English to Portuguese, Spanish, German, Italian, French Translation

Sunday, June 2, 2024

Translation of neurotechnologies - Nature.com - Translation

Abstract

Neurotechnologies combine engineering methods and neuroscientific knowledge to design devices that interface the brain with the outside world. Since the early 2000s, inspiring and encouraging neurotechnology examples have been the subject of high-profile scientific articles and made headlines in popular media. However, although neurotechnologies have the potential to improve people’s lives in ways that cannot be achieved by other solutions such as pharmaceuticals, only a few of them have established themselves as clinical solutions. In this Review, we provide a systematic, state-of-the-art assessment of the opportunities and shortcomings of neurotechnology’s engineering and scientific components, and highlight the requirements to overcome translational barriers. Finally, we present a comprehensive framework to aid the clinical and commercial translation of neurotechnologies.

Key points

Neurotechnologies provide new diagnostic and treatment solutions in ways that cannot be achieved by other strategies such as pharmaceuticals, therefore potentially addressing the unmet needs of a large number of patients.
Despite important scientific and technical breakthroughs, as well as impressive demonstrations, only a few neurotechnologies have firmly established themselves as clinical solutions.
Developing successful neurotechnologies requires addressing technical, clinical and commercial requirements to overcome translational barriers.
Rigorously assessing and validating potential benefits, incorporating and advancing neuroscientific knowledge, optimizing technology innovation, and focusing on the user and on the right problem can assist the development of new solutions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Purchase on Springer Link
Instant access to full article PDF

Buy now

Prices may be subject to local taxes which are calculated during checkout

**Fig. 2: Hardware interactions with the brain at different scales, and corresponding hardware.**

**Fig. 3: Benefit, costs, market penetration and patient benefit.**

Recent advances in neurotechnologies with broad potential for neuroscience research

Article 16 November 2020

Navigating the FDA regulatory landscape

Article 18 October 2023

Precision electronic medicine in the brain

Article 02 September 2019

References

Wolpaw, J. & Wolpaw, E. (eds.) Brain–Computer Interfaces: Principles and Practice (Oxford Univ. Press, 2012).
Stieglitz, T. Of man and mice: translational research in neurotechnology. Neuron 105, 12–15 (2020).

Article Google Scholar
Borton, D. A., Dawes, H. E., Worrell, G. A., Starr, P. A. & Denison, T. J. Developing collaborative platforms to advance neurotechnology and its translation. Neuron 108, 286–301 (2020).

Article Google Scholar
Famm, K., Litt, B., Tracey, K. J., Boyden, E. S. & Slaoui, M. Drug discovery: a jump-start for electroceuticals. Nature 496, 159–161 (2013).

Article Google Scholar
Mountcastle, V. B. Modality and topographic properties of single neurons of cat’s somatic sensory cortex. J. Neurophysiol. 20, 408–434 (1957).

Article Google Scholar
Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106 (1962).

Article Google Scholar
Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

Article Google Scholar
Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).

Article Google Scholar
Jones, K. E., Campbell, P. K. & Normann, R. A. A glass/silicon composite intracortical electrode array. Ann. Biomed. Eng. 20, 423–437 (1992).

Article Google Scholar
Shain, W. et al. Controlling cellular reactive responses around neural prosthetic devices using peripheral and local intervention strategies. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 186–188 (2003).

Article Google Scholar
Ritaccio, A. L., Brunner, P. & Schalk, G. Electrical stimulation mapping of the brain: basic principles and emerging alternatives. J. Clin. Neurophysiol. 35, 86 (2018).

Article Google Scholar
Kubanek, J., Miller, K. J., Ojemann, J. G., Wolpaw, J. R. & Schalk, G. Decoding flexion of individual fingers using electrocorticographic signals in humans. J. Neural Eng. 6, 066001 (2009).

Article Google Scholar
Miller, K. et al. Spectral changes in cortical surface potentials during motor movement. J. Neurosci. 27, 2424–2432 (2007).

Article Google Scholar
Schalk, G. et al. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J. Neural Eng. 4, 264–275 (2007).

Article Google Scholar
Berger, H. Über das Electroenkephalogramm des Menschen. Arch. Psychiat. Nervenkr. 87, 527–570 (1929).

Article Google Scholar
Buzsáki, G. & Draguhn, A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).

Article Google Scholar
Haegens, S., Nácher, V., Luna, R., Romo, R. & Jensen, O. α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proc. Natl Acad. Sci. USA 108, 19377–19382 (2011).

Article Google Scholar
Coon, W. G. et al. Oscillatory phase modulates the timing of neuronal activations and resulting behavior. NeuroImage 133, 294–301 (2016).

Article Google Scholar
Moheimanian, L., Paraskevopoulou, S. E., Adamek, M., Schalk, G. & Brunner, P. Modulation in cortical excitability disrupts information transfer in perceptual-level stimulus processing. NeuroImage 243, 118498 (2021).

Article Google Scholar
Miller, K. J., Sorensen, L. B., Ojemann, J. G. & den Nijs, M. Power-law scaling in the brain surface electric potential. PLoS Comput. Biol. 5, e1000609 (2009).

Article MathSciNet Google Scholar
Manning, J. R., Jacobs, J., Fried, I. & Kahana, M. J. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J. Neurosci. 29, 13613–13620 (2009).

Article Google Scholar
Ray, S. & Maunsell, J. Different origins of gamma rhythm and high-gamma activity in macaque visual cortex. PLoS Biol. 9, e1000610 (2011).

Article Google Scholar
Whittingstall, K. & Logothetis, N. K. Frequency-band coupling in surface EEG reflects spiking activity in monkey visual cortex. Neuron 64, 281–289 (2009).

Article Google Scholar
Gardner, A. B., Worrell, G. A., Marsh, E., Dlugos, D. & Litt, B. Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings. Clin. Neurophysiol. 118, 1134–1143 (2007).

Article Google Scholar
Crowther, L. J. et al. A quantitative method for evaluating cortical responses to electrical stimulation. J. Neurosci. Methods 311, 67–75 (2019).

Article Google Scholar
Miller, K. J. et al. Canonical response parameterization: quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. PLoS Comput. Biol. 19(5), e1011105 (2023).

Article Google Scholar
Butson, C. R. & McIntyre, C. C. Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimul. 1, 7–15 (2008).

Article Google Scholar
Blakely, T., Miller, K. J., Zanos, S. P., Rao, R. P. & Ojemann, J. G. Robust, long-term control of an electrocorticographic brain–computer interface with fixed parameters. Neurosurg. Focus. 27, E13 (2009).

Article Google Scholar
Chao, Z. C., Nagasaka, Y. & Fujii, N. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front. Neuroeng. https://ift.tt/9pmb2Dk (2010).
Schalk, G. Can electrocorticography (ECoG) support robust and powerful brain-computer interfaces? Front. Neuroeng. 3, 9 (2010).

Google Scholar
Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G. & Moran, D. W. A brain-computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–71 (2004).

Article Google Scholar
Schalk, G. et al. Two-dimensional movement control using electrocorticographic signals in humans. J. Neural Eng. 5, 75 (2008).

Article Google Scholar
Vansteensel, M. J. et al. Fully implanted brain–computer interface in a locked-in patient with ALS. N. Engl. J. Med. 375, 2060–2066 (2016).

Article Google Scholar
Herff, C. et al. Brain-to-text: decoding spoken phrases from phone representations in the brain. Front. Neurosci. 9, 217 (2015).

Article Google Scholar
Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385, 217–227 (2021).

Article Google Scholar
Heck, C. N. et al. Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS system pivotal trial. Epilepsia 55, 432–441 (2014).

Article Google Scholar
Edwardson, M., Lucas, T., Carey, J. & Fetz, E. New modalities of brain stimulation for stroke rehabilitation. Exp. Brain Res. 224, 335–358 (2013).

Article Google Scholar
de Hemptinne, C. et al. Exaggerated phase-amplitude coupling in the primary motor cortex in Parkinson disease. Proc. Natl Acad. Sci. USA 110, 4780–4785 (2013).

Article Google Scholar
Opri, E. et al. Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor. Sci. Transl. Med. 12, eaay7680 (2020).

Article Google Scholar
Scangos, K. W. et al. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat. Med. 27, 1696–1700 (2021).

Article Google Scholar
Kubanek, J. Neuromodulation with transcranial focused ultrasound. Neurosurg. Focus 44, E14 (2018).

Article Google Scholar
Philip, N. S. & Arulpragasam, A. R. Reaching for the unreachable: low intensity focused ultrasound for non-invasive deep brain stimulation. Neuropsychopharmacology 48, 251–252 (2022).

Article Google Scholar
Mirzakhalili, E., Barra, B., Capogrosso, M. & Lempka, S. F. Biophysics of temporal interference stimulation. Cell Syst. 11, 557–572 (2020).

Article Google Scholar
Acerbo, E. et al. Focal non-invasive deep-brain stimulation with temporal interference for the suppression of epileptic biomarkers. Front. Neurosci. 16, 945221 (2022).

Article Google Scholar
Macé, E. et al. Functional ultrasound imaging of the brain. Nat. Methods 8, 662–664 (2011).

Article Google Scholar
Li, C.-L. The inhibitory effect of stimulation of a thalamic nucleus on neuronal activity in the motor cortex. J. Physiol. 133, 40–53 (1956).

Article Google Scholar
Pfurtscheller, G. et al. The hybrid BCI. Front. Neurosci. https://ift.tt/DZsE8wh (2010).

Article Google Scholar
Müller-Putz, G. et al. Towards noninvasive hybrid brain–computer interfaces: framework, practice, clinical application, and beyond. Proc. IEEE 103, 926–943 (2015).

Article Google Scholar
Ross, S. D. et al. Systematic review of the literature regarding the diagnosis of sleep apnea. Evid. Rep. Technol. Assess. https://ift.tt/AbiDEHB (1998).
Smith, S. J. EEG in the diagnosis, classification, and management of patients with epilepsy. J. Neurol. Neurosurg. Psychiatry 76, ii2–ii7 (2005).

Article Google Scholar
Blackhart, G. C., Minnix, J. A. & Kline, J. P. Can EEG asymmetry patterns predict future development of anxiety and depression? A preliminary study. Biol. Psychol. 72, 46–50 (2006).

Article Google Scholar
Thibodeau, R., Jorgensen, R. S. & Kim, S. Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. J. Abnorm. Psychol. 115, 715 (2006).

Article Google Scholar
Mohan, Y., Chee, S. S., Xin, D. K. P. & Foong, L. P. Artificial neural network for classification of depressive and normal in EEG. In 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 286–290 (IEEE, 2016).
de Aguiar Neto, F. S. & Rosa, J. L. G. Depression biomarkers using non-invasive EEG: a review. Neurosci. Biobehav. Rev. 105, 83–93 (2019).

Article Google Scholar
Jiang, C., Li, Y., Tang, Y. & Guan, C. Enhancing EEG-based classification of depression patients using spatial information. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 566–575 (2021).

Article Google Scholar
Nuwer, M. R., Hovda, D. A., Schrader, L. M. & Vespa, P. M. Routine and quantitative EEG in mild traumatic brain injury. Clin. Neurophysiol. 116, 2001–2025 (2005).

Article Google Scholar
Doan, D. N. T. et al. Predicting dementia with prefrontal electroencephalography and event-related potential. Front. Aging Neurosci. 13, 659817 (2021).

Article Google Scholar
Farwell, L. A. & Donchin, E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70, 510–523 (1988).

Article Google Scholar
Sutter, E. E. The brain response interface: communication through visually-induced electrical brain responses. J. Microcomput. Appl. 15, 31–45 (1992).

Article Google Scholar
Pfurtscheller, G., Flotzinger, D. & Kalcher, J. Brain–computer interface — a new communication device for handicapped persons. J. Microcomput. Appl. 16, 293–299 (1993).

Article Google Scholar
Birbaumer, N. et al. A spelling device for the paralysed. Nature 398, 297–298 (1999).

Article Google Scholar
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G. & Vaughan, T. M. Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–791 (2002).

Article Google Scholar
Kübler, A. et al. Patients with ALS can use sensorimotor rhythms to operate a brain–computer interface. Neurology 64, 1775–1777 (2005).

Article Google Scholar
Sellers, E. W. & Donchin, E. A P300-based brain–computer interface: initial tests by ALS patients. Clin. Neurophysiol. 117, 538–548 (2006).

Article Google Scholar
Müller-Putz, G. R., Scherer, R., Brauneis, C. & Pfurtscheller, G. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J. Neural Eng. 2, 123 (2005).

Article Google Scholar
Wolpaw, J. R. in Handbook of Clinical Neurology Vol. 110, 67–74 (Elsevier, 2013).
Chen, X. et al. High-speed spelling with a noninvasive brain–computer interface. Proc. Natl Acad. Sci. USA 112, E6058–E6067 (2015).

Article Google Scholar
Soekadar, S. et al. Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia. Sci. Robot. 1, eaag3296 (2016).

Article Google Scholar
Chaudhary, U. et al. Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training. Nat. Commun. 13, 1236 (2022).

Article Google Scholar
Ang, K. K. et al. Brain–computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front. Neuroeng. 7, 30 (2014).

Article Google Scholar
Soekadar, S. R., Birbaumer, N., Slutzky, M. W. & Cohen, L. G. Brain–machine interfaces in neurorehabilitation of stroke. Neurobiol. Dis. 83, 172–179 (2015).

Article Google Scholar
Bundy, D. T. et al. Contralesional brain–computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors. Stroke 48, 1908–1915 (2017).

Article Google Scholar
Cervera, M. A. et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann. Clin. Transl. Neurol. 5, 651–663 (2018).

Article Google Scholar
Musso, M. et al. Aphasia recovery by language training using a brain–computer interface: a proof-of-concept study. Brain Commun. 4, fcac008 (2022).

Article Google Scholar
Donati, A. R. et al. Long-term training with a brain–machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci. Rep. 6, 30383 (2016).

Article Google Scholar
Sonmez, A. I. et al. Accelerated TMS for depression: a systematic review and meta-analysis. Psychiatry Res. 273, 770–781 (2019).

Article Google Scholar
Trevizol, A. P. et al. Transcranial magnetic stimulation for obsessive-compulsive disorder: an updated systematic review and meta-analysis. J. ECT 32, 262–266 (2016).

Article Google Scholar
Cole, J. C., Bernacki, C. G., Helmer, A., Pinninti, N. & O’reardon, J. P. Efficacy of transcranial magnetic stimulation (TMS) in the treatment of schizophrenia: a review of the literature to date. Innov. Clin. Neurosci. 12, 12 (2015).

Google Scholar
Cirillo, G. et al. Neurobiological after-effects of non-invasive brain stimulation. Brain Stimul. 10, 1–18 (2017).

Article Google Scholar
Mantegazza, M., Curia, G., Biagini, G., Ragsdale, D. S. & Avoli, M. Voltage-gated sodium channels as therapeutic targets in epilepsy and other neurological disorders. Lancet Neurol. 9, 413–424 (2010).

Article Google Scholar
Adaikkan, C. et al. Gamma entrainment binds higher-order brain regions and offers neuroprotection. Neuron 102, 929–943 (2019).

Article Google Scholar
Soula, M. et al. Forty-hertz light stimulation does not entrain native gamma oscillations in Alzheimer’s disease model mice. Nat. Neurosci. 26, 570–578 (2023).

Article Google Scholar
Nasr, K. et al. Breaking the boundaries of interacting with the human brain using adaptive closed-loop stimulation. Prog. Neurobiol. 226, 102311 (2022).

Article Google Scholar
Wolpaw, J. R. & Tennissen, A. M. Activity-dependent spinal cord plasticity in health and disease. Annu. Rev. Neurosci. 24, 807–843 (2001).

Article Google Scholar
Jackson, A., Mavoori, J. & Fetz, E. E. Long-term motor cortex plasticity induced by an electronic neural implant. Nature 444, 56–60 (2006).

Article Google Scholar
Haslacher, D. et al. In vivo phase-dependent enhancement and suppression of human brain oscillations by transcranial alternating current stimulation (tACS). NeuroImage 275, 120187 (2023).

Article Google Scholar
Ngo, H.-V. V., Martinetz, T., Born, J. & Mölle, M. Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron 78, 545–553 (2013).

Article Google Scholar
Santostasi, G. et al. Phase-locked loop for precisely timed acoustic stimulation during sleep. J. Neurosci. Methods 259, 101–114 (2016).

Article Google Scholar
Garcia-Molina, G. et al. Closed-loop system to enhance slow-wave activity. J. Neural Eng. 15, 066018 (2018).

Article Google Scholar
Lustenberger, C. et al. Auditory deep sleep stimulation in older adults at home: a randomized crossover trial. Commun. Med. 2, 30 (2022).

Article Google Scholar
Ehrlich, S. K., Agres, K. R., Guan, C. & Cheng, G. A closed-loop, music-based brain–computer interface for emotion mediation. PLoS ONE 14, e0213516 (2019).

Article Google Scholar
Clausen, J. et al. Help, hope, and hype: ethical dimensions of neuroprosthetics. Science 356, 1338–1339 (2017).

Article Google Scholar
Soekadar, S., Chandler, J., Ienca, M. & Bublitz, C. On the verge of the hybrid mind. Morals Mach. 1, 30–43 (2021).

Article Google Scholar
Fetz, E. E. & Finocchio, D. V. Operant conditioning of specific patterns of neural and muscular activity. Science 174, 431–435 (1971).

Article Google Scholar
Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N. & Wolpaw, J. BCI2000: a general-purpose brain–computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004).

Article Google Scholar
Schalk, G. & Mellinger, J. A Practical Guide to Brain–Computer Interfacing with BCI2000 (Springer, 2010).
Renard, Y. et al. OpenViBE: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments. Presence 19, 35–53 (2010).

Article Google Scholar
Brunner, P. & Schalk, G. in Brain–Computer Interfaces Handbook: Technological and Theoretical Advances (eds Nam, C. S. et al.) 323–336 (CRC, 2018).
Schalk, G. et al. Toward a fully implantable ecosystem for adaptive neuromodulation in humans: preliminary experience with the CorTec BrainInterchange device in a canine model. Front. Neurosci. 16, 932782 (2022).

Article Google Scholar
Dornhege, G. et al. Combined optimization of spatial and temporal filters for improving brain-computer interfacing. IEEE Trans. Biomed. Eng. 53, 2274–2281 (2006).

Article Google Scholar
McFarland, D., Anderson, C., Muller, K.-R., Schlogl, A. & Krusienski, D. BCI meeting 2005 — workshop on BCI signal processing: feature extraction and translation. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 135–138 (2006).

Article Google Scholar
Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).

Article Google Scholar
Ang, K. K., Chin, Z. Y., Zhang, H. & Guan, C. Filter bank common spatial pattern (FBCSP) in brain–computer interface. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) 2390–2397 (IEEE, 2008).
Lotte, F. & Guan, C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58, 355–362 (2011).

Article Google Scholar
Wei, Q., Wang, Y., Gao, X. & Gao, S. Amplitude and phase coupling measures for feature extraction in an EEG-based brain–computer interface. J. Neural Eng. 4, 120 (2007).

Article Google Scholar
Lotte, F. et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15, 031005 (2018).

Article Google Scholar
Aru, J. et al. Untangling cross-frequency coupling in neuroscience. Curr. Opin. Neurobiol. 31, 51–61 (2015).

Article Google Scholar
Schalk, G. A general framework for dynamic cortical function: the function-through-biased-oscillations (FBO) hypothesis. Front. Hum. Neurosci. 9, 352 (2015).

Article Google Scholar
Krusienski, D. J., Schalk, G., McFarland, D. J. & Wolpaw, J. R. A mu-rhythm matched filter for continuous control of a brain–computer interface. IEEE Trans. Biomed. Eng. 54, 273–280 (2007).

Article Google Scholar
Schalk, G., Marple, J., Knight, R. T. & Coon, W. G. Instantaneous voltage as an alternative to power- and phase-based interpretation of oscillatory brain activity. Neuroimage 157, 545–554 (2017).

Article Google Scholar
Homer, M., Nurmikko, A., Donoghue, J. & Hochberg, L. Sensors and decoding for intracortical brain computer interfaces. Annu. Rev. Biomed. Eng. 15, 383–405 (2013).

Article Google Scholar
Zhang, K., Robinson, N., Lee, S.-W. & Guan, C. Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network. Neural Netw. 136, 1–10 (2021).

Article Google Scholar
Kwon, O.-Y., Lee, M.-H., Guan, C. & Lee, S.-W. Subject-independent brain–computer interfaces based on deep convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst. 31, 3839–3852 (2020).

Article Google Scholar
Delorme, A., Sejnowski, T. & Makeig, S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 34, 1443–1449 (2007).

Article Google Scholar
Islam, M. K., Rastegarnia, A. & Yang, Z. Methods for artifact detection and removal from scalp EEG: a review. Neurophysiol. Clin. 46, 287–305 (2016).

Article Google Scholar
Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L. & Jung, T.-P. Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings. IEEE Trans. Biomed. Eng. 67, 1114–1121 (2020).

Article Google Scholar
Winkler, I., Haufe, S. & Tangermann, M. Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav. Brain Funct. 7, 1–15 (2011).

Article Google Scholar
Mullen, T. R. et al. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62, 2553–2567 (2015).

Article Google Scholar
Blum, S., Jacobsen, N. S., Bleichner, M. G. & Debener, S. A Riemannian modification of artifact subspace reconstruction for EEG artifact handling. Front. Hum. Neurosci. 13, 141 (2019).

Article Google Scholar
Schreyögg, J., Bäumler, M. & Busse, R. Balancing adoption and affordability of medical devices in Europe. Health Policy 92, 218–224 (2009).

Article Google Scholar
Hall, B. H. & Khan, B. Adoption of New Technology. https://ift.tt/eQBitLx (2003).
Kilgore, K. L. et al. An implanted upper-extremity neuroprosthesis using myoelectric control. J. Hand Surg. 33, 539–550 (2008).

Article Google Scholar
Haugland, M. et al. A preliminary non-randomised study to evaluate the safety and performance of the ActiGait implanted drop-foot stimulator in established hemiplegia. In Getting FES into Clinical Practice, Proc. IFESS-FESnet 2004 (eds Wood, D. & Taylor, J.) 153–155 (2004).
Luo, Y. H.-L. & Da Cruz, L. The Argus® II retinal prosthesis system. Prog. Retin. Eye Res. 50, 89–107 (2016).

Article Google Scholar
Bergstein, B. Paralyzed again. MIT Technol. Rev. (9 April 2015).
Strickland, E. & Harris, M. What happens when a bionic body part becomes obsolete? Blind people with Second Sight’s retinal implants found out. IEEE Spectr. 59, 24–31 (2022).

Article Google Scholar
Drew, L. Abandoned: the human cost of neurotechnology failure. Nature https://ift.tt/AIliGXM (2022).
Kramer, D. B., Xu, S. & Kesselheim, A. S. How does medical device regulation perform in the United States and the European Union? A systematic review. PLoS Med. 9, e1001276 (2012).

Article Google Scholar
Shiroiwa, T. et al. International survey on willingness-to-pay (WTP) for one additional QALY gained: what is the threshold of cost effectiveness? Heal. Econ. 19, 422–437 (2010).

Article Google Scholar
Benabid, A.-L., Pollak, P., Louveau, A., Henry, S. & De Rougemont, J. Combined (thalamotomy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson disease. Stereotact. Funct. Neurosurg. 50, 344–346 (1987).

Article Google Scholar
Kestenbaum M, L. E. & Ford, B. Estimating the proportion of essential tremor and Parkinson’s disease patients undergoing deep brain stimulation surgery: five-year data from Columbia University Medical Center (2009–2014). Mov. Disord. Clin. Pract. 4, 384–378 (2015).

Article Google Scholar
Morgante, L. et al. How many Parkinsonian patients are suitable candidates for deep brain stimulation of subthalamic nucleus? Results of a questionnaire. Park. Relat. Disord. 13, 528–531 (2007).

Article Google Scholar
Nassiri, A. M., Sorkin, D. L. & Carlson, M. L. Current estimates of cochlear implant utilization in the United States. Otol. Neurotol. 43, e558–e562 (2022).

Article Google Scholar
Smilowska, K. et al. Cost-effectiveness of device-aided therapies in Parkinson’s disease: a structured review. J. Park. Dis. 11, 475–489 (2021).

Google Scholar
Cheng, L.-J., Soon, S. S., Wu, D. B.-C., Ju, H. & Ng, K. Cost-effectiveness analysis of bilateral cochlear implants for children with severe-to-profound sensorineural hearing loss in both ears in Singapore. PLoS ONE 14, e0220439 (2019).

Article Google Scholar
Montes, F. et al. Cochlear implants versus hearing aids in a middle-income country: costs, productivity, and quality of life. Otol. Neurotol. 38, e26–e33 (2017).

Article Google Scholar
Qiu, J. et al. Cost-effectiveness of pediatric cochlear implantation in rural China. Otol. Neurotol. 38, e75 (2017).

Article Google Scholar
Tolstoy, L. Anna Karenina (The Russian Messenger, 1878).
Diamond, J. Guns, Germs, and Steel: The Fates of Human Societies 20th anniversary edn (W. W. Norton & Company, 2017).
Brooks, F. P. & Bullet, N. S. Essence and accidents of software engineering. IEEE Comput. 20, 10–19 (1987).

Article Google Scholar
Fetz, E. E. Real-time control of a robotic arm by neuronal ensembles. Nat. Neurosci. 2, 583–584 (1999).

Article Google Scholar
Chapin, J. K. Neural prosthetic devices for quadriplegia. Curr. Opin. Neurol. 13, 671–675 (2000).

Article Google Scholar
Nicolelis, M. A. Actions from thoughts. Nature 409, 403–407 (2001).

Article Google Scholar
Donoghue, J. P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 1085–1088 (2002).

Article Google Scholar
Lyle, R. C. A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int. J. Rehabil. Res. 4, 483–492 (1981).

Article Google Scholar
Buhmann, C. et al. Adverse events in deep brain stimulation: a retrospective long-term analysis of neurological, psychiatric and other occurrences. PLoS ONE 12, e0178984 (2017).

Article Google Scholar
Follett, K. A. et al. Pallidal versus subthalamic deep-brain stimulation for Parkinson’s disease. N. Engl. J. Med. 362, 2077–2091 (2010).

Article Google Scholar
Figee, M. et al. Deep brain stimulation for depression. Neurotherapeutics 19, 1229–1245 (2022).

Article Google Scholar
Levy, R. M. The need for mechanism-based medicine in neuromodulation. Neuromodulation 15, 273–279 (2012).

Article Google Scholar
Abou-Al-Shaar, H., Brock, A. A., Kundu, B., Englot, D. J. & Rolston, J. D. Increased nationwide use of stereoencephalography for intracranial epilepsy electroencephalography recordings. J. Clin. Neurosci. 53, 132–134 (2018).

Article Google Scholar
Li, G. et al. Optimal referencing for stereo-electroencephalographic (SEEG) recordings. NeuroImage 183, 327–335 (2018).

Article Google Scholar
Coon, W. & Schalk, G. A method to establish the spatiotemporal evolution of task-related cortical activity from electrocorticographic signals in single trials. J. Neurosci. Methods 271, 76–85 (2016).

Article Google Scholar
Morrell, M. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77, 1295–1304 (2011).

Article Google Scholar
Rouse, A. G. et al. A chronic generalized bi-directional brain–machine interface. J. Neural Eng. 8, 036018 (2011).

Article Google Scholar
Gilron, R. et al. Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson’s disease. Nat. Biotechnol. 39, 1078–1085 (2021).

Article Google Scholar
Stanslaski, S. et al. A chronically implantable neural coprocessor for investigating the treatment of neurological disorders. IEEE Trans. Biomed. Circuits Syst. 12, 1230–1245 (2018).

Article Google Scholar
Jimenez-Shahed, J. Device profile of the percept PC deep-brain stimulation system for the treatment of Parkinson’s disease and related disorders. Expert. Rev. Med. Devices 18, 319–332 (2021).

Article Google Scholar
Kohler, F. et al. Closed-loop interaction with the cerebral cortex: a review of wireless implant technology. Brain Comput. Interfaces 4, 146–154 (2017).

Article Google Scholar
Oxley, T. J. et al. Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity. Nat. Biotechnol. 34, 320–327 (2016).

Article Google Scholar
Musk, E. et al. An integrated brain–machine interface platform with thousands of channels. J. Med. Internet Res. 21, e16194 (2019).

Article Google Scholar
Peckham, P. H. & Kilgore, K. L. Challenges and opportunities in restoring function after paralysis. IEEE Trans. Biomed. Eng. 60, 602–609 (2013).

Article Google Scholar
Chen, X. Y. & Wolpaw, J. R. Operant conditioning of H-reflex in freely moving rats. J. Neurophysiol. 73, 411–415 (1995).

Article Google Scholar
Thompson, A. K. & Wolpaw, J. R. H-reflex conditioning during locomotion in people with spinal cord injury. J. Physiol. 599, 2453–2469 (2019).

Article Google Scholar
Sebastián-Romagosa, M. et al. Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients — a feasibility study. Front. Neurosci. 14, 591435 (2020).

Article Google Scholar
Davis, P. A. Effects of acoustic stimuli on the waking human brain. J. Neurophysiol. 2, 494–499 (1939).

Article Google Scholar
Jasper, H. & Penfield, W. Electrocorticograms in man: effect of voluntary movement upon the electrical activity of the precentral gyrus. Arch. für Psychiatr. und Nervenkrankheiten 183, 163–174 (1949).

Article Google Scholar
Gibbs, F. A., Davis, H. & Lennox, W. G. The electroencephalogram in epilepsy and in conditions of impaired consciousness. Am. J. EEG Technol. 8, 59–73 (1968).

Article Google Scholar
Cooper, I. S., Upton, A. & Amin, I. Chronic cerebellar stimulation (CCS) and deep brain stimulation (DBS) in involuntary movement disorders. Appl. Neurophysiol. 45, 209–217 (1982).

Google Scholar
Pistohl, T., Ball, T., Schulze-Bonhage, A., Aertsen, A. & Mehring, C. Prediction of arm movement trajectories from ECoG-recordings in humans. J. Neurosci. Methods 167, 105–114 (2008).

Article Google Scholar
Horsley, V. & Clarke, R. H. The structure and functions of the cerebellum examined by a new method. Brain 31, 45–124 (1908).

Article Google Scholar
Bailey, P. & Bremer, F. A sensory cortical representation of the vagus nerve: with a note on the effects of low blood pressure on the cortical electrogram. J. Neurophysiol. 1, 405–412 (1938).

Article Google Scholar
Laitinen, L. Placement of electrodes in transcutaneous stimulation for chronic pain. Neurochirurgie 22, 517–526 (1976).

Google Scholar
Jöbsis, F. F. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science 198, 1264–1267 (1977).

Article Google Scholar
Barker, A. T., Jalinous, R. & Freeston, I. L. Non-invasive magnetic stimulation of human motor cortex. Lancet 325, 1106–1107 (1985).

Article Google Scholar
Gavrilov, L., Tsirulnikov, E. & Davies, I. a. I. Application of focused ultrasound for the stimulation of neural structures. Ultrasound Med. Biol. 22, 179–192 (1996).

Article Google Scholar
Maynard, E. M., Nordhausen, C. T. & Normann, R. A. The Utah intracortical electrode array: a recording structure for potential brain–computer interfaces. Electroencephalogr. Clin. Neurophysiol. 102, 228–239 (1997).

Article Google Scholar
Xia, H., Ben-Amar Baranga, A., Hoffman, D. & Romalis, M. Magnetoencephalography with an atomic magnetometer. Appl. Phys. Lett. 89, 211104 (2006).

Article Google Scholar
Delgado, J. M., Bradley, R. J., Johnston, V. S., Weiss, G. & Wallace, J. D. Implantation of Multilead Electrode Assemblies and Radio Stimulation of the Brain in Chimpanzees. Technical Report (Yale Univ. Department of Psychiatry, 1969).
Frost, J. Jr Development of a Prototype Onboard EEG Analysis System. Technical Report CR 108508 (NASA, 1970).
Vidal, J. J. Toward direct brain–computer communication. Annu. Rev. Biophys. Bioeng. 2, 157–180 (1973).

Article Google Scholar
Taylor, D. M., Tillery, S. I. & Schwartz, A. B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

Article Google Scholar
Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

Article Google Scholar
Rossi, P. J. et al. Scheduled, intermittent stimulation of the thalamus reduces tics in Tourette syndrome. Park. Relat. Disord. 29, 35–41 (2016).

Article Google Scholar
Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M. & Shenoy, K. V. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021).

Article Google Scholar
Lorach, H. et al. Walking naturally after spinal cord injury using a brain–spine interface. Nature 618, 126–133 (2023).

Article Google Scholar
Penfield, W. & Boldrey, E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60, 389–443 (1937).

Article Google Scholar
Cerletti, U. L’elettroshock. Riv. Sper. Freniat. Med. Leg. Alien. Ment. 64, 209–310 (1940).

Google Scholar
Desoyer, I. & Hochmair, E. Implantable eight-channel stimulator for the deaf. In ESSCIRC’77: 3rd European Solid State Circuits Conference, 87–89 (IEEE, 1977).
Clark, G. M., Clark, J. C. & Furness, J. B. The evolving science of cochlear implants. JAMA 310, 1225–1226 (2013).

Article Google Scholar
Pascual-Leone, A., Rubio, B., Pallardó, F. & Catalá, M. D. Rapid-rate transcranial magnetic stimulation of left dorsolateral prefrontal cortex in drug-resistant depression. Lancet 348, 233–237 (1996).

Article Google Scholar
Brunner, P. et al. A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. Epilepsy Behav. 15, 278–286 (2009).

Article Google Scholar
Pesaran, B., Pezaris, J. S., Sahani, M., Mitra, P. P. & Andersen, R. A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat. Neurosci. 5, 805–811 (2002).

Article Google Scholar
Pinotsis, D. A., Fridman, G. & Miller, E. K. Cytoelectric coupling: electric fields sculpt neural activity and ‘tune’ the brain’s infrastructure. Prog. Neurobiol. 226, 102465 (2023).

Article Google Scholar
Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).

Article Google Scholar
Moore, G. A. Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers 3rd edn (Collins, 2014).
Kremen, V. et al. Integrating brain implants with local and distributed computing devices: a next generation epilepsy management system. IEEE J. Transl. Eng. Health Med. 6, 2500112 (2018).

Article Google Scholar
Gunduz, A. et al. Adding wisdom to ‘smart’ bioelectronic systems: a design framework for physiologic control including practical examples. Bioelectron. Med. 2, 29–41 (2019).

Article Google Scholar
Shirvalkar, P. et al. First-in-human prediction of chronic pain state using intracranial neural biomarkers. Nat. Neurosci. 26, 1090–1099 (2023).

Article Google Scholar
Alagapan, S. et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 622, 130–138 (2023).

Article Google Scholar
Skarpaas, T. L., Jarosiewicz, B. & Morrell, M. J. Brain-responsive neurostimulation for epilepsy (RNS® system). Epilepsy Res. 153, 68–70 (2019).

Article Google Scholar
Strollo, P. J. Jr et al. Upper-airway stimulation for obstructive sleep apnea. N. Engl. J. Med. 370, 139–149 (2014).

Article Google Scholar
Ryu, S. I. & Shenoy, K. V. Human cortical prostheses: lost in translation? Neurosurg. Focus 27, E5 (2009).

Article Google Scholar
Oxley, T. J. et al. Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience. J. Neurointerv. Surg. 13, 102–108 (2021).

Article Google Scholar
Levy, R. M. et al. Epidural electrical stimulation for stroke rehabilitation: results of the prospective, multicenter, randomized, single-blinded Everest trial. Neurorehabil. Neural Repair. 30, 107–119 (2016).

Article Google Scholar
Ezzyat, Y. et al. Closed-loop stimulation of temporal cortex rescues functional networks and improves memory. Nat. Commun. 9, 365 (2018).

Article Google Scholar
Krauss, J. K. et al. Technology of deep brain stimulation: current status and future directions. Nat. Rev. Neurol. 17, 75–87 (2021).

Article Google Scholar
Kostarelos, K., Vincent, M., Hebert, C. & Garrido, J. A. Graphene in the design and engineering of next-generation neural interfaces. Adv. Mater. 29, 1700909 (2017).

Article Google Scholar
Peckham, P. H. et al. Efficacy of an implanted neuroprosthesis for restoring hand grasp in tetraplegia: a multicenter study. Arch. Phys. Med. Rehabil. 82, 1380–1388 (2001).

Article Google Scholar
van der Aa, H., Alleman, E., Nene, A. & Snoek, G. Sacral anterior root stimulation for bladder control: clinical results. Arch. Physiol. Biochem. 107, 248–256 (1999).

Article Google Scholar
da Cruz, L. et al. Five-year safety and performance results from the Argus II retinal prosthesis system clinical trial. Ophthalmology 123, 2248–2254 (2016).

Article Google Scholar
Cook, M. J. et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 12, 563–571 (2013).

Article Google Scholar
Gilbert, F., Ienca, M. & Cook, M. How I became myself after merging with a computer: does human–machine symbiosis raise human rights issues? Brain Stimul. 16, 783–789 (2023).

Article Google Scholar

Download references

Acknowledgements

We acknowledge funding from the Tianqiao and Chrissy Chen Institute (G.S.), the RIE2020 AME Programmatic Fund in Singapore (A20G8b0102 (C.G.)), the NIH (U01-NS12861 (K.J.M.) and P41-EB018783 (P.B.)), the Royal Academy of Engineering (T.D.), the German Federal Ministry of Research and Education (BMBF, 01DR21025A, 01GP2121B, 03ZU1110DD, 13N16486, 01UX2211 (S.R.S.)), the European Research Council (ERC 759370, 101081905 (S.R.S.)), the Foundation for OCD Research (K.J.M.) and the Minnesota Partnership for Biotechnology and Medical Genomics (K.J.M.). We also thank S. Graepel, J. Reindorp and T. Larsson for their help with illustrations, and H. Shao for assistance with background information.

Author information

Authors and Affiliations

Chen Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, Shanghai, China

Gerwin Schalk
Department of Neurosurgery, Fudan University/Huashan Hospital, Shanghai, China

Gerwin Schalk
Neurosurgery and Biomedical Engineering, Washington University School of Medicine, St Louis, MO, USA

Peter Brunner
National Center for Adaptive Neurotechnologies, St Louis, MO, USA

Peter Brunner
Cognitive Science, University of California San Diego, La Jolla, CA, USA

Brendan Z. Allison
Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité — Universitätsmedizin Berlin, Charité Campus Mitte (CCM), Berlin, Germany

Surjo R. Soekadar
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Cuntai Guan
Department of Engineering Science, Oxford University, Oxford, UK

Tim Denison
Neudio Inc., DE, USA

Jörn Rickert
Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla, Puebla, México

Jörn Rickert
Neurological Surgery, Mayo Clinic, Rochester, MN, USA

Kai J. Miller

Authors

Gerwin Schalk

View author publications

You can also search for this author in PubMed Google Scholar
Peter Brunner

View author publications

You can also search for this author in PubMed Google Scholar
Brendan Z. Allison

View author publications

You can also search for this author in PubMed Google Scholar
Surjo R. Soekadar

View author publications

You can also search for this author in PubMed Google Scholar
Cuntai Guan

View author publications

You can also search for this author in PubMed Google Scholar
Tim Denison

View author publications

You can also search for this author in PubMed Google Scholar
Jörn Rickert

View author publications

You can also search for this author in PubMed Google Scholar
Kai J. Miller

View author publications

You can also search for this author in PubMed Google Scholar

Contributions

G.S. initiated and project-managed the creation of this Review, wrote several sections, edited the manuscript, and contributed to the generation of all figures. P.B. edited the manuscript. B.Z.A. developed Fig. 1 and edited the manuscript. S.R.S. wrote the section on macroscale aggregates. C.G. wrote the section on signal processing and AI. T.D. provided the initial requirements based on prior analysis, developed Figs. 3 and 4, and jointly wrote the business/regulatory sections. J.R. developed Fig. 3, jointly wrote the business and regulatory sections, and edited the manuscript. K.J.M. developed Fig. 2 and edited the manuscript.

Corresponding author

Correspondence to Gerwin Schalk.

Ethics declarations

Competing interests

G.S. previously consulted for CorTec and currently consults for NeuroXess. T.D. is a director and shareholder at Amber Therapeutics, and consults for CorTec. He has previously consulted for Synchron, Inspire and Galvani. J.R. previously served as CEO for CorTec and is a shareholder at CorTec and Neudio. The other authors report no competing interests.

Peer review

Peer review information

Nature Reviews Bioengineering thanks Ramses Alcaide and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Schalk, G., Brunner, P., Allison, B.Z. et al. Translation of neurotechnologies. Nat Rev Bioeng (2024). https://ift.tt/hnaV0ym

Download citation

Accepted: 05 April 2024
Published: 31 May 2024
DOI: https://ift.tt/hnaV0ym

Adblock test (Why?)

Saturday, June 1, 2024

Divergence and its impact on the market: Stocks in Translation - Yahoo Finance - Translation

Major US Indices (^GSPC, ^DJI, ^IXIC) have seen a rally with tech leading the charge in the past several months. One of the top stocks leading is Nvidia (NVDA) with Wall Street abuzz with any and all plays related to AI. Does this sector have more room to grow? How does all of this relate to the economy at large? With the Presidential election on the way, will the stock market be impacted? will the broader market and consumers be impacted?

Yahoo Finance Reporter Jared Blikre is joined by Freedom Capital Markets Chief Global Strategist Jay Woods for the latest edition of Stocks in Translation to discuss volatility in the market, the rally seen in the S&P 500, the part Nvidia plays in that rise, divergence in the market, and more.

For more Stocks in Translation, watch here: https://ift.tt/ESL1tHf

This post was written by Nicholas Jacobino

Video Transcript

Welcome to Stocks and translation, our essential conversation cutting through the market, mayhem, the noisy numbers and hyperbole to give you the information you need for your portfolio.

Today, I am joined by Jay Woods.

He is the Freedom Capital Markets Chief Global Strategist on loan from the Well, it used to be the New York Stock Exchange.

You want to say a quick hello here, I just came from the New York Stock Exchange.

It's great to be with you guys and great to be on the podcast.

Thanks for having me.

You bet.

And we're all, we also have Sydney Fried as always our intrepid producer here who's going to keep it real.

Um So today's theme, today's theme every day needs a theme.

We have mixed signals today which is not unusual in the markets, but we're seeing some non intuitive developments recently that we're going to break down for the viewers.

Our word of the day divergence.

And how can we actually measure some of these mixed signals?

Is music stopping and this episode is brought to you by the number of 35% and that's how much of the S and P five hundreds gained this year is due to NVIDIA and NVIDIA alone.

So, uh, well, let's get started here.

The story of the week, I'm talking mixed signals, Jay, we've got the Dow has been lagging the NASDAQ in a big way for the last 5 to 7 sessions.

We've got yields rising but not intuitively.

We have tech outperforming.

So usually when we have tech outperforming, you see yields going down.

But you know, what are you making of this situation?

Well, I'd like to see tech yield but something interesting happened last week when NVIDIA reported its earnings, uh we had in the S and P 500 a bearish engulfing candle.

I know you're gonna question what's a bearish.

We'll talk about that.

But uh what happened was the video was up, the stock market gapped higher and it could not carry the weight of the stock market on its shoulder.

So Atlas shrugged in this example.

And what happened was 90% of the stocks were down that day.

Since that day, we've had two days where the S and P 500 has been up one point, not 1% 1 point and Navidi was 40 points both times.

So NVIDIA has been holding the S and P 500 up and you mentioned divergence, your word of the day, we're seeing the NASDAQ go up because tech is leading semiconductors continue to do well.

And I'd like to see tech lead but the health care stocks starting to crack a little bit rotation.

Are we rotating only into semiconductors?

Now, we've had five different leaders this year.

Five months, groups, five different leaders each month.

So this time now technology takes the lead.

It's a nice rotation but something feels a little different this time.

And I think we're gonna pause.

So watch that Thursday, bearish engulfing candle.

We're in that range.

If we pierce below, we probably are gonna be testing that 50 day moving average 2.5% lower or we have a normal 5% correction.

It will be our second this year.

The one was the last one was recent, three a year.

So it wouldn't shock me to see a little bit of a pull back now.

And let's talk about some of these prior leaders and just the nature of markets.

Uh We have the opportunity here to kind of take a big picture of you.

When we talk about rotation in the markets, we're just talking about other sectors, kind of stepping up to the plate here.

Now, in the meantime, the indices, they might be treading water, they might be going higher, sometimes they go lower.

Uh But meanwhile you have under the surface here, you got some winners and you got some losers.

Talk about the winners, the other winners we've seen this year.

Yeah.

Well, the winners, there's very interesting talk about divergence.

I say bifurcation right now within certain sectors, you have certain stocks staples.

Perfect example.

You have Walmart doing well.

Target, not doing well.

Um, you have mcdonald's Starbucks.

Uh, oh, not doing well, but Starbucks isn't doing well.

But guess what is Dutch bros, coffee.

People are still buying coffee.

The consumer changing their spending habit, but the overall trend in the market continues to climb higher.

So, equities are where people are going now, seen it more into tech over the last week.

But other sectors have led utilities which was a laggard started to lead.

Yes, there was a little A I play but they were oversold.

They're coming back.

Uh We seen financials, one of my favorite sectors continue to act well in a higher for longer narrative.

So the rotation and energy led to begin the year.

Uh So we've seen different sectors take that mantle.

But when we see tech lead, that's usually a good sign.

We just need to see more tech join the NVIDIA party, the semiconductor party.

I mean, you're basically saying this, I don't get how any other sector these days wouldn't lead besides tech utilities.

I mean, it's a I play, but if I talk to you about American water, you're gonna fall asleep.

All right.

It's not gonna get clicks on Yahoo.

Finance the, the, the stocks that bring eyeballs are the ones that are mostly owned, the biggest market caps.

So when they move, they make headlines.

But as someone that follows all sectors and the market itself looks for the trends under the surface.

You wanna see what else is happening because you wanna see a broadening for too long.

We talked about the magnificent seven and it's the only seven star about it.

Oh, great.

Uh We go back to sleep.

Oh, well, American water will put you to sleep.

But yes, uh we wanted to see a broadening out.

We're getting it.

Uh but right now we're taking a little bit of a pause and I say this all the time.

I probably said it to you, Jared sideways is a direction and we're going sideways at all time.

Highs, NASDAQ 17,000 all time high yesterday.

Pulling back a little bit as we take this show S and P 500 within a percent or two of an all time high.

These, these are good things and if we pause here, it's not a bad thing.

Correction in time versus correction and price.

I wanna ask you about small caps.

You used to be a direct market maker down at the New York Stock Exchange where you were also a floor governor and uh just talk to us about maybe the market that you would make for some of these small cops.

And then maybe you mentioned, where do you think they fit into the modern modern market portfolio here?

Do you like them even?

I used to have hair too.

So for reminding me, but yes, I did spend 28 years as a market maker.

So I followed individual stocks.

Um II I think what, what is going on underneath the surface is fine.

Uh We are just having a normal rotation in a secular bull market.

So when you see pockets of strength in sectors or individual stocks within those sectors, uh as an investor, you wanna go where the puck is and you wanna go to the leadership.

People are like, well, you're buying stocks at all time highs, but all time highs usually beget all time highs, momentum continues to go in the direction of momentum.

This case up earnings growth, three straight quarters of earnings growth, something good is happening.

But there are a lot of negative headlines.

Uh and there's a lot of bifurcation divergence back to your word in the way people look at the market.

I saw a survey that said 50% of the people it surveyed.

I don't know who this survey was from.

Uh said that the market was down this year.

Well, you know, they're reading and seeing one side of the story without looking at the, you know, indexes themselves.

That's fascinating.

Let's let's break that down and kind of dig into that for a second because um we're talking about mixed signals and the big one here, I think a lot of people who are especially are new to trading as a lot of people are since the pandemic is the real economy, what you're experiencing your normal everyday life.

And then what happens in the stock market?

And I think what you just said that a significant amount of people percentage wise think that we are in a bear market right now.

Meanwhile, we're minting new highs.

Where do you think that perception comes from?

And then how do you resolve it if possible?

Uh I, I think it comes from political rhetoric to be honest with you.

Uh It's an election year and it happens to be an election year and we have two candidates that, that make headlines.

Uh Let's let's be honest about that.

Uh 2016 was a controversial election year.

And what happened when it's finally the dust settled market rally 2020 very controversial election year.

Guess what happened when the dust settled November 4th.

I believe it was the low going into it.

And then we rallied through January 6th.

We made a new high in the S and P 502 days after January 6th.

So these headlines that we're gonna hear the economy is not the stock market, the stock market is not the economy.

You have to look at these individual companies, how they're performing, how the consumer is treating them.

The consumer continues to be resilient, the companies continue to see earnings growth.

Uh And that is what's driving the stock market.

And what I'm waiting to see is even more growth in the stock market is the IP O market is starting to percolate it hasn't heated up, but there's one sector that it will heat up in the next 6 to 18 months.

Those are A I stocks.

Um, remember.com, anything with a.com after it, pets.com, everyone uses that as the example.

But there were dozens, hundreds actually that went public because they had a.com in their name.

This mania will come because I still believe this A I hype is in the early innings and we're just starting to see it get a little, you know, heated with the video because that's all people want to talk about.

I'm thinking this through as I'm saying, it so bear with me but like this A I mania, right?

It's already here.

You're saying there's gonna be more of it soon.

But when we talk about like Wall Street versus Main Street and how people feel about the economy, like I, I don't know your average person, maybe they're not paying that much attention to the stock market or A I mania just because they're paying more prices at the grocery store and that's what they care about, right?

So how do you more reconcile?

Like, forget about the politics for a second.

Like what are we really talking about when we talk about the economy versus uh versus main street?

Which is what the average person cares about?

Yeah.

And unfortunately it is a have and have not uh situation where the upper class has been doing really well and where is most of their, you know, wealth been generated, it's been generated in the stock market and we've seen great returns.

So in this case, the wealthier get even wealthier, they're doing well where, you know, the average guy on main street is worried about what they're paying at mcdonald's and we're seeing that literally in mcdonald's and then what they're paying at the pump, gas prices are a huge election issue every year.

It's the biggest tax on the American consumer because that's what they see change more than anything that fluctuates just as much as the stock market at times.

So when they have to do their everyday chores, their groceries, which have not come down in, you know, any, they never come down.

But uh they're seeing shrink.

You know, my, my box of girl scouts, the girl scouts started this shrink, by the way, we thin mints.

I am an expert.

It dangerous to blame the girl scouts.

I will start that war and I will die on that hill.

The girl scouts were the first people to reduce the amount of thin mints because I could eat a sleeve in about 60 seconds.

Then I could do it in 45 seconds.

Sounds like you're speaking from personal experience.

I may.

Uh Yes.

Uh, it's, it's something i it's one of my skills in life.

But uh, no, but to get back to the consumer.

Yes.

Uh There are parts of main street that don't understand what's going on and what they hear, they hear the mania things they hear when gamestop and Roy Kitty re you know, tweets for the first time in three years and they think they can make a quick buck and it's the long term investors that tend to do well over the long term.

Uh but when people are trying to make that quick buck and look at the market in a different light, it's great to have new investors.

But when new investors come to the market, people like us, people in my industry need to tell them one, the, the downside risks that are involved and, and two guide them say welcome.

Now, let's diversify a little bit.

It's not, stocks are going to the moon and I think we've seen it and I look at a stock like Robin Hood that's, you know, up 70% year to date.

Uh The stock is starting to see consistent user base and it's growing.

It's going after an older base of people like my, my age, which as you know, we won't diverge uh divulge, divulge, but uh you know, they're transferring their 401k, they're looking for more long term growth.

So II I think education shows like this uh help people that don't understand some of the things that people like myself have been engrossed in for 32 years and just talk like it's our everyday common language.

It's easy to do.

Uh We gotta pause here, take a quick break.

We were just talking about some of the head scratching moves in the market and we didn't quite get to our word of the day divergence, although you did touch on it and I just want to kind of break that down.

According to investopedia divergence is when the price of an asset is moving in the opposite direction of a technical indicator, it could be an oscillator or is moving contrary to data.

So divergence warns that the current price trend up or down may be weakening.

And in some cases, it may lead to the price changing direction.

And I think divergence for me is a useful indicator, not only in technical analysis but in thinking about the market.

Um if you have a couple of things going in the same direction, it could be classical Dow theory, you got the Dow industrials and the dow transports going in the same direction and then one of them diverges, well, maybe that's a signal.

Yeah.

And, and you talk about Dow theory to keep it simple.

Uh It's the industrials, those that make and then the transports, those that take the the the companies that chip and usually you want to see those go hand in hand and this goes back to the turn of the 19th century, 2019 hundreds.

I don't know what century that was the turn of.

Uh but that is, you know, a as simple as it gets.

And when you see divergences, what we're seeing in some of the consumer staples.

All right, something is changing and when you see relative strength, how well one stock is doing relative to its peers and all of a sudden it's doing a little worse, doing a little better.

It tells you that something is changing.

The tide is not lifting all boats and we have to be a little one more selective and two maybe defensive at times.

Uh I don't see a major divergence.

The ones to pause, maybe you're just pause.

Well, here's the big story.

It's the 10 year yield.

The yield in the 10 year last year, 2023 spiked up to 5%.

And when it moved, it moved quickly.

And what happened every time it took a leg hire the market, the equity market came in and now the yield is above 4.5% not necessarily heading to 5%.

I don't think we're gonna get there, but what goes up?

The market is digesting.

It is same with the higher for longer narrative with rates.

We aren't a higher historically average level, maybe below average level, 50 years.

But over the last few years, it's pretty high.

It is high, but this is the new normal.

And I think the market is telling us, let's get used to it.

It's been eight months since we've had a big move in rates and the market is trading much higher than when it paused.

If we cut, then maybe you're gonna see more participation.

You talk about the Russell 2000, those small caps that are more rate sensitive, they'll probably join the party.

If not lead the party on another leg hire.

Going back just a little bit.

You mentioned the 10 year people have their eye on treasury yields because we're thinking about what the feds gonna do with rates.

You mentioned the 10 year specifically.

What is it about the 10 year that is so important?

Why do, why is that kind of a benchmark?

Yeah, well, as rates go up, it makes it harder for smaller companies to borrow and they have to borrow at higher rates.

So their debt will increase uh for the older investors, they may take money out of equities because a 5% yield is safer.

The hey, I can get 5%.

I don't need the risk of the headlines that are going on during an election cycle.

So the yields will move the equities.

But when you get used to where the yields are, uh some of these mega cap companies are able to shake that off, they're ok.

The small cap companies struggle and they struggle to keep up and when you see volatility in yields, it really throws off their bottom line.

So the stability has been more of a story because we're not seeing volatility as much.

But the direction still is a problem right now.

Yeah, that's why I love to look at the move index, it's kind of like the vic for the bond market.

And as you just said, uh Jay, when, when you see increased bond market volatility, it just kind of spills over into everything else and, and disrupts the uh the portfolio want to move on now to our number of the day, it's 35 as in 35%.

And guess what NVIDIA is sitting on market cap gains of $1.6 trillion this year.

Meanwhile, the S and P 500 is up a cool 4.5 trillion.

So 35% of that is just NVIDIA of those gains in the S and P 500.

If you broaden this to the mag seven, the number is 60%.

So Jay, my question to you is, is it still just these seven stocks?

It's down from 70% by the way, just so, you know, yes, it is.

Um It is there the story but you can break down each of the seven stocks.

It's not Tesla anymore.

Apple has been flat for three years.

Uh Microsoft looks great.

Amazon is a stock that has yet to break out to all time.

Highs watch 190.

Uh I think that's a stock that could really lead the next leg of the mag seven.

But yes, over time, we always have had a leadership group, whether it was Exxon Mobile or AT&T back as recently as 10 years ago.

Yes.

Uh, these were stocks that everyone complained about.

It's too impactful on the market right now.

It's in the video and when you throw out 35% yeah, it's head scratching.

You're like, oh, if this thing comes in we're in trouble.

But right now there's a reason it's doing so well because the growth is there, the, it's trading at multiples that are not euphoric.

Historically speaking, when I talk about that, I talk about going back to the.com when Cisco Intel Microsoft were like 90 times earnings.

Yeah, that was euphoric.

Where, where is uh NVIDIA last?

I looked it was under 40 if I'm not mistaken because the earnings continue to grow.

Uh So right now, yes, it is alarming and it's a narrative that uh you know, a bear would definitely cling onto but how those bears been cleaning, they're falling like crazy.

They're raising their year end targets.

They've missed this boat.

Uh The boat continues to go higher.

Jay, you mentioned like there's always a leadership group in stocks going back to my like, why are we all bullish on tech?

Is, is it always going to be tech forever?

Now, these stocks that are leading, is there ever another category where the stocks are gonna, that I'm gonna lead against?

Exactly.

Yeah, I mean, there's something that some kids building in his basement right now that's gonna eventually take over the world and lead us.

I don't know what that is, if I did, I wouldn't be sitting here with you.

I'd be on a beach, I'd be on a beach in Hawaii.

Probably.

Uh, but no, yeah, the technology is where the growth continues to be as a nation.

We go back to, you know, when I started on Wall Street, the nineties and the internet was the big thing and the internet created its own economy.

It's everything we do is on the internet.

This is on the internet now A I is creating its own economy where it goes in what directions a lot of people talk about the negative.

It's gonna kill us.

We're gonna self destruct.

But there's a lot of good that's gonna come out of it and they're gonna be a lot of jobs created because there are college students, high school students, grade school students that are gonna learn this.

I'm not gonna learn it.

I'm too old.

I'm done.

My brain is full but these kids are gonna program the next technology and change how we buy things, how we shop, how things are brought to us, how we're entertained, uh how we're educated.

I think that could be the story longest model.

It also could be detrimental for a college student who didn't do their paper late at night.

And yes, I'm maybe talking to one of my child Children as we speak.

But yeah, I think that A I revolution we're still in the early infancy of it because we don't know exactly how expansive it can be then after a I your guess is as good as mine.

All right.

Well, we do have a few minutes left here and we have barely mentioned crypto a situation that we will now remedy Jay, why not?

Um sticking with the theme of the day mixed signals.

OK. Let's talk about the spot Ether ETF S that have uh they were left for dead at the SEC.

Suddenly they're on the fast track for approval.

It's reminiscent of the spot Bitcoin ETF S that were rejected until there was a court battle gray scale asset management when a key court battle that forced a reversal at the SEC.

Now it could be months before we finally get the spot Ether ETF S when you can actually trade them, but it looks like it's uh going to happen.

And based on what we know now who's wearing the sec about face on the spot ETF S better.

Is it Bitcoin or Ether?

There's no wrong answer.

There is no wrong answer.

Well, right now it's Bitcoin because they've been approved and it's been trading and it's a great way for an average investor who didn't really get involved in crypto to get involved and you know it a little safer, you know, you, you, you know the custody there.

Uh I am not a crypto expert when I look at crypto, I just look at the technicals because like you, I've studied these things and I don't understand, I can't hold it.

I can't touch it on the chart.

I understand the chart and the chart looks fantastic.

Above 70,000.

This is, this is fantastic ether.

What a run my God.

When it broke above, I think it was 3000, maybe 3200.

It kept going and guess what?

People have a strong demand.

It is a currency.

It's an asset class, it's here to stay.

It's not going anywhere, it's being accepted by, you know, those in power.

Some people look at that like, oh no, this could be the end of it.

But no, it allows the average investor to put it wisely into the retirement fund.

Uh just to hold it in their own investment accounts and they don't have to worry about custody and all these things.

So to me, when Ether gets approved and it trades an ETF form just like Bitcoin, I think that's a great thing and it's a great thing for the individual investor.

So it belongs a little bit belongs in your portfolio.

Um I have $200 worth of that.

Uh That's a little bit, it depends.

Portfolio is not that big.

No, yes.

I, I also have a thing learned by doing.

So whenever I'm not sure of something, an investment, I'll buy it and once I own it, I look at it differently, I know if it's going up or down and then I follow there was news in it now.

We don't get good news in, in big.

There's no earnings like I am so trained to be an equity trader that the Bitcoin movement, the ether movement while I've had a small part just to be in it really minimal uh exposure in my portfolio.

And I stick to what I know, which are equities.

Got you.

All right, Jay, we're coming to the, coming to the end.

We got a couple of minutes left and I am very curious about your time on the floor of the New York Stock Exchange, you got, you got some stories for us.

Uh There are some stories we cannot talk about on air, but uh the Stock exchange is a place like no other.

I got to go down there during the nineties is when I started and um it was a different, you know, it was kill or be killed.

My training was getting lunch.

Uh That is not really what we do with our interns or our young staff.

Now we, we put out linkedin Post welcoming them and we praise them and, and, and fine, we should.

Uh but uh uh I'll just edit myself and not say anything else to clarify.

No intern gets lunch anymore.

They will pick it up.

But uh you know, we usually buy it for them.

Uh But no, my training was different because it was a hand to hand world and my best training believe it or not was before I even got to the floor and I didn't know this.

Uh, I was a cox and on a crew team back in high school national champion, 1988.

Uh, yeah.

Yeah, I mean, but I, the two other coxes on the team, one won gold in Athens in 2004 and another one went into advertising and you'll see his commercials for Budweiser during the Super Bowl.

You're just what I do have a national championship from high school under my belt.

But that training, believe it or not, it actually helped me for the floor because I'm not the biggest guy in the room.

I may be the loudest guy in the room and that's where the cox and the crew helped and I was able to get the respect of my peers people bigger than me.

Let me lead, let me listen.

All right, you're a buyer or a seller.

Let's stick it together.

Uh That family that camaraderie on the floor was the best life lesson.

Those people I got to bond with and work with are some of the greatest people I've come across in my life and they helped shape me as I continue to grow in my career.

So the floor is a very special place.

It's not what it once was.

Um Yeah, I have a lot of great stories, but we could be here for three hours if I was to start and try to cherry pick one that's better than the other.

But there isn't a day I go into that building where I know I'm not lucky to still be going into that building and we're gonna leave it right there as we are out of time on stocks and translation.

Keep your dial tuned at Yahoo Finance.

Adblock test (Why?)

Persian Quran: Lost in Translation and Religious Revisionism - Worldcrunch - Translation

-Essay-

LONDON — The Quran is not merely a religious text. For Muslims, it is the literal word of God that was miraculously revealed to the Prophet Muhammad, born in Mecca in the 6th century. After the prophet's death, invading Arab armies brought Islam to Iran in the early to mid-7th century, forcibly converting a population that was already God-fearing as Zoroastrians.

For the latest news & views from every corner of the world, Worldcrunch Today is the only truly international newsletter. Sign up here.

The text can be examined from three perspectives: philosophical, religious and theological. The first two read it from "outside," supposedly with impartiality and objectivity, and seeking to corroborate their interpretations delving into multiple fields including history, linguistics, sociology and psychology.

Theology, however, is an "internal" reading of the text, reiterating its message of faith in secular or explanatory language, but always with reference to the text itself.

A late meeting

During Islam's first 400 years in the land that is today Iran, the Persian-speaking population was rarely informed of the Quran's contents. Translations into Persian, the first of which was written in Transoxiana, were limited until the 20th century. Even those were confined to princely courts or private libraries; they were inaccessible to the masses, most of whom were, in any case, illiterate.

During the 600 years or so of the caliphates, first of the Umayyads of Damascus, then the Abbasids who ruled from Baghdad, the policy was to Arabize the lands of the Islamic empire. Translating the Quran was, therefore, seen as unfit if not impious. The Quran's holiness was enmeshed with its language, in keeping with state-approved orthodoxy. Piety sourced in Arabic thus became part of the policy of making the Islamic empire an Arab empire.

The Mongols put a violent end to the Abbasid caliphate in Baghdad in 1258, though this was of little use to Quran's translation.

Under the Safavids, who ruled Persia and beyond from the early 16th to the mid-18th centuries, indifference to Persian literature had its effect on translation: There were no notable Persian versions of the Quran under the dynasty that is often considered the first 'national' dynasty after Islam (the Safavids would have spoken a Turkish dialect more commonly than Persian).

New religious thinkers

Students reading the Quran in Lahore, Pakistan

TWT/ZUMA

The Quran after the 1979 Revolution

Following the Persian Constitutional Revolution (from 1904-05), the use of written (and often simplified) Persian took off as part of a modernizing and patriotic agenda. This fueled translations of the Quran into Persian, and more literate Iranians became familiar with the book's contents.

Translations increased markedly after the 1979 Revolution, with several versions published in the 1980s and 1990s with the aim of promoting religion, not Iranian culture.

From the 1990s , some Iranians started to doubt the Quran as a divine source.

This has been of little use to the Islamic Republic, which always touted itself as the pious government par excellence. As more people became familiar with the book's contents, the contrast between its premises and the regime's double standards and cruelty became glaring. This may have contributed to a trend from the 1990s among some Iranians to doubt the Quran as a divine source, and strengthened outside or academic perspectives on the text.

Some began to consider the Quran not the Word of God but the work of the Prophet Muhammad, while others resorted to hermeneutic interpretations to explain gaps between its dictates and the needs of modern society. They were called, among other things, New Religious Thinkers, and were at times associated with political reformism inside the Islamic Republic.

Worldcrunch 🗞 Extra!

Know more • Following the 1979 Revolution, the Iranian regime imposed a Sharia legal system, meaning one that directly stems from the Quran and other pillars of Islam. While the Muslim world agrees in considering the Quran as the foundation of Sharia, there are major disagreements over the principles upon which this should be translated into law. This can result in laws based more on the interpretation by the ruling class than on religious principles.

In Iran, a major example of this phenomenon is the death penalty, which today is carried out in certain cases of adultery, homosexuality, “spreading corruption on earth” and more. Yet, this departs significantly from the Islamic doctrine, writes Beirut-based daily L’Orient-Le-Jour: “The Quran only mentions the death penalty once, as punishment for deliberate murder. Anything that has to do with drugs, espionage, homosexual relations, adultery, apostasy and other non-voluntary murders are excluded.”

According to Amnesty International, only China carried out more death sentences in 2023 than Iran, which executed 853 people, marking a 48% rise from the previous year. — Fabrizio La Rocca (read more about the Worldcrunch method here)

New Persian translations

Differences of interpretation, if not confusion, abound in Persian translations of the Quran. This was mainly because its language is ancient and complex, and the competence of most of its translators has proved to be limited. Yet even these subjective renditions broadened public familiarity with the text, and inadvertently provoked skepticism or transformed people's views on religion.

This is reminiscent of similar results in 16th century Europe, when the Protestants encouraged translations of the Bible from Latin to European languages. That broke the clergy's interpretive monopoly, and allowed ordinary people to read and interpret the text by themselves — for better or worse, acting as a prelude to significant developments in thought in Europe.

In Iran, more widespread if superficial contact with the Quran may have boosted religious revisionism and even a rejection of the Islamic religion — if not all religions — in a country suffering from 45 years of tyrannical rule in the religion's name.

It may even have strengthened rationalism in Iranian society — although without relevant studies, one can only speculate. But if Iranians are more skeptical and less accepting of received ideas and interpretations, this may be another step on that process solemnly termed as progress.

From Your Site Articles

English to Portuguese, Spanish, German, Italian, French Translation

Sunday, June 2, 2024

Translation of neurotechnologies - Nature.com - Translation

Abstract

Key points

Access options

Similar content being viewed by others

Recent advances in neurotechnologies with broad potential for neuroscience research

Navigating the FDA regulatory landscape

Precision electronic medicine in the brain

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Related links

Rights and permissions

About this article

Cite this article

Saturday, June 1, 2024

Divergence and its impact on the market: Stocks in Translation - Yahoo Finance - Translation

Video Transcript

Persian Quran: Lost in Translation and Religious Revisionism - Worldcrunch - Translation

A late meeting

New religious thinkers

The Quran after the 1979 Revolution

Worldcrunch 🗞 Extra!

New Persian translations

Blog Archive

About Me

Sunday, June 2, 2024

Translation of neurotechnologies - Nature.com - Translation

Abstract

Key points

Access options

Similar content being viewed by others

Recent advances in neurotechnologies with broad potential for neuroscience research

Navigating the FDA regulatory landscape

Precision electronic medicine in the brain

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Related links

Rights and permissions

About this article

Cite this article

Share this article

Saturday, June 1, 2024

Divergence and its impact on the market: Stocks in Translation - Yahoo Finance - Translation

Video Transcript

Persian Quran: Lost in Translation and Religious Revisionism - Worldcrunch - Translation

A late meeting

New religious thinkers

The Quran after the 1979 Revolution

Worldcrunch 🗞 Extra!

New Persian translations

Blog Archive

About Me