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    • br Summary We have previously

    2018-10-29


    Summary We have previously investigated how typical neurodevelopment (Harris et al., 2011; see also Church et al., 2012) and autistic neuropathology (Reynell and Harris, 2013) involve changes to the relationship between neural activity, blood flow and synthase energy use, which can alter the way that BOLD signal differences in these populations, compared to control groups, should be interpreted. Here, we have extended this investigation to consider the effects that pharmacological antidepressant treatment – particularly during the sensitive neurodevelopmental period of adolescence – have on the physiological basis of the BOLD signal. Fluoxetine is used to treat depression because it alters the brain in a way that significantly improves the symptoms of depression for a significant proportion of the people who take it (Hetrick et al., 2010). Although not fully understood, the primary mechanism of action is thought to be through inhibition of serotonin transporters (see Section 3). However, in this paper we have described many other pharmacological actions of fluoxetine that would not only alter neuronal activity but may also alter the signaling pathways responsible for producing blood flow responses, independent of neuronal activity, as well as brain energy use. Despite the significant amount of research into the short- and long-term effects of fluoxetine treatment on various components of the signalling pathways between neurons, glial cells, blood vessels and cellular metabolism, we are far from having a complete picture of how the drug influences the BOLD signal. Nevertheless, being aware of these possible changes in the relationship between neuronal activity and the BOLD response in different groups of interest can help researchers plan their studies in a way that will minimise the risk of incorrectly interpreting fMRI data. For instance, experiments such as those carried out by Tao et al. (2012) minimise the additional complications of comparing across populations by allowing for within-participant comparisons, before and after drug treatment. Most importantly, though, awareness of the possible non-neuronal sources of a BOLD signal difference between two groups can ensure that these differences are not incorrectly attributed to changes in neuronal activity, or vice versa for a BOLD signal similarity.
    Acknowledgements
    Introduction
    Materials and methods
    Results
    Discussion Head movement during fMRI acquisition has emerged as a critical methodological issue, not only because of its deleterious effects on data quality, but also because methods that exclude data from participants with excessive head motion have the potential to alter the characteristics of study samples and bias results. These issues may be especially pronounced in developmental samples, as evidenced by greater mean movement among children and adolescents relative to adults (Siegel et al., 2014). When motion-confounded signals are subject to group comparison, it is challenging to tease apart the effects of group differences in motion from group differences in the task or cognitive processes under study. Motivated by recent findings that adults’ head movement is highly traitlike (e.g., Van Dijk et al., 2012; Zeng et al., 2014), we examined children’s in-scanner movement and its relationship to person- and scan-related characteristics in two developmental samples.
    Acknowledgements This research project was supported by National Institutes of Health grants P50 HD052117 (JAC and JJ), R21HD081437 (JAC and ETD), and R01HD083613 (ETD). The Population Research Center at The University of Texas at Austin is supported by National Institutes of Health grant R24HD042849. Additional support came from the University of Texas Imaging Research Center Pilot Grant 20141031a. L. E. Engelhardt was supported by a National Science Foundation Graduate Research Fellowship. We wish to acknowledge the entire Texas Learning Disabilities Research Center team, especially Jack Fletcher and Sharon Vaughn. We also wish to acknowledge the Core for Advanced MRI (CAMRI) at Baylor College of Medicine. Finally, we thank our participating families for their time and effort.