Brief Report - (2025) Volume 11, Issue 6
Received: 01-Dec-2025, Manuscript No. elj-25-176215;
Editor assigned: 03-Dec-2025, Pre QC No. P-176215;
Reviewed: 17-Dec-2025, QC No. Q-176215;
Revised: 22-Dec-2025, Manuscript No. R-176215;
Published:
29-Dec-2025
, DOI: 10.37421/2472-0895.2025.11.344
Citation: Petrova, Natalia. ”Neuroimaging: Key Biomarkers for Brain Disorders.” Epilepsy J 11 (2025):344.
Copyright: © 2025 Petrova N. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
This research uses quantitative susceptibility mapping (QSM) to precisely map regional brain iron levels in individuals with Alzheimer's Disease. The findings reveal specific iron accumulation patterns in key brain regions like the hippocampus and basal ganglia, suggesting iron dysregulation is a significant biomarker for AD progression and severity. This offers a non-invasive way to track disease pathology [1].
This study investigates the utility of combining different neuroimaging techniques, such as MRI and PET, to create more robust biomarkers for Parkinson's Disease. It highlights how multimodal approaches can improve early diagnosis and provide better predictions for disease progression, moving towards more personalized management strategies [2].
This DTI study explores the white matter abnormalities in schizophrenia patients, linking disrupted integrity in specific brain tracts to cognitive impairments. It suggests that diffusion tensor imaging could serve as a valuable biomarker for identifying individuals at risk and monitoring the impact of interventions on cognitive function in schizophrenia [3].
This multi-site study uses connectome-based analyses to identify reliable neuroimaging biomarkers for Major Depressive Disorder. It reveals specific alterations in brain network connectivity that could serve as diagnostic and prognostic indicators, paving the way for more objective assessment and treatment response prediction in depression [4].
This DTI study examines persistent white matter abnormalities in individuals with a history of mild Traumatic Brain Injury (mTBI) years after the initial trauma. The findings suggest that subtle, long-lasting microstructural changes in white matter can serve as neuroimaging biomarkers for chronic mTBI effects and potentially inform prognosis and intervention strategies [5].
This multimodal MRI study explores both structural and functional brain connectivity patterns in individuals with Autism Spectrum Disorder (ASD). It identifies distinct connectivity anomalies that could function as neuroimaging biomarkers, aiding in a more precise characterization of ASD heterogeneity and supporting the development of targeted therapies [6].
This research investigates the combined use of neurofilament light chain (NfL) in blood and various MRI metrics as complementary biomarkers for Multiple Sclerosis (MS). The study demonstrates that integrating these two types of biomarkers enhances the monitoring of disease activity and progression, offering a more comprehensive picture for patient management [7].
This longitudinal study identifies altered resting-state functional connectivity patterns in individuals with Opioid Use Disorder (OUD). It highlights specific brain network dysfunctions that can serve as neuroimaging biomarkers for OUD, potentially aiding in identifying relapse risk and evaluating the effectiveness of treatment interventions over time [8].
This study leverages multimodal MRI data to develop biomarkers capable of differentiating various frontotemporal dementia (FTD) syndromes. By combining structural, functional, and diffusion imaging, the research improves diagnostic accuracy for FTD, which is crucial for early and specific patient management given the diverse presentations of these conditions [9].
This research identifies structural and functional brain connectivity patterns that serve as neuroimaging biomarkers for predicting cognitive decline in healthy older adults. It highlights the importance of network-level changes as early indicators, offering potential avenues for preventive strategies and monitoring interventions aimed at preserving cognitive function [10].
Neuroimaging biomarkers are transforming our understanding and approach to various neurological and psychiatric conditions. For instance, quantitative susceptibility mapping (QSM) offers a non-invasive way to track Alzheimer's Disease (AD) pathology by precisely mapping regional brain iron levels. Findings show specific iron accumulation patterns in key brain regions, suggesting iron dysregulation is a significant biomarker for AD progression and severity [1]. Similarly, multimodal neuroimaging, which combines techniques like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), shows great promise for Parkinson's Disease. These approaches improve early diagnosis and provide better predictions for disease progression, moving us closer to personalized management strategies [2].
Diffusion Tensor Imaging (DTI) has been pivotal in identifying white matter abnormalities across several disorders. In schizophrenia patients, DTI studies reveal disrupted integrity in specific brain tracts, which are linked to cognitive impairments. This suggests DTI could serve as a valuable biomarker for identifying at-risk individuals and monitoring the impact of interventions on cognitive function [3]. DTI also highlights persistent white matter changes years after mild Traumatic Brain Injury (mTBI), indicating that subtle, long-lasting microstructural alterations can act as neuroimaging biomarkers for chronic mTBI effects, potentially informing prognosis and intervention strategies [5].
Beyond individual tracts, connectome-based analyses are crucial for Major Depressive Disorder, identifying reliable neuroimaging biomarkers through specific alterations in brain network connectivity. These findings offer diagnostic and prognostic indicators, leading to more objective assessment and treatment response prediction [4]. In Autism Spectrum Disorder (ASD), multimodal MRI studies explore both structural and functional brain connectivity patterns, identifying distinct anomalies that could function as neuroimaging biomarkers. This helps characterize ASD heterogeneity more precisely and supports the development of targeted therapies [6].
The integration of different biomarker types significantly enhances disease monitoring. Take Multiple Sclerosis (MS), for example; research shows that combining neurofilament light chain (NfL) in blood and various MRI metrics provides complementary biomarkers. This combined approach offers a more comprehensive picture for tracking disease activity and progression, aiding patient management [7]. In the realm of addiction, longitudinal studies have identified altered resting-state functional connectivity patterns in individuals with Opioid Use Disorder (OUD). These specific brain network dysfunctions serve as neuroimaging biomarkers, potentially assisting in identifying relapse risk and evaluating the effectiveness of treatment interventions over time [8]. For neurodegenerative conditions like frontotemporal dementia (FTD), multimodal MRI dataâ??structural, functional, and diffusion imagingâ??is being leveraged to develop biomarkers capable of differentiating various FTD syndromes. This is vital for early and specific patient management, given the diverse presentations of these conditions and the need for accurate diagnosis [9].
The utility of neuroimaging biomarkers extends to understanding normal aging and predicting future health. Research highlights structural and functional brain connectivity patterns as neuroimaging biomarkers for predicting cognitive decline in healthy older adults. These network-level changes act as early indicators, suggesting avenues for preventive strategies and monitoring interventions aimed at preserving cognitive function [10]. Collectively, these studies underscore the broad utility of neuroimaging biomarkers, whether used alone or in multimodal combinations, to improve diagnosis, prognosis, and therapeutic interventions across a spectrum of brain disorders and even in the context of healthy cognitive aging. They represent a significant step towards more precise, personalized, and effective healthcare in neurology and psychiatry.
Neuroimaging techniques are becoming essential tools for understanding and managing a range of neurological and psychiatric conditions. Research uses methods like quantitative susceptibility mapping (QSM) to map brain iron levels in Alzheimer's Disease (AD), identifying specific accumulation patterns as crucial biomarkers for disease progression and severity. Multimodal neuroimaging, combining techniques such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), shows promise for improving early diagnosis and predicting the course of Parkinson's Disease, allowing for more personalized treatment plans. Diffusion Tensor Imaging (DTI) has been instrumental in exploring white matter abnormalities in schizophrenia, linking disrupted integrity in specific brain tracts to cognitive impairments and offering a biomarker for risk identification and intervention monitoring. It also reveals long-term white matter changes after mild Traumatic Brain Injury (mTBI), suggesting these subtle microstructural alterations can serve as biomarkers for chronic effects and help inform prognosis. Connectome-based analyses are uncovering reliable neuroimaging biomarkers for Major Depressive Disorder, highlighting alterations in brain network connectivity that aid in objective assessment and treatment response prediction. Studies also utilize multimodal MRI to identify distinct structural and functional brain connectivity anomalies in Autism Spectrum Disorder (ASD), which can characterize heterogeneity and support targeted therapies. Furthermore, combining neurofilament light chain (NfL) in blood with various MRI metrics provides complementary biomarkers for Multiple Sclerosis (MS), enhancing the monitoring of disease activity. Functional connectivity patterns are being identified as biomarkers for Opioid Use Disorder (OUD), helping to assess relapse risk and treatment efficacy over time. Multimodal MRI data is also crucial for differentiating various Frontotemporal Dementia (FTD) syndromes, improving diagnostic accuracy. Finally, research identifies structural and functional brain connectivity patterns as neuroimaging biomarkers for predicting cognitive decline in healthy older adults, underscoring network-level changes as early indicators for preventive strategies.
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