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Relevant bibliographies by topics / Neuroimaging

Academic literature on the topic 'Neuroimaging'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2024

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Journal articles on the topic "Neuroimaging"

1

Luetmer, Patrick H. "Neuroimaging." Mayo Clinic Proceedings 75, no. 7 (July 2000): 772. http://dx.doi.org/10.4065/75.7.772.

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Travis, Michael J., and Robert Kerwin. "Neuroimaging." Current Opinion in Psychiatry 10, no. 1 (January 1997): 16–25. http://dx.doi.org/10.1097/00001504-199701000-00005.

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Warach, Steven, and Jean-Claude Baron. "Neuroimaging." Stroke 35, no. 2 (February 2004): 351–53. http://dx.doi.org/10.1161/01.str.0000115163.59487.fd.

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HOON, ALEXANDER H., and ELIAS R. MELHEM. "Neuroimaging." Journal of Developmental & Behavioral Pediatrics 21, no. 4 (August 2000): 291–302. http://dx.doi.org/10.1097/00004703-200008000-00007.

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Butler, Katy, and Robert L. Hendren. "Neuroimaging." Journal of the American Academy of Child & Adolescent Psychiatry 36, no. 11 (November 1997): 1637–38. http://dx.doi.org/10.1097/00004583-199711000-00035.

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Klein, Joshua. "Neuroimaging." Seminars in Neurology 37, no. 05 (October 2017): 483–84. http://dx.doi.org/10.1055/s-0037-1608794.

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Luetmer, Patrick H. "Neuroimaging." Mayo Clinic Proceedings 75, no. 7 (July 2000): 772. http://dx.doi.org/10.1016/s0025-6196(11)64634-x.

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Theodore, William H. "Neuroimaging." Neurologic Clinics 4, no. 3 (August 1986): 645–68. http://dx.doi.org/10.1016/s0733-8619(18)30968-x.

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Mechtler, Laszlo L. "Neuroimaging." Neurologic Clinics 38, no. 1 (February 2020): i. http://dx.doi.org/10.1016/s0733-8619(19)30099-4.

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Araiza, Joseph, and Beatrix Araiza. "NEUROIMAGING." Emergency Medicine Clinics of North America 15, no. 3 (August 1997): 507–26. http://dx.doi.org/10.1016/s0733-8627(05)70315-1.

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Dissertations / Theses on the topic "Neuroimaging"

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Grover, Vijay Paul Bob. "Neuroimaging in liver disease." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520951.

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Punugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.

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The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-processing methods is used in three machine learning algorithms namely: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and k-Nearest Neighbors (k-nn) to recognize disease patterns and classify the diseases. While neurodegenerative diseases and psychiatric diseases overlap with a mix of diseases and resulted in fairly moderate classification, the classification between Alzheimer's disease and Cerebrovascular Small Vessel diseases yielded good results with an accuracy of up to 73.7%.

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Khusnullina, Aygul A. "Neuroimaging of chronic pain." Thesis, Bangor University, 2016. https://research.bangor.ac.uk/portal/en/theses/neuroimaging-of-chronic-pain(39542293-ad7d-4163-beab-203ac359e2a2).html.

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Chronic pain is a debilitating symptom of a wide range of conditions. These conditions are both highly prevalent and create adverse consequences for individuals and society. Whilst understanding of chronic pain conditions has improved, in a number of cases the mechanisms of chronic pain are not fully understood and no cure is available. It is appreciated that chronic pain is not only unpleasant in itself, but can also lead to a reorganisation of the nervous system resulting in further suffering. These factors present a justification for further investigation into the mechanisms and effects of chronic pain to enable progress towards more effective treatments. Neuroimaging techniques have helped our understanding the mechanisms and effects of chronic pain. Techniques have been developed to examine the structure, chemistry and activity of the brain. This thesis describes investigations that used neuroimaging to examine the effects of chronic pain on the human brain. A distinction has been drawn between chronic widespread pain (CWP) and chronic localised pain (CLP). Historically, the latter was seen as a condition of the peripheral components of the pain system. More recently, however, an understanding has been gained that central mechanisms may also be a factor in these conditions. The purpose of my investigations was to examine differences and similarities in the effects of two CWP and CLP conditions on the human brain. Fibromyalgia (FM) and Knee Osteoarthritis (OA) were chosen as representatives of these classes of condition. The effects on neurochemistry, brain structure and coordinated brain activity in these conditions were compared using magnetic resonance spectroscopy (MRS), voxel-based morphometry (VBM) and resting state functional connectivity (rs-FC). Using MRS I observed a reduction in N-Acetylaspartic acid (NAA) in the thalamus of OA patients when compared to FM. Using VBM I observed that grey matter volume (GMV) was reduced in the left brainstem and posterior cingulate cortex in FM patients when compared to OA. GMV was reduced in the left precentral, middle frontal and supramarginal gyri in OA when compared to FM. Using rs-FC I observed an increase in functional connectivity in the default mode network of FM patients when compared to OA. I observed increased functional connectivity within the default mode network (DMN) in both pain conditions compared to healthy controls. I also observed increased functional connectivity between the precuneus and regions in both the DMN and executive attention networks. Consideration is given to these findings in the context of previous relevant research. The implications of the results are related the patients’ own experience of their condition and links to clinical measures are also discussed. The findings provide further evidence for the neural basis of elements of patients’ experience of their condition and further understanding of the differences between the wider presentations of these conditions. The findings are drawn together to demonstrate where the effects of CWP and CLP overlap and where their effects contrast. Consideration is given to the mechanisms at work in these conditions that suggest differing effects on the components of the pain system and also to demonstrate where prolonged abnormal peripheral input may be a factor driving adaptation in CLP.

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Ottino, González Jonatan. "Overweight, Allostatic Load and Neuroimaging." Doctoral thesis, Universitat de Barcelona, 2019. http://hdl.handle.net/10803/666987.

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Overweight and stress interact in complex ways. Excess weight promotes chronic low-grade inflammatory states that can mobilise the hypothalamic-pituitary-adrenal (HPA) axis. HPA axis activation resulting from frequent stress situations can modify energy uptake and expenditure. Separately, both conditions have been linked to changes in brain integrity and executive performance. The organism adapts to situations of caloric surplus through boosting immune, neuroendocrine and cardiometabolic systems to restore energy homeostasis. The allostatic load model establishes that the cumulative effects of adapting to challenging scenarios may result in adverse health situations in the future. There is sufficient evidence to consider that a state of overweight is inherently linked to a higher chronic physiological stress, or allostatic load. Our hypothesis was that, independently of the effects of visceral adiposity, the aggregated effects of the biological alterations related to overweight would be enough detrimental to brain structure and executive functioning. Lean-to-obese volunteers aged 21 to 40 years were recruited from primary health care centres belonging to the Consorci Sanitari de Terrassa. Subjects underwent a medical and neuropsychological examination, as well as a magnetic resonance imaging acquisition at the Hospital Clínic de Barcelona. The allostatic load index consisted of the sum of several biomarkers representing physiological stress. Overweight subjects had a greater allostatic load than healthy weight participants. The allostatic load escalation was negatively correlated with the morphology of cortical areas and tracts known to be ascribed to circuits involved in cognitive control, reward-processing and the integration of visceral-sensory signalling. Finally, the intensification in this index correlated with worse cognitive flexibility.
El sobrepès i l'estrès interactuen de formes complexes. L'excés de pes promou estats inflamatoris crònics de baix grau que poden mobilitzar l'eix hipotalàmic-pituitari-adrenal (HPA). L'activació de l'eix HPA resultant de situacions d'estrès freqüents pot modificar la captació i la despesa d'energia. Les dues condicions s'han vinculat per separat a canvis en la integritat cerebral i l'acompliment executiu. L'organisme s'adapta a situacions de superàvit calòric a través de impulsar sistemes immunes, neuroendocrins i cardiometabòlics per restaurar l'homeòstasi energètica. El model de càrrega alostàtica estableix que els efectes acumulatius de l'adaptació a escenaris desafiadors poden resultar en situacions adverses per a la salut en el futur. Hi ha evidència suficient per a considerar que un estat de sobrepès està inherentment vinculat a un major estrès fisiològic crònic, o càrrega alostàtica. La nostra hipòtesi va ser que, independentment dels efectes de l'adipositat visceral, els efectes agregats de les alteracions biològiques relacionades amb l'excés de pes resultarien suficientment perjudicials per a la estructura cerebral i el funcionament executiu. Es van reclutar voluntaris amb normopès i sobrepès amb edats compreses entre els 21 i els 40 anys de centres d'atenció primària de salut pertanyents al Consorci Sanitari de Terrassa. Els subjectes es van sotmetre a un examen mèdic i neuropsicològic, així com a l'adquisició d'imatges per ressonància magnètica a l'Hospital Clínic de Barcelona. L'índex de càrrega alostàtica va consistir en la suma de diversos biomarcadors representant estrès fisiològic. Els subjectes amb sobrepès van presentar major càrrega alostàtica que els participants de pes saludable. L'escalada de càrrega alostàtica es va correlacionar negativament amb la morfologia d'àrees corticals i tractes coneguts per estar adscrits a circuits implicats en el control cognitiu, el processament de recompenses i la integració de la senyalització visceral-sensorial. Finalment, la intensificació en l'esmentat índex va correlacionar amb una pitjor flexibilitat cognitiva.
El sobrepeso y el estrés interactúan de formas complejas. El exceso de peso promueve estados inflamatorios crónicos de bajo grado que pueden movilizar el eje hipotalámico- pituitario-adrenal (HPA). La activación del eje HPA resultante de situaciones de estrés frecuentes puede modificar la captación y el gasto de energía. Ambas condiciones se han vinculado por separado a cambios en la integridad cerebral y el desempeño ejecutivo. El organismo se adapta a situaciones de superávit calórico a través de varias modificaciones fisiológicas. Esto incluye impulsar sistemas inmunes, neuroendocrinos y cardiometabólicos para restaurar la homeostasis energética. El modelo de carga alostática establece que los efectos acumulativos de la adaptación a escenarios desafiantes pueden resultar en situaciones adversas para la salud en el futuro. Existe evidencia suficiente para considerar que un estado de sobrepeso está inherentemente vinculado a un mayor estrés fisiológico crónico, o carga alostática. Nuestra hipótesis fue que, independientemente de los efectos de la adiposidad visceral, los efectos agregados de las alteraciones biológicas relacionadas con el sobrepeso resultarían suficientemente perjudiciales para la estructura cerebral y el funcionamiento ejecutivo. Se reclutaron voluntarios con normopeso y sobrepeso con edades comprendidas entre los 21 y los 40 años de centros de atención primaria de salud pertenecientes al Consorci Sanitari de Terrassa. Los sujetos se sometieron a un examen médico y neuropsicológico, así como a la adquisición de imágenes por resonancia magnética en el Hospital Clínic de Barcelona. El índice de carga alostática consistió en la suma de varios biomarcadores que representan estrés fisiológico. Los sujetos con sobrepeso presentaron mayor carga alostática que los participantes de peso saludable. La escalada de carga alostática se correlacionó negativamente con la morfología de áreas corticales y tractos conocidos por estar adscritos a circuitos implicados en el control cognitivo, el procesamiento de recompensas y la integración de la señalización visceral-sensorial. Finalmente, la intensificación en dicho índice correlacionó con una peor flexibilidad cognitiva.

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Mcguire, Philip Kevin. "Functional neuroimaging of auditory hallucinations." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286719.

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Patel, N. "Development of radiotracers for neuroimaging." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1469649/.

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Nuclear imaging enables quantitative measurements of biological processes in vivo and has revolutionised biomedical research, drug development and clinical practice. Despite the advances made in this field, the ability to image fundamental aspects of neurological diseases remains a challenge. This is partly due to the limited availability of radiotracers for imaging excitatory neurotransmission and detection of inflammation as well as an array of other biochemical processes central to the operational function of the brain. The aim of this research was to expand the arsenal of radiotracers available for neuroimaging in order to study key pathological processes involved in neurological diseases. With the aim to target neuronal Voltage Gated Sodium Channels (VGSCs), Vascular Cell Adhesion Molecule – 1 (VCAM-1) and N-methyl-D-Aspartate Receptors (NMDARs), radiotracers have been synthesised and evaluated. Abnormal expression of these receptors has been implicated in a number of pathological conditions including epilepsy, multiple sclerosis and neurodegeneration. The radiotracers were characterised and evaluated via in vivo imaging (MRI and SPECT/CT) and ex-vivo studies (phosphorimaging, biodistribution and metabolite analysis) in order to determine if they hold significant potential as tools to study neuronal pathways as well as for diagnostic imaging and treatment monitoring. Iodinated analogues of the iminodihydroquinoline WIN17317-3, and the 1-benzazepin-2-one BNZA have been evaluated as neuronal VGSC tracer candidates in healthy mice. Whilst the WIN17317-3 analogue suffered from poor brain uptake and was rapidly metabolised in vivo, the BNZA analogue exhibited excellent in vivo stability and its promising uptake in the brain warrants further investigations. Even though N-(1-Napthyl)-N’-(3-[123I]-iodophenyl)-N’-methylguanidine ([123I]CNS-1261) has demonstrated favourable pharmacokinetics for brain imaging in clinical studies, [125I]CNS-1261 was not successful in discriminating NMDAR expression between naïve rats and those induced with status epilepticus using lithium and pilocarpine. Promisingly, a multi modal contrast agent comprising micron sized particles of iron oxide conjugated to I-125 radiolabelled antibodies, highlighted the up-regulation of VCAM-1 in rat models of cerebral inflammation and in the lithium pilocarpine model of status epilepticus. This versatile imaging agent presents an exciting opportunity to identify an early biomarker for epileptogenesis.

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Jha, Preeti. "Receptor based radioligands for neuroimaging." Thesis, IIT Delhi, 2019. http://eprint.iitd.ac.in:80//handle/2074/8064.

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Aston, John Alexander David. "Statistical methods for functional neuroimaging data." Thesis, Imperial College London, 2002. http://hdl.handle.net/10044/1/7185.

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Steven, M. S. "Neuroimaging of multisensory processing and synaesthesia." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410663.

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Deng, Yi, and 鄧藝. "From neuroimaging to proteomics in schizophrenia." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43278516.

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Books on the topic "Neuroimaging"

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Zimmerman, Robert A., Wendell A. Gibby, and Raymond F. Carmody, eds. Neuroimaging. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1152-5.

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International Symposium of Neuroimaging (1983 Innsbruck, Austria). Neuroimaging. Edited by Gerstenbrand F, Grčević Nenad, and Aichner F. Stuttgart: G. Fischer, 1985.

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W, Orrison William, ed. Neuroimaging. Philadelphia: W.B. Saunders, 2000.

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F, Glabus Michael, ed. Neuroimaging. San Diego, Calif: Elsevier, 2005.

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Liu, Hongsheng, and Xiaoan Zhang, eds. Pediatric Neuroimaging. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7928-5.

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Bigler, Erin D., ed. Neuroimaging I. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-1701-0.

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Bigler, Erin D., ed. Neuroimaging II. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-1769-0.

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Michel, Christoph M., Thomas Koenig, Daniel Brandeis, Lorena R. R. Gianotti, and Jiri Wackermann, eds. Electrical Neuroimaging. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511596889.

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Bravo-Rodríguez, Francisco de Asís, Rocío Diaz-Aguilera, and L. Celso Hygino Cruz, eds. Learning Neuroimaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-22999-2.

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Ginat, Daniel Thomas, Juan E. Small, and Pamela Whitney Schaefer, eds. Neuroimaging Pharmacopoeia. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12715-6.

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Book chapters on the topic "Neuroimaging"

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Rees, Elliott, and Tilak Das. "Neuroimaging." In Management of Adult Glioma in Nursing Practice, 21–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76747-5_2.

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Knops, André. "Neuroimaging." In Encyclopedia of Personality and Individual Differences, 3190–200. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1333.

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van Duinen, Martin Th A. "Neuroimaging." In The Transorbital Intracranial Penetrating Injury, 57–70. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4457-5_11.

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Cobia, Derin, Chaz Rich, and Erin D. Bigler. "Neuroimaging." In Cognitive Rehabilitation and Neuroimaging, 1–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48382-1_1.

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Sharma, Anuj, and Alan Weintraub. "Neuroimaging." In Encyclopedia of Clinical Neuropsychology, 2409–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_52.

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Forbes, Angela. "Neuroimaging." In Nursing Care of the Pediatric Neurosurgery Patient, 573–96. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49319-0_18.

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Beaton, Elliott A. "Neuroimaging." In Encyclopedia of Behavioral Medicine, 1482–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1154.

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Tiro, Jasmin, Simon J. Craddock Lee, Steven E. Lipshultz, Tracie L. Miller, James D. Wilkinson, Miriam A. Mestre, Barbara Resnick, et al. "Neuroimaging." In Encyclopedia of Behavioral Medicine, 1317–20. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1154.

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Sharma, Anuj, and Alan Weintraub. "Neuroimaging." In Encyclopedia of Clinical Neuropsychology, 1–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-56782-2_52-3.

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Delfyett, William T., and David T. Fetzer. "Neuroimaging." In Neurological illness in pregnancy, 15–69. Oxford, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118430903.ch3.

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Conference papers on the topic "Neuroimaging"

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Masoud, Mohamed, Pratyush Reddy, Farfalla Hu, and Sergey Plis. "Brainchop: Advancing the Frontier of Web-Based Neuroimaging." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635670.

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Hillman, Elizabeth. "Optical Neuroimaging." In Bio-Optics: Design and Application. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/boda.2013.jm1a.1.

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Luo, Qingming. "Optoelectronic Neuroimaging Approaches." In Asia Communications and Photonics Conference and Exhibition. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/acp.2009.thv1.

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ORZI, FRANCESCO. "Models for quantitative Neuroimaging." In Frontiers in Imaging Science: High Performance Nuclear Medicine Imagers for Vascular Disease Imaging (Brain and Heart). Trieste, Italy: Sissa Medialab, 2008. http://dx.doi.org/10.22323/1.039.0013.

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Hale, Luke. "Voxel Printing of Neuroimaging." In Design Computation Input/Output 2020. Design Computation Ltd., 2020. http://dx.doi.org/10.47330/dcio.2020.kgqd8189.

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Lemanski, Francisco Costa Beber, Vitor Dalepiane Rossato, Nathalia Beck Corrêa, Letícia Reginato, and Gabriel Tarasconi Zanin. "Wilson’s disease: neuroimaging features." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.002.

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Background: Wilson’s disease (DW) is a genetic disorder characterized by the accumulation of copper in the body. The copper accumulation is systemic and occurs in several tissues, with the central nervous system (CNS) being one of the most affected sites. The use of imaging tests is not necessary for the diagnosis. However, in the suspicion of neurological damage, Magnetic Resonance Imaging (MRI) plays an important role in the assessment of the metal deposit in the CNS and in the clinico-anatomical correlation in symptomatic patients. Objectives: to identify the characteristic findings of DW in neuroimaging exams. Methods: a narrative literature review. Results: in MRI, the most affected sites in the CNS are the basal ganglia (mainly the outermost portion of the putamen), followed by the midbrain, the pons, and the thalamus. Alterations are bilaterally and symmetrically. T2 sequence reveals hyperintensity in putamen, the most common abnormality, as well as in the rest of the basal ganglia. Eventually, it is possible to identify the “panda sign” in the axial section of the midbrain, due to the involvement of the tegmental region associated with the normal signal of the red nuclei and hypointensity of the superior colliculus, characteristic of DW. In the T1 sequence, patients with neurological symptoms present hypointense images. Conclusions: MRI has a diagnostic and prognostic role in DW. The putamen is the most affected structure, but abnormalities in the pons, midbrain, and thalamus are part of the neuroimaging spectrum of Wilson’s disease. The “panda sign” is the classic MRI finding.

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Nielsen, Finn Årup, and Lars Kai Hansen. "Interactive information visualization in neuroimaging." In the 1997 workshop. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/275519.275533.

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Lei, Tianhu, John Dell, Ralphy Magee, and Timothy P. L. Roberts. "Source counting in MEG neuroimaging." In SPIE Medical Imaging, edited by Xiaoping P. Hu and Anne V. Clough. SPIE, 2009. http://dx.doi.org/10.1117/12.813655.

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van Buchem, M. A., J. van der Grond, M. Versluis, M. J. P. van Osch, W. Teeuwisse, H. Kan, and A. G. Webb. "High field clinical MRI neuroimaging." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490280.

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Mohammadian Foroushani, Hossein, Rajat Dhar, Yasheng Chen, Jenny Gurney, Ali Hamzehloo, Jin-Moo Lee, and Daniel S. Marcus. "SNIPR: Stroke Neuroimaging Phenotype Repository." In Imaging Informatics for Healthcare, Research, and Applications, edited by Thomas M. Deserno and Po-Hao Chen. SPIE, 2020. http://dx.doi.org/10.1117/12.2549622.

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Reports on the topic "Neuroimaging"

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Lowe, Mark. Cleveland Clinic Next Generation Neuroimaging. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/1130571.

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Sherman, Paul M., M. A. Foster, B. D. Tharin, and J. G. Smirniotopoulos. An Algorithmic Approach to Neuroimaging in AIDS. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada437671.

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Janowsky, Jeri. Exploration of Prostate Cancer Treatment Induced Neurotoxicity with Neuroimaging. Fort Belvoir, VA: Defense Technical Information Center, May 2008. http://dx.doi.org/10.21236/ada484214.

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Rao, Stephen M. Neuroimaging of Brain Injuries and Disorders at Cleveland Clinic. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada595250.

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Janowsky, Jenri. Exploration of Prostate Cancer Treatment Induced Neurotoxicity with Neuroimaging. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada472360.

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Li, Jia, Yuan Liu, Jing Zhang, and Mingxing Yuan. Neuroimaging studies of acupuncture on knee osteoarthritis: a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2022. http://dx.doi.org/10.37766/inplasy2022.4.0110.

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Abstract:

Review question / Objective: This study was conducted in order to investigate the study design and main outcomes of acupuncture neuroimaging studies on knee osteoarthritis (KOA),and reveal the potential mechanism of the pain-relieving effect of acupuncture on knee osteoarthritis. Condition being studied: Knee osteoarthritis is a very common disease that seriously affects people's quality of life. Acupuncture, as an effective treatment option, can achieve pain relief and treat the disease, but the mechanism of acupuncture analgesia is still unclear to us. Therefore, we set certain criteria to include eligible clinical trials to reveal its principles.

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Gou, Xinyun, Jiaxi Huang, Liuxue Guo, Jin Zhao, Dongling Zhong, Yuxi Li, Xiaobo Liu, et al. The conscious recognition of emotion in depression disorder: A meta-analysis of neuroimaging studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0057.

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Zhang, Yao, and Xin Zhao. The Neural Mechanism of Working Memory Training: A meta-analysis of Functional Neuroimaging Studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0105.

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Cohen, Jonathan D. Second Generation Flexible Computing Environment for Computational Modeling of Brain Function and Neuroimaging Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada530764.

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Radovitzky, R., S. Socrate, K. Taber, R. Hurley, David Moore, Steve Son, Wayne Chen, James Deshler, Ghatu Subhash, and Cameron Bass. Investigations of Tissue-Level Mechanisms of Primary Blast Injury Through Modeling, Simulation, Neuroimaging and Neuropathological Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada573887.

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