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Individual BF 10 and percent error for each stimulation condition (as compared to sham) for average task-unrelated thought across all experimental trials for Filmer et al. 4 .
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Blinding in non-invasive brain stimulation research is a topic of intense debate, especially regarding the efficacy of sham-controlled methods for transcranial direct current stimulation (tDCS). A common approach to assess blinding success is the inclusion of correct guess rate. However, this method cannot provide insight into the effect of unblind...
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... this finding seemingly calls into question the conclusions of Filmer et al. 4 and, more broadly, those of any study with purely a sham control. However, a key limitation of the Fassi and Kadosh 35 approach was that they examined all conditions on the Filmer et al. 4 study in a single analysis, whereas only effects were observed for cathodal 2.0 mA stimulation as compared to sham comparison with moderate evidence (see Table 1 for specific BF 10 values for the experimental conditions from Filmer et al. 4 ). ...Citations
... To further understand the effect of subjective belief on the frequency of the dynamic thought types, we implemented two Bayesian ANOVAs for each thought probe, employing objective intervention and subjective intervention as between-subject factors and the outcome measure of the average ratings for each thought probe (Gordon et al., 2022). The first set of ANOVAs compared all stimulation groups for each region (see Table S2); however, the second set of ANOVAs excluded conditions and probes which showed limited evidence for differences between stimulation and sham according to the modelling analyses, thus only evaluating conditions that demonstrate meaningful results (see Table 2). ...
Mind wandering is a common phenomenon in our daily lives and can have both an adaptive and detrimental impact. Recently, a dynamic framework has been proposed to characterise the heterogeneity of internal thoughts, suggesting there are three distinct thought types which can change over time - freely moving, deliberately constrained, and automatically constrained (thoughts). There is very little evidence on how different types of dynamic thought map onto the brain. Previous research has applied non-invasive transcranial direct current stimulation (tDCS) to causally implicate the prefrontal cortex and inferior parietal lobule in general mind wandering. However, a more recently developed and nuanced technique, high-definition tDCS (HD-tDCS), delivers more focal stimulation able to target specific brain regions. Therefore, the current study investigated the effect of anodal HD-tDCS applied to the left prefrontal and right inferior parietal cortices (with the occipital cortex included as an active control) on mind wandering, and specifically, the causal neural substrates of the three internal dynamic thought types. This was a single session study using a novel task which allows investigation into how dynamic thoughts are associated with behavioural variability and the recruitment of executive control operations across the three brain regions. Anodal stimulation to the prefrontal cortex decreased freely moving thought and anodal stimulation to the parietal lobule decreased deliberately constrained thought, with preliminary evidence for an increase in freely moving thought in the occipital cortex as well. These findings support the heterogenous nature of mind wandering, revealing that different brain regions are implicated in distinct dynamic thought types.
... In order to test for a causal role of the involvement of the dlPFC in increased task shielding, targeted stimulation of focal brain regions, e.g., by high-definition (HD) tDCS (e.g., 49,64,65 ) would be is required. The use of an active control group allows checking for specificity of tDCS effects 66 and to avoid blinding issues arising from perceptual sensation 67 . It becomes even more important if inefficient blinding leads to increase in demand characteristics, such as arousal or motivation on performance 68 . ...
Task shielding is an important executive control demand in dual-task performance enabling the segregation of stimulus–response translation processes in each task to minimize between-task interference. Although neuroimaging studies have shown activity in left dorsolateral prefrontal cortex (dlPFC) during various multitasking performances, the specific role of dlPFC in task shielding, and whether non-invasive brain stimulation (NIBS) may facilitate task shielding remains unclear. We therefore applied a single-blind, crossover sham-controlled design in which 34 participants performed a dual-task experiment with either anodal transcranial direct current stimulation (atDCS, 1 mA, 20 min) or sham tDCS (1 mA, 30 s) over left dlPFC. Task shielding was assessed by the backward-crosstalk effect, indicating the extent of between-task interference in dual tasks. Between-task interference was largest at high temporal overlap between tasks, i.e., at short stimulus onset asynchrony (SOA). Most importantly, in these conditions of highest multitasking demands, atDCS compared to sham stimulation significantly reduced between-task interference in error rates. These findings extend previous neuroimaging evidence and support modulation of successful task shielding through a conventional tDCS setup with anodal electrode over the left dlPFC. Moreover, our results demonstrate that NIBS can improve shielding of the prioritized task processing, especially in conditions of highest vulnerability to between-task interference.
In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual’s subjective experience of the type of treatment she or he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment - the thought of receiving the active or placebo condition during an experiment - can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N = 387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Studies 1 and 2; transcranial direct current stimulation: Studies 3 and 4). Our findings show that the inclusion of subjective treatment can provide a better model fit either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive, and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.
In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual’s subjective experience of the type of treatment s/he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment—the thought of receiving the active or placebo condition during an experiment—can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N=387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Study 1 & 2; transcranial direct current stimulation: Study 3 & 4). Our findings show that the inclusion of subjective treatment can provide a better model fit, either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.
We demonstrate that individual differences in subjective treatment—the belief of receiving the active or placebo condition during an experiment—can explain variability in research outcomes better than objective treatment, the actual treatment to which participants are assigned. Even though it is a standard practice for intervention studies to collect data on subjective treatment, its contribution to research outcomes has been overlooked. By demonstrating the explanatory power of subjective treatment beyond objective treatment in four independent datasets, we show its potential to provide further insights into the effectiveness of different interventions. We, therefore, encourage researchers to adopt our approach in existing and new studies, to improve experimental design and ultimately increase the rigour and robustness of clinical and non-clinical interventions.
In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual’s subjective experience of the type of treatment s/he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment—the thought of receiving the active or placebo condition during an experiment—can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N=387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Study 1 & 2; transcranial direct current stimulation: Study 3 & 4). Our findings consistently show that the inclusion of subjective treatment provides a better model fit than objective treatment alone—the condition to which participants are assigned in the experiment. These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive and behavioural outcomes. Based on these findings, we advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.
We demonstrate that individual differences in subjective treatment—the belief of receiving the active or placebo condition during an experiment—can explain variability in research outcomes better than objective treatment, the actual treatment to which participants are assigned. Even though it is a standard practice for intervention studies to collect data on subjective treatment, its contribution to research outcomes has been overlooked. By demonstrating the explanatory power of subjective treatment beyond objective treatment in four independent datasets, we show its potential to provide further insights into the effectiveness of different interventions. We, therefore, encourage researchers to adopt our approach in existing and new studies, to improve experimental design and ultimately increase the rigour and robustness of clinical and non-clinical interventions.
ELife digest
Neuromodulation is a type of intervention that relies on various non-invasive techniques to temporarily stimulate the brain and nervous system. It can be used for the treatment of depression or other medical conditions, as well as the improvement of cognitive abilities such as attention. However, there is conflicting evidence regarding whether this approach has beneficial effects.
Most studies aiming to assess the efficiency of a treatment rely on examining the outcomes of people who received the intervention in comparison to participants who undergo a similar procedure with no therapeutic effect (or placebo). However, the influence of other, ‘subjective’ factors on these results – such as the type of intervention participants think they have received – remains poorly investigated.
To bridge this gap, Fassi and Hochman et al. used statistical modeling to assess how patients’ beliefs about their treatment affected the results of four neuromodulation studies on mind wandering, depression and attention deficit hyperactivity disorder symptoms. In two studies, participants' perceptions of their treatment status were more strongly linked to changes in depression scores and mind-wandering than the actual treatment. Results were more nuanced in the other two studies. In one of them, participants who received the real neuromodulation but believed they received the placebo showed the most improvement in depressive symptoms; in the other study, subjective beliefs and objective treatment both explained changes in inattention symptoms.
Taken together, the results by Fassi and Hochman et al. suggest that factoring in patients’ subjective beliefs about their treatment may be necessary in studies of neuromodulation and other interventions like virtual reality or neurofeedback, where participants are immersed in cutting-edge research settings and might therefore be more susceptible to develop beliefs about treatment efficacy.
The application of transcranial direct current stimulation (tDCS) to the prefrontal cortex has the potential to improve performance more than cognitive training alone. Such stimulation-induced performance enhancements can generalize beyond trained tasks, leading to benefits for untrained tasks/processes. We have shown evidence that stimulation intensity has non-linear effects on augmenting cognitive training outcomes. However, it is currently unclear how stimulation intensity augments cognitive processing to impact training and transfer effects. Here, we applied decision-making modelling via the linear ballistic accumulator framework to understand what aspects of cognitive processes underlying speeded single-/dual-task decision-making performance change with tDCS intensity. One hundred and twenty-three participants were split into four groups: sham, 0.7 mA, 1.0 mA and 2.0 mA stimulation intensities. Participants completed four training sessions whilst tDCS was delivered. The 0.7 mA & 1.0 mA intensities provided the greatest benefit for performance (increased decision-making efficiency as measured by drift rates) on the trained task - more than sham or 2.0 mA stimulation. The latent decision components integrated both accuracy and reaction times to estimate performance more broadly. We see an inverted u-shaped function of stimulation intensity and cognitive performance in the trained-on task, where either no stimulation or too much stimulation is sub-optimal for performance. By contrast, 1.0 mA and 2.0 mA intensities led to increased drift rates in an untrained (transfer) single task. In sum, tDCS intensity non-linearly modulates cognitive processes related to decision-making efficiency.
