No CrossRef data available.
Article contents
Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking
Published online by Cambridge University Press: 27 June 2023
Abstract
Objective:
Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance.
Method:
Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions.
Results:
Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02).
Conclusions:
Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.
- Type
- Research Article
- Information
- Journal of the International Neuropsychological Society , Volume 30 , Issue 2 , February 2024 , pp. 117 - 127
- Copyright
- Copyright © INS. Published by Cambridge University Press 2023
References
Amunts, K., & Zilles, K. (2012). Architecture and organizational principles of Broca’s region. Trends in Cognitive Sciences, 16(8), 418–426. https://doi.org/10.1016/j.tics.2012.06.005
CrossRefGoogle ScholarPubMed
Angsuwatanakul, T., O'Reilly, J., Ounjai, K., Kaewkamnerdpong, B., & Iramina, K. (2020). Multiscale entropy as a new feature for EEG and fNIRS analysis. Entropy (Basel), 22(2), 189. https://doi.org/10.3390/e22020189
Google Scholar
Armbruster-Genç, D. J., Ueltzhöffer, K., & Fiebach, C. J. (2016). Brain signal variability differentially affects cognitive flexibility and cognitive stability. Journal of Neuroscience, 36(14), 3978–3987. https://doi.org/10.1523/jneurosci.2517-14.2016
Google Scholar
Atkinson, H. H., Rosano, C., Simonsick, E. M., Williamson, J. D., Davis, C., Ambrosius, W. T., Rapp, S. R., Cesari, M., Newman, A. B., Harris, T. B., Rubin, S. M., Yaffe, K., Satterfield, S., & Kritchevsky, S. B. (2007). Cognitive function, gait speed decline, and comorbidities: The health, aging and body composition study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 62(8), 844–850. https://doi.org/10.1093/gerona/62.8.844
Google Scholar
Ayaz, H., Izzetoglu, M., Platek, S. M., Bunce, S., Izzetoglu, K., Pourrezaei, K., & Onaral, B. (2006). Registering fNIR data to brain surface image using MRI templates. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2671–2674. https://doi.org/10.1109/iembs.2006.260835
Google Scholar
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Google Scholar
Bhaya-Grossman, I., & Chang, E. F. (2022). Speech computations of the human superior temporal gyrus. Annual Review of Psychology, 73(1), 79–102. https://doi.org/10.1146/annurev-psych-022321-035256
Google Scholar
Bishnoi, A., Holtzer, R., & Hernandez, M. E. (2021). Brain activation changes while walking in adults with and without neurological disease: Systematic review and meta-analysis of functional near-infrared spectroscopy studies. Brain Sciences, 11(3), 291. https://doi.org/10.3390/brainsci11030291
Google Scholar
Boylan, M. A., Foster, C. M., Pongpipat, E. E., Webb, C. E., Rodrigue, K. M., & Kennedy, K. M. (2021). Greater BOLD variability is associated with poorer cognitive function in an adult lifespan sample. Cerebral Cortex, 31(1), 562–574. https://doi.org/10.1093/cercor/bhaa243
Google Scholar
Bunce, S. C., Izzetoglu, M., Izzetoglu, K., Onaral, B., & Pourrezaei, K. (2006). Functional near-infrared spectroscopy. IEEE Engineering in Medicine and Biology Magazine, 25(4), 54–62. https://doi.org/10.1109/memb.2006.1657788
Google Scholar
Burzynska, A. Z., Nagel, I. E., Preuschhof, C., Gluth, S., Bäckman, L., Li, S-C., Lindenberger, U., & Heekeren, H. R. (2012). Cortical thickness is linked to executive functioning in adulthood and aging. Human Brain Mapping, 33(7), 1607–1620. https://doi.org/10.1002/hbm.21311
Google Scholar
Cant, J. S., & Goodale, M. A. (2007). Attention to form or surface properties modulates different regions of human occipitotemporal cortex. Cerebral Cortex, 17(3), 713–731. https://doi.org/10.1093/cercor/bhk022
Google Scholar
Cavanna, A. E., & Trimble, M. R. (2006). The precuneus: A review of its functional anatomy and behavioural correlates. Brain, 129(Pt 3), 564–583. https://doi.org/10.1093/brain/awl004
Google Scholar
Cohen, L., Dehaene, S., Naccache, L., Lehéricy, S., Dehaene-Lambertz, G., Hénaff, M. A., & Michel, F. (2000). The visual word form area: Spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain, 123(Pt 2), 291–307. https://doi.org/10.1093/brain/123.2.291
Google Scholar
Costa, A. S., Dogan, I., Schulz, J. B., & Reetz, K. (2019). Going beyond the mean: Intraindividual variability of cognitive performance in prodromal and early neurodegenerative disorders. Clinical Neuropsychologist, 33(2), 369–389. https://doi.org/10.1080/13854046.2018.1533587
Google Scholar
Curiati, P. K., Tamashiro, J. H., Squarzoni, P., Duran, F. L. S., Santos, L. C., Wajngarten, M., Leite, C. C., Vallada, H., Menezes, P. R., Scazufca, M., Busatto, G. F., & Alves, T. C. (2009). Brain structural variability due to aging and gender in cognitively healthy elders: Results from the Sao Paulo Ageing and Health study. American Journal of Neuroradiology, 30(10), 1850–1856. https://doi.org/10.3174/ajnr.A1727
Google Scholar
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194. https://doi.org/10.1006/nimg.1998.0395
Google Scholar
Daselaar, S. M., Iyengar, V., Davis, S. W., Eklund, K., Hayes, S. M., & Cabeza, R. E. (2015). Less wiring, more firing: Low-performing older adults compensate for impaired white matter with greater neural activity. Cerebral Cortex, 25(4), 983–990. https://doi.org/10.1093/cercor/bht289
Google Scholar
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Google Scholar
Dinstein, I., Heeger, D. J., & Behrmann, M. (2015). Neural variability: Friend or foe? Trends in Cognitive Sciences, 19(6), 322–328. https://doi.org/10.1016/j.tics.2015.04.005
Google Scholar
Dubois, J., & Adolphs, R. (2016). Building a science of individual differences from fMRI. Trends in Cognitive Sciences, 20(6), 425–443. https://doi.org/10.1016/j.tics.2016.03.014
Google Scholar
England, S. E., Verghese, J., Mahoney, J. R., Trantzas, C., & Holtzer, R. (2015). Three-level rating of turns while walking. Gait & Posture, 41(1), 300–303. https://doi.org/10.1016/j.gaitpost.2014.09.010
Google Scholar
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Google Scholar
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., & Dale, A. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355. https://doi.org/10.1016/s0896-6273(02)00569-x
Google Scholar
Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., Busa, E., Seidman, L. J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., & Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14(1), 11–22. https://doi.org/10.1093/cercor/bhg087
Google Scholar
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2010). Blood oxygen level-dependent signal variability is more than just noise. Journal of Neuroscience, 30(14), 4914–4921. https://doi.org/10.1523/jneurosci.5166-09.2010
Google Scholar
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. Journal of Neuroscience, 31(12), 4496–4503. https://doi.org/10.1523/jneurosci.5641-10.2011
CrossRefGoogle ScholarPubMed
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2013). The modulation of BOLD variability between cognitive states varies by age and processing speed. Cerebral Cortex, 23(3), 684–693. https://doi.org/10.1093/cercor/bhs055
Google Scholar
Giorgio, A., Santelli, L., Tomassini, V., Bosnell, R., Smith, S., De Stefano, N., & Johansen-Berg, H. (2010). Age-related changes in grey and white matter structure throughout adulthood. NeuroImage, 51(3), 943–951. https://doi.org/10.1016/j.neuroimage.2010.03.004
Google Scholar
Grady, C. L., & Garrett, D. D. (2018). Brain signal variability is modulated as a function of internal and external demand in younger and older adults. NeuroImage, 169, 510–523. https://doi.org/10.1016/j.neuroimage.2017.12.031
Google Scholar
Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and its role in object recognition. Vision Research, 41(10-11), 1409–1422. https://doi.org/10.1016/s0042-6989(01)00073-6
Google Scholar
Guitart-Masip, M., Salami, A., Garrett, D., Rieckmann, A., Lindenberger, U., & Bäckman, L. (2016). BOLD variability is related to dopaminergic neurotransmission and cognitive aging. Cerebral Cortex, 26(5), 2074–2083. https://doi.org/10.1093/cercor/bhv029
Google Scholar
Haaland, K. Y., Harrington, D. L., & Knight, R. T. (2000). Neural representations of skilled movement. Brain, 123(Pt 11), 2306–2313. https://doi.org/10.1093/brain/123.11.2306
Google Scholar
Haar, S., Donchin, O., & Dinstein, I. (2017). Individual movement variability magnitudes are explained by cortical neural variability. Journal of Neuroscience, 37(37), 9076–9085. https://doi.org/10.1523/jneurosci.1650-17.2017
Google Scholar
Habeck, C., & Stern, Y. (2007). Neural network approaches and their reproducibility in the study of verbal working memory and Alzheimer’s disease. Clinical Neuroscience Research, 6(6), 381–390. https://doi.org/10.1016/j.cnr.2007.05.004
Google Scholar
Haier, R. J., Siegel, B. V., Neucheterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., Browning, H. L., & Buchsbaum, M. S. (1988). Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12(2), 199–217. https://doi.org/10.1016/0160-2896(88)90016-5
Google Scholar
Haynes, B. I., Bauermeister, S., & Bunce, D. (2017). A systematic review of longitudinal associations between reaction time intraindividual variability and age-related cognitive decline or impairment, dementia, and mortality. Journal of the International Neuropsychological Society, 23(5), 431–445. https://doi.org/10.1017/s1355617717000236
Google Scholar
Herlin, B., Navarro, V., & Dupont, S. (2021). The temporal pole: From anatomy to function-A literature appraisal. Journal of Chemical Neuroanatomy, 113, 101925. https://doi.org/10.1016/j.jchemneu.2021.101925
Google Scholar
Holtzer, R., Izzetoglu, M., Chen, M., & Wang, C. (2019). Distinct fNIRS-derived HbO2 trajectories during the course and over repeated walking trials under single- and dual-task conditions: Implications for within session learning and prefrontal cortex efficiency in older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 74(7), 1076–1083. https://doi.org/10.1093/gerona/gly181
Google Scholar
Holtzer, R., Mahoney, J. R., Izzetoglu, M., Wang, C., England, S., & Verghese, J. (2015). Online fronto-cortical control of simple and attention-demanding locomotion in humans. NeuroImage, 112, 152–159. https://doi.org/10.1016/j.neuroimage.2015.03.002
Google Scholar
Holtzer, R., Ross, D., & Izzetoglu, M. (2020). Intraindividual variability in neural activity in the prefrontal cortex during active walking in older adults. Psychology and Aging, 35(8), 1201–1214. https://doi.org/10.1037/pag0000583
Google Scholar
Holtzer, R., Schoen, C., Demetriou, E., Mahoney, J. R., Izzetoglu, M., Wang, C., & Verghese, J. (2017). Stress and gender effects on prefrontal cortex oxygenation levels assessed during single and dual-task walking conditions. European Journal of Neuroscience, 45(5), 660–670. https://doi.org/10.1111/ejn.13518
Google Scholar
Holtzer, R., Verghese, J., Wang, C., Hall, C. B., & Lipton, R. B. (2008). Within-person across-neuropsychological test variability and incident dementia. JAMA, 300(7), 823–830. https://doi.org/10.1001/jama.300.7.823
Google Scholar
Holtzer, R., Wang, C., & Verghese, J. (2012). The relationship between attention and gait in aging: Facts and fallacies. Motor Control, 16(1), 64–80. https://doi.org/10.1123/mcj.16.1.64
Google Scholar
Holtzer, R., Wang, C., & Verghese, J. (2014). Performance variance on walking while talking tasks: Theory, findings, and clinical implications. Age (Dordrecht), 36(1), 373–381. https://doi.org/10.1007/s11357-013-9570-7
Google Scholar
Hutton, C., Draganski, B., Ashburner, J., & Weiskopf, N. (2009). A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. NeuroImage, 48(2), 371–380. https://doi.org/10.1016/j.neuroimage.2009.06.043
Google Scholar
Izzetoglu, M., & Holtzer, R. (2020). Effects of processing methods on fNIRS signals assessed during active walking tasks in older adults. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(3), 699–709. https://doi.org/10.1109/tnsre.2020.2970407
Google Scholar
Kakimoto, A., Ito, S., Okada, H., Nishizawa, S., Minoshima, S., & Ouchi, Y. (2016). Age-related sex-specific changes in brain metabolism and morphology. Journal of Nuclear Medicine, 57(2), 221–225. https://doi.org/10.2967/jnumed.115.166439
Google Scholar
Kim, J. G., & Liu, H. (2007). Variation of haemoglobin extinction coefficients can cause errors in the determination of haemoglobin concentration measured by near-infrared spectroscopy. Physics in Medicine and Biology, 52(20), 6295–6322. https://doi.org/10.1088/0031-9155/52/20/014
Google Scholar
Koechlin, E., Ody, C., & Kouneiher, F. (2003). The architecture of cognitive control in the human prefrontal cortex. Science, 302(5648), 1181–1185. https://doi.org/10.1126/science.1088545
Google Scholar
Kumral, D., Şansal, F., Cesnaite, E., Mahjoory, K., Al, E., Gaebler, M., Nikulin, V. V., & Villringer, A. (2020). BOLD and EEG signal variability at rest differently relate to aging in the human brain. NeuroImage, 207, 116373. https://doi.org/10.1016/j.neuroimage.2019.116373
Google Scholar
Leech, R., & Sharp, D. J. (2014). The role of the posterior cingulate cortex in cognition and disease. Brain, 137(Pt 1), 12–32. https://doi.org/10.1093/brain/awt162
Google Scholar
Levine, D. A., Gross, A. L., Briceño, E. M., Tilton, N., Giordani, B. J., Sussman, J. B., Hayward, R. A., Burke, J. F., Hingtgen, S., Elkind, M. S. V., Manly, J. J., Gottesman, R. F., Gaskin, D. J., Sidney, S., Sacco, R. L., Tom, S. E., Wright, C. B., Yaffe, K., & Galecki, A. T. (2021). Sex differences in cognitive decline among US adults. JAMA Network Open, 4(2), e210169. https://doi.org/10.1001/jamanetworkopen.2021.0169
Google Scholar
Lim, S. H., Dinner, D. S., Pillay, P. K., Lüders, H., Morris, H. H., Klem, G., Wyllie, E., & Awad, I. A. (1994). Functional anatomy of the human supplementary sensorimotor area: Results of extraoperative electrical stimulation. Electroencephalography and Clinical Neurophysiology, 91(3), 179–193. https://doi.org/10.1016/0013-4694(94)90068-x
Google Scholar
Lucas, M., Wagshul, M. E., Izzetoglu, M., & Holtzer, R. (2018). Moderating effect of white matter integrity on brain activation during dual-task walking in older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 74(4), 435–441. https://doi.org/10.1093/gerona/gly131
Google Scholar
MacDonald, S. W., Nyberg, L., & Bäckman, L. (2006). Intra-individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity. Trends in Neurosciences, 29(8), 474–480. https://doi.org/10.1016/j.tins.2006.06.011
Google Scholar
Maidan, I., Hacham, R., Galperin, I., Giladi, N., Holtzer, R., Hausdorff, J. M., & Mirelman, A. (2022). Neural variability in the prefrontal cortex as a reflection of neural flexibility and stability in patients with Parkinson disease. Neurology, 98(8), e839–e847. https://doi.org/10.1212/wnl.0000000000013217
Google Scholar
Maidan, I., Mirelman, A., Hausdorff, J. M., Stern, Y., & Habeck, C. G. (2021). Distinct cortical thickness patterns link disparate cerebral cortex regions to select mobility domains. Scientific Reports, 11(1), 6600. https://doi.org/10.1038/s41598-021-85058-z
Google Scholar
Marinescu, R. V., Eshaghi, A., Alexander, D. C., & Golland, P. (2019). BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy (2019), 11846, 112–120. https://doi.org/10.1007/978-3-030-33226-6_13
Google Scholar
Mechelli, A., Humphreys, G. W., Mayall, K., Olson, A., & Price, C. J. (2000). Differential effects of word length and visual contrast in the fusiform and lingual gyri during reading. Proceedings of the Royal Society of London. Series B: Biological Sciences, 267(1455), 1909–1913. https://doi.org/10.1098/rspb.2000.1229
Google Scholar
Menant, J. C., Maidan, I., Alcock, L., Al-Yahya, E., Cerasa, A., Clark, D. J., de Bruin, E. D., Fraser, S., Gramigna, V., Hamacher, D., Herold, F., Holtzer, R., Izzetoglu, M., Lim, S., Pantall, A., Pelicioni, P., Peters, S., Rosso, A. L., St George, R., …Mirelman, A. (2020). A consensus guide to using functional near-infrared spectroscopy in posture and gait research. Gait & Posture, 82, 254–265. https://doi.org/10.1016/j.gaitpost.2020.09.012
Google Scholar
Miyai, I., Tanabe, H. C., Sase, I., Eda, H., Oda, I., Konishi, I., Tsunazawa, Y., Suzuki, T., Yanagida, T., & Kubota, K. (2001). Cortical mapping of gait in humans: A near-infrared spectroscopic topography study. NeuroImage, 14(5), 1186–1192. https://doi.org/10.1006/nimg.2001.0905
Google Scholar
Molavi, B., & Dumont, G. A. (2012). Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement, 33(2), 259–270. https://doi.org/10.1088/0967-3334/33/2/259
Google Scholar
Montero-Odasso, M. M., Sarquis-Adamson, Y., Speechley, M., Borrie, M. J., Hachinski, V. C., Wells, J., Riccio, P. M., Schapira, M., Sejdic, E., Camicioli, R. M., Bartha, R., McIlroy, W. E., & Muir-Hunter, S. (2017). Association of dual-task gait with incident dementia in mild cognitive impairment: Results from the gait and brain study. JAMA Neurology, 74(7), 857–865. https://doi.org/10.1001/jamaneurol.2017.0643
Google Scholar
Moon, Y., Sung, J., An, R., Hernandez, M. E., & Sosnoff, J. J. (2016). Gait variability in people with neurological disorders: A systematic review and meta-analysis. Human Movement Science, 47, 197–208. https://doi.org/10.1016/j.humov.2016.03.010
Google Scholar
Motl, R. W., Cohen, J. A., Benedict, R., Phillips, G., LaRocca, N., Hudson, L. D., & Rudick, R. (2017). Validity of the timed 25-foot walk as an ambulatory performance outcome measure for multiple sclerosis. Multiple Sclerosis, 23(5), 704–710. https://doi.org/10.1177/1352458517690823
Google Scholar
Nesselroade, J. R., & Salthouse, T. A. (2004). Methodological and theoretical implications of intraindividual variability in perceptual-motor performance. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 59(2), P49–55. https://doi.org/10.1093/geronb/59.2.p49
Google Scholar
Neubauer, A. C., & Fink, A. (2009). Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews, 33(7), 1004–1023. https://doi.org/10.1016/j.neubiorev.2009.04.001
Google Scholar
Paraskevoudi, N., Balcı, F., & Vatakis, A. (2018). “Walking” through the sensory, cognitive, and temporal degradations of healthy aging. Annals of the New York Academy of Sciences, 1426(1), 72–92. https://doi.org/10.1111/nyas.13734
Google Scholar
Passarelli, L., Gamberini, M., & Fattori, P. (2021). The superior parietal lobule of primates: A sensory-motor hub for interaction with the environment. Journal of Integrative Neuroscience, 20(1), 157–171. https://doi.org/10.31083/j.jin.2021.01.334
Google Scholar
Ptak, R., & Valenza, N. (2005). The inferior temporal lobe mediates distracter-resistant visual search of patients with spatial neglect. Journal of Cognitive Neuroscience, 17(5), 788–799. https://doi.org/10.1162/0898929053747676
Google Scholar
Quan, M., Xun, P., Chen, C., Wen, J., Wang, Y., Wang, R., Chen, P., & He, K. (2017). Walking pace and the risk of cognitive decline and dementia in elderly populations: A meta-analysis of prospective cohort studies. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 72(2), 266–270. https://doi.org/10.1093/gerona/glw121
Google Scholar
Randolph, C., Tierney, M. C., Mohr, E., & Chase, T. N. (1998). The repeatable battery for the assessment of neuropsychological status (RBANS): Preliminary clinical validity. Journal of Clinical and Experimental Neuropsychology, 20(3), 310–319. https://doi.org/10.1076/jcen.20.3.310.823
Google Scholar
Ross, D., Wagshul, M. E., Izzetoglu, M., & Holtzer, R. (2021). Prefrontal cortex activation during dual-task walking in older adults is moderated by thickness of several cortical regions. Geroscience, 43(4), 1959–1974. https://doi.org/10.1007/s11357-021-00379-1
Google Scholar
Salat, D. H., Buckner, R. L., Snyder, A. Z., Greve, D. N., Desikan, R. S., Busa, E., Morris, J. C., Dale, A. M., & Fischl, B. (2004). Thinning of the cerebral cortex in aging. Cerebral Cortex, 14(7), 721–730. https://doi.org/10.1093/cercor/bhh032
Google Scholar
Scholkmann, F., Spichtig, S., Muehlemann, T., & Wolf, M. (2010). How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiological Measurement, 31(5), 649–662. https://doi.org/10.1088/0967-3334/31/5/004
Google Scholar
Scholkmann, F., & Wolf, M. (2013). General equation for the differential pathlength factor of the frontal human head depending on wavelength and age. Journal of Biomedical Optics, 18(10), 105004. https://doi.org/10.1117/1.Jbo.18.10.105004
Google Scholar
Smith, E., Cusack, T., Cunningham, C., & Blake, C. (2017). The influence of a cognitive dual task on the gait parameters of healthy older adults: A systematic review and meta-analysis. Journal of Aging and Physical Activity, 25(4), 671–686. https://doi.org/10.1123/japa.2016-0265
Google Scholar
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(Suppl 1), S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051
Google Scholar
Steinberg, S. N., Malins, J. G., Liu, J., & King, T. Z. (2022). Within-individual BOLD signal variability in the N-back task and its associations with vigilance and working memory. Neuropsychologia, 173, 108280. https://doi.org/10.1016/j.neuropsychologia.2022.108280
Google Scholar
Studenski, S., Perera, S., Patel, K., Rosano, C., Faulkner, K., Inzitari, M., Brach, J., Chandler, J., Cawthon, P., Connor, E. B., Nevitt, M., Visser, M., Kritchevsky, S., Badinelli, S., Harris, T., Newman, A. B., Cauley, J., Ferrucci, L., & Guralnik, J. (2011). Gait speed and survival in older adults. JAMA, 305(1), 50–58. https://doi.org/10.1001/jama.2010.1923
Google Scholar
Sui, J., Huster, R., Yu, Q., Segall, J. M., & Calhoun, V. D. (2014). Function-structure associations of the brain: Evidence from multimodal connectivity and covariance studies. NeuroImage, 102(Pt 1), 11–23. https://doi.org/10.1016/j.neuroimage.2013.09.044
CrossRefGoogle ScholarPubMed
Uddin, L. Q. (2020). Bring the noise: Reconceptualizing spontaneous neural activity. Trends in Cognitive Sciences, 24(9), 734–746. https://doi.org/10.1016/j.tics.2020.06.003
Google Scholar
Uddin, L. Q., Nomi, J. S., Hébert-Seropian, B., Ghaziri, J., & Boucher, O. (2017). Structure and function of the human insula. Journal of Clinical Neurophysiology, 34(4), 300–306. https://doi.org/10.1097/wnp.0000000000000377
Google Scholar
van Kemenade, B. M., Arikan, B. E., Podranski, K., Steinsträter, O., Kircher, T., & Straube, B. (2019). Distinct roles for the cerebellum, angular gyrus, and middle temporal gyrus in action-feedback monitoring. Cerebral Cortex, 29(4), 1520–1531. https://doi.org/10.1093/cercor/bhy048
Google Scholar
Verghese, J., Holtzer, R., Lipton, R. B., & Wang, C. (2012). Mobility stress test approach to predicting frailty, disability, and mortality in high-functioning older adults. Journal of the American Geriatrics Society, 60(10), 1901–1905. https://doi.org/10.1111/j.1532-5415.2012.04145.x
Google Scholar
Wagshul, M. E., Lucas, M., Ye, K., Izzetoglu, M., & Holtzer, R. (2019). Multi-modal neuroimaging of dual-task walking: Structural MRI and fNIRS analysis reveals prefrontal grey matter volume moderation of brain activation in older adults. NeuroImage, 189, 745–754. https://doi.org/10.1016/j.neuroimage.2019.01.045
Google Scholar
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007). Visual field maps in human cortex. Neuron, 56(2), 366–383. https://doi.org/10.1016/j.neuron.2007.10.012
Google Scholar
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., Duggirala, R., & Glahn, D. C. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, 53(3), 1135–1146. https://doi.org/10.1016/j.neuroimage.2009.12.028
Google Scholar
Yogev-Seligmann, G., Hausdorff, J. M., & Giladi, N. (2008). The role of executive function and attention in gait. Movement Disorders, 23(3), 329–342; quiz 472. https://doi.org/10.1002/mds.21720
Google Scholar
Ross et al. supplementary material
Table S1
File
174.6 KB