Alavash, M., Lim, S.-J., Thiel, C., Sehm, B., Deserno, L., & Obleser, J. (2018). Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance. NeuroImage, 15, 341–356. https://doi.org/10.1016/j.neuroimage.2018.01.048

Article  CAS  Google Scholar 

Aris, I. M., Rifas-Shiman, S. L., Jimenez, M. P., Li, L.-J., Hivert, M.-F., Oken, E., & James, P. (2021). Neighborhood Child Opportunity Index and adolescent cardiometabolic risk. Pediatrics, 147, e2020018903. https://doi.org/10.1542/peds.2020-018903

Armbruster-Genc, D. J. N., Ueltzhoffer, K., & Fiebach, C. J. (2016). Brain signal variability differentially affects cognitive flexibility and cognitive stability. The Journal of Neuroscience, 36, 3978–3987. https://doi.org/10.1523/JNEUROSCI.2517-14.2016

Article  CAS  PubMed  PubMed Central  Google Scholar 

Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29. https://doi.org/10.1146/annurev-psych-120710-100422

Article  PubMed  Google Scholar 

Bonakdarpour, B., Beeson, P. M., DeMarco, A. T., & Rapcsak, S. Z. (2015). Variability in blood oxygen level dependent (BOLD) signal in patients with stroke-induced and primary progressive aphasia. NeuroImage: Clinical, 8, 87–94. https://doi.org/10.1016/j.nicl.2015.03.014

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, 1–13. https://doi.org/10.1093/cercor/bhaa243

Brown, J. (1958). Some tests of the decay theory of immediate memory. Quarterly Journal of Experimental Psychology, 10, 12–21. https://doi.org/10.1080/17470215808416249

Article  Google Scholar 

Burzynska, A. Z., Wong, C. N., Voss, M. W., Cooke, G. E., Gothe, N. P., Fanning, J., McAuley, E., & Kramer, A. F. (2015a). Physical activity is linked to greater moment-to-moment variability in spontaneous brain activity in older adults. PloS One, 10, e0134819. https://doi.org/10.1371/journal.pone.0134819

Burzynska, A. Z., Wong, C. N., Voss, M. W., Cooke, G. E., McAuley, E., & Kramer, A. F. (2015b). White matter integrity supports BOLD signal variability and cognitive performance in the aging human brain. PloS One, 10, e0120315. https://doi.org/10.1371/journal.pone.0120315

Calamia, M., Markon, K., & Tranel, D. (2013). The robust reliability of neuropsychological measures: meta-analyses of test–retest correlations. The Clinical Neuropsychologist, 27, 1077–1105. https://doi.org/10.1080/13854046.2013.809795

Clark, S. V., Semmel, E. S., Aleksonis, H. A., Steinberg, S. N., & King, T. Z. (2021). Cerebellar-subcortical-cortical systems as modulators of cognitive functions. Neuropsychology Review, 31, 422–446. https://doi.org/10.1007/s11065-020-09465-1

Article  PubMed  Google Scholar 

Cools, R. (2007). Dopaminergic modulation of flexible cognitive control: the role of the striatum. In Bunge, S.A. & Wallis, J.D. (Eds.) Neuroscience of rule-guided behavior. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195314274.003.0017

Cools, R., & D’Esposito, M. (2010). Dopaminergic modulation of flexible cognitive control in humans. In Iversen, L., Iversen, S., Dunnett, S., & Bjorklund, A. (Eds.) Dopamine handbook. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195373035.003.0017

Cools, R., & D’Esposito, M. (2011). Inverted-U-shaped dopamine actions on human working memory and cognitive control. Biological Psychiatry, 69, e113-125. https://doi.org/10.1016/j.biopsych.2011.03.028

Article  CAS  PubMed  Google Scholar 

Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33, 1914–1928. https://doi.org/10.1002/hbm.21333

Day, T. K. M., Madhyastha, T. M., Lee, A., Zabetian, C. P., Montine, T. J., & Grabowski, T. J. (2019). Effect of dopaminergic medications on blood oxygen level-dependent variability and functional connectivity in Parkinson’s disease and healthy aging. Brain Connectivity, 9, 554–565. https://doi.org/10.1089/brain.2019.0677

Article  PubMed  PubMed Central  Google Scholar 

Depue, B. E., Burgess, G. C., Wilcutt, E. G., Bidwell, L. C., Ruzic, L., & Banich, M. T. (2010). Symptom-correlated brain regions in young adults with combined-type ADHD: Their organization, variability, and relation to behavioral performance. Psychiatry Research, 182, 96–102. https://doi.org/10.1016/j.pscychresns.2009.11.011

Article  PubMed  PubMed Central  Google Scholar 

Diamond, A. (2012). Executive functions. Annual Review of Psychology, 64, 135–168. https://doi.org/10.1146/annurev-psych-113011-143750

Article  PubMed  PubMed Central  Google Scholar 

Dotson, V. M., & Duarte, A. (2019). The importance of diversity in cognitive neuroscience. Annals of the New York Academy of Sciences, 1464, 181–191. https://doi.org/10.1111/nyas.14268

Article  PubMed  Google Scholar 

Easson, A. K., & McIntosh, A. R. (2019). BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder. Developmental Cognitive Neuroscience, 36, 100630. https://doi.org/10.1016/j.dcn.2019.100630

Fox, M., & King, T. Z. (2018). Functional connectivity in adult brain tumor patients: A systematic review. Brain Connectivity, 8, 381–397. https://doi.org/10.1089/brain.2018.0623

Article  Google Scholar 

Galambos, A., Szabo, E., Nagy, Z., Edes, A. E., Kocsel, N., Juhasz, G., & Kokonyei, G. (2019). A systematic review of structural and functional MRI studies on pain catastrophizing. Journal of Pain Research, 12, 1155–1178. https://doi.org/10.2147/JPR.S192246

Article  PubMed  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. The Journal of Neuroscience, 30, 4914–4921. https://doi.org/10.1523/JNEUROSCI.5166-09.2010

Article  CAS  PubMed  PubMed Central  Google Scholar 

Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. The Journal of Neuroscience, 31, 4496–4503. https://doi.org/10.1523/JNEUROSCI.5641-10.2011

Article  CAS  PubMed  PubMed Central  Google Scholar 

Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W., Lindenberger, U., McIntosh, A. R., & Grady, C. L. (2013a). Moment-to-moment brain signal variability: A next frontier in human brain mapping? Neuroscience and Biobehavioral Reviews, 37, 610–624. https://doi.org/10.1016/j.neubiorev.2013.02.015

Article  PubMed  PubMed Central  Google Scholar 

Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2013b). The modulation of BOLD signal variability between cognitive states varies by age and cognitive performance. Cerebral Cortex, 23, 684–693. https://doi.org/10.1093/cercor/bhs055

Article  PubMed  Google Scholar 

Garrett, D. D., McIntosh, A. R., & Grady, C. L. (2014). Brain signal variability is parametrically modifiable. Cerebral Cortex, 24, 2931–2940. https://doi.org/10.1093/cercor/bht150

Article  PubMed  Google Scholar 

Garrett, D. D., Nagel, I. E., Preuschhof, C., Burzynska, A. Z., Marchner, J., Wiegert, S., Jungehulsing, G. J., Nyberg, L., Villringer, A., Li, S.-C., Heekeren, H. R., Backman, L., & Lindenberger, U. (2015). Amphetamine modulates brain signal variability and working memory in younger and older adults. PNAS, 112, 7593–7598. https://doi.org/10.1073/pnas.1504090112

Article  CAS  PubMed  PubMed Central  Google Scholar 

Garrett, D. D., Lindenberger, U., Hoge, R. D., & Gauthier, C. J. (2017). Age differences in brain signal variability are robust to multiple vascular controls. Scientific Reports, 7, 10149. https://doi.org/10.1038/s41598-017-09752-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Garrett, D. D., Epp, S. M., Perry, A., & Lindenberger, U. (2018). Local temporal variability reflects functional integration in the human brain. NeuroImage, 183, 776–787. https://doi.org/10.1016/j.neuroimage.2018.08.019

Article  PubMed  Google Scholar 

Garrett, D. D., Epp, S. M., Kleemeyer, M., Lindenberger, U., & Polk, T. A. (2020). Higher performers upregulate brain signal variability in response to more feature-rich visual input. NeuroImage, 217, 116836. https://doi.org/10.1016/j.neuroimage.2020.116836

Garrett, D. D., Skowron, A., Wiegert, S., Adolf, J., Dahle, C. L., Lindenberger, U., & Raz, N. (2021). Lost dynamics and the dynamics of Loss: Longitudinal compression of brain signal variability is coupled with declines in functional integration and cognitive performance. Cerebral Cortex. https://doi.org/10.1093/cercor/bhab154

Article  PubMed  PubMed Central  Google Scholar 

Gaut, G., Turner, B., Lu, Z-L., Li, X., Cunningham, W.A., & Steyvers, M. (2019a). Predicting task and subject differences with functional connectivity and blood-oxygen-level-dependent variability. Brain Connectivity, 9. https://doi.org/10.1089/brain.2018.0632

Gaut, G., Li, X., Lu, Z-L., & Steyvers, M. (2019b). Experimental design modulates variance in BOLD activation: the variance design general linear model. Human Brain Mapping, 40, 3918–3929. https://doi.org/10.1002/hbm.24677

Gimenez, M., Guinea-Izquierdo, A., Villalta-Gil, V., Martinez-Zalacain, I., Segalas, C., Subira, M., Real, E., Pujol, J., Harrison, B. J., Haro, J. M., Sato, J. R., Hoexter, M. Q., Cardoner, N., Alonso, P., Menchon, J. M., & Soriano-Mas, C. (2017). Brain alterations in low-frequency fluctuations across multiple bands in obsessive compulsive disorder. Brain Imaging and Behavior, 11, 1690–1706. https://doi.org/10.1007/s11682-016-9601-y

Article  Google Scholar 

Good, T. J., Villafuerte, J., Ryan, J. D., Grady, C. L., & Barense, M. D. (2020). Resting state BOLD variability of the posterior medial temporal lobe correlates with cognitive performance in older adults with and without risk for cognitive decline. eNeuro, 7. https://doi.org/10.1523/ENEURO.0290-19.2020

Grady, C. L., & Garrett, D. D. (2014). Understanding variability in the BOLD signal and why it matters for aging. Brain Imaging & Behavior, 8, 274–283. https://doi.org/10.1007/s11682-013-9253-0

Article  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

Article  PubMed  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, 2074–2083. https://doi.org/10.1093/cercor/bhv029

Article  PubMed  Google Scholar 

Halliday, D. W. R., Mulligan, B. P., Garrett, D. D., Schmidt, S., Hundza, S. R., Garcia-Barrera, M. A., Stawski, R. S., & MacDonald, S. W. S. (2018). Mean and variability in functional brain activations differentially predict executive function in older adults: an investigation employing functional near-infrared spectroscopy. Neurophotonics, 5, 011013. https://doi.org/10.1117/1.NPh.5.1.011013

Heisz, J. J., Shedden, J. M., & McIntosh, A. R. (2012). Relating brain signal variability to knowledge representation. NeuroImage, 63, 1384–1392. https://doi.org/10.1016/j.neuroimage.2012.08.018

Article  PubMed  Google Scholar 

Ito, T., Brincat, S. L., Siegel, M., Mill, R. D., He, B. J., Miller, E. K., Rotstein, H. G., & Cole, M. W. (2020). Task-evoked activity quenches neural correlations and variability across cortical areas. PloS Computational Biology, 16, e1007983. https://doi.org/10.1371/journal.pcbi.1007983

Jacola, L. M., Willard, V. W., Ashford, J. M., Ogg, R. J., Scoggins, M. A., Jones, M. M., Wu, S., & Conklin, H. M. (2014). Clinical utility of the n-back task in functional neuroimaging studies of working memory. Journal of Clinical and Experimental Neuropsychology, 36, 875–886. https://doi.org/10.1080/13803395.2014.953039

Article  PubMed Central  Google Scholar 

Kannurpatti, S. S., Motes, M. A., Rypma, B., & Biswal, B. B. (2011a). Increasing measurement accuracy of age-related BOLD signal change: Minimizing vascular contributions by resting-state-fluctuation-of-amplitude scaling. Human Brain Mapping, 32, 1125–1140. https://doi.org/10.1002/hbm.21097

Article  PubMed  Google Scholar 

Kannurpatti, S. S., Motes, M. A., Rypma, B., & Biswal, B. B. (2011b). Non-neural BOLD variability in block and event-related paradigms. Magnetic Resonance Imaging, 29, 140–146. https://doi.org/10.1016/j.mri.2010.07.006

Article  PubMed  Google Scholar