Table of Contents
Overview
Definition and Purpose
Neuroimaging refers to a variety of techniques used to visualize the structure and function of the living brain. These techniques allow researchers to examine both the anatomical and functional properties of the brain, providing insights into its workings during cognitive processes. The feasibility of neuroimaging in patient populations is notable, as it can be conducted as long as individuals are able to remain still for a brief period, typically ranging from 10 to 60 minutes.[5.1] The development and application of neuroimaging techniques in humans have significantly advanced over the past decade, resulting in a substantial increase in research focused on understanding the neural basis of cognitive processes.[6.1]Multidisciplinary Nature
Neuroimaging is a multidisciplinary field that plays a crucial role in the care of patients with neurological disorders by utilizing various techniques to image the brain and spinal cord, as well as to measure blood flow and neural function.[1.1] Among the functional brain imaging techniques, functional magnetic resonance imaging (fMRI) has become a preferred method for assessing brain activity due to its lack of radiation exposure, higher temporal resolution compared to positron emission tomography (PET), and greater availability in medical settings.[4.1] Although PET scanning was once the primary method for functional brain imaging, it is now limited by the rapid decay of its radioisotopes, which restricts its use to short tasks.[4.1] Despite these limitations, PET continues to contribute significantly to neuroscience.[4.1] The evolution of these neuroimaging techniques underscores the importance of technological advancements in enhancing our understanding of brain structure and function.[1.1] The integration of positron emission tomography (PET) and magnetic resonance imaging (MRI) represents a significant advancement in neuroimaging, as these modalities provide complementary information essential for studying the human brain. Simultaneous PET/MRI data acquisition allows for the spatial and temporal correlation of measured signals, creating opportunities that are unattainable with stand-alone instruments.[7.1] This combined approach enhances neurological clinical care and research by offering spatially and temporally matched anatomical MR imaging alongside advanced MR physiologic imaging and metabolic PET imaging.[9.1] Furthermore, the features of this technology are particularly beneficial for applications in neuroscience and translational neurologic and psychiatric research, as MRI serves as the first-line diagnostic imaging modality for numerous indications, while a variety of specific PET tracers are available to assess functional and molecular processes in the brain.[8.1] Recent advancements in neuroimaging techniques, including resting-state functional MRI, diffusion tensor imaging, and magnetoencephalography, have significantly improved our ability to detect and monitor neurological disorders, particularly Alzheimer's disease. These technologies have provided unprecedented insights into the brain's structure and function, facilitating earlier and more accurate diagnoses of neurodegenerative conditions.[18.1] Over the past two decades, neuroimaging modalities have evolved to become quicker, less invasive, and more comfortable for patients, which is essential for enhancing diagnostic processes.[26.1] As neuroimaging continues to advance, its role in diagnosis and personalized medicine is expected to grow, underscoring the importance of these innovations in improving patient care.[26.1]In this section:
History
Early Techniques
The ethical challenges associated with early neuroimaging technologies were recognized as a significant priority within the emerging discipline of neuroimaging. These challenges encompassed various concerns that arose in both research and clinical settings, highlighting the need for careful consideration of the implications of these advanced capabilities.[65.1] As the field developed, there was a growing acknowledgment that ethical issues in neuroscience warranted a distinct area of inquiry, termed "neuroethics." This emerging view was supported by initiatives such as those from the Stanford Center for Bioethics, which emphasized the relevance of neuroimaging techniques as a focal point for neuroethical discussions.[66.1] Public perceptions of neuroimaging also played a crucial role in shaping the discourse surrounding its ethical implications. Research conducted in the Netherlands revealed that understanding public attitudes and concerns could inform the development of neuroimaging technologies, ensuring that future innovations align with societal desires and expectations.[67.1] This interplay between technological advancement and societal perception underscores the importance of addressing ethical considerations as neuroimaging techniques continue to evolve.Key Milestones in Development
Neuroimaging has undergone significant advancements since its inception in the late 19th century. The journey began in the 1880s with the invention of the human circulation balance, which laid the groundwork for subsequent imaging techniques such as x-rays, air ventriculography, and cerebral angiography.[35.1] A pivotal moment in the history of neuroimaging occurred on October 1, 1971, when Godfrey Hounsfield and Jamie Ambrose operated a new machine that marked the beginning of modern neurosurgery and neuroimaging with the introduction of the computed tomography (CT) scanner.[36.1] The development of the CT scanner was further highlighted by Dr. William Stuart's visit to Massachusetts General Hospital in 1972, where he witnessed the revolutionary potential of this technology. Upon returning to Atlanta, he acquired one of the earliest CT scanners, which significantly impacted the practice of neurology.[37.1] The post-World War II era saw remarkable progress in neuroimaging, with the CAT scan emerging as a critical milestone that transformed diagnostic capabilities in healthcare.[39.1] In the years following the introduction of CT, advancements in magnetic resonance imaging (MRI) began to reshape the field. MRI technology provides high-resolution images of brain structures, facilitating early detection of conditions such as tumors and multiple sclerosis, and enhancing pre-surgical planning through detailed mapping of brain connectivity.[38.1] The Society for Brain Mapping and Therapeutics, founded in 2004, has played a crucial role in promoting these advancements and their applications in understanding brain function and disorders.[38.1] The evolution of neuroimaging techniques has not only improved diagnostic accuracy but has also expanded our understanding of neurological function and disease. Each technological advancement, from the early CT scans to contemporary MRI and functional MRI (fMRI), has contributed to a deeper comprehension of brain activity and its alterations in various neurological conditions.[40.1] As neuroimaging continues to evolve, it remains an essential tool for both research and clinical practice, providing invaluable insights into the complexities of the human brain.Recent Advancements
Neuroimaging Techniques
Recent advancements in neuroimaging techniques have significantly enhanced our understanding of brain function and its associated disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two prominent neuroimaging modalities that have revolutionized the field of neuroscience. These techniques allow researchers to observe changes in brain activity in real-time, providing insights into both normal and atypical brain functioning.[69.1] fMRI, in particular, has emerged as a powerful tool for investigating brain connectivity and activity. It enables non-invasive visualization of the awake, behaving human brain, allowing for the tracking of whole-brain signals across various cognitive and behavioral states.[75.1] Recent improvements in fMRI technology have facilitated the identification of abnormal functional brain activity in diverse patient populations, including those with neurodegenerative and cerebrovascular disorders.[72.1] Moreover, the integration of artificial intelligence into fMRI analysis has opened new avenues for rapid pathology detection, enhancing the diagnostic capabilities of this technique.[73.1] Quantitative neuroimaging methods are also gaining traction, as they allow for systematic image analysis and statistical modeling of brain activity. These approaches utilize canonical templates or brain atlases to measure changes in specific regions of interest, which is crucial for understanding the integrated structural and functional networks of the brain.[71.1] For instance, resting-state fMRI has been employed to detect early signs of Alzheimer's disease by observing disruptions in the Default Mode Network (DMN), highlighting its potential in clinical applications.[74.1] Furthermore, the combination of EEG and fMRI techniques presents promising clinical applications, particularly in the context of epilepsy. This integration allows for the detection of cortical areas involved in epileptic activity and can aid in the planning of neurosurgery or rehabilitation.[81.1] Overall, the advancements in neuroimaging techniques not only enhance our understanding of brain function but also hold significant implications for the diagnosis and treatment of neurological disorders.Impact on Neuroscience Research
Recent advancements in neuroimaging, particularly through the integration of machine learning and artificial intelligence (AI), have significantly influenced neuroscience research and clinical diagnostics. The application of machine learning methods to neuroimaging has expanded the field from traditional population-based analyses to the development of individualized biomarkers for diseases and functional brain states, marking a transformative shift in the discipline.[102.1] This evolution allows for a more nuanced understanding of brain function and pathology, as machine learning models can link neuroimaging data, such as the blood-oxygen-level-dependent (BOLD) signals from functional magnetic resonance imaging (fMRI), to specific experimental conditions.[103.1] Moreover, the combination of structural and functional neuroimaging techniques enables a multi-modal description of the brain, facilitating the modeling of its structure-function relationship as a marker for various diseases.[103.1] For instance, supervised machine learning approaches utilizing functional connectivity data have revealed disease correlates that are not detectable through structural imaging alone, such as in the identification of characteristics associated with autism spectrum disorders.[103.1] The rapid advancement of machine learning in clinical translational imaging is also noteworthy, as it aids in the early detection, prediction, and treatment of diseases that threaten brain health.[104.1] As AI techniques continue to evolve, deep learning has been proposed to enhance healthcare monitoring and regulation of brain health, improving the accuracy of neuroimaging reconstructions and enabling faster imaging processes.[105.1] This capability allows for the mining of extensive pathological and genetic data, thereby assisting pathologists in evaluating pathological sections more efficiently and improving disease diagnosis and prognosis.[105.1] In addition to these technological advancements, ethical considerations surrounding neuroimaging research have gained prominence. Issues such as human subjects protection, medical privacy, and the responsible communication of research results are critical in ensuring that neuroimaging data is utilized ethically in both research and clinical settings.[98.1] The International Neuroethics Society has highlighted the importance of transparency, consent, and the security of personal databases in the context of neuroimaging research.[99.1] Furthermore, the obligation to disclose incidental findings, particularly those of uncertain significance, raises complex ethical questions regarding the responsibilities of researchers towards participants.[100.1]In this section:
Applications In Clinical Settings
Diagnosis of Neurological Disorders
Neuroimaging techniques are essential in diagnosing neurological disorders, offering critical insights into the brain's structure and function. Advanced imaging modalities, including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), are frequently utilized to visualize the brain, allowing healthcare providers to identify abnormalities associated with various conditions such as tumors, multiple sclerosis, and strokes.[119.1] When comparing MRI and CT scans, several factors must be considered, including image quality, radiation exposure, diagnostic capabilities, patient characteristics, and cost.[121.1] MRI is particularly noted for providing high-resolution images that enhance the early detection of neurological disorders, while CT scans are advantageous for rapid assessment in acute clinical settings.[119.1] Overall, neuroimaging serves as a non-invasive diagnostic procedure that significantly contributes to understanding brain anatomy and function, ultimately improving patient outcomes.[113.1] The diagnosis of neurological disorders has greatly benefited from advancements in neuroimaging techniques, which are essential for visualizing the intricate structures and functions of the brain and nervous system. Positron emission tomography (PET) is a nuclear medicine diagnostic technique that provides functional brain scans, playing a crucial role in localizing areas of abnormal brain function, particularly in cases where surgical treatment is considered.[114.1] Additionally, advanced MRI techniques, such as diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI), have transformed our understanding of brain connectivity and activity patterns. These modalities allow for the mapping of blood flow and tracking of neural activity, which are vital for identifying biomarkers of neurological diseases and personalizing treatment strategies.[118.1] Furthermore, MRI and DTI are instrumental in pre-surgical planning, as they help map brain structures and connectivity, thereby minimizing risks during neurosurgery.[119.1] Overall, neurological imaging serves as a cornerstone of modern medicine, providing critical insights that inform diagnosis, treatment planning, and research in neurology.[115.1] Recent advancements in neuroimaging technologies, including diffusion tensor imaging (DTI) and resting-state fMRI, have significantly improved the early detection and monitoring of neurological disorders.[118.1] These techniques allow for the analysis of white matter integrity and functional connectivity, which are critical in identifying biomarkers for diseases and tailoring personalized treatment approaches.[118.1] The collaborative efforts seen in initiatives such as the Human Connectome Project have further underscored the importance of neuroimaging in advancing our understanding of brain networks and disease processes.[112.1]Treatment Monitoring and Evaluation
Neuroimaging techniques, particularly MRI, have become essential in the monitoring and evaluation of treatment responses in psychiatric disorders. These methods facilitate research aimed at understanding brain circuit alterations associated with the etiology, pathophysiology, and treatment outcomes of various mental health conditions. For instance, studies have utilized neuroimaging to identify cognitive tasks that serve as surrogate markers for specific disorders, such as working memory in schizophrenia and theory of mind in autism. This approach not only aids in diagnosing but also in prognostic assessments by comparing different diagnostic or at-risk groups.[148.1] Moreover, neuroimaging has been instrumental in linking brain structure and function to treatment responses. A notable example includes the use of functional MRI (fMRI) data from a large cohort of patients with diverse psychiatric diagnoses, which revealed common alterations in interoception-related brain activation across various conditions, including schizophrenia, bipolar disorder, and anxiety disorders.[149.1] Such findings underscore the potential of neuroimaging to inform targeted therapeutic interventions, such as transcranial magnetic stimulation (TMS), which can be guided by neuroimaging data to enhance treatment efficacy. For instance, a NIRS-guided repetitive TMS treatment protocol has been successfully applied to patients with panic disorder, targeting specific brain regions implicated in emotion processing.[148.1]Emerging Technologies
Multimodal Imaging
Multimodal imaging in neuroimaging combines various techniques to enhance the understanding of brain function and disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two prominent modalities that, when used together, provide complementary insights into neural activity. fMRI measures hemodynamic changes associated with neural activity, offering high spatial resolution (2-3 mm isotropic) but lower temporal resolution (1-3 seconds).[167.1] In contrast, EEG provides a direct measurement of electrical activity in the brain, capturing rapid changes in neural dynamics with high temporal resolution but coarser spatial resolution.[168.1] This complementary nature allows researchers to leverage the strengths of both techniques, using EEG to track fast electrical activity while employing fMRI to investigate the corresponding whole-brain correlates, particularly in deep subcortical structures.[166.1] The integration of EEG and fMRI has proven particularly valuable in studying cognitive processes and brain disorders. For instance, conditions such as schizophrenia, autism, and depression often involve disruptions in both the timing and spatial aspects of brain activity, making the combined approach essential for a comprehensive understanding of these disorders.[165.1] Moreover, the fusion of these modalities facilitates the exploration of brain networks and state changes, enhancing the ability to decipher complex brain circuitry and its dysfunctions.[152.1] Recent advancements in multimodal imaging techniques have enhanced the reliability and validity of neuroimaging data. Quantitative neuroimaging is particularly well-suited for statistical modeling and systematic image analysis, utilizing canonical templates or brain atlases to measure changes in specific regions of interest.[160.1] These quantitative methods are beginning to evaluate the brain as an integrated structural and functional network, which is critical for deciphering brain circuitry and understanding the dysfunction of brain network connectivity that occurs during the progression of various neurological and psychiatric disorders.[160.1] The role of digital brain atlases is crucial in modern neuroimaging research, as they standardize and simplify the analysis of complex brain data obtained through techniques such as fMRI.[161.1] The significance of these brain atlases is expected to grow due to the substantial increase in neuroimaging data, and it can be asserted that many major discoveries in clinical neuroscience over the past two decades would not have been possible without them.[160.1]Artificial Intelligence in Neuroimaging
Recent advancements in neuroimaging have increasingly incorporated artificial intelligence (AI) and machine learning techniques, significantly enhancing the analysis and interpretation of complex brain data. The integration of machine learning algorithms into neuroimaging data analysis allows for the identification of imaging signatures associated with various neurological diseases and disorders, including Alzheimer's disease, schizophrenia, and autism spectrum disorders.[182.1] These machine learning models can link observed neuroimaging data, such as the blood-oxygen-level-dependent (BOLD) signals from functional magnetic resonance imaging (fMRI), to specific experimental conditions, thereby revealing disease correlates that may not be visible through traditional structural imaging methods.[180.1] Moreover, the combination of functional and structural neuroimaging techniques facilitates a multi-modal description of the brain, enabling researchers to model the relationship between brain structure and function as potential markers for disease.[180.1] This approach is particularly beneficial in clinical settings, where machine learning has been employed to improve diagnostic accuracy and accelerate treatment discovery for conditions like Alzheimer's disease.[156.1] The use of sophisticated machine learning algorithms is further supported by the availability of large-scale neuroimaging datasets, which enhance the robustness and generalizability of these models across different populations and imaging modalities.[179.1] The integration of machine learning techniques in neuroimaging, particularly in the context of brain hemorrhage research, presents significant challenges due to the necessity of acquiring patient data from computed tomography (CT) scans. This complexity is highlighted by the fact that approximately seven million individuals worldwide experience brain injuries annually from various causes, including accidents and medical conditions.[181.1] In clinical neuroimaging, machine learning studies have successfully identified imaging signatures for a range of diseases and disorders, such as Alzheimer's Disease, schizophrenia, mood disorders, and autism.[182.1] However, the development of machine learning methods that not only make accurate predictions but also provide insights into the biological processes underlying these conditions is still in its infancy.[182.1] Furthermore, quantitative neuroimaging is increasingly adept at evaluating the brain as an integrated structural and functional network, which is essential for understanding the dysfunction of brain connectivity associated with neurological and psychiatric disorders.[155.1] These advancements in neuroimaging techniques may play a crucial role in addressing current challenges in clinical psychology and cognitive neuroscience, particularly in studying mechanisms of change and developing future applications that can enhance real-world designs and methods.[154.1]Neuroimaging And Mental Health
Understanding Psychiatric Disorders
Neuroimaging techniques have significantly advanced the understanding of psychiatric disorders by elucidating the biological underpinnings associated with various mental health conditions. Magnetic resonance imaging (MRI) methods, particularly functional MRI (fMRI), have become essential in psychiatric research, allowing for the exploration of brain circuit alterations linked to the etiology, pathophysiology, and treatment responses of mental illnesses. Studies utilizing fMRI have demonstrated altered brain activation patterns in patients with diverse psychiatric diagnoses, including schizophrenia, bipolar disorder, and depression, highlighting common interoception-related activation in the left dorsal mid-insula across these groups.[197.1] Neuroimaging techniques play a crucial role in enhancing our understanding of psychiatric disorders by providing insights into their biological underpinnings. Established methods such as computed tomography (CT) and electroencephalography (EEG) are complemented by more contemporary techniques like functional MRI (fMRI) and diffusion tensor imaging (DTI), which are still demonstrating their full translational potential in clinical practice.[190.1] For instance, research has linked the e4 allele of the apolipoprotein E (APOE) gene to reductions in grey matter volume and functional connectivity, suggesting that brain imaging could aid in the early detection of abnormalities in individuals at risk for Alzheimer’s disease.[190.1] Additionally, neuroimaging has been utilized to explore the pathophysiology of mental disorders by identifying cognitive tasks that serve as surrogate markers, such as working memory deficits in schizophrenia and theory of mind impairments in autism.[195.1] Furthermore, specific neuroimaging studies have demonstrated the relationship between dopamine D2 receptor occupancy and the alleviation of positive symptoms in schizophrenia, highlighting the therapeutic window for antipsychotic efficacy.[194.1] This integration of neuroimaging findings not only informs our understanding of mental disorders but also influences the development of personalized treatment plans, as evidenced by targeted interventions like transcranial magnetic stimulation (TMS) based on identified symptom-related brain regions.[195.1] Moreover, neuroimaging findings have begun to influence personalized treatment plans for patients with severe mental illnesses. For example, targeted interventions like transcranial magnetic stimulation (TMS) have been guided by neuroimaging data, allowing for precise targeting of brain regions implicated in emotional processing.[195.1] Additionally, the identification of specific brain alterations associated with treatment responses has the potential to refine therapeutic approaches, as evidenced by studies linking fMRI data to treatment outcomes in various psychiatric conditions.[197.1]Future Directions
Innovations in Imaging Modalities
Innovations in neuroimaging modalities are poised to significantly enhance our understanding and treatment of neurological conditions. The integration of advanced techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) is becoming increasingly important. These modalities serve as critical biomarkers for the identification, tracking, diagnosis, and treatment monitoring of various neurological diseases, including Alzheimer's disease, frontotemporal lobar degeneration, and Parkinson's disease.[239.1] Recent advancements in neuroimaging are also focusing on the development of specific radiotracers that target underlying proteinopathies, such as tau and amyloid, which are crucial in the evaluation of neurodegenerative disorders.[241.1] The combination of PET and MRI into a hybrid modality offers the advantage of high spatial resolution alongside specific molecular targeting, thereby providing a more comprehensive understanding of brain pathology.[241.1] Moreover, the application of machine learning techniques within neuroimaging is revolutionizing the field. These methods are being utilized to analyze complex imaging data, revealing disease correlates that may not be visible through traditional structural imaging alone.[230.1] Machine learning models can link neuroimaging information to experimental conditions, enhancing the precision of diagnoses and potentially leading to more personalized treatment strategies.[228.1] As the field progresses, the future of neuroimaging looks promising, with emerging technologies expected to yield even more detailed and accurate images of the brain and nervous system. This evolution is likely to have a profound impact on the diagnosis and treatment of a wide range of neurological conditions, ultimately improving patient outcomes.[233.1]Integration with Precision Medicine
The integration of neuroimaging with machine learning is revolutionizing precision medicine by enhancing the diagnosis and treatment of neurological disorders. Functional neuroimaging, particularly, offers insights into neurophysiological and molecular properties that structural imaging cannot reveal, enabling a comprehensive understanding of the brain's structure-function relationship as a disease marker.[237.1] This approach is pivotal in precision medicine, where individualized treatment strategies are developed based on specific brain function insights. Machine learning models are instrumental in analyzing neuroimaging data, such as the BOLD signals from fMRI, to identify brain regions associated with specific conditions. This capability allows for the detection of disease correlates that structural imaging might overlook, as demonstrated in studies on autism spectrum disorders.[237.1][232.1] The use of supervised machine learning techniques in this context underscores the potential for uncovering critical insights into brain function and pathology. Large-scale collaborations are crucial for advancing precision medicine, as they facilitate the collection and analysis of extensive qMRI data. These initiatives help address the replication crisis in neuroimaging by enabling robust data-driven analyses through machine and deep learning, which are essential for identifying reliable imaging signatures for various neurological disorders, including Alzheimer's disease and schizophrenia.[236.1] The integration of AI into neuroimaging is transformative, offering enhanced disease detection, predictive modeling, and treatment planning. However, it also introduces ethical challenges, such as algorithmic bias and data privacy concerns, which must be addressed to ensure equitable use of these technologies.[238.1] A collaborative approach involving researchers, clinicians, and ethicists is essential to maximize AI's benefits in improving patient outcomes in neurodegenerative diseases.[238.1]References
[1] History of Neuroimaging | The American Society of Neuroimaging — History of Neuroimaging. Presented to the ASN from the Archives by William McKinney. Edited and adapted for the ASN website by Rohit Bakshi. ... because the advent of new techniques for imaging the brain and spinal cord, for measuring its blood flow and neural function, are of greatest importance for care of patients with neurologic disorders
[4] Neuroimaging - Wikipedia — Functional brain imaging techniques, such as functional magnetic resonance imaging (fMRI), are common in neuroimaging but rarely used in neuroradiology. The biggest drawback of PET scanning is that because the radioactivity decays rapidly, it is limited to monitoring short tasks.: 60 Before fMRI technology came online, PET scanning was the preferred method of functional (as opposed to structural) brain imaging, and it continues to make large contributions to neuroscience. PET radioisotopes have limited exposure time in the body as they commonly have very short half-lives (~2 hours) and decay rapidly. Currently, fMRI is a preferred method of imaging brain activity compared to PET, since it does not involve radiation, has a higher temporal resolution than PET, and is more readily available in most medical settings.
[5] Neuroimaging - an overview | ScienceDirect Topics — A brief overview of select neuroimaging techniques is provided in Box 1. In addition to the variety of techniques that can be used to examine structural and functional properties of the living brain, neuroimaging is feasible in patient populations so long as they are able to lay still for a short amount of time (10-60 min). In fact
[6] Cognition: An Overview of Neuroimaging Techniques - ResearchGate — The advent of neuroimaging techniques for use in humans has led to an explosion of research on the neural basis of cognitive processes in humans during the past decade.
[7] PET/MRI for neurologic applications - PubMed — PET and MRI provide complementary information in the study of the human brain. Simultaneous PET/MRI data acquisition allows the spatial and temporal correlation of the measured signals, creating opportunities impossible to realize using stand-alone instruments. This paper reviews the methodologic im …
[8] PET/MRI for Neurological Applications - PMC - PubMed Central (PMC) — The features of this new technology may be particularly appealing to applications in neuroscience and translational neurologic/psychiatric research, considering that MR represents the first-line diagnostic imaging modality for numerous indications and that a great number of specific PET tracers are available today to assess functional and molecular processes in the brain. The concept of baseline state is an essential, though often overlooked, aspect of brain activation studies because the change in signal reported in response to a stimulus (measured either with PET or MR imaging techniques) is typically assessed with respect to an (unquantified) baseline condition. Simultaneous MR/PET imaging of the human brain: feasibility study.
[9] Neurological Applications of PET/MR - PMC - PubMed Central (PMC) — PET/MR benefits neurological clinical care and research by providing spatially and temporally matched anatomic MR imaging, advanced MR physiologic imaging, and metabolic PET imaging. ... Work published in this article was supported NIH grants P50 AG05681, ... Journal of magnetic resonance imaging: JMRI. 2012 Jan;35(1):56-63. doi: 10.1002/jmri
[18] PDF — https://www.eejournals.org Open Access Page | 5 Advancements in Neuroimaging for Early Detection of Neurological Disorders Kato Jumba K. This paper examines the most recent breakthroughs in neuroimaging, with a focus on resting-state functional MRI, presymptomatic detection in Alzheimer's research, diffusion tensor imaging for white matter analysis, and magnetoencephalography. Keywords: Neuroimaging, Early Detection, Alzheimer's Disease, Functional MRI, Diffusion Tensor Imaging INTRODUCTION Advancements in neuroimaging for early detection of neurological disorders. APPLICATIONS OF NEUROIMAGING IN EARLY DETECTION Neuroimaging is becoming increasingly useful in the early diagnosis and monitoring of neurological disorders. Technologies such as resting-state functional MRI, diffusion tensor imaging, and magnetoencephalography have provided unprecedented insights into the brain's structure and function, allowing for earlier and more accurate detection of diseases like Alzheimer’s and other neurodegenerative conditions. Advancements in Neuroimaging for Early Detection of Neurological Disorders.
[26] Exploring the Future of Neuro-Diagnostics: - InMed — Exploring the Future of Neuro-Diagnostics: ... Neuroimaging modalities/techniques have progressed in the past two decades. The techniques are quicker, less invasive and more comfortable. ... The role of neuroimaging in diagnosis and personalized medicine-current position and likely future directions. Dialogues Clin Neurosci [Internet]. 2009
[35] History of neuroimaging - Wikipedia — Neuroimaging first came about as a medical technique in the 1880s with the invention of the human circulation balance and has since lead to other inventions such as the x-ray, air ventriculography, cerebral angiography, PET/SPECT scans, magnetoencephalography, and xenon CT scanning. "Brain Imaging Explained." Online at http://www.nature.com/nsu/010712/010712-13.html "Connecting Cortex to Machines: Recent Advance in Brain Interfaces." Online at http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v5/n11s/full/nn947.html "UCLA Researchers Map Brain Growth in Four Dimensions, Revealing Stage-Specific Growth Patterns in Children." Online at https://web.archive.org/web/20041204085436/http://www.loni.ucla.edu/~thompson/MEDIA/press_release.html and https://web.archive.org/web/20041204083259/http://www.loni.ucla.edu/~thompson/JAY/Growth_REVISED.html "Bioinformatics and Brain Imaging: Recent Advances and Neuroscience Applications." Online at https://web.archive.org/web/20050118095748/http://www.loni.ucla.edu/~thompson/SFN2002/SFN2002coursePT_v4.pdf "Brain Scan Technology Poised to Play Policy Roll." Online at https://web.archive.org/web/20041204084542/http://www.loni.ucla.edu/~thompson/MEDIA/RH/rh.html
[36] PDF — On October 1, 1971 Godfrey Hounsfield and Jamie Am-brose positioned a patient inside a new machine in the basement of the hospital turned a switch and launched the era of modern neurosurgery and neuroimaging.
[37] History of Neuroimaging | The American Society of Neuroimaging — In recounting a history of his life in neuroimaging to the American Society of Neuroimaging (ASN) in 1992, Oldendorf discussed Godfrey Hounsfield and his successful introduction of the CT scanner. In 1975 James Toole, MD chairman of the Ad hoc committee on Neuroimaging of the American Academy of Neurology (AAN) wrote to Oldendorf regarding his opinion of the effect of CT scanning on the practice of neurology. In 1972 Dr. William Stuart of Atlanta, Georgia visited the Massachusetts General Hospital to see a new piece of equipment that “was going to revolutionize the practice of neurology.” He returned to Atlanta with Polaroid images from the “primitive CT scan of that period.” The Atlanta neurologists bought the 16th scanner off the EMI assembly line.
[38] Historical Advancements in Neuroimaging Techniques — Historical Advancements in Neuroimaging Techniques - Society for Brain Mapping and Therapeutics SOCIETY FOR BRAIN MAPPING AND THERAPEUTICS What is Brain Mapping Annual Brain Mapping Day at the US Congress SBMT Initiatives ME + Brain ME + Brain Impact: This discovery revolutionized understanding of how the brain operates in a baseline state and its alterations in disorders like Alzheimer’s, schizophrenia, and depression. MRI provides high-resolution images of brain structures, enabling early detection of tumors, multiple sclerosis, and stroke. Pre-surgical Planning: MRI and DTI help map brain structures and connectivity, minimizing risks during neurosurgery. The Society for Brain Mapping and Therapeutics (SBMT) was founded in 2004 to break boundaries in healthcare. What Is Brain Mapping Copyright © 2024 Society for Brain Mapping and Therapeutics (SBMT).
[39] History of neuroimaging: the legacy of William Oldendorf — The field of neuroimaging witnessed remarkable progress in the post-World War II era, resulting in tremendous benefits for healthcare today. One such important milestone was the development of the computerized axial tomography (CAT) scan. This state of the art technique has paved the way for modern …
[40] PDF — strategies, and a deeper understanding of brain function . Conclusion The evolution of brain imaging, from the early days of CT scans to the sophisticated techniques of today, has profoundly expanded our knowledge of the brain. Each advancement has provided new insights into neurological function and disease, improving diagnosis and treatment.
[65] Neuroethics in a New Era of Neuroimaging | American Journal of ... — The ethical challenges introduced by advanced capabilities in neuroimaging were recognized as a priority for the new discipline, taking into consideration significant concerns and potentially thorny issues that have surfaced both in research and in the clinical environment.
[66] Legal and ethical issues in neuroimaging research: human subjects ... — Current efforts to delineate the field of "neuroethics" reflect an emerging view that ethical problems in the neurosciences merit a distinct domain within the broader arena of bioethics (Stanford Center for Bioethics, 2002).As evidenced by this special issue of the journal Brain and Cognition, neuroimaging techniques are a logical focus for this new "neuroethical" inquiry, given the
[67] Exploring responsible innovation: Dutch public perceptions of the ... — Insight into public perceptions provides opportunities to take public desires and concerns into account in an early phase of innovation development in order to maximise the potential benefits for users of the future. Public perceptions of neuroimaging in health care are presented in this article, based on research undertaken in the Netherlands.
[69] Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in ... — Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders - PubMed Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Neuroimaging techniques such as fMRI (A) and EEG (B) have revolutionized our understanding of brain function and have become essential tools in studying neurological disorders.
[71] The vast potential and bright future of neuroimaging - PMC — Quantitative neuroimaging is well-suited for statistical modeling and systematic image analysis approaches that utilize canonical templates or brain atlases to measure changes in specific regions of interest.7 Quantitative methods are beginning to evaluate the brain as an integrated structural and functional network.8 These new approaches might be critical not only for deciphering brain circuitry but also for understanding the dysfunction of brain network connectivity that occurs during the progression of many neurological and psychiatric disorders.
[72] Advances in functional magnetic resonance imaging: Technology and ... — Investigators have used fMRI to identify abnormal functional brain activity during task performance in a variety of patient populations, including those with neurodegenerative, demyelinating, cerebrovascular, and other neurological disorders that highlight the potential utility of fMRI in both basic and clinical spheres of research.
[73] Functional MRI: The Revolutionary Innovations of 2023 — Functional magnetic resonance imaging (fMRI) is a revolutionary technique in medicine that allows real-time visualization of brain activity. The integration of artificial intelligence into fMRI image analysis also opens new perspectives for the rapid detection of pathologies. Advances in imaging, new analysis methods, and better awareness among practitioners will enable optimal use of this technology, thus transforming the diagnosis and treatment of neurological diseases. A new imaging technology against cancer powered by artificial intelligence in Hong Kong Recevez directement dans votre boîte mail les dernières actualités, articles, et ressources pour rester informé et inspiré, où que vous soyez. GlobalSante.org est une plateforme dédiée aux professionnels de la santé, offrant des articles spécialisés, des ressources pratiques, et les dernières actualités médicales.
[74] Case Studies: fMRI in Neurological Disorders - Society for Brain ... — Case Study 1: Memory and Aging Objective: Analyze how resting-state fMRI uncovers changes in functional connectivity linked to aging. Example: A longitudinal study involving older adults used resting-state fMRI to detect early signs of Alzheimer's disease by observing disruptions in the Default Mode Network (DMN). Researchers identified that decreased connectivity in specific DMN regions
[75] Functional neuroimaging as a catalyst for integrated neuroscience — Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Q. Typical and atypical development of functional human brain networks: insights from resting-state fMRI. This article presents a method for concurrent widefield optical imaging and fMRI, enabling cell-type-specific investigations of how different neural populations contribute to the fMRI signal as well as more precise translation between mouse models and human studies. M. Functional brain connectivity Using fMRI in aging and Alzheimer’s disease. L. Identifying natural images from human brain activity. & Shine, J.M. Functional neuroimaging as a catalyst for integrated neuroscience.
[81] Integration of multimodal neuroimaging methods: a rationale for ... — The EEG-fMRI approach has several promising clinical applications. The first is the detection of cortical areas involved in interictal and ictal epileptic activity. Second, combining evoked potentials with fMRI could be an accurate way to study eloquent cortical areas for the planning of neurosurgery or rehabilitation, circumventing the above-mentioned limitation of fMRI. Finally, the use of
[98] Legal and ethical issues in neuroimaging research: human subjects ... — Legal and ethical issues in neuroimaging research: human subjects protection, medical privacy, and the public communication of research results - ScienceDirect Legal and ethical issues in neuroimaging research: human subjects protection, medical privacy, and the public communication of research results The focus will be on three issues of contemporary significance in clinical research: compliance with regulatory requirements for the protection of human subjects, safeguarding privacy and confidentiality, and ethical behavior in the public communication of research results. This paper has explored ethical issues of significance to neuroimaging researchers, including protections for human subjects, issues of privacy and confidentiality, and a researcher’s obligation to communicate science responsibly.
[99] Ethical considerations of neuroscience research and the application of ... — The International Neuroethics Society (INS) recently commented on the ethical considerations of neuroscience research and the application of neuroscience research findings for the Presidential Commission for the Study of Bioethical Issues, published in the Federal Register Jan. 31, 2014. COMMENTS BY THE INTERNATIONAL NEUROETHICS SOCIETY (INS) ON THE ETHICAL CONSIDERATIONS OF NEUROSCIENCE RESEARCH AND THE APPLICATION OF NEUROSCIENCE RESEARCH FINDINGS FOR THE PRESIDENTIAL COMMISSION FOR THE STUDY OF BIOETHICAL ISSUES These include: issues of transparency; issues of consent; safety of neuroimaging techniques; use of large databases; security of personal databases (containing e.g. disease or genetic information); obsolescence of data in a rapidly developing field where technology and methodology are updated frequently; secondary findings of potential clinical significance and return of results.
[100] A Just Standard: The Ethical Management of Incidental Findings in Brain ... — Moreover, disclosing incidental findings of unknown or uncertain significance may impede the satisfaction of researchers’ institutional obligations, by precipitating the expenditure of significant health care resources for participant follow-up, with only a small chance of benefit. We have argued that researchers have an obligation to look for and disclose incidental findings arising from neuroimaging research only insofar as this is a requirement of the basic care to which participants are entitled as a matter of distributive justice. While attempts to justify disclosure on the basis of respect for participant autonomy or beneficence (arising either from the researcher as a moral agent, or in their role as a researcher) provide a reasonable justification for disclosing incidental findings in cases where there is clear clinical benefit, they inadequately address researcher’s responsibilities regarding findings of uncertain or unknown significance.
[102] Machine learning in neuroimaging: Progress and challenges — The application of machine learning methods to neuroimaging has risen more rapidly than could have been predicted 15 years ago. It has been a very exciting new direction in neuroimaging, as it has expanded the field from population-based analyses into individualized biomarkers of diseases or functional brain states. From a clinical perspective
[103] Machine learning in neuroimaging: from research to clinical practice — Compared to structural imaging, functional neuroimaging can detect underlying neurophysiological and molecular properties, and combining both enables a multi-modal description of the brain and the modeling of its structure–function relationship as a marker for disease [17–19]. Machine learning models link the observed neuroimaging information such as the sequential BOLD signal observed for each image voxel of fMRI data to experiment conditions, aiming to identify brain regions whose functional signal is associated with the condition (Fig. 2). Supervised machine learning using functional connectivity data revealed disease correlates not visible in structural imaging, for instance, deep learning models for the identification of characteristics of autism spectrum disorders or a generative autoencoder model to classify autism . Machine learning offers a means to align individual brains based on their functional imaging data.
[104] How Machine Learning is Powering Neuroimaging to Improve Brain Health — This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. ... How Machine Learning is Powering Neuroimaging to Improve Brain Health Neuroinformatics. 2022 Oct;20(4) :943
[105] Deep Learning Aided Neuroimaging and Brain Regulation — As AI techniques continue to be refined and improved, deep learning has been proposed to dramatically change the health care monitoring and regulation of the brain , which can not only improve the reconstruction accuracy of neuroimaging and achieve fast imaging, but also mine a large amount of pathological and genetic data by processing and cross-referencing health and medical big data such as images, pathology, and genes, and help pathologists to evaluate pathological sections faster to improve the efficiency and prognosis of disease diagnosis . In recent years, several deep learning-based medical image analysis methods have been introduced to facilitate health monitoring and diagnosis using brain MRI scans .
[112] Neuroimaging techniques and their applications - conpas — Neuroimaging techniques have revolutionized our understanding of the brain’s structure and function. Multi-modal imaging combines information from different neuroimaging techniques to provide a comprehensive understanding of the brain. While neuroimaging techniques have significantly improved in terms of resolution, there are still limitations in capturing fine details of brain structures and functions. Initiatives like the Human Connectome Project and the Alzheimer’s Disease Neuroimaging Initiative have already demonstrated the power of collaborative research in advancing our understanding of brain networks and disease processes. Neuroimaging techniques have transformed the field of neuroscience, providing unprecedented insights into the structure and function of the human brain. Despite challenges such as cost, resolution limitations, and ethical considerations, continuous advancements in technology and interdisciplinary collaborations are paving the way for a future where neuroimaging plays an even more significant role in understanding and improving brain health.
[113] Top Neuroimaging Techniques for Diagnosing Brain Disorders — Whether you’re curious about how neuroimaging identifies brain disorders or seeking information on the best imaging tests for memory loss, this guide provides clear, patient-centered insights. Using advanced technologies like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), neuroimaging offers vital information about the brain’s anatomy and function. By employing advanced techniques such as MRI, CT scans, and functional imaging, healthcare providers can visualize the brain’s structure and function. Neuroimaging, often referred to as brain imaging, is a non-invasive diagnostic procedure that provides detailed images of the brain’s structure and function. The specific steps involved depend on the type of imaging test, such as an MRI brain scan, CT scan, or functional neuroimaging.
[114] Selecting Neuroimaging Techniques: A Review for the Clinician — Positron emission tomography (PET) is a nuclear medicine diagnostic technique used to obtain functional brain scans, similar to fMRI and different from standard CT and MRI scans that provide structural information. PET has an important role in the localization of the focus for cases in which surgical treatment is considered.22,23 Other less common applications of nuclear medicine techniques in neuropsychiatry are the evaluation of brain tumors (which tend to be hypermetabolic) and head trauma (particularly in the chronic phase if affective, behavioral, and cognitive symptoms are present and structural imaging is unrevealing).24,25
[115] Neurology Images: Complete Guide to Brain Imaging Techniques & Analysis — Neurological imaging is a crucial component of modern medicine that allows healthcare professionals to visualize and understand the complex structures and functions of the brain and nervous system. Neurological images are medical visualizations of the brain and nervous system used for diagnosis, treatment planning, and research. Beyond basic structural imaging, advanced MRI techniques can now map blood flow, track neural activity, and even measure the direction of water movement within brain tissue, providing insights into neural connectivity. Also Read: Dr. Imran Lasker's Monthly Case Webinar #6: Neurological MRI to Trauma Bone Imaging CT Neurological imaging represents a cornerstone of modern neurology, providing crucial insights into brain structure and function.
[118] The connectomics revolution: Utilizing Resting State fMRI and DTI to ... — The connectomics revolution: Utilizing Resting State fMRI and DTI to personalize the treatment of neurological and psychocognitive disorders - ScienceDirect The connectomics revolution: Utilizing Resting State fMRI and DTI to personalize the treatment of neurological and psychocognitive disorders Resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) are critical imaging modalities that provide insights into the brain's functional and structural connectivity without the need for active patient participation. This presentation is designed to showcase the pivotal role of rs-fMRI and DTI in the burgeoning field of personalized functional imaging. Employing rs-fMRI and DTI for connectomic analysis has yielded promising results in pinpointing neurological disease biomarkers, deciphering psychiatric disorder pathways, and crafting tailored therapeutic interventions. Journal of Cardiovascular Magnetic Resonance, Volume 19, Issue 1, 2016, Article 21
[119] Historical Advancements in Neuroimaging Techniques — Historical Advancements in Neuroimaging Techniques - Society for Brain Mapping and Therapeutics SOCIETY FOR BRAIN MAPPING AND THERAPEUTICS What is Brain Mapping Annual Brain Mapping Day at the US Congress SBMT Initiatives ME + Brain ME + Brain Impact: This discovery revolutionized understanding of how the brain operates in a baseline state and its alterations in disorders like Alzheimer’s, schizophrenia, and depression. MRI provides high-resolution images of brain structures, enabling early detection of tumors, multiple sclerosis, and stroke. Pre-surgical Planning: MRI and DTI help map brain structures and connectivity, minimizing risks during neurosurgery. The Society for Brain Mapping and Therapeutics (SBMT) was founded in 2004 to break boundaries in healthcare. What Is Brain Mapping Copyright © 2024 Society for Brain Mapping and Therapeutics (SBMT).
[121] MRI vs. CT Scan: A Comparative Study in Radiology Diagnosis — When comparing MRI and CT scan, several factors should be considere d, including image quality, radiation exposure, diagnostic capabilities, patient characteristics, and cost. MR I generally
[148] Neuroimaging in Psychiatry: From Bench to Bedside — We will focus on studies that tried to utilise neuroimaging to understand the pathophysiology of mental disorders by identifying cognitive tasks acting as a surrogate marker (e.g., working memory in schizophrenia, theory of mind in autism), those that compared different diagnostic or at-risk groups in order to obtain diagnostic or prognostic markers, and those that integrated neuroimaging in treatment protocols. The identification of these symptom-related regions and networks may inform treatment, for example targeted interference with transcranial magnetic stimulation (TMS), as shown for example with a NIRS-guided repetitive TMS treatment protocol in a patient with panic disorder targeted on a frontal brain region implicated in emotion processing (Dresler et al., 2009).
[149] Understanding the Value and Limitations of MRI Neuroimaging in Psychiatry — In psychiatry, MRI neuroimaging methods have become a critical tool for performing research with the intent of developing a better understanding of brain circuit alterations that are associated with etiology, pathophysiology, and treatment response. In this issue of the Journal, we present a series of papers that highlight the use of various neuroimaging techniques that are aimed at linking brain structure and function to 1) understanding pathophysiological processes associated with illnesses, 2) factors related to normative development and early-life risk, and 3) treatment response. By comparing fMRI data from 626 patients with a range of psychiatric diagnoses to 610 control subjects, the authors found altered interoception-related left dorsal mid-insula activation to be common across the patient groups (diagnoses included schizophrenia, bipolar disorder, depression, anxiety disorders, posttraumatic stress disorder, eating disorders, and substance use disorders).
[152] The vast potential and bright future of neuroimaging - PMC — Quantitative neuroimaging is well-suited for statistical modeling and systematic image analysis approaches that utilize canonical templates or brain atlases to measure changes in specific regions of interest.7 Quantitative methods are beginning to evaluate the brain as an integrated structural and functional network.8 These new approaches might be critical not only for deciphering brain circuitry but also for understanding the dysfunction of brain network connectivity that occurs during the progression of many neurological and psychiatric disorders.
[154] Future Applications of Real-World Neuroimaging to Clinical Psychology ... — Therefore, the present article describes the concept of ecological validity, fNIRS as an emerging neuroimaging method, current challenges in clinical psychology and cognitive neuroscience to studying mechanisms of change, and future applications of real-world designs and methods that can address these issues in theory and practice.
[155] The vast potential and bright future of neuroimaging - PMC — Quantitative neuroimaging is well-suited for statistical modeling and systematic image analysis approaches that utilize canonical templates or brain atlases to measure changes in specific regions of interest.7 Quantitative methods are beginning to evaluate the brain as an integrated structural and functional network.8 These new approaches might be critical not only for deciphering brain circuitry but also for understanding the dysfunction of brain network connectivity that occurs during the progression of many neurological and psychiatric disorders.
[156] Current and future uses of neuroimaging for cognitively impaired ... — Developments in diagnosis and treatment of age-related cognitive decline have led to better detection of and symptomatic treatment for Alzheimer’s disease (AD), and neuroimaging is often used to assist physicians in making an accurate diagnosis. The prospect of more effective treatments for AD and other dementias, and the potential for improving memory and other cognitive functions in people with mild, age-related decline, as well as delaying their progression, has led to new brain imaging technologies that might not only further improve diagnostic accuracy but also accelerate treatment discovery. In-vivo imaging of Alzheimer disease β-amyloid with [11C]SB-13 PET. PET imaging of cortical 11C-nicotine binding correlates with the cognitive function of attention in Alzheimer’s disease. Changes in brain 11C-nicotine binding sites in patients with mild Alzheimer’s disease following rivastigmine treatment as assessed by PET.
[160] Professor Wieslaw Nowinski: 25 years of contribution to the development ... — The role of digital brain atlases is therefore absolutely crucial in modern neuroimaging research, and will continue to grow due to the huge increase in data. It can be safely assumed that without brain atlases, many major discoveries in clinical neuroscience over the last 20 years would not have been possible.
[161] A short investigation of the effect of the selection of human brain ... — These brain atlases are used to standardize and simplify the process of analyzing complex brain data using neuroimaging techniques, such as fMRI. Brain atlases can be categorized into two classes, "Anatomical" and "Functional" where the anatomical atlas is related to the physical structures of the brain and the functional atlas is
[165] Brain Wars: EEG vs. fMRI — The Battle for Neuroimaging Supremacy — Another area where EEG and fMRI complement each other is in the investigation of brain disorders. Conditions like schizophrenia, autism, and depression involve disruptions in both the timing and
[166] Multimodal EEG-fMRI: advancing insight into large-scale human brain ... — Keywords: functional neuroimaging, brain networks, state changes, hemodynamics, EEG-fMRI Scalp EEG provides a non-invasive view into brain electrical activity, and is complementary to fMRI: EEG captures fast electrical activity, with the ability to resolve temporal markers of natural or pathological state changes (e.g., sleep and seizure activity), but with coarser spatial resolution and different physiological origins compared to fMRI. Therefore, one major framework in fusing EEG-fMRI has involved the use of EEG – with its high temporal resolution - to track the occurrence or continuous modulation of electrical activity over time, and fMRI - with its superior spatial resolution - to investigate the corresponding whole-brain correlates, particularly in deep subcortical structures that regulate brain states but which are less accessible to scalp EEG.
[167] Multimodal Neuroimaging with Simultaneous fMRI and EEG — Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are noninvasive techniques used to measure neural activity in the human brain. fMRI measures the magnetic resonance signal associated with hemodynamic changes driven by neural activity and has a good spatial resolution (2-3 mm isotropic) and low temporal resolution (1-3 s).
[168] Simultaneous EEG and fMRI: towards the characterization of structure ... — Both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are extremely efficient methods for the assessment of human brain function. The specific appeal of the combination is related to the fact that both methods are complementary in terms of basic aspects: EEG is a direct measurement of neural mass activity and
[179] Introduction to Machine Learning in Neuroimaging - PubMed — Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical steps for the analysis of neuroimaging data using machine learning (ML), while the field of radiomics will be addressed separately (c.f., Chap. 18 -Radiomics).
[180] Machine learning in neuroimaging: from research to clinical practice — Compared to structural imaging, functional neuroimaging can detect underlying neurophysiological and molecular properties, and combining both enables a multi-modal description of the brain and the modeling of its structure–function relationship as a marker for disease [17–19]. Machine learning models link the observed neuroimaging information such as the sequential BOLD signal observed for each image voxel of fMRI data to experiment conditions, aiming to identify brain regions whose functional signal is associated with the condition (Fig. 2). Supervised machine learning using functional connectivity data revealed disease correlates not visible in structural imaging, for instance, deep learning models for the identification of characteristics of autism spectrum disorders or a generative autoencoder model to classify autism . Machine learning offers a means to align individual brains based on their functional imaging data.
[181] Deep-Learning Based Multi-Modalities Fusion for the ... - Springer — The machine learning applications in brain haemorrhage research are particularly difficult since it requires the acquisition of patient data from computed tomography (CT) scan pictures . Every year, roughly seven million people worldwide encounter brain injuries as a result of car accidents, falls, attacks, brain disorders, or blood vessel
[182] Machine learning in neuroimaging: Progress and challenges — For example, in clinical neuroimaging, machine learning studies revealed imaging signatures for a number of diseases and disorders, such as Alzheimer's Disease ((Kloppel et al., 2008; Zhang et al., 2011); see (Rathore et al., 2017) for a recent review), brain development and aging (Franke et al., 2010; Habes et al., 2016; Xia et al., 2018), preclinical states (Davatzikos et al., 2009), schizophrenia (Davatzikos et al., 2005a) and its prodromal stages (Koutsouleris et al., 2009), mood disorders (Koutsouleris et al., 2015), and autism (Ecker et al., 2010), amongst others. The literature certainly uses such models (Brodersen et al., 2011), and adaptations of GAN deep networks might hold promise, however the development of machine learning methods that seek primarily to provide insights into the biology of disease processes or brain function, in addition to making the right decision, is still in its infancy.
[190] Everything you wanted to know about neuroimaging and psychiatry, but ... — Some techniques, such as computed tomography (CT), electroencephalography (EEG) and structural magnetic resonance imaging (sMRI), are well established in clinical practice, whereas more contemporary techniques, such as functional MRI (fMRI) and diffusion tensor imaging (DTI), have yet to show their full translational potential. For example, research has enabled us to link the increased risk of developing Alzheimer’s disease associated with e4 allele of the apolipoprotein E (APOE) gene to reductions in grey matter volume (Reference Shaw, Lerch and PruessnerShaw 2007) and functional connectivity (Reference Filippini, MacIntosh and HoughFilippini 2009) in young people, suggesting that brain imaging on people with this mutation might help early detection of brain abnormalities.
[194] Neuroimaging in Psychiatric Disorders - PMC — An example of the use of neuroimaging in preclinical models is the demonstration that a new PET compound, [11C]GSK931145, binds the glycine transporter 1, an important modulator of NMDA receptors, which are considered to be hypoactive in schizophrenia . For instance, by imaging striatal dopamine (DA) D2 receptors using [11C]raclopride PET, a link was found between D2 receptor occupancy by medication and a reduction in positive symptoms in schizophrenia: ascending doses of up to 80% receptor occupancy were progressively more effective in relieving delusions and hallucinations . As mentioned, the original PET studies with neuroleptics showed that DA D2 antagonists have antipsychotic efficacy with minimal extrapyramidal syndrome side effects within a 'therapeutic window' of 65–80% striatal D2 receptor occupancy. Dose-occupancy study of striatal and extrastriatal dopamine D(2) receptors by aripiprazole in schizophrenia with PET and [(18)F]Fallypride.
[195] Neuroimaging in Psychiatry: From Bench to Bedside — We will focus on studies that tried to utilise neuroimaging to understand the pathophysiology of mental disorders by identifying cognitive tasks acting as a surrogate marker (e.g., working memory in schizophrenia, theory of mind in autism), those that compared different diagnostic or at-risk groups in order to obtain diagnostic or prognostic markers, and those that integrated neuroimaging in treatment protocols. The identification of these symptom-related regions and networks may inform treatment, for example targeted interference with transcranial magnetic stimulation (TMS), as shown for example with a NIRS-guided repetitive TMS treatment protocol in a patient with panic disorder targeted on a frontal brain region implicated in emotion processing (Dresler et al., 2009).
[197] Understanding the Value and Limitations of MRI Neuroimaging in ... — In psychiatry, MRI neuroimaging methods have become a critical tool for performing research with the intent of developing a better understanding of brain circuit alterations that are associated with etiology, pathophysiology, and treatment response. In this issue of the Journal, we present a series of papers that highlight the use of various neuroimaging techniques that are aimed at linking brain structure and function to 1) understanding pathophysiological processes associated with illnesses, 2) factors related to normative development and early-life risk, and 3) treatment response. By comparing fMRI data from 626 patients with a range of psychiatric diagnoses to 610 control subjects, the authors found altered interoception-related left dorsal mid-insula activation to be common across the patient groups (diagnoses included schizophrenia, bipolar disorder, depression, anxiety disorders, posttraumatic stress disorder, eating disorders, and substance use disorders).
[228] Machine learning in neuroimaging: Progress and challenges — For example, in clinical neuroimaging, machine learning studies revealed imaging signatures for a number of diseases and disorders, such as Alzheimer's Disease ((Kloppel et al., 2008; Zhang et al., 2011); see (Rathore et al., 2017) for a recent review), brain development and aging (Franke et al., 2010; Habes et al., 2016; Xia et al., 2018), preclinical states (Davatzikos et al., 2009), schizophrenia (Davatzikos et al., 2005a) and its prodromal stages (Koutsouleris et al., 2009), mood disorders (Koutsouleris et al., 2015), and autism (Ecker et al., 2010), amongst others. The literature certainly uses such models (Brodersen et al., 2011), and adaptations of GAN deep networks might hold promise, however the development of machine learning methods that seek primarily to provide insights into the biology of disease processes or brain function, in addition to making the right decision, is still in its infancy.
[230] Machine learning in neuroimaging: from research to clinical practice — Compared to structural imaging, functional neuroimaging can detect underlying neurophysiological and molecular properties, and combining both enables a multi-modal description of the brain and the modeling of its structure–function relationship as a marker for disease [17–19]. Machine learning models link the observed neuroimaging information such as the sequential BOLD signal observed for each image voxel of fMRI data to experiment conditions, aiming to identify brain regions whose functional signal is associated with the condition (Fig. 2). Supervised machine learning using functional connectivity data revealed disease correlates not visible in structural imaging, for instance, deep learning models for the identification of characteristics of autism spectrum disorders or a generative autoencoder model to classify autism . Machine learning offers a means to align individual brains based on their functional imaging data.
[232] Machine learning in neuroimaging: from research to clinical practice — Compared to structural imaging, functional neuroimaging can detect underlying neurophysiological and molecular properties, and combining both enables a multi-modal description of the brain and the modeling of its structure–function relationship as a marker for disease [17–19]. Machine learning models link the observed neuroimaging information such as the sequential BOLD signal observed for each image voxel of fMRI data to experiment conditions, aiming to identify brain regions whose functional signal is associated with the condition (Fig. 2). Supervised machine learning using functional connectivity data revealed disease correlates not visible in structural imaging, for instance, deep learning models for the identification of characteristics of autism spectrum disorders or a generative autoencoder model to classify autism . Machine learning offers a means to align individual brains based on their functional imaging data.
[233] How Neuroimaging changes diagnosis of neurological disorders - Medicai — The future of neuroimaging is bright, with new technologies and techniques emerging that promise to provide even more detailed and accurate images of the brain and nervous system. These advances are likely to have a significant impact on the diagnosis and treatment of a wide range of neurological conditions. Artificial Intelligence (AI)
[236] Machine learning in neuroimaging: Progress and challenges — For example, in clinical neuroimaging, machine learning studies revealed imaging signatures for a number of diseases and disorders, such as Alzheimer's Disease ((Kloppel et al., 2008; Zhang et al., 2011); see (Rathore et al., 2017) for a recent review), brain development and aging (Franke et al., 2010; Habes et al., 2016; Xia et al., 2018), preclinical states (Davatzikos et al., 2009), schizophrenia (Davatzikos et al., 2005a) and its prodromal stages (Koutsouleris et al., 2009), mood disorders (Koutsouleris et al., 2015), and autism (Ecker et al., 2010), amongst others. The literature certainly uses such models (Brodersen et al., 2011), and adaptations of GAN deep networks might hold promise, however the development of machine learning methods that seek primarily to provide insights into the biology of disease processes or brain function, in addition to making the right decision, is still in its infancy.
[237] Machine learning in neuroimaging: from research to clinical practice — Compared to structural imaging, functional neuroimaging can detect underlying neurophysiological and molecular properties, and combining both enables a multi-modal description of the brain and the modeling of its structure–function relationship as a marker for disease [17–19]. Machine learning models link the observed neuroimaging information such as the sequential BOLD signal observed for each image voxel of fMRI data to experiment conditions, aiming to identify brain regions whose functional signal is associated with the condition (Fig. 2). Supervised machine learning using functional connectivity data revealed disease correlates not visible in structural imaging, for instance, deep learning models for the identification of characteristics of autism spectrum disorders or a generative autoencoder model to classify autism . Machine learning offers a means to align individual brains based on their functional imaging data.
[238] Artificial intelligence in neuroimaging: Opportunities and ethical ... — Artificial intelligence in neuroimaging: Opportunities and ethical challenges - ScienceDirect Artificial intelligence in neuroimaging: Opportunities and ethical challenges open access The integration of artificial intelligence (AI) into neuroimaging represents a transformative shift in the diagnosis and treatment of neurodegenerative diseases. This letter discusses the opportunities AI presents in neuroimaging, including improved disease detection, predictive modeling, and treatment planning. Issues such as algorithmic bias, data privacy, and the interpretability of AI-driven insights must be addressed to ensure that these technologies are used responsibly and equitably. As neuroimaging continues to evolve, a collaborative approach involving researchers, clinicians, and ethicists is essential to navigate these challenges and maximize the benefits of AI in improving patient outcomes in neurodegenerative diseases. Next article in issue No articles found. For all open access content, the relevant licensing terms apply.
[239] Neuroimaging Advances in Neurologic and Neurodegenerative Diseases — Neuroimaging Advances in Neurologic and Neurodegenerative Diseases - PMC Neuroimaging tools, including magnetic resonance imaging (MRI), positron emission tomography (PET), and others (computerized tomography (CT), single-photon emission computerized tomography (SPECT), etc.) can be important biomarkers for identification, tracking, diagnosis, and treatment monitoring of neurologic diseases. Widely used in neurodegenerative disorders like Alzheimer’s disease (AD), frontotemporal lobar degeneration (FTLD), and Parkinson’s disease (PD) and associated disorders, neuroimaging methods are also applied to many other neurologic diseases to uncover important information about underlying biology, diagnostic classification, and treatment response. Finally, Brooks (2020) provides a comprehensive review of neuroimaging in PD and related disorders (such as LBD), focusing on MRI, SPECT (i.e., DATScan), and PET findings (primarily dopamine-focused and other neurotransmitter-focused tracers) .
[241] Hybrid PET/MRI in Neurodegenerative Disorders - Advances in Clinical ... — Imaging will play a major role in this transition, particularly specific radiotracers designed to target underlying proteinopathies, such as tau and amyloid. In parallel, PET/MRI is emerging as a hybrid modality with the advantage of combining specific molecular targeting with high spatial resolution in the evaluation of neurodegenerative