6.3 Methodology considerations
6.3.3 Limitations and Advantages
The number of participants in the present studies was relatively small, which may have decreased the statistical power of the analyses. Future developmental studies of brain activity and functional network connectivity will benefit from increasing the subject number to make strong conclusions. Another limitation is that although the results revealed task-specific relationships in the FC between the PFC and visual areas (Study I and II), the analysis method does not allow assessment of directionality of connectivity. In future studies, the effective connectivity for the directionality of the connections between the PFC and visual areas should be studied. In addition, due to the limitation of the block design, the WM was studied as a whole process in the present studies. In the future, it would be good to apply, for example, an event-related fMRI design that allows investigation of the developmental changes of brain activity and network connectivity in the separate processes of the WM (i.e., encoding, memory maintenance, and response period) in children.
The strength of the present studies may reside in the task paradigm that was designed to also be suitable for children and was based on information obtained from earlier studies in children and adults performed in our group (Vuontela et al. 2003, 2009).
This was reflected in the relatively good performance of the tasks – both groups had over 80% mean hits percentage and less than 5% false alarms. Another advantage of the present study is that the fMRI data were recorded during both the resting state and tasks in children and adults, which allowed examination of changes in the FC of the networks between different states in the two age groups. Data-driven methods are currently popular in studies of human resting state networks, but are not commonplace in task-related functional imaging. Study III shows that data-driven approaches are also applicable to task-related functional imaging data analyses. However, due to the scanning order of the fMRI data where the task-related fMRI was performed before the resting state fMRI, it is possible that the immediately preceding WM task performance could have affected the spontaneous FC of the brain networks. These possible influences are still unsettled (Fox and Raichle 2007; Hasson et al. 2009; Pyka et al. 2009; Rzucidlo et al. 2013), and further studies are needed to explore whether or how the preceding experience could affect the intrinsic brain networks.
7 Conclusion and suggestions for future work
The first study of the thesis found that the neural mechanisms related to face processing need a longer time to achieve the adult level compared to those related to the processing of scenes and space. One possible reason is that during childhood, children gain plenty of experience in spatial compared to facial information, which may promote the faster maturational rate of the scene compared to face processing.
The second study of the thesis compared the activity and FC of two cortical regions, the PPA and RSC that are involved in the processing of scene- and spatial information.
The study found that these two brain regions and related networks involved in navigation have a different maturational pace over the course of development. The results support the interactive specialization model of functional brain development, suggesting that the activity and FC of brain regions become progressively more selective to particular task demands during development.
The third study of the thesis provided preliminary evidence about the FC of cognitive brain networks during tasks performance compared to the resting state. The study found that 7–11-year-old children have already established adult-like resting state networks, however, the FC strength differs between children and adults, especially during task performance. On the other hand, the differences between the two age groups in their ability to perform the attention/memory tasks may be associated with the observed group differences in the task-related FC of the networks, especially regarding the DMN and FPN, which was also reflected in the different functional recruitment of brain networks during task performance in children and adults. The results of the brain network FC in the third study conform to the whole brain activation results of the first two studies of this thesis which showed differential activation patterns between the two groups in the core regions of these networks.
Taken together, Studies I and II of this thesis found weaker or otherwise immature top-down modulation of the face processing-related visual association cortices that could partially be explained by the observed weaker FC between the PFC and the visual association cortex in the typically developing 7–11-year-old children compared to the young adults. Moreover, there were age-dependent differences in the recruitment of the PFC during visual WM tasks. These age-dependent differences between the two groups are in line with the observed differences in the performance of the WM tasks that was poorer in children than young adults. Study III showed that the 7-11-year-old children have already established an adult-like pattern of resting state networks, but especially during task performance, the FC within and between the ICNs differed from that in young adults.
The group differences observed in the brain activation and FC are likely partly related to the morphological developmental state of the grey- and white matter in the 7–11-year-old children (i.e., the ongoing synaptic pruning and myelination of axons that continue up to young adulthood) (Sowell et al. 2001; Supekar et al. 2010; Bazargani et al. 2014). The findings of this thesis conform to the suggestion that during development, the function of brain regions, especially the PFC, and the FC of brain networks, undergo dynamic changes, and that the same cognitive function may rely on different brain networks at different ages (Johnson 2011).
In the future, it would be interesting to investigate how different tasks and task difficulty levels influence the top-down regulation of the brain activity and network FC, and what are the neural signatures underlying successful performance in different age groups.
It will also be interesting to understand whether there are cultural differences in the development of information processing in cognitive tasks, for example, whether children with different cultural backgrounds have a similar maturational rate of face and scene information processing.
Acknowledgements
This doctoral thesis was carried out in the Neuroscience Unit of the Department of Physiology, Faculty of Medicine, University of Helsinki and in the Department of Neuroscience and Biomedical Engineering, and in the Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University School of Science. I want to thank the heads of the department for providing the facilities, and the support from the Academy of Finland, the Finnish Graduate School of Neuroscience, the Doctoral Program Brain and Mind, the National Science Foundation of China, Helsinki University Central Hospital Research Funds (EVO), the Päivikki and Sakari Sohlberg Foundation, the Sigrid Juselius Foundation, the Finnish Cultural Foundation and the University of Helsinki.
I wish to express my deepest gratitude and respect to everyone who supported and encouraged me throughout the years. I’m glad we made the wonderful journey together.
I would especially like to thank my supervisor Synnöve Carlson for her patience, insightful advice, and constructive feedback during the entire period of my research work. I am very grateful for her encouragement of my independent thinking and letting me make mistakes.
I am indebted to my co-supervisor Prof. Yuanye Ma for introducing me to neuroscience and believing in me. I am most thankful to Prof. Antti Pertovaara for his inspiring passion for science and good sense of humor. I especially thank Synnöve and Antti’s concerns regarding our daily life in Finland, and making us not feel like foreigners.
I remain grateful to all of my co-authors, Prof. Eeva T. Aronen, Oili Salonen, Tuija Fontell, Petri Savolainen, Tiina Liiri, and Matti Ahlström. Particular thanks to Virve Vuontela, Maksym Tokariev and Hai Lin for your kind help on the project and lively discussions about our manuscripts. I further wish to thank my colleagues: Robert Boldt, Zuyue Chen, Juha Gogulski, Masamichi Hayashi, Moona, Aino, Rasmus, Mikko, Hong Wei, Hanna, Boriss and Nora, for your inspiring discussions and cheerful chatting. The
help of Anne Simola and Mari Kaarni in secretarial assistance, and the help and encouragement of Katri Wegelius during my time in the FGSN and B&M program are appreciated. I also thank all of the volunteer subjects who participated in the thesis.
Many thanks go to all my dear friends in Finland for all of the parties and activities we enjoyed together, only with your friendship could I survive the cold winters and the
loneliness of this journey. Thanks to all my friends in Biomedicum for the fun and delightful chats we had together during our daily lunch time. Special thanks go to Tang Yurui, Sun Xiaoyu, Wang Wei, Sun Xuemei, Ma Li, Fang Shentong, Huang Danmei, Cheng Lu, Chen Zuyue, and Liu Liwei for helping and supporting me during some dark times on this journey. I also wish to thank Wei Hong, Wang Bei, Wei Gonghong, Li Songping, Cheng Fang and Jin Congyu for your great advice and support in my career path and life difficulties.
Finally, my deepest and most sincere gratitude goes to the most important people in my life. I thank my parents for their constant care, selfless love and unconditional support throughout my life, without your encouragement I would not have had the courage to pursue my dreams. I hope my effort will make you proud. I also thank my brother and his family for their help and support. In the end, I am immensely grateful to my beloved Xing. It is your understanding, love, patience, and faith that carried me through the whole journey.
Thank you for bringing me happiness and security, and for tolerating my occasional temper.
References
Andersson JLR, Jenkinson M, Smith S. 2007. Non-linear registration, aka spatial normalisation.
Tech. Rep. TR07JA2, Oxford Centre for Functional MRI of the Brain.
Adleman NE, Menon V, Blasey CM, White CD, Warsofsky IS, Glover GH, Reiss AL. 2002. A developmental fMRI study of the Stroop color-word task. Neuroimage 16(1):61-75.
Amodio DM, Frith CD. 2006. Meeting of minds: the medial frontal cortex and social cognition. Nat Rev Neurosci 7(4):268-77.
Anand A, Li Y, Wang Y, Lowe MJ, Dzemidzic M. 2009. Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression. Psychiatry Res 171(3):189-98.
Anand A, Li Y, Wang Y, Wu J, Gao S, Bukhari L, Mathews VP, Kalnin A, Lowe MJ. 2005. Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biol Psychiatry 57(10):1079-88.
Anticevic A, Cole MW, Murray JD, Corlett PR, Wang XJ, Krystal JH. 2012. The role of default network deactivation in cognition and disease. Trends Cogn Sci 16(12):584-92.
Avidan G, Tanzer M, Hadj-Bouziane F, Liu N, Ungerleider LG, Behrmann M. 2014. Selective dissociation between core and extended regions of the face processing network in congenital prosopagnosia. Cereb Cortex 24(6):1565-78.
Baddeley AD. 2007. Working memory, thought, and action. Oxford: Oxford University Press.
Barnea-Goraly N, Menon V, Eckert M, Tamm L, Bammer R, Karchemskiy A, Dant CC, Reiss AL.
2005. White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study. Cereb Cortex 15(12):1848-54.
Bazargani N, Hillebrandt H, Christoff K, Dumontheil I. 2014. Developmental changes in effective connectivity associated with relational reasoning. Hum Brain Mapp 35:3262-3276.
Beck DM, Kastner S. 2009. Top-down and bottom-up mechanisms in biasing competition in the human brain. Vision Res 49(10):1154-65.
Beckmann CF, DeLuca M, Devlin JT, Smith SM. 2005. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci
360(1457):1001-13.
Beckmann CF, Jenkinson M, Smith SM. 2003. General multilevel linear modeling for group analysis in FMRI. Neuroimage 20(2):1052-63.
Beckmann CF, Smith SM. 2004. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23(2):137-52.
Beckmann CF, Smith SM. 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25(1):294-311.
Behzadi Y, Restom K, Liau J, Liu TT. 2007. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37(1):90-101.
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.
Bentin S, Allison T, Puce A, Perez E, McCarthy G. 1996. Electrophysiological Studies of Face Perception in Humans. J Cogn Neurosci 8(6):551-565.
Binder JR. 2012. Task-induced deactivation and the "resting" state. Neuroimage 62(2):1086-91.
Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537-41.
Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld RW, Theberge J, Schaefer B, Williamson P. 2007. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 33(4):1004-12.
Bodamer J. 1947. On a Frontal Brain Syndrome Following Electro-Cramp-Treatment. Nervenarzt 18(9):385-393.
Braver TS, Ruge H. 2006. Functional neuroimaging of executive functions. In R. Cabeza & A.
Kingstone (Eds.), Handbook of functional neuroimaging of cognition (pp. 307–348).
Cambridge: The MIT Press.
Bressler SL, Menon V. 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci 14(6):277-90.
Buckner RL, Andrews-Hanna JR, Schacter DL. 2008. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1-38.
Bunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JD. 2002. Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron 33(2):301-11.
Burgund ED, Kang HC, Kelly JE, Buckner RL, Snyder AZ, Petersen SE, Schlaggar BL. 2002. The feasibility of a common stereotactic space for children and adults in fMRI studies of development. Neuroimage 17(1):184-200.
Calhoun VD, Eichele T, Pearlson G. 2009. Functional brain networks in schizophrenia: a review.
Front Hum Neurosci 3:17.
Casey BJ, Cohen JD, Jezzard P, Turner R, Noll DC, Trainor RJ, Giedd J, Kaysen D, Hertz-Pannier L, Rapoport JL. 1995. Activation of prefrontal cortex in children during a nonspatial working memory task with functional MRI. Neuroimage 2(3):221-9.
Casey BJ, Trainor RJ, Orendi JL, Schubert AB, Nystrom LE, Giedd JN, Castellanos FX, Haxby JV, Noll DC, Cohen JD et al. . 1997. A Developmental Functional MRI Study of Prefrontal Activation during Performance of a Go-No-Go Task. J Cogn Neurosci 9(6):835-47.
Caviness, V.S., Kennedy, D.N., Richelme, C., Rademacher, J., Filipek, P.A. (1996). The Human Brain Age 7-11 Years: A Volumetric Analysis Based on Magnetic Resonance Images.
Cerebral Cortex 6:726-736.
Chai XJ, Castanon AN, Ongur D, Whitfield-Gabrieli S. 2012. Anticorrelations in resting state networks without global signal regression. Neuroimage 59(2):1420-8.
Chai XJ, Ofen N, Jacobs LF, Gabrieli JD. 2010. Scene complexity: influence on perception, memory, and development in the medial temporal lobe. Front Hum Neurosci 4:21.
Cherkassky VL, Kana RK, Keller TA, Just MA. 2006. Functional connectivity in a baseline resting-state network in autism. Neuroreport 17(16):1687-90.
Ciesielski KT, Lesnik PG, Savoy RL, Grant EP, Ahlfors SP. 2006. Developmental neural networks
Cohen Kadosh K, Cohen Kadosh R, Dick F, Johnson MH. 2011. Developmental changes in effective connectivity in the emerging core face network. Cereb Cortex 21(6):1389-94.
Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. 2014. Intrinsic and task-evoked network architectures of the human brain. Neuron 83(1):238-51.
Cook RD. 1977. Detection of Influential Observation in Linear-Regression. Technometrics 19(1):15-18.
Corbetta M, Kincade JM, Shulman GL. 2002. Neural systems for visual orienting and their relationships to spatial working memory. J Cogn Neurosci 14(3):508-23.
Corbetta M, Miezin FM, Dobmeyer S, Shulman GL, Petersen SE. 1990. Attentional modulation of neural processing of shape, color, and velocity in humans. Science 248(4962):1556-9.
Corbetta M, Shulman GL. 2002. Control of goal-directed and stimulus-driven attention in the brain.
Nat Rev Neurosci 3(3):201-15.
Curtis CE, D'Esposito M. 2003. Persistent activity in the prefrontal cortex during working memory.
Trends Cogn Sci 7(9):415-423.
D'Esposito M, Postle BR. 2015. The cognitive neuroscience of working memory. Annu Rev Psychol 66:115-42.
Damadian R, Goldsmith M, Minkoff L. 1977. NMR in cancer: XVI. FONAR image of the live human body. Physiol Chem Phys 9(1):97-100, 108.
Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF. 2006.
Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103(37):13848-53.
Daselaar SM, Prince SE, Cabeza R. 2004. When less means more: deactivations during encoding that predict subsequent memory. Neuroimage 23(3):921-7.
Davidson MC, Amso D, Anderson LC, Diamond A. 2006. Development of cognitive control and executive functions from 4 to 13 years: evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia 44(11):2037-78.
De Renzi E, Perani D, Carlesimo GA, Silveri MC, Fazio F. 1994. Prosopagnosia can be associated with damage confined to the right hemisphere--an MRI and PET study and a review of the literature. Neuropsychologia 32(8):893-902.
de Reus MA, van den Heuvel MP. 2013. The parcellation-based connectome: limitations and extensions. Neuroimage 80:397-404.
Dennis EL, Thompson PM. 2014. Reprint of: Mapping connectivity in the developing brain. Int J Dev Neurosci 32:41-57.
Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. 2008. A dual-networks architecture of top-down control. Trends Cogn Sci 12(3):99-105.
Dosenbach NU, Visscher KM, Palmer ED, Miezin FM, Wenger KK, Kang HC, Burgund ED, Grimes AL, Schlaggar BL, Petersen SE. 2006. A core system for the implementation of task sets.
Neuron 50(5):799-812.
Douw L, Wakeman DG, Tanaka N, Liu H, Stufflebeam SM. 2016. State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility. Neuroscience 339:12-21.
Driver J, Frackowiak RS. 2001. Neurobiological measures of human selective attention.
Neuropsychologia 39(12):1257-62.
Duncan J, Emslie H, Williams P, Johnson R, Freer C. 1996. Intelligence and the frontal lobe: the organization of goal-directed behavior. Cogn Psychol 30(3):257-303.
Elton A, Gao W. 2015. Task-positive Functional Connectivity of the Default Mode Network Transcends Task Domain. J Cogn Neurosci 27(12):2369-81.
Engel AK, Fries P, Singer W. 2001. Dynamic predictions: oscillations and synchrony in top-down processing. Nat Rev Neurosci 2(10):704-16.
Epstein R, Deyoe EA, Press DZ, Rosen AC, Kanwisher N. 2001. Neuropsychological evidence for a topographical learning mechanism in parahippocampal cortex. Cogn Neuropsychol 18(6):481-508.
Epstein R, Kanwisher N. 1998. A cortical representation of the local visual environment. Nature
Epstein RA. 2008. Parahippocampal and retrosplenial contributions to human spatial navigation.
Trends Cogn Sci 12(10):388-96.
Epstein RA, Parker WE, Feiler AM. 2007. Where am I now? Distinct roles for parahippocampal and retrosplenial cortices in place recognition. J Neurosci 27(23):6141-9.
Epstein RA, Vass LK. 2014. Neural systems for landmark-based wayfinding in humans. Philos Trans R Soc Lond B Biol Sci 369(1635):20120533.
Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, Barch DM, Raichle ME, Petersen SE, Schlaggar BL. 2008. The maturing architecture of the brain's default network. Proc Natl Acad Sci U S A 105(10):4028-32.
Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM, Schlaggar BL, Petersen SE. 2009. Functional brain networks develop from a "local to distributed" organization.
PLoS Comput Biol 5(5):e1000381.
Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, Barch DM, Raichle ME, Petersen SE, Schlaggar BL. 2007. Development of distinct control networks through segregation and integration. Proc Natl Acad Sci U S A 104(33):13507-12.
Filippini N, Rao A, Wetten S, Gibson RA, Borrie M, Guzman D, Kertesz A, Loy-English I, Williams J, Nichols T et al. . 2009. Anatomically-distinct genetic associations of APOE epsilon4 allele load with regional cortical atrophy in Alzheimer's disease. Neuroimage 44(3):724-8.
Fougnie D. 2008. Chapter 1: The relationship between attention and working memory. In: New Research on Short-Term Memory. Nova Science Publishers, Inc.
Forsyth JK, McEwen SC, Gee DG, Bearden CE, Addington J, Goodyear B, Cadenhead KS, Mirzakhanian H, Cornblatt BA, Olvet DM et al. . 2014. Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: analysis from the North American Prodrome Longitudinal Study. Neuroimage 97:41-52.
Fox MD, Raichle ME. 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700-11.
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102(27):9673-8.
Freiwald W, Duchaine B, Yovel G. 2016. Face Processing Systems: From Neurons to Real-World Social Perception. Annu Rev Neurosci 39:325-46.
Friston KJ, Rotshtein P, Geng JJ, Sterzer P, Henson RN. 2006. A critique of functional localisers.
Friston KJ, Rotshtein P, Geng JJ, Sterzer P, Henson RN. 2006. A critique of functional localisers.