The effects of psychiatric history and age on self-regulation of the default mode network

Abstract

Real-time neurofeedback enables human subjects to learn to regulate their brain activity, effecting behavioral changes and improvements of psychiatric symptomatology. Neurofeedback up-regulation and downregulation have been assumed to share common neural correlates. Neuropsychiatric pathology and aging incur suboptimal functioning of the default mode network. Despite the exponential increase in real-time neuroimaging studies, the effects of aging, pathology and the direction of regulation on neurofeedback performance remain largelyunknown. Using open-access analyses and real-time fMRI data shared through the Rockland Sample Real-Time Neurofeedback project (N=136), we first modeled neurofeedback performance and learning in a group of subjects with psychiatric history (n a =74) and a healthy control group (n b =62). Subsequently, we examined the relationship between up-regulation and down-regulation learning, the relationship between age and neurofeedback performance in each group and differences in neurofeedback performance between the two groups. Results show that in an initial session of default mode network neurofeedback with real-time fMRI, up-regulation and down-regulation learning scores are negatively correlated. Moreover, age correlates negatively with default mode network neurofeedback performance, only in absence of psychiatric history. Finally, adults with psychiatric history outperform healthy controls in default mode network up-regulation. Interestingly, the performance difference is related to no upregulation learning in controls. attention) is unknown. Third, key performance constraints, such as the effect of age, in relation to self-regulation of brain function and pathology, have not been investigated.Here we address these three open issues regarding self-regulation of brain function, using the largest publicly available rt-fMRI NF repository, comprising data from healthy participants and psychiatric patients, during bidirectional self-regulation of the default mode network (DMN). The DMN is a large-scale cerebral network (Raichle et al., 2001) associated with a variety of brain functions, including perception (Kelly et al., 2008), attention (Weissman et al., 2006 and working memory (Mayer et al. 2010), which has become highly relevant for clinical applications (Zhang and Raichle, 2010;Brakowski et al., 2017;Mulders et al., 2015;Hamilton et al., 2015).Similarly to psychiatric pathology, aging also incurs suboptimal functioning of the human brain's DMN (Whitfield-Gabrieli and Ford, 2012;Damoiseaux et al., 2008). Recent studies have demonstrated that DMN activity can be

A systematic review of fMRI neurofeedback reporting and effects in clinical populations

“…Small sample sizes are one of the primary reasons for underpowered studies ( Algermissen and Mehler, 2018 ) and yet only three studies ( Jaeckle et al, 2019 , Mehler et al, 2018 , Skouras and Scharnowski, 2019 ) justified their sample size with power calculation (see Table 2 ). Although two studies ( Papoutsi et al, 2018a , Subramanian et al, 2016 ) estimated the number of participants for future studies based on their early-phase results, this procedure can be problematic because it can lead to exaggerated estimates of effect sizes and thus underpowered efficacy studies.…”

“…In publications with multiple control groups, an average control group size was calculated first. The two studies using a large dataset from a repository ( McDonald et al, 2017 , Skouras and Scharnowski, 2019 ) were excluded from the count as outliers. Finally, if no control group was included in the study, the study was omitted from the control group count in order to provide a realistic average control group size.…”

“…The number has doubled over the past 10 years. Note that two studies were excluded from this calculation due to much larger sample sizes ( McDonald et al, 2017 , Skouras and Scharnowski, 2019 ). …”

“… – – – – Mehler et al, 2018 @ Depression 16 Mainly anterior brain areas such as insula and striatum Sug Yes ✗ * Clin Diff str ✓ ↑ Yes No Sess Clin ↑ – 18w (clin ↑) NCT01544205 Jaeckle et al, 2019 ^@$ Depression 19 rSATL and pSCC No No * Clin Diff str ✓ ↓ Yes – Yes Clin ↑ No – ISRCTN10526888 Zahn et al, 2019 $ Depression 2 14 aSCC and aSTC No No ** Clin Diff con ✗ ↑ Yes Yes Pre-post Clin – – – NCT01920490 Rance et al, 2018 Misc 10 (A), 20 (TS) 5 OFC (A), SMA (TS) Yes – ** Clin Yoked – ↓↑ same – – – Clin ↑ – 2, 4, 6, 8w (clin ↑) NCT02206945 NCT01702077 McDonald et al, 2017 Misc 76 6 DMN Yes No – ↓↑ same Yes – – (n.a.) – – – – Skouras and Scharnowski, 2019 @ Misc 74 DMN Yes No Healthy NFB ✓ …”

Neuromodulation of brain activation associated with addiction: A review of real-time fMRI neurofeedback studies

“…Others used much wider age ranges of participants ( Hanlon et al, 2013 , Li et al, 2013 ), with some studies including participants ages 18–50 years old ( Hamilton et al, 2011 ) and 18 to 60 years old ( Canterberry et al, 2013 , Hartwell et al, 2016 , Karch et al, 2015 ). Other work beyond the focus of our review has indeed identified age differences in neurofeedback performance, such as a negative correlation between age and default mode network neurofeedback performance in a non-pathological sample of adults aged 20–45 years old ( Skouras & Scharnowski, 2019 ).…”