Brain activation during vocal motor imagery: a pilot functional near-infrared spectroscopy (fNIRS) study

Bauer RM, Johnson MK, Smith LB (2015) Learning novel concepts: a neuroimaging approach. Human Brain Mapping, 36(8):3213–3226. https://doi.org/10.1002/hbm.22846

Beauchet O, Fantino B, Allali G, Muir SW, Montero-Odasso M, Annweiler C (2010) Timed up and go test and risk of falls in older adults: a systematic review. J Nutrition Health Aging 14(10):933–938. https://doi.org/10.1007/s12603-010-0294-8

Article  Google Scholar 

Dash D, Ferrari P, Angrick M (2020) Decoding speech imagery using MEG. Neuroimage 211:116665. https://doi.org/10.1016/j.neuroimage.2020.116665

Article  Google Scholar 

Hall CR, Martin KA (1997) Measuring movement imagery abilities: a revision of the movement imagery questionnaire. J Ment Imag 21(1–2):143–154

Google Scholar 

Hall CR, Pongrac J, Buckolz E (1985) The measurement of imagery ability. Hum Mov Sci 4(2):107–118

Article  Google Scholar 

Hardwick RM, Caspers S, Eickhoff SB, Swinnen SP (2018) Neural correlates of action: comparing meta-analyses of imagery, observation, and execution. Neurosci Biobehav Rev 94:31–44. https://doi.org/10.1016/j.neubiorev.2018.08.003

Article  PubMed  Google Scholar 

Herff C, Heger D, de Pesters A, Telaar D, Brunner P, Schalk G, Schultz T (2016) Brain-to-text: decoding spoken phrases from phone representations in the brain. Front Neurosci 10:429. https://doi.org/10.3389/fnins.2016.00429

Article  PubMed  PubMed Central  Google Scholar 

Hétu S, Grégoire M, Saimpont A, Coll M-P, Eugène F, Michon P-E, Jackson PL (2013) The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav Rev 37(5):930–949. https://doi.org/10.1016/j.neubiorev.2013.03.017

Article  PubMed  Google Scholar 

Ikeda S, Shibata T, Nakano N, Okada Y, Tsuyuguchi N, Kato A, Hara M (2014) Neural decoding of single vowels during covert articulation using electrocorticography. Front Hum Neurosci 8:125. https://doi.org/10.3389/fnhum.2014.00125

Article  PubMed  PubMed Central  Google Scholar 

Isaac AR, Marks DF, Russell DG (1986) Measurement of imagery vividness: a test of the validity of the vividness of movement imagery questionnaire (VMIQ). J Ment Imag 10(4):59–72

Google Scholar 

Itotani K, Taniguchi T, Kodama N, Yano Y (2023) The relationship between restrictions on going out and motor imagery among medical university students in Japan—research with small samples. J Phys Ther Sci 35(3):243–248

Google Scholar 

Jeannerod M (1994) The representing brain: neural correlates of motor intention and imagery. Behav Brain Sci 17(2):187–245

Article  Google Scholar 

Kim JS, Kroliczak G, Vesia M (2018) A distributed frontoparietal network in covert visuomotor integration for hand actions. Hum Brain Mapp 39(8):3342–3357. https://doi.org/10.1002/hbm.24173

Article  Google Scholar 

Kotegawa K, Yasumura A, Iwakura T, Ichikawa H (2020) Prefrontal activation during motor imagery of gait measured by fNIRS. NeuroReport 31(2):142–147. https://doi.org/10.1097/WNR.0000000000001363

Article  CAS  Google Scholar 

Kumawat A, Raza A, Singh NJ, Saini M (2024) Motor imagery modulates distinct networks across age groups: A resting-state fMRI study. Neuropsychologia 192:108243. https://doi.org/10.1016/j.neuropsychologia.2024.108243

Article  Google Scholar 

Mishra RK, Taly AB, Srivastava A, Kumar S (2022) Effect of concurrent transcranial direct current stimulation on instrumented timed up and go task performance in people with Parkinson’s disease: a double-blind and cross-over study. Clin Neurophysiol 138:74–82. https://doi.org/10.1016/j.clinph.2022.03.006

Article  Google Scholar 

Okamoto M, Dan H, Sakamoto K, Takeo K, Shimizu K, Kohno S, Dan I (2004) Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. Neuroimage 21(1):99–111. https://doi.org/10.1016/j.neuroimage.2003.08.026

Article  PubMed  Google Scholar 

Podsiadlo D, Richardson S (1991) The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39(2):142–148. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x

Article  PubMed  CAS  Google Scholar 

Roberts R, Callow N, Hardy L, Markland D, Bringer J (2008) Movement imagery ability: development and assessment of a revised version of the vividness of movement imagery questionnaire. J Sport Exercise Psychol 30(2):200–221

Article  Google Scholar 

Scholkmann F, Wolf M (2013) General equation for the differential pathlength factor of the frontal human head depending on wavelength and age. J Biomed Opt 18(10):105004. https://doi.org/10.1117/1.JBO.18.10.105004

Article  PubMed  CAS  Google Scholar 

Sheffield SW, Larson E, Butera IM, DeFreese A, Rogers BP, Wallace MT, Stecker GC, Lee AKC, Gifford RH (2023) Sound level changes the auditory cortical activation detected with functional near-infrared spectroscopy. J Neural Eng 20(1):016004. https://doi.org/10.1007/s10548-023-00981-w

Article  Google Scholar 

Shiromoto O, Fujiki R, Nishizawa M (2006) Effectiveness of voice therapy in elderly patients with glottal insufficiency. J Voice 20(4):625–629. https://doi.org/10.1016/j.jvoice.2005.09.007

Article  Google Scholar 

Tachtsidis I, Scholkmann F (2016) False positives and false negatives in functional near-infrared spectroscopy: Issues, challenges, and the way forward. Neurophotonics 3(3):031405. https://doi.org/10.1117/1.NPh.3.3.031405

Article  PubMed  PubMed Central  Google Scholar 

Tian X, Poeppel D (2010) Mental imagery of speech and movement implicates the dynamics of internal forward models. Front Psychol 1:166. https://doi.org/10.3389/fpsyg.2010.00166

Article  PubMed  PubMed Central  Google Scholar 

Toriyama H, Ushiba J, Ushiba H (2018) Motor imagery vividness can be assessed by the similarity in EEG frequency components. Front Hum Neurosci 12:243. https://doi.org/10.3389/fnhum.2018.00243

Article  Google Scholar 

Van Uem JMT, Walgaard S, Ainsworth E, Hasmann SE, Heger T, Nussbaum S, Hobert MA, Micó-Amigo EM, Van Lummel RC, Berg D, Maetzler W (2016) Quantitative Timed-Up-and-Go parameters in relation to cognitive parameters and health-related quality of life in mild-to-moderate Parkinson’s disease. PLoS ONE 11(4):e0151997. https://doi.org/10.1371/journal.pone.0151997

Article  PubMed  PubMed Central  CAS  Google Scholar 

Vry M-S, Saur D, Rijntjes M, Umarova R, Kellmeyer P, Schnell S, Weiller C (2012) Ventral and dorsal fiber systems for imagined and executed movement. Experim Brain Res 219(2):203–216. https://doi.org/10.1007/s00221-012-3079-7

Article  Google Scholar 

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