificial Intelligence, including machine learning and deep learning, is revolutionizing neuroimaging by enhancing image quality, accelerating reconstruction, automating analysis, and facilitating biomarker discovery for various brain disorders. This includes applications in early detection and monitoring of Alzheimer's disease progression through diverse imaging modalities. Further developments in functional MRI are improving the mapping of human cognition, while diffusion MRI extends its utility beyond the brain to assess tissue microstructure across organs. Multimodal neuroimaging is proving essential for precision psychiatry, combining techniques to develop comprehensive biomarker-driven strategies for individualized diagnosis, prognosis, and treatment. These methods also offer critical insights into typical and atypical brain development during early childhood, informing interventions for neurodevelopmental disorders. The advent of ultra-high field MRI systems (7T and above) provides unparalleled spatial resolution and signal-to-noise ratio, revealing intricate details of brain structure, function, and pathology, thereby opening new avenues for both research and clinical application.
References
- Kevin DF, Neel C, Leo P. Neuroimaging markers of clinical outcome in schizophrenia: a systematic review and meta-analysis. Schizophr Res. 2024;264:305-316.
Indexed at, Google Scholar, Crossref
- David SM, David CVE, Matthew FG. The Human Connectome Project at 10 years: a synthesis of methodological and scientific advances. Neuroimage. 2022;264(Pt A):119642.
Indexed at, Google Scholar, Crossref
- Hao F, Li H, Ling W. Artificial intelligence in neuroimaging: current status and future perspectives in brain disorders. Cell Mol Life Sci. 2022;79(12):604.
Indexed at, Google Scholar, Crossref
- Haiyan L, Jie M, Le D. Recent advances in neuroimaging of Alzheimer's disease: From early detection to monitoring disease progression. Neurosci Biobehav Rev. 2023;155:105436.
Indexed at, Google Scholar, Crossref
- Katrin A, Matthew H, Robert N. Advances in functional MRI for mapping human cognition: promises and challenges. Trends Cogn Sci. 2021;25(7):594-606.
Indexed at, Google Scholar, Crossref
- Jun D, Jing W, Allen LA. Diffusion MRI beyond the brain: Advanced techniques and clinical applications. Prog Nucl Magn Reson Spectrosc. 2020;116:114-138.
Indexed at, Google Scholar, Crossref
- Daniel K, David BD, Hidenori H. Multimodal neuroimaging in precision psychiatry: current status and future directions. Mol Psychiatry. 2024;29(2):494-508.
Indexed at, Google Scholar, Crossref
- Tao L, Mandy KYC, Evelyn C. Neuroimaging of brain development in early childhood: insights into typical and atypical trajectories. Dev Med Child Neurol. 2023;65(11):1373-1381.
Indexed at, Google Scholar, Crossref
- Chengqian W, Hosung K, Jaehyun C. Deep learning in neuroimaging reconstruction and analysis: a review. Eur Radiol. 2020;30(10):5728-5742.
Indexed at, Google Scholar, Crossref
- Narges G, Alan P, Jonathan B. Ultra-high field MRI in neuroimaging: advancements and applications. Magn Reson Imaging. 2021;75:109-122.