Posts Tagged Neuroplasticity

[Abstract+References] Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke

Objective. Somatosensory function is critical to normal motor control. After stroke, dysfunction of the sensory systems prevents normal motor function and degrades quality of life. Structural neuroplasticity underpinnings of sensory recovery after stroke are not fully understood. The objective of this study was to identify changes in bilateral cortical thickness (CT) that may drive recovery of sensory acuity. Methods. Chronic stroke survivors (n = 20) were treated with 12 weeks of rehabilitation. Measures were sensory acuity (monofilament), Fugl-Meyer upper limb and CT change. Permutation-based general linear regression modeling identified cortical regions in which change in CT was associated with change in sensory acuity. Results. For the ipsilesional hemisphere in response to treatment, CT increase was significantly associated with sensory improvement in the area encompassing the occipital pole, lateral occipital cortex (inferior and superior divisions), intracalcarine cortex, cuneal cortex, precuneus cortex, inferior temporal gyrus, occipital fusiform gyrus, supracalcarine cortex, and temporal occipital fusiform cortex. For the contralesional hemisphere, increased CT was associated with improved sensory acuity within the posterior parietal cortex that included supramarginal and angular gyri. Following upper limb therapy, monofilament test score changed from 45.0 ± 13.3 to 42.6 ± 12.9 mm (P = .063) and Fugl-Meyer score changed from 22.1 ± 7.8 to 32.3 ± 10.1 (P < .001). Conclusions. Rehabilitation in the chronic stage after stroke produced structural brain changes that were strongly associated with enhanced sensory acuity. Improved sensory perception was associated with increased CT in bilateral high-order association sensory cortices reflecting the complex nature of sensory function and recovery in response to rehabilitation.

Keywords 

1. Wolf, SL, Winstein, CJ, Miller, JP; EXCITE Investigators. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA. 2006;296:20952104. doi:10.1001/jama.296.17.2095. Google ScholarCrossrefMedlineISI
2. Lo, AC, Guarino, PD, Richards, LG. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:17721783. doi:10.1056/NEJMoa0911341.Google ScholarCrossrefMedlineISI
3. McCabe, J, Monkiewicz, M, Holcomb, J, Pundik, S, Daly, JJ. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96:981990. doi:10.1016/j.apmr.2014.10.022. Google ScholarCrossrefMedlineISI
4. Johansen-Berg, H, Dawes, H, Guy, C, Smith, SM, Wade, DT, Matthews, PM. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy. Brain. 2002;125(pt 12):27312742Google ScholarCrossrefMedline
5. Luft, AR, McCombe-Waller, S, Whitall, J. Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA. 2004;292:18531861. doi:10.1001/jama.292.15.1853. Google ScholarCrossrefMedlineISI
6. Pundik, S, McCabe, JP, Hrovat, K. Recovery of post stroke proximal arm function, driven by complex neuroplastic bilateral brain activation patterns and predicted by baseline motor dysfunction severity. Front Hum Neurosci. 2015;9:394. doi:10.3389/fnhum.2015.00394. Google ScholarCrossrefMedline
7. Desrosiers, J, Noreau, L, Rochette, A, Bourbonnais, D, Bravo, G, Bourget, A. Predictors of long-term participation after stroke. Disabil Rehabil. 2006;28:221230. doi:10.1080/09638280500158372. Google ScholarCrossrefMedlineISI
8. Carey, L, Macdonell, R, Matyas, TA. SENSe: Study of the Effectiveness of Neurorehabilitation on Sensation: a randomized controlled trial. Neurorehabil Neural Repair. 2011;25:304313. doi:10.1177/1545968310397705. Google ScholarSAGE JournalsISI
9. Cramer, SC, Nelles, G, Benson, RR. A functional MRI study of subjects recovered from hemiparetic stroke. Stroke. 1997;28:25182527Google ScholarCrossrefMedlineISI
10. Carey, JR, Kimberley, TJ, Lewis, SM. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain. 2002;125(pt 4):773788Google ScholarCrossrefMedline
11. Carey, LM, Abbott, DF, Lamp, G, Puce, A, Seitz, RJ, Donnan, GA. Same intervention-different reorganization: the impact of lesion location on training-facilitated somatosensory recovery after stroke. Neurorehabil Neural Repair. 2016;30:9881000. doi:10.1177/1545968316653836.Google ScholarSAGE JournalsISI
12. Carey, LM, Matyas, TA. Frequency of discriminative sensory loss in the hand after stroke in a rehabilitation setting. J Rehabil Med. 2011;43:257263. doi:10.2340/16501977-0662. Google ScholarCrossrefMedlineISI
13. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training and neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedlineISI
14. Gauthier, LV, Taub, E, Perkins, C, Ortmann, M, Mark, VW, Uswatte, G. Remodeling the brain: plastic structural brain changes produced by different motor therapies after stroke. Stroke. 2008;39:15201525. doi:10.1161/STROKEAHA.107.502229. Google ScholarCrossrefMedlineISI
15. Zheng, X, Schlaug, G. Structural white matter changes in descending motor tracts correlate with improvements in motor impairment after undergoing a treatment course of tDCS and physical therapy. Front Hum Neurosci. 2015;9:229. doi:10.3389/fnhum.2015.00229. Google ScholarCrossrefMedlineISI
16. Bailey, CH, Kandel, ER. Structural changes accompanying memory storage. Annu Rev Physiol. 1993;55:397426. doi:10.1146/annurev.ph.55.030193.002145. Google ScholarCrossrefMedlineISI
17. Jones, TA, Chu, CJ, Grande, LA, Gregory, AD. Motor skills training enhances lesion-induced structural plasticity in the motor cortex of adult rats. J Neurosci. 1999;19:1015310163Google ScholarCrossrefMedlineISI
18. Wang, L, Conner, JM, Rickert, J, Tuszynski, MH. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. Proc Natl Acad Sci U S A. 2011;108:25452550. doi:10.1073/pnas.1014335108. Google ScholarCrossrefMedline
19. Maguire, EA, Gadian, DG, Johnsrude, IS. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A. 2000;97:43984403. doi:10.1073/pnas.070039597. Google ScholarCrossrefMedlineISI
20. Draganski, B, Gaser, C, Busch, V, Schuierer, G, Bogdahn, U, May, A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004;427:311312. doi:10.1038/427311a. Google ScholarCrossrefMedlineISI
21. Sampaio-Baptista, C, Scholz, J, Jenkinson, M. Gray matter volume is associated with rate of subsequent skill learning after a long term training intervention. Neuroimage. 2014;96:158166. doi:10.1016/j.neuroimage.2014.03.056. Google ScholarCrossrefMedline
22. Nouri, S, Cramer, SC. Anatomy and physiology predict response to motor cortex stimulation after stroke. Neurology. 2011;77:10761083. doi:10.1212/WNL.0b013e31822e1482. Google ScholarCrossrefMedlineISI
23. Gauthier, LV, Taub, E, Mark, VW, Barghi, A, Uswatte, G. Atrophy of spared gray matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke. Stroke. 2012;43:453457. doi:10.1161/STROKEAHA.111.633255. Google ScholarCrossrefMedlineISI
24. Sterr, A, Dean, PJ, Vieira, G, Conforto, AB, Shen, S, Sato, JR. Cortical thickness changes in the non-lesioned hemisphere associated with non-paretic arm immobilization in modified CI therapy. Neuroimage Clin. 2013;2:797803. doi:10.1016/j.nicl.2013.05.005. Google ScholarCrossrefMedline
25. Schaechter, JD, Moore, CI, Connell, BD, Rosen, BR, Dijkhuizen, RM. Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain. 2006;129(pt 10):27222733. doi:10.1093/brain/awl214. Google ScholarCrossrefMedline
26. Kopp, B, Kunkel, A, Flor, H. The Arm Motor Ability Test: reliability, validity, and sensitivity to change of an instrument for assessing disabilities in activities of daily living. Arch Phys Med Rehabil. 1997;78:615620Google ScholarCrossrefMedlineISI
27. Duncan, PW, Lai, SM, Keighley, J. Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology. 2000;39:835841Google ScholarCrossrefMedlineISI
28. Fischl, B. FreeSurfer. Neuroimage. 2012;62:774781. doi:10.1016/j.neuroimage.2012.01.021.Google ScholarCrossrefMedlineISI
29. Reuter, M, Rosas, HD, Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53:11811196. doi:10.1016/j.neuroimage.2010.07.020. Google ScholarCrossrefMedlineISI
30. Greve, DN, Van der Haegen, L, Cai, Q. A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci. 2013;25:14771492. doi:10.1162/jocn_a_00405. Google ScholarCrossrefMedlineISI
31. Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuro-image. 2014;92:381397. doi:10.1016/j.neuroimage.2014.01.060. Google ScholarCrossrefMedlineISI
32. Smith, SM, Nichols, TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:8398. doi:10.1016/j.neuroimage.2008.03.061. Google ScholarCrossrefMedlineISI
33. Nichols, T, Holmes, A. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:125. doi:10.1016/B978-012264841-0/50048-2.Google ScholarCrossrefMedlineISI
34. Winkler, AM, Ridgway, GR, Douaud, G, Nichols, TE, Smith, SM. Faster permutation inference in brain imaging. Neuroimage. 2016;141:502516. doi:10.1016/j.neuroimage.2016.05.068.Google ScholarCrossrefMedline
35. Desikan, RS, Ségonne, F, Fischl, B. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968980. doi:10.1016/j.neuroimage.2006.01.021. Google ScholarCrossrefMedlineISI
36. Thut, G, Théoret, H, Pfennig, A. Differential effects of low-frequency rTMS at the occipital pole on visual-induced alpha desynchronization and visual-evoked potentials. Neuroimage. 2003;18:334347Google ScholarCrossrefMedline
37. Amedi, A, Floel, A, Knecht, S, Zohary, E, Cohen, LG. Transcranial magnetic stimulation of the occipital pole interferes with verbal processing in blind subjects. Nat Neurosci. 2004;7:12661270. doi:10.1038/nn1328. Google ScholarCrossrefMedlineISI
38. Merabet, LB, Swisher, JD, McMains, SA. Combined activation and deactivation of visual cortex during tactile sensory processing. J Neurophysiol. 2006;97:16331641. doi:10.1152/jn.00806.2006. Google ScholarCrossrefMedline
39. Amedi, A, Malach, R, Hendler, T, Peled, S, Zohary, E. Visuo-haptic object-related activation in the ventral visual pathway. Nat Neurosci. 2001;4:324330. doi:10.1038/85201. Google ScholarCrossrefMedlineISI
40. Stoesz, MR, Zhang, M, Weisser, VD, Prather, SC, Mao, H, Sathian, K. Neural networks active during tactile form perception: common and differential activity during macrospatial and microspatial tasks. Int J Psychophysiol. 2003;50:4149Google ScholarCrossrefMedline
41. Lacey, S, Tal, N, Amedi, A, Sathian, K. A putative model of multisensory object representation. Brain Topogr. 2009;21:269274. doi:10.1007/s10548-009-0087-4. Google ScholarCrossrefMedline
42. Kim, JK, Zatorre, RJ. Tactile-auditory shape learning engages the lateral occipital complex. J Neurosci. 2011;31:78487856. doi:10.1523/JNEUROSCI.3399-10.2011. Google ScholarCrossrefMedline
43. Botvinick, M, Cohen, J. Rubber hands “feel” touch that eyes see. Nature. 1998;391:756. doi:10.1038/35784. Google ScholarCrossrefMedlineISI
44. Zangaladze, A, Epstein, CM, Grafton, ST, Sathian, K. Involvement of visual cortex in tactile discrimination of orientation. Nature. 1999;401:587590. doi:10.1038/44139. Google ScholarCrossrefMedlineISI
45. Sathian, K. Analysis of haptic information in the cerebral cortex. J Neurophysiol. 2016;116:17951806. doi:10.1152/jn.00546.2015. Google ScholarCrossrefMedline
46. Hsiao, S, Gomez-Ramirez, M. Touch. In: Gottfried, JA ed. Neurobiology of Sensation and Reward. Frontiers in Neuroscience. Boca Raton, FLCRC Press/Taylor & Francis2011Google ScholarCrossref
47. Vincent, JL, Snyder, AZ, Fox, MD. Coherent spontaneous activity identifies a hippocampal-parietal memory network. J Neurophysiol. 2006;96:35173531. doi:10.1152/jn.00048.2006.Google ScholarCrossrefMedlineISI
48. Roland, PE, O’Sullivan, B, Kawashima, R. Shape and roughness activate different somatosensory areas in the human brain. Proc Natl Acad Sci U S A. 1998;95:32953300Google ScholarCrossrefMedlineISI
49. Bodegård, A, Geyer, S, Grefkes, C, Zilles, K, Roland, PE. Hierarchical processing of tactile shape in the human brain. Neuron. 2001;31:317328Google ScholarCrossrefMedline
50. Karhu, J, Tesche, CD. Simultaneous early processing of sensory input in human primary (SI) and secondary (SII) somatosensory cortices. J Neurophysiol. 1999;81:20172025Google ScholarCrossrefMedline
51. Fabri, M, Polonara, G, Pesce, MD, Quattrini, A, Salvolini, U, Manzoni, T. Posterior corpus callosum and interhemispheric transfer of somatosensory information: an fMRI and neuropsychological study of a partially callosotomized patient. J Cogn Neurosci. 2001;13:10711079. doi:10.1162/089892901753294365. Google ScholarCrossrefMedlineISI
52. Chung, Y, Han, S, Kim, HS. Intra- and inter-hemispheric effective connectivity in the human somatosensory cortex during pressure stimulation. BMC Neurosci. 2014;15:43. doi:10.1186/1471-2202-15-43. Google ScholarCrossrefMedline
53. Mohajerani, MH, Aminoltejari, K, Murphy, TH. Targeted mini-strokes produce changes in interhemispheric sensory signal processing that are indicative of disinhibition within minutes. Proc Natl Acad Sci U S A. 2011;108:E183E191. doi:10.1073/pnas.1101914108. Google ScholarCrossrefMedlineISI
54. Blankenburg, F, Ruff, CC, Bestmann, S. Interhemispheric effect of parietal TMS on somatosensory response confirmed directly with concurrent TMS-fMRI. J Neurosci. 2008;28:1320213208. doi:10.1523/JNEUROSCI.3043-08.2008. Google ScholarCrossrefMedline
55. Inoue, K, Kawashima, R, Sugiura, M. Activation in the ipsilateral posterior parietal cortex during tool use: a PET study. Neuroimage. 2001;14:14691475. doi:10.1006/nimg.2001.0942. Google ScholarCrossrefMedlineISI
56. Takatsuru, Y, Fukumoto, D, Yoshitomo, M, Nemoto, T, Tsukada, H, Nabekura, J. Neuronal circuit remodeling in the contralateral cortical hemisphere during functional recovery from cerebral infarction. J Neurosci. 2009;29:1008110086. doi:10.1523/JNEUROSCI.1638-09.2009.Google ScholarCrossrefMedlineISI
57. Nelles, G, Spiekermann, G, Jueptner, M. Reorganization of sensory and motor systems in hemiplegic stroke patients. A positron emission tomography study. Stroke. 1999;30:15101516Google ScholarCrossrefMedlineISI
58. Jang, SH, Lee, MY. Correlation between somatosensory function and cortical activation induced by touch stimulation in patients with intracerebral hemorrhage. Int J Neurosci. 2013;123:248252. doi:10.3109/00207454.2012.755968. Google ScholarCrossrefMedlineISI
59. Bannister, LC, Crewther, SG, Gavrilescu, M, Carey, LM. Improvement in touch sensation after stroke is associated with resting functional connectivity changes. Front Neurol. 2015;6:165. doi:10.3389/fneur.2015.00165. Google ScholarCrossrefMedline
60. Kang, X, Herron, TJ, Cate, AD, Yund, EW, Woods, DL. Hemispherically-unified surface maps of human cerebral cortex: reliability and hemispheric asymmetries. PloS One. 2012;7:e45582. doi:10.1371/journal.pone.0045582. Google ScholarCrossrefMedline
61. Maingault, S, Tzourio-Mazoyer, N, Mazoyer, B, Crivello, F. Regional correlations between cortical thickness and surface area asymmetries: a surface-based morphometry study of 250 adults. Neuropsychologia. 2016;93(pt B):350364. doi:10.1016/j.neuropsychologia.2016.03.025. Google ScholarCrossrefMedline
62. Van de Winckel, A, Wenderoth, N, De Weerdt, W. Frontoparietal involvement in passively guided shape and length discrimination: a comparison between subcortical stroke patients and healthy controls. Exp Brain Res. 2012;220:179189. doi:10.1007/s00221-012-3128-2. Google ScholarCrossrefMedline
63. Borstad, A, Schmalbrock, P, Choi, S, Nichols-Larsen, DS. Neural correlates supporting sensory discrimination after left hemisphere stroke. Brain Res. 2012;1460:7887. doi:10.1016/j.brainres.2012.03.060. Google ScholarCrossrefMedlineISI
64. Lindberg, PG, Schmitz, C, Engardt, M, Forssberg, H, Borg, J. Use-dependent up- and down-regulation of sensorimotor brain circuits in stroke patients. Neurorehabil Neural Repair. 2007;21:315326. doi:10.1177/1545968306296965. Google ScholarSAGE JournalsISI
65. Dechaumont-Palacin, S, Marque, P, De Boissezon, X. Neural correlates of proprioceptive integration in the contralesional hemisphere of very impaired patients shortly after a subcortical stroke: an FMRI study. Neurorehabil Neural Repair. 2008;22:154165. doi:10.1177/1545968307307118. Google ScholarSAGE JournalsISI
66. Zatorre, RJ, Fields, RD, Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528536. doi:10.1038/nn.3045.Google ScholarCrossrefMedlineISI
67. Fields, RD. Changes in brain structure during learning: fact or artifact? Reply to Thomas and Baker. Neuroimage. 2013;73:260267. doi:10.1016/j.neuroimage.2012.08.085. Google ScholarCrossrefMedline
68. Xu, T, Yu, X, Perlik, AJ. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature. 2009;462:915919. doi:10.1038/nature08389. Google ScholarCrossrefMedlineISI
69. Kleim, JA, Hogg, TM, VandenBerg, PM, Cooper, NR, Bruneau, R, Remple, M. Cortical synaptogenesis and motor map reorganization occur during late, but not early, phase of motor skill learning. J Neurosci. 2004;24:628633. doi:10.1523/JNEUROSCI.3440-03.2004. Google ScholarCrossrefMedlineISI
70. Sampaio-Baptista, C, Khrapitchev, AA, Foxley, S. Motor skill learning induces changes in white matter microstructure and myelination. J Neurosci. 2013;33:1949919503. doi:10.1523/JNEUROSCI.3048-13.2013. Google ScholarCrossrefMedlineISI
71. Erickson, KI. Evidence for structural plasticity in humans: comment on Thomas and Baker (2012). Neuroimage. 2013;73:237238. doi:10.1016/j.neuroimage.2012.07.003. Google ScholarCrossrefMedline
72. Feydy, A, Carlier, R, Roby-Brami, A. Longitudinal study of motor recovery after stroke: recruitment and focusing of brain activation. Stroke. 2002;33:16101617Google ScholarCrossrefMedlineISI
73. Hamzei, F, Liepert, J, Dettmers, C, Weiller, C, Rijntjes, M. Two different reorganization patterns after rehabilitative therapy: an exploratory study with fMRI and TMS. Neuroimage. 2006;31:710720. doi:10.1016/j.neuroimage.2005.12.035. Google ScholarCrossrefMedlineISI

via Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke – Svetlana Pundik, Aleka Scoco, Margaret Skelly, Jessica P. McCabe, Janis J. Daly, 2018

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[TED TALK] How To Rewire Your Brain: Neuroscientist Dr. Joe Dispenza Explains The Incredible Science Behind Neuroplasticity – YouTube

Dr Joe Dispenza, D.C., studied biochemistry at Rutgers University in New Brunswick, N.J. He has a Bachelor of Science degree with an emphasis in Neuroscience and also received his Doctor of Chiropractic Degree at Life University in Atlanta, Georgia, graduating magna cum laude.

Over the last 10 years, Dr. Dispenza has lectured in over 17 different countries on six continents educating people about the role and function of the human brain.

His approach, taught in a very simple method, creates a bridge between true human potential and the latest scientific theories of neuroplasticity. He explains how thinking in new ways, as well as changing beliefs, can literally rewire one’s brain. The premise of his work is founded in his total conviction that every person on this planet has within them, the latent potential of greatness and true unlimited abilities.

His new book, Evolve Your Brain: The Science of Changing Your Mind connects the subjects of thought and consciousness with the brain, the mind, and the body. The book explores “the biology of change.” That is, when we truly change our mind, there is a physical evidence of change in the brain.

As an author of several scientific articles on the close relationship between the brain and the body, Dr. Dispenza ties information together to explain the roles these functions play in physical health and disease.

In his research into spontaneous remissions, Dr. Dispenza has found similarities in people who have experienced so-called miraculous healings, showing that they have actually changed their mind, which then changed their health.

One of the scientists, researchers, and teachers featured in the award winning film, “What the BLEEP Do We Know!?” Dr. Dispenza is often remembered for his comments on how a person can create their day, which he discussed in the film. He also has guest appearances in the theatrical directors cut, “What the BLEEP Down the Rabbit Hole.. as well as the extended Quantum Edition DVD set.

To find out more information on Joe Dispenza goto http://www.drjoedispenza.com/

via Dr Joe Dispenza- TED Talks with Dr Joe Dispenza – YouTube

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[Abstract] Transcranial Direct Current Stimulation Enhances Motor Skill Learning but Not Generalization in Chronic Stroke

Background. Motor training alone or combined with transcranial direct current stimulation (tDCS) positioned over the motor cortex (M1) improves motor function in chronic stroke. Currently, understanding of how tDCS influences the process of motor skill learning after stroke is lacking.

Objective. To assess the effects of tDCS on the stages of motor skill learning and on generalization to untrained motor function.

Methods. In this randomized, sham-controlled, blinded study of 56 mildly impaired chronic stroke patients, tDCS (anode over the ipsilesional M1 and cathode on the contralesional forehead) was applied during 5 days of training on an unfamiliar, challenging fine motor skill task (sequential visual isometric pinch force task). We assessed online and offline learning during the training period and retention over the following 4 months. We additionally assessed the generalization to untrained tasks.

Results. With training alone (sham tDCS group), patients acquired a novel motor skill. This skill improved online, remained stable during the offline periods and was largely retained at follow-up. When tDCS was added to training (real tDCS group), motor skill significantly increased relative to sham, mostly in the online stage. Long-term retention was not affected by tDCS. Training effects generalized to untrained tasks, but those performance gains were not enhanced further by tDCS.

Conclusions. Training of an unfamiliar skill task represents a strategy to improve fine motor function in chronic stroke. tDCS augments motor skill learning, but its additive effect is restricted to the trained skill.

 

via Transcranial Direct Current Stimulation Enhances Motor Skill Learning but Not Generalization in Chronic Stroke – Manuela Hamoudi, Heidi M. Schambra, Brita Fritsch, Annika Schoechlin-Marx, Cornelius Weiller, Leonardo G. Cohen, Janine Reis, 2018

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[Abstract+References] Brain Plasticity and Modern Neurorehabilitation Technologies

Abstract

In recent decades, interest in studies on basic and applied aspects of how the nervous system functions has been growing rapidly around the world. The recovery of lost functions rests on processes of neuroplasticity, which is determined by the ability of the brain to transform its structures in response to injury. The effects of both routine and state-of-the-art neurorehabilitation technologies are ensured by synaptic plasticity— long-term potentiation and long-term depression, which influence learning and the preservation of new knowledge and skills obtained during rehabilitation. The introduction of new methods of neuroimaging, neurophysiology, and mathematical statistics have powerfully stimulated the development of the neuroplasticity doctrine. It has become clear that the main role in the recovery of injured functions is played by the reorganization of cortical nets and not by tissue reparation as such. The Research Center of Neurology has accumulated significant experience in the use of innovative treatment methods based on modern neurorehabilitation principles. Some of them are used for acute stroke; among other things, their effectiveness and safety have been shown with regard to patients in intensive care units (cyclic robotic mechanotherapy) and patients with severe motor deficit and an associated somatic pathology (stimulation of plantar support zones). Opportunities to assess neuroplasticity under various rehabilitation methods using fMRI and navigated transcranial magnetic stimulation (TMS) are revealed. The center also studies the fundamentals of consciousness using original neuroimaging and neurophysiological protocols for the sake of its recovery. The center is actively introducing its data into the practice of domestic clinics specializing in recovery medicine and neurorehabilitation.

References

  1. 1.
    C. H. Rankin, T. Abrams, R. J. Barry, et al., “Habituation revisited: An updated and revised description of the behavioral characteristics of habituation,” Neurobiol. Learn. Mem. 92 (2), 135–138 (2009).CrossRefGoogle Scholar
  2. 2.
    I. Jin, E. R. Kandel, and R. D. Hawkins, “Whereas short-term facilitation is presynaptic, intermediateterm facilitation involves both presynaptic and postsynaptic protein kinases and protein synthesis,” Learn. Mem. Cold Spring Harb. 18, 96–102 (2011).CrossRefGoogle Scholar
  3. 3.
    C. Lüscher, R. A. Nicoll, R. C. Malenka, and D. Muller, “Synaptic plasticity and dynamic modulation of the postsynaptic membrane,” Nat. Neurosci., No. 3, 545–550 (2000).CrossRefGoogle Scholar
  4. 4.
    M. Lenz, A. Vlachos, and N. Maggio, “Ischemic longterm-potentiation (iLTP): Perspectives to set the threshold of neural plasticity toward therapy,” Neural Regen. Res., No. 10, 1537–1539 (2015).CrossRefGoogle Scholar
  5. 5.
    N. Hardingham, J. Dachtler, and K. Fox, “The role of nitric oxide in pre-synaptic plasticity and homeostasis,” Front Cell Neurosci., No. 7, 1–19 (2013).CrossRefGoogle Scholar
  6. 6.
    S. D. Bury and T. A. Jones, “Unilateral sensorimotor cortex lesions in adult rats facilitate motor skill learning with the ‘unaffected’ forelimb and training-induced dendritic structural plasticity in the motor cortex,” J. Neurosci. Off. J. Soc. Neurosci. 22, 8597–8606 (2002).CrossRefGoogle Scholar
  7. 7.
    R. J. Nudo, “Postinfarct cortical plasticity and behavioral recovery,” Stroke 38, 840–845 (2007).CrossRefGoogle Scholar
  8. 8.
    A. Arvidsson, T. Collin, D. Kirik, et al., “Neuronal replacement from endogenous precursors in the adult brain after stroke,” Nat. Med. 8, 963–970 (2002).CrossRefGoogle Scholar
  9. 9.
    Y. Bach and P. Rita, “Central nervous system lesions: Sprouting and unmasking in rehabilitation,” Arch. Phys. Med. Rehabil. 62, 413–417 (1981).Google Scholar
  10. 10.
    W. T. Greenough, H. M. Hwang, and C. Gorman, “Evidence for active synapse formation or altered postsynaptic metabolism in visual cortex of rats reared in complex environments,” Proc. Natl. Acad. Sci. U. S. A. 82, 4549–4552 (1985).CrossRefGoogle Scholar
  11. 11.
    J. Liepert, H. Bauder, H. R. Wolfgang, et al., “Treatment-induced cortical reorganization after stroke in humans,” Stroke J. Cereb. Circ. 31, 1210–1216 (2000).CrossRefGoogle Scholar
  12. 12.
    Y. Sagi, I. Tavor, S. Hofstetter, et al., “Learning in the fast lane: New insights into neuroplasticity,” Neuron 73, 1195–1203 (2012).CrossRefGoogle Scholar
  13. 13.
    E. Auriel, B. L. Edlow, Y. D. Reijmer, et al., “Microinfarct disruption of white matter structure: A longitudinal diffusion tensor analysis,” Neurology 83, 182–188 (2014).CrossRefGoogle Scholar
  14. 14.
    L. A. Chernikova, M. A. Piradov, N. A. Suponeva, et al., “High-tech methods of neurorehabilitation in nervous system diseases,” in Neurology of the 21st Century: Diagnostic, Treatment, and Research Technologies: Manual for Doctors, Ed. by M. A. Piradov, S. N. Illarioshkin, and M. M. Tanashyan (ATMO, Moscow, 2015) [in Russian].Google Scholar
  15. 15.
    L. G. Tarasova, L. A. Chernikova, and A. S. Chubukov, “Hand motion recovery in poststroke hemiparesis patients by the method of intensive training of the paretic upper limb,” Lech. Fizkul’t. Sport. Med., No. 8, 34–39 (2008).Google Scholar
  16. 16.
    P. R. Prokazova, M. A. Piradov, Yu. V. Ryabinkina, et al., “Robotic mechanotherapy using the Motomed Letto 2 simulator in complex early stroke rehabilitation in the resuscitation and intensive care unit,” Annaly Klinich. Eksp. Nevrolog., No. 2, 11–15 (2013).Google Scholar
  17. 17.
    A. A. Belkin, I. A. Avdyunina, N. A. Varako, et al., “Intensive care rehabilitation: Clinical recommendations,” Vestn. Vosstanov. Med., No. 2, 139–143 (2017).Google Scholar
  18. 18.
    K. Ustinova, N. Epstein, L. Chernikova, et al., “Effect of robotic locomotor training in an individual with Parkinson’s disease: A case report,” Disab. Rehab.: Assist. Technol. 6 (1), 77–85 (2011).Google Scholar
  19. 19.
    S. N. Morozova, E. A. Zmeykina, R. N. Konovalov, et al., “Changes in functional connectivity of motor zones in the course of treatment with a Regent multimodal complex exoskeleton in neurorehabilitation of poststroke patients.” Hum. Physiol., No. 1, 54–60 (2016).Google Scholar
  20. 20.
    E. I. Kremneva, L. A. Chernikova, R. N. Konovalov, et al., “Assessing supraspinal control of locomotion in norm and in pathology using a passive motor fMRT paradigm,” Annaly Klinich. Eksp. Nevrol., No. 1, 31–37 (2012).Google Scholar
  21. 21.
    L. A. Chernikova, E. I. Kremneva, A. V. Chervyakov, et al., “New approaches in the study of the neuroplasticity process in patients with central nervous system lesions,” Hum. Physiol., No. 3, 272–277 (2013).CrossRefGoogle Scholar
  22. 22.
    O. V. Glebova, M. Yu. Maksimova, and L. A. Chernikova, “Mechanical stimulation of plantar support zones during acute moderate and severe stroke,” Vestn. Vosstanov. Med., No. 1, 71–75 (2014).Google Scholar
  23. 23.
    I. V. Saenko, S. N. Morozova, E. A. Zmeikina, et al., “Change in functional connectivity of motor zones using the Regent multimodal exoskeleton complex in stroke patients,” Fiziol. Chel., No. 1, 64–72 (2016).Google Scholar
  24. 24.
    M. A. Piradov, S. N. Illarioshkin, A. O. Gushcha, et al., “State-of-the-art neuromodulation technologies,” in Neurology of the 21st Century: Diagnostic, Treatment, and Research Technologies: Manual for Doctors, Ed. by M. A. Piradov, S. N. Illarioshkin, and M. M. Tanashyan (ATMO, Moscow, 2015), pp. 46–98 [in Russian].Google Scholar
  25. 25.
    N. A. Suponeva, I. S. Bakulin, A. G. Poidasheva, and M. A. Piradov, “Safety of transcranial magnetic stimulation: A review of international recommendations and new data,” Nervno-Myshech. Bol., No. 2, 21–36 (2017).Google Scholar
  26. 26.
    M. A. Piradov, M. V. Krotenkova, R. N. Konovalov, et al., “Neuroimaging technologies,” in Neurology of the 21st Century: Diagnostoc, Treatment, and Research Technologies: Manual for Doctors, Ed. by M. A. Piradov, S. N. Illarioshkin, and M. M. Tanashyan (ATMO, Moscow, 2015), pp. 11–82 [in Russian].Google Scholar
  27. 27.
    L. A. Legostaeva, E. A. Zmeikina, A. G. Poidasheva, et al., “Navigated transcranial magnetic stimulation under fMRT resting control during rehabilitation of patients with chronic consciousness disorders: Blind intervention study,” in VI Baltic Congress on Child Neurology: A Collection of Abstracts, (St. Petersburg, 2016), pp. 221–222 [in Russian].Google Scholar
  28. 28.
    O. A. Mokienko, R. K. Lyukmanov, L. A. Chernikova, et al., “Brain–computer interface: The first experience of clinical use in Russia,” Hum. Physiol., No. 1, 24–31 (2016).CrossRefGoogle Scholar
  29. 29.
    O. A. Mokienko, A. V. Chervyakov, S. Kulikova, et al., “Increased motor cortex excitability during motor imagery in brain–computer interface trained subjects,” Front. Comput. Neurosci. 7, 168 (2013).CrossRefGoogle Scholar
  30. 30.
    A. G. Poidasheva, G. A. Aziatskaya, A. Yu. Chernyavskii, et al., “Dynamics of cortical motor representation of the common digital extensor when teaching motor imaging using the brain–computer interface: A controlled study,” Zh. Vyssh. Nerv. Deyat. im. I.P. Pavlova, No. 4, 473–484 (2017).Google Scholar

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[VIDEO] Neuroplasticity – YouTube

The Sentis Brain Animation Series takes you on a tour of the brain through a series of short and sharp animations.

The fourth in the series explains how our most complex organ is capable of changing throughout our lives. This inspiring animation demonstrates how we all have the ability to learn and change by rewiring our brains.

Who is Sentis? We are a global team assisting individuals and organisations change their lives for the better.

The human mind is our focus and we believe the mind is an individual’s most important performance tool.

We are the world leaders in the application of psychology and neuroscience to safety, leadership development, and wellbeing in the workplace.

Find out more at http://www.sentis.com.au/

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[WEB SITE] Human brains make new nerve cells — and lots of them — well into old age.

Previous studies have suggested neurogenesis tapers off or stops altogether

BY
LAUREL HAMERS, APRIL 5, 2018
nerve cells in hippocampi

NEURON NURSERY  Roughly the same number of new nerve cells (dots) exist in the hippocampus of people in their 20s (three hippocampi shown, top row) as in people in their 70s (bottom). Blue marks the dentate gyrus, where new nerve cells are born.
M. BOLDRINI/COLUMBIA UNIV.

Your brain might make new nerve cells well into old age.

Healthy people in their 70s have just as many young nerve cells, or neurons, in a memory-related part of the brain as do teenagers and young adults, researchers report in the April 5 Cell Stem Cell. The discovery suggests that the hippocampus keeps generating new neurons throughout a person’s life.

The finding contradicts a study published in March, which suggested that neurogenesis in the hippocampus stops in childhood (SN Online: 3/8/18). But the new research fits with a larger pile of evidence showing that adult human brains can, to some extent, make new neurons. While those studies indicate that the process tapers off over time, the new study proposes almost no decline at all.

Understanding how healthy brains change over time is important for researchers untangling the ways that conditions like depression, stress and memory loss affect older brains.

When it comes to studying neurogenesis in humans, “the devil is in the details,” says Jonas Frisén, a neuroscientist at the Karolinska Institute in Stockholm who was not involved in the new research. Small differences in methodology — such as the way brains are preserved or how neurons are counted — can have a big impact on the results, which could explain the conflicting findings. The new paper “is the most rigorous study yet,” he says.

Researchers studied hippocampi from the autopsied brains of 17 men and 11 women ranging in age from 14 to 79. In contrast to past studies that have often relied on donations from patients without a detailed medical history, the researchers knew that none of the donors had a history of psychiatric illness or chronic illness. And none of the brains tested positive for drugs or alcohol, says Maura Boldrini, a psychiatrist at Columbia University. Boldrini and her colleagues also had access to whole hippocampi, rather than just a few slices, allowing the team to make more accurate estimates of the number of neurons, she says.

To look for signs of neurogenesis, the researchers hunted for specific proteins produced by neurons at particular stages of development. Proteins such as GFAP and SOX2, for example, are made in abundance by stem cells that eventually turn into neurons, while newborn neurons make more of proteins such as Ki-67. In all of the brains, the researchers found evidence of newborn neurons in the dentate gyrus, the part of the hippocampus where neurons are born.

Although the number of neural stem cells was a bit lower in people in their 70s compared with people in their 20s, the older brains still had thousands of these cells. The number of young neurons in intermediate to advanced stages of development was the same across people of all ages.

Still, the healthy older brains did show some signs of decline. Researchers found less evidence for the formation of new blood vessels and fewer protein markers that signal neuroplasticity, or the brain’s ability to make new connections between neurons. But it’s too soon to say what these findings mean for brain function, Boldrini says. Studies on autopsied brains can look at structure but not activity.

Not all neuroscientists are convinced by the findings. “We don’t think that what they are identifying as young neurons actually are,” says Arturo Alvarez-Buylla of the University of California, San Francisco, who coauthored the recent paper that found no signs of neurogenesis in adult brains. In his study, some of the cells his team initially flagged as young neurons turned out to be mature cells upon further investigation.

But others say the new findings are sound. “They use very sophisticated methodology,” Frisén says, and control for factors that Alvarez-Buylla’s study didn’t, such as the type of preservative used on the brains.

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[Review] Current evidence on transcranial magnetic stimulation and its potential usefulness in post-stroke neurorehabilitation: Opening new doors to the treatment of cerebrovascular disease – Full Text

Abstract

Introduction

Repetitive transcranial magnetic stimulation (rTMS) is a therapeutic reality in post-stroke rehabilitation. It has a neuroprotective effect on the modulation of neuroplasticity, improving the brain’s capacity to retrain neural circuits and promoting restoration and acquisition of new compensatory skills.

Development

We conducted a literature search on PubMed and also gathered the latest books, clinical practice guidelines, and recommendations published by the most prominent scientific societies concerning the therapeutic use of rTMS in the rehabilitation of stroke patients. The criteria of the International Federation of Clinical Neurophysiology (2014) were followed regarding the inclusion of all evidence and recommendations.

Conclusions

Identifying stroke patients who are eligible for rTMS is essential to accelerate their recovery. rTMS has proven to be safe and effective for treating stroke complications. Functional brain activity can be optimised by applying excitatory or inhibitory electromagnetic pulses to the hemisphere ipsilateral or contralateral to the lesion, respectively, as well as at the level of the transcallosal pathway to regulate interhemispheric communication. Different studies of rTMS in these patients have resulted in improvements in motor disorders, aphasia, dysarthria, oropharyngeal dysphagia, depression, and perceptual-cognitive deficits. However, further well-designed randomised controlled clinical trials with larger sample size are needed to recommend with a higher level of evidence, proper implementation of rTMS use in stroke subjects on a widespread basis.

Introduction

Stroke patients should receive early neurorehabilitation after convalescence. For many years, researchers have aimed to identify new therapeutic targets to hasten recovery from stroke. However, we continue to lack a universally accepted, approved pharmacological therapy for these patients.1234 ;  5 After stroke, organisational changes in brain interneuronal activity in the affected area and the surrounding healthy tissue may on occasion promote functional recovery. Neurorehabilitation may help achieve this aim. Unfortunately, there are also occasions when neural reorganisation is suboptimal; in these cases, the problem persists and becomes chronic. In this context, transcranial magnetic stimulation (TMS) emerged as a tool for studying the brain and has been used since the mid-1980s to treat certain neuropsychiatric disorders. Neurorehabilitation is based on the idea that the brain is a dynamic entity able to adapt to internal and external homeostatic changes. This adaptive capacity, called neuroplasticity, is also present in patients with acquired brain injuries. The degree of recovery and the functional prognosis of these patients depend on the extent of neuroplastic changes.12345 ;  6 When performed by experienced physicians, TMS is a safe, non-invasive technique which enables the organisation of these neural changes (Fig. 1). The technique’s applications are expanding rapidly.12345678 ;  9

Modern TMS device.

Figure 1.

Modern TMS device.

We present the results of a literature review of the most relevant articles, manuals, and clinical practice guidelines addressing TMS (background information, diagnostic and therapeutic uses, and especially its usefulness for stroke neurorehabilitation) and published between 1985 (when the technique was first used) and 2015.

 

Development

The organisation of language in the brain

The left hemisphere of the brain is the anatomo-functional seat of language in 96% of right-handed and 70% of left-handed individuals. Language processing in the left hemisphere involves certain anatomical pathways for language comprehension, repetition, and production (Fig. 2). Positron emission tomography and functional magnetic resonance imaging (fMRI) studies conducted during multiple language tasks have shown brain activation not only in the main language centres (lesions to these areas may cause Broca aphasia, Wernicke aphasia, etc.) (Fig. 3) but also in many other locations, such as the thalamus (alertness), the basal ganglia (motor modulation), and the limbic system (affect and memory). Language is the perfect model for understanding how the central nervous system works as a whole.10 ;  11

Figure 2. The functional pathways involved in comprehension, repetition, and production of written, gesture, and spoken language, according to the Wernicke-Geschwind model. Within the left hemisphere, language organisation follows certain anatomical pathways for language comprehension, repetition, and production. Sounds are processed by the bilateral auditory cortex, in the superior temporal gyrus (primary auditory area), and decoded in the posterior area of the left temporal cortex (Wernicke area); the latter is connected to other cortical areas or networks which assign meaning to words. During reading, output from the primary visual area (bilaterally) travels to other parieto-occipital association areas for word and phrase recognition (especially the left fusiform gyrus, located in the inferior surface of the temporal lobe, where there is a key word recognition centre) and reaches the angular gyrus, which processes language-related visual and auditory information. In spontaneous language repetition and production, auditory information must travel through the arcuate fasciculus towards the left inferior frontal region (Broca area), which is responsible for language production; this area is also known to be involved in such other functions as action comprehension (mirror neurons). To produce written or spoken language, output from the Wernicke area, the Broca area, and nearby association areas must reach the primary motor cortex.10 ;  11
Adapted with permission from Bear et al.10

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[ARTICLE] Cerebral Reorganization in Subacute Stroke Survivors after Virtual Reality-Based Training: A Preliminary Study – Full Text

Abstract

Background

Functional magnetic resonance imaging (fMRI) is a promising method for quantifying brain recovery and investigating the intervention-induced changes in corticomotor excitability after stroke. This study aimed to evaluate cortical reorganization subsequent to virtual reality-enhanced treadmill (VRET) training in subacute stroke survivors.

Methods

Eight participants with ischemic stroke underwent VRET for 5 sections per week and for 3 weeks. fMRI was conducted to quantify the activity of selected brain regions when the subject performed ankle dorsiflexion. Gait speed and clinical scales were also measured before and after intervention.

Results

Increased activation in the primary sensorimotor cortex of the lesioned hemisphere and supplementary motor areas of both sides for the paretic foot (p < 0.01) was observed postintervention. Statistically significant improvements were observed in gait velocity (p < 0.05). The change in voxel counts in the primary sensorimotor cortex of the lesioned hemisphere is significantly correlated with improvement of 10 m walk time after VRET (r = −0.719).

Conclusions

We observed improved walking and increased activation in cortical regions of stroke survivors after VRET training. Moreover, the cortical recruitment was associated with better walking function. Our study suggests that cortical networks could be a site of plasticity, and their recruitment may be one mechanism of training-induced recovery of gait function in stroke. This trial is registered with ChiCTR-IOC-15006064.

1. Introduction

Gait impairment is a common consequence of stroke, and the decreases in gait velocity, stride length, and cadence are hallmark features of gait pattern alterations in stroke survivors [12]. Previous studies found that early intervention with physical therapy and gait training to restore walking after stroke was recommended to improve motor function and decrease disability [34]. As gait impairments are a result of deficient neuromuscular control, a better understanding of the impact and mechanism of those interventions on gait pattern recovery after stroke is therefore essential.

Environmental factors act as critical determinants for the level of community ambulation of stroke patient [5]. The development of computers has resulted in virtual reality (VR) tools which can create life-like scenarios via visual, auditory, and tactile feedback and can provide subjects with a safe and stimulating learning environment [6]. VR has been increasingly used in poststroke rehabilitation; therapy interventions using VR may improve motor function for those patients [715]. VR system might represent the main neural substrate for relearning or resuming impaired motor functions following stroke. A key challenge in neurorehabilitation is to establish optimal training protocols for the given patient [10]. VR could provide a person with senses of encouragement and accomplishment [1619]. However, two main concerns need to be investigated. What kind of rehabilitation strategies can combine with VR, and what degree for those VR combined rehabilitation strategies can facilitate stroke patients? Recently, motor relearning strategies can be applied in VR-enhanced treadmill (VRET) training by numerous movement repetitions and a multisensory approach to stimulate brain plasticity and patients receive visual feedback which is close to real-life experience [12]. While the positive benefits of VRET exercise on gait speed, cadence, step length, community walking time, and balance have been demonstrated [7911121415], the associated changes of brain activity with this training have not been investigated yet.

Advances in imaging, such as blood oxygenation level-dependent functional magnetic resonance imaging (fMRI), have been allowed for the observation of changes in cerebral plasticity and the exploration of recovery mechanisms. The control of gait involves the planning and execution from multiple cortical areas, such as secondary and premotor cortex [11]. Ankle dorsiflexion is an important kinematic aspect of the gait cycle. Using ankle movement, Enzinger et al. [20] observed increased activation in the unlesioned hemisphere associated with increasing functional impairment of the paretic leg in patients with stroke. fMRI studies of patients after stroke have suggested that VR could increase neural activations in the primary motor areas and improve lateralization of primary sensorimotor cortex (SMC) activity [2123]. We hypothesized that recovery of lower limb function after VRET would be associated with changes in brain activation during ankle dorsiflexion.

Therefore, the primary aim of this preliminary study was to investigate if functional reorganization takes place after VRET in subacute stroke survivors with gait impairment, using fMRI and an ankle dorsiflexion paradigm. Correlation between clinical scale changes after VERT and brain activation alterations was also studied to see the relations of the induction of cortical plasticity and functional recovery in subacute stroke survivors. We hope that the results of the current study could help to understand the mechanism of VRET as an early intervention for gait recovery for stroke.

2. Methods

2.1. Participants

Eight stroke survivors were recruited in this study, aged 41–72 years (mean: 58.38 years) and included 6 males and 2 females (Table 1 and Figure 1). Inclusion criteria: (i) 18 to 80 years in ages; (ii) right-foot dominant; (iii) first incident of ischemic cortical or subcortical stroke which resulted in gait impairment; (iv) stroke was confirmed by MRI within the past 3 months of inclusion; (v) at least 10° of dorsiflexion is available at the ankle. Exclusion criteria: (i) contraindication to MRI scan (implanted medical devices incompatible with MRI testing or claustrophobia); (ii) history of stroke resulted in function impairment; (iii) history of mental disorder or the use of antipsychotic medication; (iv) cognitive impairment (Mini-Mental State Examination score of less than 24 points); (v) unable to speak or hear; (vi) history of recent deep vein thrombosis of the lower limbs; (vii) recent myocardial infarction; (viii) medically unstable; (ix) existing lower extremity pathology. This study was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (SYSU), and all subjects provided informed consent before the experiments.

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Figure 1
Axial structural T1-weighted MRI scans at the level of maximum infarct volume for each patient. And right hemisphere patients flipped on the sagittal axis for better comparison.

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[WEB SITE] DARPA Is Planning to Hack the Human Brain to Let Us “Upload” Skills

IN BRIEF

The DARPA Targeted Neuroplasticity Training (TNT) program is exploring ways to speed up skill acquisition by activating synaptic plasticity. If the program succeeds, downloadable learning that happens in a flash may be the result.

MINDHACK FOR FASTER LEARNING

In March 2016, DARPA — the U.S. military’s “mad science” branch — announced their Targeted Neuroplasticity Training(TNT) program. The TNT program aims to explore various safe neurostimulation methods for activating synaptic plasticity, which is the brain’s ability to alter the connecting points between neurons — a requirement for learning. DARPA hopes that building up that ability by subjecting the nervous system to a kind of workout regimen will enable the brain to learn more quickly.

[Taken]Military Researchers Are Hacking the Human Brain So We Can Learn Much Faster
Credit: DARPA

The ideal end benefit for this kind of breakthrough would be downloadable learning. Rather than needing to learn, for example, a new language through rigorous study and practice over a long period of time, we could basically “download” the knowledge after putting our minds into a highly receptive, neuroplastic state. Clearly, this kind of research would benefit anyone, but urgent military missions can succeed or fail based on the timing. In those situations, a faster way to train personnel would be a tremendous boon.

FIRST NEUROSTIMULATION, THEN APPLICATION

As part of the TNT program, DARPA is funding eight projects at seven institutions. All projects are part of a coordinated effort that will first study the fundamental science undergirding brain plasticity and will conclude with human trials. The first portion of the TNT program will work to unravel the neural mechanisms that allow nerve stimulation to influence brain plasticity. The second portion of the program will practically apply what has been learned in a variety of training exercises.

To ensure the work stays practical, foreign language specialists, intelligence analysts, and others who train personnel now will work with researchers to help refine the TNT platform to suit military training needs. Researchers will compare the efficacy of using an implanted device to stimulate the brain versus non-invasive stimulation. They will also explore both the ethics of enhanced learning through neurostimulation and ways to avoid side effects and potential risks.

The Evolution of Brain-Computer Interfaces [INFOGRAPHIC]
Click to View Full Infographic

“The Defense Department operates in a complex, interconnected world in which human skills such as communication and analysis are vital, and the Department has long pushed the frontiers of training to maximize those skills,” Doug Weber, the TNT Program Manager, said in a DARPA press release. “DARPA’s goal with TNT is to further enhance the most effective existing training methods so the men and women of our Armed Forces can operate at their full potential.”

If the TNT program succeeds, striving to be all you can be may mean learning at a much faster pace, and not just for military personnel. Downloadable learning may be one of the ways we achieve next-level humanity.

 

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[BLOG POST] Where does the controversial finding that adult human brains don’t grow new neurons leave ongoing research?

Scientists have known for about two decades that some neurons – the fundamental cells in the brain that transmit signals – are generated throughout life. But now a controversial new study from the University of California, San Francisco, casts doubt on whether many neurons are added to the human brain after birth.

As a translational neuroscientist, this work immediately piqued my interest. It has direct implications for the research my lab does: We transplant young neurons into damaged brain areas in mice in an attempt to treat epileptic seizures and the damage they’ve caused. Like many labs, part of our work is based on a foundational belief that the hippocampus is a brain region where new neurons are born throughout life.

If the new study is right, and human brains for the most part don’t add new neurons after infancy, researchers like me need to reconsider the validity of the animal models we use to understand various brain conditions – in my case temporal lobe epilepsy. And I suspect other labs that focus on conditions including drug addiction, depression and post-traumatic stress disorder are thinking about what the UCSF study means for their investigations, too.

In the brain of a baby who died soon after birth, there are many new neurons (green in this image) in the hippocampus. Sorrells et alCC BY-ND

When and where are new neurons born?

No doubt, the adult human brain is able to learn throughout life and to change and adapt – a capability brain scientists call neuroplasticity, the brain’s ability to reorganize itself by rewiring connections. Yet, a central dogma in the field of neuroscience for nearly 100 years had been that a child is born with all the neurons she will ever have because the adult brain cannot regenerate neurons.

Just over half a century ago, researchers devised a way to study proliferation of cells in the mature brain, based on techniques to incorporate a radioactive label into new cells as they divide. This approach led to the startling discovery in the 1960s that rodent brains actually could generate new neurons.

Neurogenesis – the production of new neurons – was previously thought to only occur during embryonic life, a time of extremely rapid brain growth and expansion, and the rodent findings were met with considerable skepticism. Then researchers discovered that new neurons are also born throughout life in the songbird brain, a species scientists use as a model for studying vocal learning. It started to look like neurogenesis plays a key role in learning and neuroplasticity – at least in some brain regions in a few animal species.

Even so, neuroscientists were skeptical that many nerve cells could be renewed in the adult brain; evidence was scant that dividing cells in mammalian brains produced new neurons, as opposed to other cell types. It wasn’t until researchers extracted neural stem cells from adult mouse brains and grew them in cell culture that scientists showed these precursor cells could divide and differentiate into new neurons. Now it is generally well accepted that neurogenesis takes place in two areas of the adult rodent brain: the olfactory bulbs, which process smell information, and the hippocampus, a region characterized by neuroplasticity that is required for forming new declarative memories.

Adult neural stem cells cluster together in what scientists call niches – hotbeds for cultivating the birth and growth of new neurons, recognizable by their distinctive architecture. Despite the mounting evidence for regional growth of new neurons, these studies underscored the point that the adult brain harbors only a few stem cell niches and their capacity to produce neurons is limited to just a few types of cells.

With this knowledge, and new tools for labeling proliferating cells and identifying maturing neurons, scientists began to look for postnatal neurogenesis in primate and human brains.

What’s happening in adult human brains?

Many neuroscientists believe that by understanding the process of adult neurogenesis we’ll gain insights into the causes of some human neurological disorders. Then the next logical step would be trying to develop new treatments harnessing neurogenesis for conditions such as Alzheimer’s disease or trauma-induced epilepsy. And stimulating resident stem cells in the brain to generate new neurons is an exciting prospect for treating neurodegenerative diseases.

Because neurogenesis and learning in rodents increases with voluntary exercise and decreases with age and early life stress, some workers in the field became convinced that older people might be able to enhance their memory as they age by maintaining a program of regular aerobic exercise.

However, obtaining rigorous proof for adult neurogenesis in the human and primate brain has been technically challenging – both due to the limited experimental approaches and the larger sizes of the brains, compared to reptiles, songbirds and rodents.

Researchers injected a compound found in DNA, nicknamed BrdU to identify brand new neurons in human adult hippocampus – but the labeled cells were extremely rare. Other groups demonstrated that adult human brain tissue obtained during neurosurgery contained stem cell niches that housed progenitor cells that could generate new neurons in the lab, showing that these cells had an inborn neurogenic capacity, even in adults.

But even when scientists saw evidence for new neurons in the brain, they tended to be scarce. Some neurogenesis experts were skeptical that evidence based on incorporating BrdU into DNA was a reliable method for proving that new cells were actually being born through cell division, rather than just serving as a marker for other normal cell functions.

Further questions about how long human brains retain the capacity for neurogenesis arose in 2011, with a study that compared numbers of newborn neurons migrating in the olfactory bulbs of infants versus older individuals up to 84 years of age. Strikingly, in the first six months of life, the baby brains contained lots of chains of young neurons migrating into the frontal lobes, regions that guide executive function, long-range planning and social interactions. These areas of the human cortex are hugely increased in size and complexity compared to rodents and other species. But between 6 to 18 months of age, the migrating chains dwindled to a thin stream. Then, a very different pattern emerged: Where the migrating chains of neurons had been in the infant brain, a cell-free gap appeared, suggesting that neural stem cells become depleted during the first six months of life.

Questions still lingered about the human hippocampus and adult neurogenesis as a source for its neuroplasticity. One group came up with a clever approach based on radiocarbon dating. They measured how much atmospheric ¹⁴C – a radioactive isotope derived from nuclear bomb tests – was incorporated into people’s DNA. This method suggested that as many as 700 new cells are added to the adult human hippocampus every day. But these findings were contradicted by a 2016 study that found that the neurogenic cells in the adult hippocampus could only produce non-neuronal brain cells called microglia.

Rethinking neurogenesis research

Now the largest and most comprehensive study conducted to date presents even stronger evidence that robust neurogenesis doesn’t continue throughout adulthood in the human hippocampus – or if it does persist, it is extremely rare. This work is controversial and not universally accepted. Critics have been quick to cast doubt on the results, but the finding isn’t totally out of the blue.

So where does this leave the field of neuroscience? If the UCSF scientists are correct, what does that mean for ongoing research in labs around the world?

Because lots of studies of neurological diseases are done in mice and rats, many scientists are invested in the possibility that adult neurogenesis persists in the human brain, just as it does in rodents. If it doesn’t, how valid is it to think that the mechanisms of learning and neuroplasticity in our model animals are comparable to those in the human brain? How relevant are our models of neurological disorders for understanding how changes in the hippocampus contribute to disorders such as the type of epilepsy I study?

In my lab, we transplant embryonic mouse or human neurons into the adult hippocampus in mice, after damage caused by epileptic seizures. We aim to repair this damage and suppress seizures by seeding the mouse hippocampus with neural stem cells that will mature and form new connections. In temporal lobe epilepsy, studies in adult rodents suggest that naturally occurring hippocampal neurogenesis is problematic. It seems that the newborn hippocampal neurons become highly excitable and contribute to seizures. We’re trying to inhibit these newborn hyperexcitable neurons with the transplants. But if humans don’t generate new hippocampal neurons, then maybe we’re developing a treatment in mice for a problem that has a different mechanism in people.

Perhaps our species has evolved separate mechanisms for neuroplasticity, distinct from those used by species such as rats and mice. One possibility is that there are other sites in the human brain where neurogenesis occurs – its a big structure and more exploration will be necessary. If it turns out to be true that the human brain has a diminished capacity for neurogenesis after birth, the finding will have important implications for how neuroscientists like me think about tackling brain disorders.

Perhaps most importantly, this work underscores how crucial it is to learn how to increase the longevity of the neurons we do have, born early in life, and how we might replace or repair neurons that become damaged.

via Where does the controversial finding that adult human brains don’t grow new neurons leave ongoing research?

 

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