Neuroimaging

Resources

Annenberg Foundation (Producer). (2015). We all have different brains: Brain imaging techniques [multiple videos]. Los Angeles, CA: Author.

Ansado, J., Chasen, C., Bouchard, S., & Northoff, G. (2021). How brain imaging provides predictive biomarkers for therapeutic success in the context of virtual reality cognitive training. Neuroscience & Biobehavioral Reviews, 120, 583-594.https://doi.org/10.1016/j.neubiorev.2020.05.018

Arredondo, M. M. (2021). Shining a light on cultural neuroscience: Recommendations on the use of fNIRS to study how sociocultural contexts shape the brain. Cultural Diversity and Ethnic Minority Psychology. [online advance publication]. https://doi.org/10.1037/cdp0000469

Avena-Koenigsberger, A., Misic, B., & Sporns, O. (2018). Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1), 17-33. https://earbmc.sitehost.iu.edu/pubs/Sporns_comm_dynamics_NRN.pdf

Azhari, A., Truzzi, A., Neoh, M. J. Y., Balagtas, J. P. M., Tan, H. H., Goh, P. P., ... & Esposito, G. (2020). A decade of infant neuroimaging research: What have we learned and where are we going?. Infant Behavior and Development, 58, 101389. https://doi.org/10.1016/j.infbeh.2019.101389

Boto, E., Hill, R. M., Rea, M., Holmes, N., Seedat, Z. A., Leggett, J., Shah, V., Osborne, J., Bowtell R., & Brookes, M. J. (2021). Measuring functional connectivity with wearable MEG. NeuroImage, 230, Article 117815. https://doi.org/10.1016/j.neuroimage.2021.117815

Bullmore, E. & Sporn, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186-198. https://doi.org/10.1038/nrn2575

Cao, M., Wang, J. H., Dai, Z. J., Cao, X. Y., Jiang, L. L., Fan, F. M., ... & He, Y. (2014). Topological organization of the human brain functional connectome across the lifespan. Developmental Cognitive Neuroscience, 7, 76-93. https://doi.org/10.1016/j.dcn.2013.11.004

Carmon, J., Heege, J., Necus, J. H., Owen, T. W., Pipa, G., Kaiser, M., ... & Wang, Y. (2020). Reliability and comparability of human brain structural covariance networks. NeuroImage, 220, 117104. https://doi.org/10.1016/j.neuroimage.2020.117104

Casey, B. J., Tottenham, N., Liston, C., & Durston, S. (2005). Imaging the developing brain: what have we learned about cognitive development? Trends in Cognitive Sciences, 9(3), 104-110. https://doi.org/10.1016/j.tics.2005.01.011

Cassiers, L. L., Sabbe, B. G., Schmaal, L., Veltman, D. J., Penninx, B. W., & Van Den Eede, F. (2018). Structural and functional brain abnormalities associated with exposure to different childhood trauma subtypes: A systematic review of neuroimaging findings. Frontiers in Psychiatry, 9, 329. https://doi.org/10.3389/fpsyt.2018.00329

Cohen, D. (1968). Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents. Science, 161(3843), 784-786. https://doi.org/10.1126/science.161.3843.784

Cole, J. H. (2020). Multi-modality neuroimaging brain-age in UK Biobank: relationship to biomedical, lifestyle and cognitive factors. Neurobiology of Aging. https://doi.org/10.1016/j.neurobiolaging.2020.03.014

Cooper, R. J. (2014). Bioimaging: Watching the brain at work. Nature Photonics, 8(6), 425-426. https://doi-org.ezp-prod1.hul.harvard.edu/10.1038/nphoton.2014.116

Dai, Z., Sun, Y., Zhao, X., & Pu, X. (2020). Novel imaging and related techniques for studies of diseases of the central nervous system: a review. Cell and Tissue Research, 1-10. https://doi.org/10.1007/s00441-020-03183-z

Davidesco, I., Matuk, C., Bevilacqua, D., Poeppel, D., & Dikker, S. (2021). Neuroscience research in the classroom: portable brain Technologies in Education Research. Educational Researcher, 50(9), 649-656. https://doi.org/10.3102/0013189X211031563

de Wael, R. V., Benkarim, O., Paquola, C., Lariviere, S., Royer, J., Tavakol, S., ... & Bernhardt, B. C. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications Biology, 3(1), 1-10. https://doi.org/10.1038/s42003-020-0794-7

Deoni, S. C., D’Sa, V., Volpe, A., Beauchemin, J., Croff, J. M., Elliott, A. J., ... & Fifer, W. P. (2022). Remote and at-home data collection: Considerations for the NIH HEALthy Brain and Cognitive Development (HBCD) study. Developmental Cognitive Neuroscience, 101059.https://doi.org/10.1016/j.dcn.2022.101059

Don, A. P., Peters, J. F., Ramanna, S., & Tozzi, A. (2020). Topological view of flows inside the BOLD spontaneous activity of the human brain. Frontiers in Computational Neuroscience, 14, 34. https://doi.org/10.3389/fncom.2020.00034

Farhoodi, R., Lansdell, B. J., & Kording, K. P. (2019). Quantifying how staining methods bias measurements of neuron morphologies. Frontiers in Neuroinformatics, 13, 36. https://doi.org/10.3389/fninf.2019.00036

Frijia, E. M., Billing, A., Lloyd-Fox, S., Rosas, E. V., Collins-Jones, L., Crespo-Llado, M. M., ... & Cooper, R. J. (2021). Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment. NeuroImage, 225, Article 117490. https://doi.org/10.1016/j.neuroimage.2020.117490

George Mason University. (n.d.). Digitally reconstructed neurons. NeuroMorpho.Org. Retrieved December 5, 2020, from http://neuromorpho.org/

Galler, J. R., Bringas-Vega, M. L., Tang, Q., Rabinowitz, A. G., Musa, K. I., Chai, W. J., ... & Valdés-Sosa, P. A. (2021). Neurodevelopmental effects of childhood malnutrition: A neuroimaging perspective. NeuroImage, 231, 117828. https://doi.org/10.1016/j.neuroimage.2021.117828

Gilman, J. M., Schmitt, W. A., Potter, K., Kendzior, B., Pachas, G. N., Hickey, S., Makary, M., Huestis, M. A., & Evins, A. E. (2022). Identification of∆ 9-tetrahydrocannabinol (THC) impairment using functional brain imaging. Neuropsychopharmacology, 1-9. https://doi.org/10.1038/s41386-021-01259-0

Goldstein-Piekarski, A. N., Holt-Gosselin, B., O’Hora, K., & Williams, L. M. (2020). Integrating sleep, neuroimaging, and computational approaches for precision psychiatry. Neuropsychopharmacology, 45(1), 192-204.https://doi.org/10.1038/s41386-019-0483-8

Guell, X., Goncalves, M., Kaczmarzyk, J. R., Gabrieli, J. D., Schmahmann, J. D., & Ghosh, S. S. (2019). LittleBrain: a gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings.  Plos One, 14(1), e0210028. https://doi.org/10.1371/journal.pone.0210028

Guell, X., Schmahmann, J. (2020). Cerebellar functional anatomy: A didactic summary based on human fMRI evidence. Cerebellum 19, 1–5. https://doi.org/10.1007/s12311-019-01083-9

Hannula, D. E., Simons, D. J., & Cohen, N. J. (2005). Imaging implicit perception: Promise and pitfalls. Nature Reviews Neuroscience, 6(3), 247-255. https://doi-org.ezp-prod1.hul.harvard.edu/10.1038/nrn1630

Harvard Medical School. (2011, March 2). Brain circuits: Harvard researchers crawl a neural network. [Video]. (2:36). YouTube. https://youtu.be/bZtXgZjbshs

Hirsch, G. V., Bauer, C. M., & Merabet, L. B. (2015). Using structural and functional brain imaging to uncover how the brain adapts to blindness. Annals of Neuroscience and Psychology, 2.

Hubbard, N. A., Siless, V., Frosch, I. R., Goncalves, M., Lo, N., Wang, J., ... & Whitfield-Gabrieli, S. (2020). Brain function and clinical characterization in the Boston adolescent neuroimaging of depression and anxiety study. NeuroImage: Clinical, 27, 102240. https://doi.org/10.1016/j.nicl.2020.102240

Human Connectome Project. (2021). About. [Webpage]. http://www.humanconnectomeproject.org/

Janssen, T. W., Grammer, J. K., Bleichner, M. G., Bulgarelli, C., Davidesco, I., Dikker, S., ... & van Atteveldt, N. (2021). Opportunities and limitations of mobile neuroimaging technologies in educational neuroscience. Mind, Brain, and Education, 15(4), 354-370. https://doi.org/10.1111/mbe.12302

Jepsen, M.L. (2018, April). How we can use light to see deep inside our bodies and brains. [Video]. (16:30). Ted Talks. https://www.ted.com/talks/mary_lou_jepsen_how_we_can_use_light_to_see_deep_inside_our_bodies_and_brains

Ji, S., Zhang, Y., Chen, N., Liu, X., Li, Y., Shao, X., ... & Hu, B. (2022). Shared increased entropy of brain signals across patients with different mental illnesses: A coordinate-based activation likelihood estimation meta-analysis. Brain Imaging and Behavior, 1-8.https://doi.org/10.1007/s11682-021-00507-7

Jones, A. (2011, July). A map of the brain. [Video]. (15:14). TedTalk. https://www.ted.com/talks/allan_jones_a_map_of_the_brain

Karnath, H. O., Sperber, C., & Rorden, C. (2019). Mapping human brain lesions and their functional consequences. Neuroimage, 165, 180–189. doi:10.1016/j.neuroimage.2017.10.028

Kim, M., & Maguire, E. A. (2019). Can we study 3D grid codes non-invasively in the human brain? Methodological considerations and fMRI findings.  NeuroImage, 186, 667-678. https://doi.org/10.1016/j.neuroimage.2018.11.041

Koshiyama, D., Miura, K., Nemoto, K., Okada, N., Matsumoto, J., Fukunaga, M., & Hashimoto, R. (2020). Neuroimaging studies within Cognitive Genetics Collaborative Research Organization aiming to replicate and extend works of ENIGMA. Human Brain Mapping. DOI: 10.1002/hbm.25040

Krishnamurthy, R., Wang, D. J., Cervantes, B., McAllister, A., Nelson, E., Karampinos, D. C., & Hu, H. H. (2019). Recent advances in pediatric brain, spine, and neuromuscular magnetic resonance imaging techniques. Pediatric Neurology, 96, 7-23. https://doi.org/10.1016/j.pediatrneurol.2019.03.001

Liu, Z., Rolls, E. T., Liu, Z., Zhang, K., Yang, M., Du, J., ... & Ugurbil, K. (2019). Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results. Bioinformatics. https://www.oxcns.org/papers/577%20Liu%20Rolls%20et%20al%202019%20Brain%20Annotation%20Toolbox.pdf

Lunkova, E., Guberman, G. I., Ptito, A., & Saluja, R. S. (2021). Noninvasive magnetic resonance imaging techniques in mild traumatic brain injury research and diagnosis. Human Brain Mapping, 42(16), 5477-5494. https://doi.org/10.1002/hbm.25630

Luyckx, F., Nili, H., Spitzer, B., & Summerfield, C. (2019). Neural structure mapping in human probabilistic reward learning. eLife, 8, e42816. https://doi.org/10.7554/eLife.42816

Mateos-Pérez, J. M., Dadar, M., Lacalle-Aurioles, M., Iturria-Medina, Y., Zeighami, Y., & Evans, A. C. (2018). Structural neuroimaging as clinical predictor: A review of machine learning applications. NeuroImage: Clinical, 20, 506-522. https://doi.org/10.1016/j.nicl.2018.08.019

Maudsley, A. A., Andronesi, O. C., Barker, P. B., Bizzi, A., Bogner, W., Henning, A., Nelson, S. J., & Soher, B. J. (2021). Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations. NMR in Biomedicine, 34(5), Article e4309. https://doi.org/10.1002/nbm.4309

Michon, K. J., Khammash, D., Simmonite, M., Hamlin, A. M., & Polk, T. A. (2022). Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage, 119589. https://doi.org/10.1016/j.neuroimage.2022.119589

Mosher, V. A., Liebenthal, E., & Goodyear, B. G. (2014). Active and passive fMRI for presurgical mapping of motor and language cortex. In Papageorgiou, G., Christopoulos, G., & Smirnakis, S. (2014). Advanced brain neuroimaging topics in health and disease: Methods and applications. InTech Open. http://dx.doi.org/10.5772/30844

Neto, O. L., Haenni, S., Phuka, J., Ozella, L., Paolotti, D., Cattuto, C., ... & Lichand, G. (2021). Combining wearable devices and mobile surveys to study child and youth development in Malawi: Implementation study of a multimodal Approach. JMIR Public Health and Surveillance, 7(3), e23154. doi:10.2196/23154

Nozawa, T., & Miyake, Y. (2020, June). Capturing individual differences in prefrontal activity with wearable fNIRS for daily use. In 2020 13th International Conference on Human System Interaction (HSI) (pp. 249-254). IEEE.DOI: 10.1109/HSI49210.2020.9142689

Park, J. L., Dudchenko, P. A., & Donaldson, D. I. (2018). Navigation in real-world environments: New opportunities afforded by advances in mobile brain imaging. Frontiers in Human Neuroscience, 12, 361. https://doi.org/10.3389/fnhum.2018.00361

Pavlov, B. (2012). A multi-gap RPC based detector for gamma rays. PoS (RPC2012), 38.

Peters, R., White, D., Cleeland, C., & Scholey, A. (2020). Fuel for thought? A systematic review of neuroimaging studies into glucose enhancement of cognitive performance. Neuropsychology Review, 30(2), 234-250. https://doi.org/10.1007/s11065-020-09431-x

Pinti, P., Tachtsidis, I., Hamilton, A., Hirsch, J., Aichelburg, C., Gilbert, S., & Burgess, P. W. (2020). The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464(1), 5.10.1111/nyas.13948

Piredda, G. F., Hilbert, T., Thiran, J. P., & Kober, T. (2021). Probing myelin content of the human brain with MRI: A review. Magnetic Resonance in Medicine, 85(2), 627-652. https://doi.org/10.1002/mrm.28509

Popovych, O. V., Jung, K., Manos, T., Diaz-Pier, S., Hoffstaedter, F., Schreiber, J., Yeo, B. T T., & Eickhoff, S. B. (2021). Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling. NeuroImage, Article 118201. https://doi.org/10.1016/j.neuroimage.2021.118201

Rajapandian, M., Amico, E., Abbas, K., Ventresca, M., & Goñi, J. (2020). Uncovering differential identifiability in network properties of human brain functional connectomes. Network Neuroscience, 4(3), 698-713. https://doi.org/10.1162/netn_a_00140

Risacher, S. L., & Saykin, A. J. (2021). Neuroimaging advances in neurologic and neurodegenerative diseases. Neurotherapeutics, 18(2), 659-660. http://doi.org/10.1007/s13311-021-01105-7

Rizvi, Q. M. (2020). Analysis of human brain by magnetic resonance imaging using content-based image retrieval. International Journal of Health Sciences, 14(2), 3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069661/pdf/IJHS-14-3.pdf

Sanders, S. (2014, January 30). Beautiful 3-D brain scans show every synapse. [Video]. (4:39 minutes). YouTube. https://youtu.be/nvXuq9jRWKE

Sarubbo, S., Tate, M., De Benedictis, A., Merler, S., Moritz-Gasser, S., Herbet, G., & Duffau, H. (2020). Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain. Neuroimage, 205, 116237. https://doi.org/10.1016/j.neuroimage.2019.116237

Selvaggi, P., Rizzo, G., Mehta, M. A., Turkheimer, F. E., & Veronese, M. (2021). Integration of human whole-brain transcriptome and neuroimaging data: Practical considerations of current available methods. Journal of Neuroscience Methods, 355, Article 109128. https://doi.org/10.1016/j.jneumeth.2021.109128

See, A. A. Q., & King, N. K. K. (2017). Improving surgical outcome using diffusion tensor imaging techniques in deep brain stimulation. Frontiers in Surgery, 4, Article 54. https://doi.org/10.3389/fsurg.2017.00054

Seung, S. (2010, July). I am my connectome. [Video]. (19:25 minutes). Ted Talk. https://www.ted.com/talks/sebastian_seung_i_am_my_connectome?language=en

Shen, G., Horikawa, T., Majima, K., & Kamitani, Y. (2019). Deep image reconstruction from human brain activity. PLoS Computational Biology, 15(1), e1006633. https://doi.org/10.1371/journal.pcbi.1006633 

Shepherd, G. M., & Grillner, S. (Eds.). (2018). Handbook of brain microcircuits. Oxford University Press. http://www.cns.nyu.edu/wanglab/publications/pdf/wang.circuit2010.pdf

Siddiquee, M. R., Atri, R., Marquez, J. S., Hasan, S. M., Ramon, R., & Bai, O. (2020). Sensor location optimization of wireless wearable fnirs system for cognitive workload monitoring using a data-driven approach for improved wearability. Sensors, 20(18), 5082.https://doi.org/10.3390/s20185082

Snoek, L., Miletić, S., & Scholte, H. S. (2019). How to control for confounds in decoding analyses of neuroimaging data. NeuroImage, 184, 741-760. https://doi.org/10.1016/j.neuroimage.2018.09.074

Sprooten, E., O'halloran, R., Dinse, J., Lee, W. H., Moser, D. A., Doucet, G. E., ... & Leibu, E. (2019). Depth-dependent intracortical myelin organization in the living human brain determined by in vivo ultra-high field magnetic resonance imaging. NeuroImage, 185, 27-34. https://doi.org/10.1016/j.neuroimage.2018.10.023

Srilahari, N., Satyanarayana, N., Sunitha, P., Uma Sanker, A., Bala Sundaram, M., & Sobana, R. Non-invasive functional optical brain imaging methods: A review. International Journal of Research and Review, 7(5), 118-128. https://www.ijrrjournal.com/IJRR_Vol.7_Issue.5_May2020/IJRR0020.pdf

Sun, L., Zhao, T., Liang, X., Xia, M., Li, Q., Liao, X., ... & DIDA-MDD Working Group. (2023). Functional connectome through the human life span. bioRxiv. doi: 10.1101/2023.09.12.557193

Sun, Y., Ayaz, H., & Akansu, A. N. (2020). Multimodal affective state assessment using fNIRS+ EEG and spontaneous facial expression. Brain Sciences, 10(2), 85.https://doi.org/10.3390/brainsci10020085

Tazawa, Y., Liang, K. C., Yoshimura, M., Kitazawa, M., Kaise, Y., Takamiya, A., ... & Kishimoto, T. (2020). Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning. Heliyon, 6(2), e03274.https://doi.org/10.1016/j.heliyon.2020.e03274

Tournier, N., Comtat, C., Lebon, V., & Gennisson, J. L. (2021). Challenges and perspectives of the hybridization of PET with functional MRI or ultrasound for neuroimaging. Neuroscience, 474, 80-93. https://doi.org/10.1016/j.neuroscience.2020.10.015

Tsow, F., Kumar, A., Hosseini, S. H., & Bowden, A. (2021). A low-cost, wearable, do-it-yourself functional near-infrared spectroscopy (DIY-fNIRS) headband. HardwareX, 10, e00204. https://doi.org/10.1016/j.ohx.2021.e00204

Tulay, E. E., Metin, B., Tarhan, N., & Arıkan, M. K. (2019). Multimodal neuroimaging: basic concepts and classification of neuropsychiatric diseases. Clinical EEG and Neuroscience, 50(1), 20-33. https://doi.org/10.1177/1550059418782093

Turkheimer, F. E., Rosas, F. E., Dipasquale, O., Martins, D., Fagerholm, E. D., Expert, P., Vasa, R., Lord, L.-D.,  & Leech, R. (2021). A complex systems perspective on neuroimaging studies of behavior and its disorders. The Neuroscientist, Article 1073858421994784. https://doi.org/10.1177/1073858421994784

Urchs, S., Armoza, J., Moreau, C., Benhajali, Y., St-Aubin, J., Orban, P., & Bellec, P. (2019). MIST: A multi-resolution parcellation of functional brain networks. MNI Open Research, 1, 3. https://doi.org/10.12688/mniopenres.12767.2

Wang, H., Sun, J., Cui, D., Wang, X., Jin, J., Li, Y., ... & Yin, T. (2021). Quantitative assessment of inter-individual variability in fMRI-based human brain atlas. Quantitative Imaging in Medicine and Surgery, 11(2), 810. http://doi.org/10.21037/qims-20-404

White, T., Blok, E., & Calhoun, V. D. (2020). Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Human Brain Mapping. https://doi.org/10.1002/hbm.25120

Wigley, I. L. C. M., Mascheroni, E., Peruzzo, D., Giorda, R., Bonichini, S., & Montirosso, R. (2021). Neuroimaging and DNA methylation: An innovative approach to study the effects of early life stress on developmental plasticity. Frontiers in Psychology, 12. http://doi.org/10.3389/fpsyg.2021.672786

Wu, L. C., Zhang, Y., Steinberg, G., Qu, H., Huang, S., Cheng, M., Bliss, T., Du, F., Rao, J., Song, G., Pisani, L., Doyle, T., Conolly, S., Krishnan, K., Grant G., & Wintermark, M. (2019). A review of magnetic particle imaging and perspectives on neuroimaging. American Journal of Neuroradiology, 40(2), 206-212. https://doi.org/10.3174/ajnr.A5896

Yang, D., Shin, Y. I., & Hong, K. S. (2021). Systemic review on Transcranial Electrical Stimulation parameters and EEG/fNIRS features for brain diseases. Frontiers in Neuroscience, 15, 274.

Yeung, M. K. (2021). An optical window into brain function in children and adolescents: A systematic review of functional near-infrared spectroscopy studies. Neuroimage, 227, 117672. https://doi.org/10.1016/j.neuroimage.2020.117672

Zhao, T., Xu, Y., & He, Y. (2019). Graph theoretical modeling of baby brain networks. NeuroImage, 185, 711-727. https://doi.org/10.1016/j.neuroimage.2018.06.038

Zhao, H., Frijia, E. M., Rosas, E. V., Collins-Jones, L., Smith, G., Nixon-Hill, R., ... & Cooper, R. J. (2021, June). ANIMATE: wearable, flexible, and ultra-lightweight high-density diffuse optical tomography technologies for functional neuroimaging of newborns. In European Conference on Biomedical Optics (pp. ETu4C-3). Optical Society of America.

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Other Resources

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Wikipedia. (2021). CT Scan. [website] https://en.wikipedia.org/wiki/CT_scan

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Date of last update: 14-Dec-2022 CB

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