Brain Imaging Technologies - A Mathematical Perspective
Keywords:
Nanocarriers, RNAi, miRNA, siRNA, Gene delivery, Gene silencingAbstract
The brain is a multilayered and multicompartment system structured for accomplishing robust behaviours which leads to cognitive and physiological functioning of the human system [1]. The total volume of the human brain is 1450cm3 on an average. It comprises of innumerable neurons and glial cells which make up the building blocks of the brain [2]. Scientists have been trying to understand the brain and unfold it’s mysteries for many decades. Inspite of taking avid interest in this field the information gathered does not provide a complete comprehensive perception of the brain [3, 4]. The effort to comprehend the brain has evolved over the years [5]. Machine learning techniques are being widely used to make relevant outcomes for neuro imaging. The advent of imaging technologies has helped to facilitate our understanding of the brain and its complexities [6-8]. Brain imaging technologies provide an unprecedented
tool to analyse the changes in the central nervous system (CNS). These technologies have bought with them higher resolution and deeper penetration into the brain thus exposing newer functionalities of it. Exploring the brain to absorb its secrets and unravel its enigma is one of the most promising and rewarding applications of biomedical brain imaging technologies. Brain imaging technologies provide non invasive techniques of viewing the brain and its activi[9]. Doctors and researchers can monitor the brain without having to intrude into it via painful and risk staking neurosurgeries. This is one of the biggest advantages of the emerging and ever evolving brain imaging technologies. Today, a number of these techniques are being put to practical application to save innumerable patients around the globe. The current brain imaging technologies include Functional magnetic resonance imaging (fMRI), Computed tomography (CT), Positron Emission Tomography (PET), Electroencephalography (EEG), Magnetoencephalography (MEG), Near infrared spectroscopy, Two Photon Microscopy and Photoacoustic tomography (PAT). Of these fMRI and PAT are the recent imaging tools of particular interest.
References
Stiles J, Jernigan TL. “The basics of brain development”. Neuropsychology review (2010) 20: 327-348.
Batista-García-Ramó K, Fernández-Verdecia CI. “What We Know About the Brain Structure-Function Relationship”. Behavioral sciences (2018) 8: 39.
Raichle ME. “Functional Brain Imaging and Human Brain Function”. J Neuroscience (2003) 23: 3959-3962.
Raichle ME. “A Paradigm Shift in Functional Brain Imaging”. J Neuroscience (2009) 29: 12729-12734.
Singer W. “Understanding the brain. How can our intuition fail so fundamentally when it comes to studying the organ to which it owes its existence?” EMBO reports Spec No 8 (2007) : S16-S19.
Insel TR, Landis SC, Collins FS. “Research priorities. The NIH BRAIN Initiative”. Science (2013) 340: 687-688.
Devor A, Bandettini PA, Boas DA, Bower JM, Buxton RB et al. “The challenge of connecting the dots in the B.R.A.I.N”. Neuron (2013) 80: 270-274.
Gordon E. “Brain imaging technologies: how, what, when and why?”. Aust N Z J Psychiatry 33(1999): 187-196.
Tucker RL, Gavin PR. “Brain imaging. The Veterinary clinics of North America”. Small animal practice (1996) 26: 735-758.
Paulus MP, Kuplicki R, Yeh HW. “Machine Learning and Brain Imaging: Opportunities and Challenges”. Trends Neurosci (2019) 42: 659-661.
Nichols JA, Herbert Chan HW, Baker MAB. “Machine learning: applications of artificial intelligence to imaging and diagnosis”. Biophys Rev (2019) 11: 111-118.
Wu H, Deng Z, Zhang B, Liu Q, Chen J. “Classifier Model Based on Machine Learning Algorithms: Application to Differential Diagnosis of Suspicious Thyroid Nodules via Sonography”. AJR Am J Roentgenol (2016)207: 859-864.
Kim J, Ahn Y, Lee K, Park SH, Kim S. “A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis”. BMC Bioinformatics (2010) 11: 434.
Zhu G, Jiang B, Tong L, Xie Y, Zaharchuk G, et al. “Applications of Deep Learning to Neuro-Imaging Techniques”. Front Neurol (2019) 10: 869.
Zhang R, Xu P, Guo L, Zhang Y, Li P, Yao D. “Z-score linear discriminant analysis for EEG based brain-computer interfaces”. PloS one (2013) 8: e74433.
Tarca AL, Carey VJ, Chen XW, Romero R, Draghici S. “Machine learning and its applications to biology”. PLoS Comput Biol (2007) 3: e116.
Van der Walt S, Schonberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, et al. “Scikit-image: image processing in Python”. PeerJ (2014) 2: e453.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T. “SciPy 1.0: fundamental algorithms for scientific computing in Python”. Nature methods (2020) 17: 261-272.
Mannige R. “The BackMAP Python module: how a simpler Ramachandran number can simplify the life of a protein simulator”. Peer J (2018) 6: e5745.
Shamir L, Long J. “Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging”. Curr Aging Sci (2016) 9: 310-317.
Deo RC. “Machine Learning in Medicine”. Circulation 132(2015): 1920-1930.
Davatzikos C. “Machine learning in neuroimaging: Progress and challenges”. Neuroimage (2019) 197: 652-656.
Michel CM, Murray MM. “Towards the utilization of EEG as a brain imaging tool”. Neuroimage (2012) 61: 371-385.
Singh SP. “Magnetoencephalography: Basic principles”. Ann Indian Acad Neurol (2014) 17: S107-S112.
Nasrallah I, Dubroff J. “An overview of PET neuroimaging”. Semin Nucl Med 43(2013): 449-461.
John S, Stock S, Cerejo R, Uchino K, Winners S, et al. “Brain Imaging Using Mobile CT: Current Status and Future Prospects”. J Neuroimage (2016) 26: 5-15.
Glover GH. “Overview of functional magnetic resonance imaging”. Neurosurgery clinics North America 22(2011): 133- 139.
Benninger RKP, Piston DW. “Two-photon excitation microscopy for the study of living cells and tissues”. Curr Protoc Cell Biol (2013)11: 24.
Ellis-Davies GCR. “Two-photon microscopy for chemical neuroscience”. ACS chemical neurosci 2(2011): 185-197.
Bonmassar G, Schwartz DP, Liu AK, Kwong KK, Dale AM et al. “Spatiotemporal brain imaging of visual-evoked activity using interleaved EEG and fMRI recordings”. Neuroimage, (2001) 13: 1035-1043.
Zhang Z, Liu X, Wang L. “Spectral Clustering Algorithm Based on Improved Gaussian Kernel Function and Beetle Antennae Search with Damping Factor”. Computational Intelligence and Neurosci (2020): 1648573.
Michel CM, Murray MM. “Towards the utilization of EEG as a brain imaging tool”. Neuroimage (2012) 61: 371-385.
Suarez E, Viegas MD, Adjouadi M, Barreto A. “Relating induced changes in EEG signals to orientation of visual stimuli using the ESI-256 machine”. Biomedical sciences instrumentation, (2000) 36: 33-38. 34.
Seeck M, Koessler L, Bast T, Leijten F, Michel C, et al. “The standardized EEG electrode array of the IFCN”. Clinical Neurophysiol (2017) 128: 2070-2077.
Bai X, He B. “On the estimation of the number of dipole sources in EEG source localization. Clinical neurophysiology” Clinical Neurophysiology (2005) 116: 2037-2043.
Salek-Haddadi A, Friston KJ, Lemieux L, Fish DR. “Studying spontaneous EEG activity with fMRI”. Brain Res Brain Res Rev (2003) 43: 110-133.
Mouraux A, Iannetti GD. “Across-trial averaging of eventrelated EEG responses and beyond”. Magnetic Resonance Imaging (2008) 26: 1041-1054.
Beres AM. “Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research”. Applied psychophysiol biofeedback (2017) 42: 247-255.
Lenkov DN, Volnova AB, Pope AR, Tsytsarev V. “Advantages and limitations of brain imaging methods in the research of absence epilepsy in humans and animal models”. J Neurosci Methods (2013) 212: 195-202.
Pernet CR, Appelhoff S, Gorgolewski KJ, Flandin G, Phillips C, et al. “EEG-BIDS, an extension to the brain imaging data structure for electroencephalography”. Scientific Data (2019) 6: 103.
Machac J. “Cardiac positron emission tomography imaging”. Semin Nucl Med (2005) 35: 17-36.
Townsend D W. “Physical principles and technology of clinical PET imaging”. Ann Acad Med Singapore (2004) 33: 133-145.
Thompson PM, Hayashi KM, Simon SL, Geaga JA, Hong MS, et al. “Structural abnormalities in the brains of human subjects who use methamphetamine”. J neuroscience : the official journal of the Society for Neuroscience (2004) 24: 6028-6036.
Vandenberghe S. “Three-dimensional positron emission tomography imaging with 124I and 86Y”. Nucl Med Commun (2006) 27: 237-245.
Matsumoto K, Kitamura K, Mizuta T, Tanaka K, Yamamoto S, et al. “Performance characteristics of a new 3-dimensional continuous-emission and spiral-transmission high-sensitivity and high-resolution PET camera evaluated with the NEMA NU 2-2001 standard”. J Nucl Med 47(2006): 83-90.
Nordberg A. “PET imaging of amyloid in Alzheimer’s disease”. The Lancet Neurol (2004) 3: 519-527.
Liu E, Schmidt ME, Margolin R, Sperling R, Koeppe R et al. “Amyloid-ᶴ 11C-PiB-PET imaging results from 2 randomized bapineuzumab phase 3 AD trials” Clinical Trial I Neurology (2015) 85: 692-700.
Marcus C, Mena E, Subramaniam RM. “Brain PET in the diagnosis of Alzheimer’s disease”. Clinical nuclear medicine (2014) 39: e413-e426.
Hannawi Y, Stevens RD. “Mapping the Connectome Following Traumatic Brain Injury”. Curr Neurol Neurosci Rep (2016) 16: 44.
Musafargani S, Ghosh KK, Mishra S, Mahalakshmi P, Padmanabhan P, et al. “PET/MRI: a frontier in era of complementary hybrid imaging”. Eur J Hybrid Imaging (2018) 2: 12.
Wijdicks EFM. “The First CT Scan of the Brain: Entering the Neurologic Information Age”. Neurocrit Care (2018) 28: 273-275.
Hongo M, Okubo S, Amemiya H, Yamada H, Matsuoka K et al. “[Diagnosis of left atrial masses by computed tomography: with special reference to the differentiation between mural thrombi and myxomas]”. J Cardiogr 13(1983): 935-947.
Chua ME, Gomez OR, Sapno LD, Lim SL, Morales ML Jr. “Use of computed tomography scout film and Hounsfield unit of computed tomography scan in predicting the radioopacity of urinary calculi in plain kidney, ureter and bladder radiographs”. Urology annals (2014) 6: 218-223.
Bolus D, Morgan D, Berland L. “Effective use of the Hounsfield unit in the age of variable energy CT”. Abdom Radiol (NY) (2017) 42: 766-771.
Sohn W, Clayman RV, Lee JY, Cohen A, Mucksavage P. “Low-dose and standard computed tomography scans yield equivalent stone measurements”. Urology (2013) 81: 231-234.
Khojastepour L, Mohammadzadeh S, Jazayeri M, Omidi M. “In vitro Evaluation of the Relationship between Gray Scales in Digital Intraoral Radiographs and Hounsfield Units in CT Scans”. J Biomed phys eng (2017) 7: 289-298.
Audebert HJ, Fiebach JB.. Brain imaging in acute ischemic stroke—MRI or CT?”. Curr Neurol Neurosci Rep (2015) 15: 6.
Broder JS. “Head computed tomography interpretation in trauma: a primer”. Psychiatr Clin North Am (2010) 33: 821- 854.
Kular S, Martin A. “A primer in interpretation of head CT scans”. Br J Hosp Med (Lond) (2019) 80: C156-c161.
Halley MK, Silva PD, Foley J, Rodarte A. “Loss of consciousness: when to perform computed tomography?”. Pediatr Crit Care Med 5(2004): 230-233.
Glover GH. “Overview of functional magnetic resonance imaging”. Neurosurgery clinics North America 22(2011): 133-vii.
Chow MSM, Wu SL, Webb SE, Gluskin K, Yew DT. “Functional magnetic resonance imaging and the brain: A brief review”. World J Radiology 9(2017): 5-9.
Gore JC. “Principles and practice of functional MRI of the human brain”. J clinical investigation (2003) 112: 4-9.
Chen JE, Glover GH. “Functional Magnetic Resonance Imaging Methods”. Neuropsychol review 25(2015): 289-313.
Buxton RB. “The physics of functional magnetic resonance imaging (fMRI)”. Rep Prog Phys (2013) 76: 096601.
Balardin JB, Zimeo Morais GA, Furucho RA, Trambaiolli L, Vanzella P, et al. “Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments”. Front hum neurosci (2017) 11: 258-258.
Lv H, Wang Z, Tong E, Williams LM, Zaharchuk G, et al. “Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know”. AJNR Am J Neuroradiol 2018. 39: 1390-99.
Neugarten J, Golestaneh L. “Blood oxygenation level-dependent MRI for assessment of renal oxygenation”. International J Nephrology Renovascular Dis (2014) 7: 421-435.
Chen S, Li X. “Functional magnetic resonance imaging for imaging neural activity in the human brain: the annual progress”. Computational and mathematical methods in medicine, (2012): 613465-613465.
Petcharunpaisan S, Ramalho J, Castillo M. “Arterial spin labeling in neuroimaging”. World J Radiology (2010) 2: 384- 398.
Wolk DA, Detre JA. “Arterial spin labeling MRI: an emerging biomarker for Alzheimer’s disease and other neurodegenerative conditions”. Current opinion Neurol (2012) 25: 421-428.
Orringer DA, Vago DR, Golby AJ. “Clinical applications and future directions of functional MRI”. Seminars in neurology (2012) 32: 466-475.
Cho HJ, Chun HJ, Kim ES, Cho BR. “Multiphoton microscopy: an introduction to gastroenterologists”. World j gastroenterol 17(2011): 4456-4460.
Quinn KP, Leal EC, Tellechea A, Kafanas A, Auster ME, et al. “Diabetic Wounds Exhibit Distinct Microstructural and Metabolic Heterogeneity through Label-Free Multiphoton Microscopy”. J Investigative Dermatol (2016) 1: 342-344.
König K. “Multiphoton microscopy in life sciences”. J Microsc (2001) 200: 83-104.
Hoover, EE, Squier JA. “Advances in multiphoton microscopy technology”. Nature photonics 7(2013): 93-101.
Dunn KW, Young PA. “Principles of multiphoton microscopy”. Nephron Exp Nephrol (2006) 103: e33-40.
Smith DR, Mock JJ, Starr AF, Schurig D. “Gradient index metamaterials”. Phys Rev E Stat Nonlin Soft Matter Phys (2005) 71: 036609.
Jung W, Benalcazar W, Ahmad A, Sharma U, Tu H et al. “Numerical analysis of gradient index lens-based optical coherence tomography imaging probes”. J Biomed Opt 15(2010): 066027.
Ustione A, Piston DW. “A simple introduction to multiphoton microscopy”. J Microsc (2011) 243: 221-226.
Diaspro A, Bianchini P, Vicidomini G, Faretta M, Ramoino P et al. “Multi-photon excitation microscopy”. Biomedical engineering online (2006) 5: 36.
Young MD, Field JJ, Sheetz KE, Bartels RA, Squier J. “A pragmatic guide to multiphoton microscope design”. Advances optics photonics (2015)7: 276-378.
Tauer U. “Advantages and risks of multiphoton microscopy in physiology”. Exp Physiol (2002) 87: 709-714.
Weiskopf N, Suckling J, Williams G, Correia MM, Inkster B, et al. “Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation”. Front Neuroscience (2013) 7: 95-95.
Johnson KA, Fox NC, Sperling RA, Klunk WE. “Brain imaging in Alzheimer disease”. Cold Spring Harb perspect Med 2(2012): a006213-a006213.
Seif M, Gandini Wheeler-Kingshott CA, Cohen-Adad J, Flanders AE, Freund P. “Guidelines for the conduct of clinical trials in spinal cord injury: Neuroimaging biomarkers”. Spinal cord (2019) 57: 717-728.
McLellan ME, Kajdasz ST, Hyman BT, Bacskai B J. “In vivo imaging of reactive oxygen species specifically associated with thioflavine S-positive amyloid plaques by multiphoton microscopy”. Journal of Neuroscience : the official journal of the Society for Neuroscience, 23(6), 2212-2217.
Burgold S, Bittner T, Dorostkar MM, Kieser D, Fuhrmann M et al. “In vivo multiphoton imaging reveals gradual growth of newborn amyloid plaques over weeks”. Acta Neuropathologica (2011) 121: 327-335.
Liu H, Deng X, Tong S, He C, Cheng H et al. “In Vivo Deep- Brain Structural and Hemodynamic Multiphoton Microscopy Enabled by Quantum Dots”. Nano Lett (2019) 19: 5260-5265.
Bacskai BJ, Hickey GA, Skoch J, Kajdasz ST, Wang Y, et al. “Four-dimensional multiphoton imaging of brain entry, amyloid binding, and clearance of an amyloid-beta ligand in transgenic mice”. PNAS (2003) 100: 12462-12467.
Hierro-Bujalance C, Bacskai BJ, Garcia-Alloza M. “In Vivo Imaging of Microglia With Multiphoton Microscopy”. Front Aging Neurosci 10: 218.
Larson AM. “Multiphoton microscopy”. Nature photonics 5(2011): 1-1.
Yao J, Wang L, Yang JM, Maslov KI, Wong TTW, et al. “Highspeed label-free functional photoacoustic microscopy of mouse brain in action”. Nat methods (2015) 12: 407-410.
Shenouda M, Yevick D. “Temperature effects in optical fiber dispersion compensation modules”. Optics Letters (2014) 39: 977-980.
Cai X, Li L, Krumholz A, Guo Z, Erpelding TN et al. “Multiscale molecular photoacoustic tomography of gene expression”. PloS one (2012) 7: e43999-e43999.
Xia J, Yao J, Wang LV. “Photoacoustic tomography: principles and advances”. Electromagn Waves (Camb) (2014b) 147: 1-22.
Bai X, Gong X, Hau W, Lin R, Zheng J, et al. “Intravascular optical-resolution photoacoustic tomography with a 1.1 mm diameter catheter”. PloS one (2014) 9: e92463-e92463.
Hu S, Wang LV. “Optical-resolution photoacoustic microscopy: auscultation of biological systems at the cellular level”. Biophysical J (2013) 105: 841-847.
Zhang C, Maslov K, Wang LV. “Subwavelength-resolution label-free photoacoustic microscopy of optical absorption in vivo”. Opt Lett (2010) 35: 3195-3197.
Moothanchery M, Bi R, Kim JY, Jeon S, Kim C, et al. “Optical resolution photoacoustic microscopy based on multimode fibers”. Biomedical optics express (2018) 9: 1190-1197.
Wang D, Wu Y, Xia J. “Review on photoacoustic imaging of the brain using nanoprobes”. Neurophotonics (2016) 3: 010901- 010901.
Jeon S, Song HB, Kim J, Lee BJ, Managuli R, et al. “In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach”. Scientific Reports (2017) 7: 4318.
Moothanchery M, Dev K, Balasundaram G, Bi R, Olivo M. “Acoustic resolution photoacoustic microscopy based on microelectromechanical systems scanner”. J Biophotonics (2020) 13: e201960127.
Vienneau E, Liu W, Yao J. “Dual-view acoustic-resolution photoacoustic microscopy with enhanced resolution isotropy”. Opt Lett (2018) 43: 4413-4416.
Periyasamy V, Das N, Sharma A, Pramanik M. “1064 nm acoustic resolution photoacoustic microscopy”. J Biophotonics (2019) 12: e201800357.
Zhou Y, Yao J, Wang LV. “Tutorial on photoacoustic tomography”. J Biomed Opt 21(2016): 61007-61007.
Wang LV, Hu S. “Photoacoustic tomography: in vivo imaging from organelles to organs”. Science (New York, N.Y.) (2012) 335:1458-1462.
Yao J, Wang LV. “Photoacoustic tomography: fundamentals, advances and prospects”. Contrast media molecular imaging (2011) 6: 332-345.
Steinberg I, Huland DM, Vermesh O, Frostig HE, Tummers WS, et al. “Photoacoustic clinical imaging”. Photoacoustics (2019)14: 77-98.
Li L, Xia J, Li G, Garcia-Uribe A, Sheng Q et al. “Label-free photoacoustic tomography of whole mouse brain structures ex vivo”. Neurophotonics (2016) 3: 035001-035001.
Xia J, Yao J, Wang LV. “Photoacoustic Tomography: principles and advances”. Electromagnetic waves (Cambridge, Mass.) (2014) 147: 1-22.
Li C, Wang LV. “Photoacoustic tomography and sensing in biomedicine”. Physics in medicine and biol (2009) 54: R59-R97.
Yao J, Wang LV. “Photoacoustic Brain Imaging: from Microscopic to Macroscopic Scales”. Neurophotonics (2014).) 1: 011003.
Wang LV, Yao J. “A practical guide to photoacoustic tomography in the life sciences”. Nat methods (2016) 13: 627-638.
Zhang Y, Hong H, Cai W. “Photoacoustic imaging”. Cold Spring Harbor protoc (2011).
Wang LV. “Prospects of photoacoustic tomography”. Med Phys (2008) 35: 5758-5767.
