Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8(4), 170–177. https://doi.org/10.1016/j.tics.2004.02.010

Article  Google Scholar 

Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2014). Inhibition and the right inferior frontal cortex: one decade on. Trends in Cognitive Sciences, 18(4), 177–185. https://doi.org/10.1016/j.tics.2013.12.003

Article  Google Scholar 

Blazhenets, G., Ma, Y., Sörensen, A., Schiller, F., Rücker, G., Eidelberg, D., … Meyer, P. T. (2020). Predictive value of (18)F-Florbetapir and (18)F-FDG PET for Conversion from mild cognitive impairment to Alzheimer Dementia. Journal of Nuclear Medicine, 61(4), 597–603. https://doi.org/10.2967/jnumed.119.230797

Blum, D., la Fougère, C., Pilotto, A., Maetzler, W., Berg, D., Reimold, M., & Liepelt-Scarfone, I. (2018). Hypermetabolism in the cerebellum and brainstem and cortical hypometabolism are independently associated with cognitive impairment in Parkinson’s disease. European Journal of Nuclear Medicine and Molecular Imaging, 45(13), 2387–2395. https://doi.org/10.1007/s00259-018-4085-1

Article  CAS  Google Scholar 

Burgess, N., Maguire, E. A., & O’Keefe, J. (2002). The human hippocampus and spatial and episodic memory. Neuron, 35(4), 625–641. https://doi.org/10.1016/s0896-6273(02)00830-9

Article  CAS  Google Scholar 

Canosa, A., Pagani, M., Cistaro, A., Montuschi, A., Iazzolino, B., Fania, P., … Chiò, A. (2016). 18F-FDG-PET correlates of cognitive impairment in ALS. Neurology, 86(1), 44–49. https://doi.org/10.1212/wnl.0000000000002242

Cao, H., Duan, J., Lin, D., Shugart, Y. Y., Calhoun, V., & Wang, Y. P. (2014). Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs. NeuroImage, 102 Pt 1, 220–228. https://doi.org/10.1016/j.neuroimage.2014.01.021

Article  Google Scholar 

Chen, J., Yang, S., Wang, Z., & Mao, H. (2021). Efficient sparse representation for learning with high-dimensional data. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2021.3119278

Article  Google Scholar 

Chen, T., Zhang, C., Liu, Y., Zhao, Y., Lin, D., Hu, Y., … Li, G. (2019). A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine. BMC Genomics, 20(1), 846. https://doi.org/10.1186/s12864-019-6135-x

de Leon, M. J., Convit, A., Wolf, O. T., Tarshish, C. Y., DeSanti, S., Rusinek, H., … Fowler, J. (2001). Prediction of cognitive decline in normal elderly subjects with 2-[(18)F]fluoro-2-deoxy-D-glucose/poitron-emission tomography (FDG/PET). Proceedings of the National Academy of Sciences of the United States of America, 98(19), 10966–10971. https://doi.org/10.1073/pnas.191044198

Dolui, S., Li, Z., Nasrallah, I. M., Detre, J. A., & Wolk, D. A. (2020). Arterial spin labeling versus (18)F-FDG-PET to identify mild cognitive impairment. Neuroimage Clin, 25, 102146. https://doi.org/10.1016/j.nicl.2019.102146

Article  Google Scholar 

Falkenberg, L. E., Westerhausen, R., Specht, K., & Hugdahl, K. (2012). Resting-state glutamate level in the anterior cingulate predicts blood-oxygen level-dependent response to cognitive control. Proceedings of the National Academy of Sciences of the United States of America, 109(13), 5069–5073. https://doi.org/10.1073/pnas.1115628109

Article  Google Scholar 

Fellows, R. P., & Schmitter-Edgecombe, M. (2019). Symbol digit modalities test: regression-based normative data and clinical utility. Archives of Clinical Neuropsychology: the Official Journal of the National Academy of Neuropsychologists, 35(1), 105–115. https://doi.org/10.1093/arclin/acz020

Article  Google Scholar 

Festa, J. R., Schwarz, L. R., Pliskin, N., Cullum, C. M., Lacritz, L., Charbel, F. T., … Lazar, R. M. (2010). Neurocognitive dysfunction in adult moyamoya disease. Journal of Neurology, 257(5), 806–815. https://doi.org/10.1007/s00415-009-5424-8

Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C., Frackowiak, R. S., & Turner, R. (1995). Analysis of fMRI time-series revisited. NeuroImage, 2(1), 45–53. https://doi.org/10.1006/nimg.1995.1007

Article  CAS  Google Scholar 

Gorelick, P. B., Scuteri, A., Black, S. E., Decarli, C., Greenberg, S. M., Iadecola, C., … Seshadri, S. (2011). Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the american heart association/american stroke association. Stroke, 42(9), 2672–2713. https://doi.org/10.1161/STR.0b013e3182299496

Guidelines for diagnosis and treatment of moyamoya disease (spontaneous occlusion of the circle of Willis). (2012). Neurologia Medico-Chirurgica (Tokyo), 52(5), 245–266. https://doi.org/10.2176/nmc.52.245

Guo, Q., Hong, Z., & Shi, W. (1991). Boston naming test in Chinese elderly, patient with mild cognitive impairment and Alzheimer’s dementia. Chinese Mental Health Journal.

Guo, Q., Lv, C., & Hong, Z. (2001). Reliability and validity of auditory verbal learning test on Chinese elderly patients. Journal of Chinese Mental Health, 15, 13–15.

Guo, Q. H., Zhou, B., Zhao, Q. H., Wang, B., & Hong, Z. (2012). Memory and executive screening (MES): a brief cognitive test for detecting mild cognitive impairment. BMC Neurology, 12, 119. https://doi.org/10.1186/1471-2377-12-119

Article  Google Scholar 

Hosoda, C., Nariai, T., Ishiwata, K., Ishii, K., Matsushima, Y., & Ohno, K. (2010). Correlation between focal brain metabolism and higher brain function in patients with Moyamoya disease. International Journal of Stroke: Official Journal of the International Stroke Society, 5(5), 367–373. https://doi.org/10.1111/j.1747-4949.2010.00461.x

Article  Google Scholar 

Kang, C. G., Chun, M. H., Kang, J. A., Do, K. H., & Choi, S. J. (2017). Neurocognitive dysfunction according to Hypoperfusion Territory in patients with Moyamoya Disease. Annals of Rehabilitation Medicine, 41(1), 1–8. https://doi.org/10.5535/arm.2017.41.1.1

Article  Google Scholar 

Kapitán, M., Ferrando, R., Diéguez, E., de Medina, O., Aljanati, R., Ventura, R., … Buzó, R. (2009). [Regional cerebral blood flow changes in Parkinson’s disease: correlation with disease duration]. Revista Espaola de Medicina Nuclear, 28(3), 114–120.

Karzmark, P., Zeifert, P. D., Bell-Stephens, T. E., Steinberg, G. K., & Dorfman, L. J. (2012). Neurocognitive impairment in adults with moyamoya disease without stroke. Neurosurgery, 70(3), 634–638. https://doi.org/10.1227/NEU.0b013e3182320d1a

Article  Google Scholar 

Karzmark, P., Zeifert, P. D., Tan, S., Dorfman, L. J., Bell-Stephens, T. E., & Steinberg, G. K. (2008). Effect of moyamoya disease on neuropsychological functioning in adults. Neurosurgery, 62(5), 1048–1051. discussion 1051 – 1042. https://doi.org/10.1227/01.neu.0000325866.29634.4c

Kazumata, K., Tha, K. K., Narita, H., Kusumi, I., Shichinohe, H., Ito, M., … Houkin, K. (2015). Chronic ischemia alters brain microstructural integrity and cognitive performance in adult moyamoya disease. Stroke, 46(2), 354–360. https://doi.org/10.1161/strokeaha.114.007407

Kazumata, K., Tokairin, K., Ito, M., Uchino, H., Sugiyama, T., Kawabori, M., … Houkin, K. (2020). Combined structural and diffusion tensor imaging detection of ischemic injury in moyamoya disease: relation to disease advancement and cerebral hypoperfusion. Case Report Journal of Neurosurgery, 134(3), 1155–1164. https://doi.org/10.3171/2020.1.Jns193260

Kilpatrick, L., & Cahill, L. (2003). Amygdala modulation of parahippocampal and frontal regions during emotionally influenced memory storage. NeuroImage, 20(4), 2091–2099. https://doi.org/10.1016/j.neuroimage.2003.08.006

Article  Google Scholar 

Kim, J. M., Lee, S. H., & Roh, J. K. (2009). Changing ischaemic lesion patterns in adult moyamoya disease. Journal of Neurology, Neurosurgery and Psychiatry, 80(1), 36–40. https://doi.org/10.1136/jnnp.2008.145078

Article  Google Scholar 

Kronenburg, A., van den Berg, E., van Schooneveld, M. M., Braun, K. P. J., Calviere, L., van der Zwan, A., & Klijn, C. J. M. (2018). Cognitive functions in children and adults with Moyamoya Vasculopathy: a systematic review and Meta-analysis. Journal of Stroke, 20(3), 332–341. https://doi.org/10.5853/jos.2018.01550

Article  Google Scholar 

Lei, Y., Guo, Q., Li, Y., Jiang, H., Ni, W., & Gu, Y. (2014a). Characteristics of cognitive impairment in adults with cerebral ischemia. Zhonghua Yi Xue Za Zhi, 94(13), 984–989.

Lei, Y., Li, Y., Ni, W., Jiang, H., Yang, Z., Guo, Q., … Mao, Y. (2014b). Spontaneous brain activity in adult patients with moyamoya disease: a resting-state fMRI study. Brain Research, 1546, 27–33. https://doi.org/10.1016/j.brainres.2013.12.022

Lei, Y., Song, B., Chen, L., Su, J., Zhang, X., Ni, W., … Mao, Y. (2020). Reconfigured functional network dynamics in adult moyamoya disease: a resting-state fMRI study. Brain Imaging and Behavior, 14(3), 715–727. https://doi.org/10.1007/s11682-018-0009-8

Lei, Y., Su, J., Jiang, H., Guo, Q., Ni, W., Yang, H., … Mao, Y. (2017). Aberrant regional homogeneity of resting-state executive control, default mode, and salience networks in adult patients with moyamoya disease. Brain Imaging and Behavior, 11(1), 176–184. https://doi.org/10.1007/s11682-016-9518-5

Meyer, P. T., Frings, L., Rücker, G., & Hellwig, S. (2017). (18)F-FDG PET in parkinsonism: Differential diagnosis and evaluation of cognitive impairment. Journal of Nuclear Medicine, 58(12), 1888–1898. https://doi.org/10.2967/jnumed.116.186403

Article  CAS  Google Scholar 

Nakagawara, J., Osato, T., Kamiyama, K., Honjo, K., Sugio, H., Fumoto, K., … Nakamura, H. (2012). Diagnostic imaging of higher brain dysfunction in patients with adult moyamoya disease using statistical imaging analysis for [123I]iomazenil single photon emission computed tomography. Neurologia Medico-Chirurgica (Tokyo), 52(5), 318–326. https://doi.org/10.2176/nmc.52.318

Rektor, I., Bares, M., Brázdil, M., Kanovský, P., Rektorová, I., Sochurková, D., … Daniel, P. (2005). Cognitive- and movement-related potentials recorded in the human basal ganglia. Movement Disorders, 20(5), 562–568. https://doi.org/10.1002/mds.20368

Riva, D., Taddei, M., & Bulgheroni, S. (2018). The neuropsychology of basal ganglia. European Journal of Paediatric Neurology: Ejpn: Official Journal of the European Paediatric Neurology Society, 22(2), 321–326. https://doi.org/10.1016/j.ejpn.2018.01.009

Article  Google Scholar 

Schubert, G. A., Czabanka, M., Seiz, M., Horn, P., Vajkoczy, P., & Thomé, C. (2014). Perfusion characteristics of Moyamoya disease: an anatomically and clinically oriented analysis and comparison. Stroke, 45(1), 101–106. https://doi.org/10.1161/strokeaha.113.003370

Article  Google Scholar 

Silverman, D. H. (2004). Brain 18F-FDG PET in the diagnosis of neurodegenerative dementias: comparison with perfusion SPECT and with clinical evaluations lacking nuclear imaging. Journal of Nuclear Medicine, 45(4), 594–607.

Google Scholar 

Su, J. B., Xi, S. D., Zhou, S. Y., Zhang, X., Jiang, S. H., Xu, B., … Gu, Y. X. (2019). Microstructural damage pattern of vascular cognitive impairment: a comparison between moyamoya disease and cerebrovascular atherosclerotic disease. Neural Regeneration Research, 14(5), 858–867. https://doi.org/10.4103/1673-5374.249234

Suzuki, J., & Takaku, A. (1969). Cerebrovascular “moyamoya” disease. Disease showing abnormal net-like vessels in base of brain. Archives of Neurology, 20(3), 288–299. https://doi.org/10.1001/archneur.1969.00480090076012

Article  CAS  Google Scholar 

Van Heertum, R. L., Greenstein, E. A., & Tikofsky, R. S. (2004). 2-deoxy-fluorglucose-positron emission tomography imaging of the brain: current clinical applications with emphasis on the dementias. Seminars in Nuclear Medicine, 34(4), 300–312. https://doi.org/10.1053/j.semnuclmed.2004.03.003

Article  Google Scholar 

Vicente, J. S., Prudencio, L. F., Torre, J. R. I., & Madrid, J. I. R. (2018). Mismatch in brain perfusion and metabolism detected with (99m)Tc-hexamethyl propylene amine oxime single photon emission computed tomography and (18)F-fluorodeoxyglucose positron emission tomography in moyamoya disease. Indian Journal of Nuclear Medicine, 33(2), 154–157. https://doi.org/10.4103/ijnm.IJNM_2_18

Article  Google Scholar 

Wen, D., Jia, P., Lian, Q., Zhou, Y., & Lu, C. (2016). Review of sparse representation-based classification methods on EEG Signal Processing for Epilepsy Detection, brain-computer interface and cognitive impairment. Frontiers in Aging Neuroscience, 8, 172. https://doi.org/10.3389/fnagi.2016.00172

Article  Google Scholar 

Wityk, R. J., Hillis, A., Beauchamp, N., Barker, P. B., & Rigamonti, D. (2002). Perfusion-weighted magnetic resonance imaging in adult moyamoya syndrome: characteristic patterns and change after surgical intervention: case report. Neurosurgery, 51(6), 1499–1505, discussion 1506.

Zhao, Q., Guo, Q., Li, F., Zhou, Y., Wang, B., & Hong, Z. J. P. O. (2013). The Shape Trail Test: application of a new variant of the Trail making test. PLoS One, 8(2), e57333.

Zhao, Q., Guo, Q., Shi, W., Zhou, Y., & Hong, Z. J. C. (2007). Category verbal fluency test in identification and differential diagnosis of dementia. Chinese Journal of Clinical Psychology, 3, 241–245.

Google Scholar