[1] Kolb, B. (2013). Brain Plasticity and Behavior. American physiological society. 12(1), 1-5 [2] Jeans, A. and Erisi M. (2008). Brain Histology. Practical Neurology. 8, 303-310 [3] Marblestone, A.H. (2013). Physical Principles for Scalable Neural Recording. Frontiers in Computational Neuroscience. doi: 10.3389/fncom.2013.00137 [4] Van Praag, H. (1999). Running Enhances Neurogenesis, Learning, and Long-term Potentiation in Mice. PNAS. 96(23), 13427-13431 [5] Fordyce, D.E. and Wehner, J.M. (1993). Physical Activity Enhances Spatial Learning Performances with an Associated Alternation in the Hippocampal Protein Kinase C Activity in C57BL/6 and DBA/2 Mice. Brain Research. 619, 111-119 [6] Anderson, B.J. (2000). Exercise Influences Spatial Learning in the Radial Arm Maze. Physiology & behavior. 70, 425-429 [7] Vaynman, S. (2004). Hippocampal BDNF mediates the Efficacy of Exercise on Synaptic Plasticity. European Journal of Neuroscience. 20, 2580-2590 [8] Kerr, A.L. (2010). Angiogenesis but not Neurogenesis is Critical for Normal Learning and Memory Acquisition. Neuroscience. 171, 214-226 [9] Zhang, P. (2013). Early Exercise Improves Cerebral Blood Flow through Increased Angiogenesis in Experimental Stroke Rat Model. Journal of Neuroengineering and rehabilitation. 10(43), 1-10 [10] Swain, R.A. (2003). Prolonged Exercise Induces Angiogenesis and Increases Cerebral Blood Volume in Primary Motor Cortex of the Rat. Neuroscience. 117, 1037-1046 [11] Girouard, H. and Iadecola C. (2006). Neurovascular coupling in the normal brain and in hypertension, stroke and Alzheimer disease. Journal of Applied Physiology. 100, 328-335 [12] Gauthier, C.J. (2015). Hearts and Minds: Linking Vascular Rigidity and Aerobic Fitness with Cognitive Aging. Neurobiology of Aging. 36, 304-314 [13] Petersen, T.H. (2012). The Motor Cortex Drives the Muscles during Walking in Human Subjects. The Journal of Physiology. DOI: 10.1113/jphysiol.2012.227397 [14] Armstrong, D.M. (1988). The Supraspinal Control of Mammalian Locomotion. Journal of Physiology. 405, 1-37 [15] Black, J.E. (1990). Learning Causes Synaptogenesis, Where Motor Activity Causes Angiogenesis, in Cerebellar Cortex of Adult Rays. Neurobiology. 87, 5568-5572 [16] Gilbert, P.F. (1977). Purkinje Cell Activity during Motor Learning. Brain Research. 128, 309-328 [17] Tabatabaei-Jafari, H. (2015). Cerebral Atrophy in Mild Cognitive Impairment: A Systematic Review with meta-Analysis. Alzheimer’s and Dementia. 1(4), 487-504 [18] Raz, N. (1999). Aging of the Brain and its Impact on Cognitive Performance: Integration of Structural and Functional Findings. Aging of the brain. Chapter 1 [19] Cabeza, R. (2002). Aging Gracefully: Compensory Brain Activity in High-Performing Older Adults. Neuroimage. 17, 1394-1402 doi:10.1006/nimg.2002.1280 [20] D’Esposito, M. (2003). Alternations in the BOLD fMRI Signal with Ageing and Disease: a Challenging for Neuroimaging. Nature Review. 4, 863-872 doi:10.1038/nrn1246 [21] Meltzer, C.C. (2000). Does Cerebral Blood Flow Decline in Healthy Aging? A PET Study with Partial-Volume Correction. The Journal of Nuclear Medicine. 41, 1842-1848 [22] Kastrup, A. Changes of Cerebrovascular C02 Reactivity during Normal Aging. Stroke. 29, 1311-1314 https://doi.org/10.1161/01.STR.29.7.1311 [23] Leoni, R.F. (2017). Cerebral Blood Flow and Vascoreactivity in Aging: an Arterial Spin Labeling Study. Brazilian Journal of Medical and Biological Research. doi: 10.1590/1414-431X20175670 [24] Middleton, L.E. (2008). Changes in Cognition and Mortality in Relation to Exercise in Late Life: a Population Based Study. doi:10.1371/journal.pone.0003124 [25] Cox. E.P. (2016). Relationship between physical activity and cognitive function in apparently healthy young to middle-aged adults: A systematic review. Journal of Science and Medicine in Sport. 19, 616-628 [26] Bherer, L. (2014). A Review of the Effects of Physical Activity and Exercise on Cognitive and Brain Functions in Older Adults. Journal of Aging Research. doi: 10.1155/2013/657508 [27] Bergmann, O. (2015). Adults Neurogenesis in Humans. Cold Spring Harbor Perspective in Biology. doi: 10.1101/cshperspect.a018994 [28] Vaupel P. (1989). Blood flow, oxygen and nutrient supply, and microenvironment of human tumors: a review. Cancer Research. 49, 6449-6465 [29] Brown A.D. (2010). Effects of Cardiorespiratory Fitness and Cerebral Blood Flow on Cognitive Outcome in Older Woman. Neurobiology of Aging. 31, 2047-2057 [30] Sweatt, J.D. (2004). Hippocampal Function in Cognition. Psychopharmacology. 174, 99-110 doi 10.1007/s00213-004-1795-9 [31] Maass, A. (2014). Vascular hippocampal plasticity after aerobic exercise in older adults. Molecular Psychiatry. doi:10.1038/mp.2014.114 [32] Park, D.C. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annual Review in Psychology. 60, 173-196 doi 10.1146/annurev.psych.59.103006.093656 [33] Davis, T.L. (1998). Calibrated functional MRI: mapping the dynamics of oxidative metabolism. The National Academy of Sciences. 95, 1834-1839 [34] Chiarelli, P.A. (2007) A calibrated method for quantitative BOLD fMRI based on hyperoxia. NeuroImage. 37, 808-820 [35] Gauthier, C.J. (2013). A generalized procedure for calibrated MRI incorporating hyperoxia and hypercapnia. Human Brain Mapping. 34, 1053-1069 [36] Gauthier, C.J. (2011). Magnetic resonance imaging of resting OEF and CMRO2 using a generalized calibration model for hypercapnia and hyperoxia. NeuroImage. 60, 1212-1225 [37] Gauthier, C.J. (2012). Age dependence of hemodynamic response characteristics in human functional magnetic resonance imaging. Neurobiology of Aging. 34, 1469-1485 [38] Chiarelli, P.A. (2007). Sources of systematic bias in hypercapnia-calibrated functional MRI estimation of oxygen metabolism. NeuroImage. 34, 35-43 [39] Chen, J.J. (2009). BOLD-specific cerebral blood volume and blood flow changes during neuronal activation in humans. NMR in Biomedicine. 22, 1054-1062 DOI:10.1002/nbm.1411 [40] Bruce, F. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. PNAS. 97(20), 11050-11055 [41] Gagnon, L. (2016). Validation and optimization of hypercapnic-calibrated fMRI from oxygen-sensitive two-photon microscopy. Philosophical transections B. http://dx.doi.org/10.1098/rstb.2015.0359 [42] Wang, Y. and Liu, T. (2014). Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magnetic Resonance in Medicine. 73(1), 82-109. [43] Bammer, R. (2003). Basic principles of diffusion-weighted imaging. European Journal of Radiology. 45, 169-184 [44] Mukherjee, P. (2008). Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. American Journal of Neuroradiology. 29(4), 632-641 [45] Marques J. P. (2010). MP2RAGE, a self bias-field corrected sequence for improving segmentation and T1-mapping at high field. NeuroImaging. [46] Martijn, P. H. (2010). Exploring the brain newtwork: a review on resting-state fMRI functional connectivity. European Neuropsychopharmacology. 20, 519-534 [47] Gryga, M. (2012). Bidirectional gray matter changes after complex motor skill learning. Frontiers in Systems Neuroscience. doi: 10.3389/fnsys.2012.00037 [48] Kandel, R. K. (2013). Principles of neural science, fifth edition. Chapter 22, 475-497 [49] Cheyne D. (1990). Homuncular organization of human motor cortex as indicated by neuromagnetic recordings. Neuroscience Letters. 122, 17-20. [50] Maguire E. A. (2006). London taxi driver : a structural MRI and Neuropsychological analysis. Hippocampus. 16, 1091-1101. [51] Gaser, C. (2003). Brain structure differ between musicians and non-musicians. The Journal of Neuroscience. 23(27), 9240-9245. [52] Rizzolatti, G. (2001). The cortical motor system. Neuron. 31, 889-901. [53] Kandel, R. K. (2013). Principles of neural science, fifth edition. Chapter 37, 835-864. [54] Nudo, R. J. (1996). Use-dependent alternations of movement representations in primary motor cortex of adult squirrel monkeys. The Journal of Neuroscience. 16(2), 785-807. [55] Kleim, J. A. (1998). Functional reorganization of the rat motor cortex following motor skill learning. Journal of Neurophysiology. 80, 3321-3325. [56] Classen, J. (1998). Rapid plasticity of the human cortical movement representation induced by practice. Journal of Neurobiology. 79(2), 1117-1123. [57] Kleim, J. A. (2004). Cortical synaptogenesis and motor map reorganization occur during late, but not early, phase of motor skill learning. The Journal of Neuroscience. 24(3), 628-633. [58] Alexander G. E. (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience. 9, 357-381. [59] Kandel, R. K. (2013). Principles of neural science, fifth edition. Chapter 42, 960-981 [60] Houk, J. C. (1995). Distributed modular architectures linking basal ganglia, cerebellum and cerebral cortex: their role in planning and controlling action. Cerebral Cortex. 2, 95-110. [61] Steele, C. J. (2010). Specific increases within global decreases: a functional magnetic resonance imaging investigation of five days of motor sequence learning. The Journal of Neuroscience. 30(24), 8332-8341. [62] Penhune, V. B. (2011). Parallel contributions of cerebellar, striatal and M1 mechanisms to motor sequence learning. Behavioural Brain Research. 226, 579-591 [63] Tardif, C.L. (2017). Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies. Neuroimage. 1(149), 233-243. doi: 10.1016/j.neuroimage.2017.01.025 [64] Chapman, S.B. (2013). Neural Mechanisms of Brain Plasticity with Complex Cognitive Training in Healthy Seniors. Cerebral Cortex. doi:10.1093/cercor/bht234 [65] Abragam, A. (1961). Principles of nuclear magnetism. Oxford Science Publication. [66] Slichter, C.P. (1990). Principles of Magnetic resonance – third enlarged and updated edition. Sprinter. [67] Nishimura, G.N. (2010). Principles of magnetic resonance imaging. Stanford University. [68] Huettel S.A. (2014). Functional magnetic resonance imaging – third edtion. Sinauer [69] Cuenod C.A. and Balvay D. (2013). Perfusion and vascular permeability: Basic concepts and measurement in DCE-CT and DCE-MRI. Diagnostic and Interventional Imaging. 94, 1187-1204 [70] Bokkers, R.P.H. (2010). Arterial spin labeling perfusion MRI at multiple delay times: a correlative study with H2 15O positron emission tomography in patients with symptomatic carotid artery occlusion. Jouranl of Cerebral Blood Floow and Metabolism. 30, 222-229 [71] Federau, C. (2015). Functional Mapping of the Human Visual Cortex with Intravoxel Incoherent Motion MRI. doi:10.1371/journal.pone.0117706 [72] Dai, W. (2008). Continuous Flow Driven Inversion for Arterial Spin Labeling Using Pulsed Radiofrequency and Gradient Fields. Magnetic Resonance in Medicine. 60(6), 1488-1497 [73] Wong E.C. (1997). Implementation of Quantitative Perfusion Imaging Techniques for Functional Brain Mapping using Pulsed Arterial Spin Labeling. NMR in Biomedicine. 10, 237-249 [74] Buxton R.B. (1998). A General Kinetic Model for Quantitative Perhsion Imaging with Arterial Spin Labeling. Magnetic Resonance in Medicine. 40, 383-396 [75] Frank, Q.Y. (1996). Perfusion Imaging of the Human Brain at 1.5 T Using a Single-Shot EPI Spin Tagging Approach. Magnetic Resonance in Medicine. 36, 219-224 [76] Stanisz, G.J. (2005). T1, T2 Relaxation and Magnetization Transfer in Tissue at 3T. Magnetic Resonance in Medicine. 54, 507–512 [77] Johnston, M.E. (2015). Multi-TI Arterial Spin Labeling MRI with Variable TR and Bolus Duration for Cerebral Blood Flow and Arterial Transit Time Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING. 1392-1402 [78] Buxton, E.C. (1998). Quantitative Imaging of Perhsion Using a Single Subtraction (QUIPSS and QUIPSS 11). Magnetic Resonance in Medicine. 39, 702-708 [79] Liu, T.T. (2004). A signal processing model for arterial spin labeling functional MRI. NeuroImage. 24, 207– 215 [80] Kang, H.R. (2006). Computationnel color technology. The Society of Photo-optical Instrumentation Engineers [81] Triantafyllou, C. (2005). Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. NeuroImage. 26, 243– 250 [82] Krüger, G. and Glover, G.H. (2001). Physiological Noise in Oxygenation-Sensitive MagneticResonance Imaging. Magnetic Resonance in Medicine. 46,631–637 [83] Cavusoglu, M. (2009). Comparison of pulsed arterial spin labeling encoding schemes and absolute perfusion quantification. Magnetic Resonance Imaging. 27, 1039-1045 [84] Jenkinson, M., Bannister, P., Brady, J. M. and Smith, S. M. Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841, 2002 [85] Fan, A.P. (2017). Long-Delay Arterial Spin Labeling Provides More Accurate Cerebral Blood Flow Measurements in Moyamoya Patients. Stroke. https://doi.org/10.1161/STROKEAHA.117.017773 [86] Chen, Y. (2011). Test–Retest Reliability of Arterial Spin Labeling With Common Labeling Strategies. Journal of Magnetic Resinance Imaging. 33, 940–949