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      Transcranial stimulation of the developing brain: a plea for extreme caution

      editorial
      Frontiers in Human Neuroscience
      Frontiers Media S.A.
      TMS, tDCS, non-invasive, neuroethics, safety, paediatric

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          Abstract

          Introduction Transcranial stimulation can be used to modulate the activity of the brain. Recent developments in our understanding of technologies such as transcranial magnetic or electrical stimulation have afforded reasonable grounds for optimism that techniques such as TMS or tDCS might be effective treatments for neurally-mediated disorders. Researchers have demonstrated encouraging benefits of TMS and tDCS in treating conditions such as tinnitus (Burger et al., 2011), depression (Arul-Anandam and Loo, 2009), and stroke (Nowak et al., 2010). Collectively these techniques are often referred to as “non-invasive brain stimulation” (NIBS), although I would argue that this term is not appropriate since in all cases energy is being transferred across the skull (Davis and van Koningsbruggen, 2013), and the use of this term may be misleading to the general public who are not aware of the documented risks associated with these procedures. More recently it has been suggested that brain stimulation be used to treat neurological disorders in pediatric cases. A recent review by Vicario and Nitsche (2013a) identified a number of opportunities and challenges for the use of brain stimulation in children. Here I offer a plea for calm and for caution. The ethical stakes in clinical and research procedures with children are high enough that a conservative approach is warranted. Many of the ethical issues, relevant both to adult and child participants, have been touched on by other authors (e.g., Cohen Kadosh et al., 2012; Krause and Cohen Kadosh, 2013); however this paper will focus on the gaps in our knowledge that affect our ability to assess risk in translating brain stimulation procedures to pediatric cases. There are a number of known risks associated with brain stimulation. Mild side-effects may include scalp tenderness, headache or dizziness, which are typically associated with the mechanism of delivery or with being immobilized in a chair or frame, and which may be under-reported (Brunoni et al., 2011). More serious effects may include seizure, mood changes or induction of hyper- or hypo-mania. However, the risk of seizure is low, at around 0.1% of adult cases and around 0.2% of pediatric reports, although these figures may not reflect unreported off-label use of the techniques (Rossi et al., 2009). These more serious symptoms are largely associated with people who already possess a degree of susceptibility, such as people with a history of epilepsy (Davis et al., 2013). Adult brain stimulation is thought be reasonably safe when used within defined limits (see below), however here I wish to focus on a number of factors that complicate the translation of TMS and tDCS protocols to pediatric cases. I will focus on the key unknowns in brain stimulation research: The unknown effects of stimulation; The unknown side-effects of stimulation; The lack of clear dosing guidelines; The lack of translational studies from adults to children. I will set out these “known unknowns” in translating our knowledge about TMS and tDCS effects to clinical pediatric applications, and touch on the practical and ethical barriers to their widespread usage. Gaps in our knowledge The unknown effects of stimulation It is thought that the effects of stimulation on the brain involve modulating the excitability of cortical areas near to the tCS electrode or to the TMS coil. However, there are considerable gaps in our knowledge of how this modulation is achieved and maintained. It is assumed that long term depression- or potentiation-like processes mediate a change in the resting potential of neurons (e.g., Fritsch et al., 2010), and it is likely that the induced electric currents induce plastic changes in neurotransmitter availability (Stagg et al., 2009; Stagg and Nitsche, 2011), but the biophysical mechanism for the induction of these processes from electric fields is obscure. It is not clear to what extent white matter is involved in mediating the effects of brain stimulation. Children are known to show less myelination in some brain regions than adults (Klingberg et al., 1999; Barnea-Goraly et al., 2005), and it is thought that non-uniformity in brain tissue has a large role in determining the spread of current (Shahid et al., 2013). It is even less clear to what extent glial cells are involved during brain stimulation, although it is known that many of the changes in brain structure that occur during childhood and adolescence are due to changes in glial density (Caviness et al., 1996). These architectonic differences between child and adult brains are likely to affect the spread of applied current through brain tissue, making it more difficult to predict the electric field at, or away from, target brain areas. The unknown side-effects of stimulation As well as the short-term effects of transcranial stimulation, we do not yet understand the effects of long-term use. It seems likely that repeated sessions of TMS or tCS lead to longer-lasting neural effects; these long-duration effects are what makes brain stimulation an attractive possibility for clinical treatment. However, no brain region exists in isolation, and researchers are only now beginning to understand the knock-on effects of modulating one brain area on other areas in the brain. For example, there is evidence that enhancing one aspect of cognition may be detrimental to other cognitive faculties, making neuromodulation a zero-sum intervention (Brem et al., 2014). Conversely, reduction in activation of a brain area may induce a paradoxical overall facilitation in function (Earp et al., 2014), through disinhibition in a network or through changes in neural noise. These notions suggest that we should be checking more widely for possible adverse effects of brain stimulation, since the resulting effect of stimulation may not be seen in the hypothesized behavior, but in behaviors governed elsewhere in a brain network. There is also the worrying possibility that electrical stimulation of the skull may induce or inhibit bone growth, an issue of particular importance in children whose cranial bones are not yet fused (Friedenberg et al., 1971, 1974). This latter possibility has not been explored in human volunteers in brain stimulation experiments. The lack of clear dosing guidelines It is currently not known how to determine the appropriate dose of stimulation to give to an individual person to achieve a given size of effect. At present our best knowledge in dose-setting comes from studies that model the electric and magnetic fields generated in stimulation, and attempt to relate these fields to physical effects on brain tissue. For example, the current applied between two tDCS electrodes placed on the scalp induces an electric field across the brain surface (Miranda et al., 2006). Modeling this electric field may in principle afford predictions of the behavioral effect of specified levels of current (e.g., Mendonca et al., 2011). However, there are known to be considerable differences in the modeled field between individuals, depending on such factors as fat deposits, cortical folding and skull thickness. Importantly, one recent modeling study suggests that the transmission of electric current to the brain is more efficient in children than in adults, implying that clinicians should be more conservative in dose-setting for children than for adults (Kessler et al., 2013). This latter study suggested that the same electric field magnitude at the brain surface might be achieved with half of the applied current in children compared to adults. However, it is interesting to note that TMS-induced motor potentials are generated at a higher TMS intensity in children than older people, possibly as a result of different levels of inhibitory processing in the cortex (Mall et al., 2004). While not a complete solution, developing individual MRI-derived models for dose prediction is likely to remain the most effective strategy for safe delivery of brain stimulation. The lack of translational studies from adults to children It is a well-established principle that children should not be considered as “small adults” when testing medical interventions. A recent study suggested that most medical devices used in children are never tested in pediatric populations before approval (Hwang et al., 2014). I argued above that modeling studies can inform our ability to safely apply the correct level of dose in individual children. However, we are left with an ethical dilemma: how to judge the safety of a procedure in children without exposing children to the procedure's potential risks during testing? This is not an uncommon problem in vulnerable groups. For example, in order to be certain that a drug is safe for use in pregnancy, it must be tested on pregnant women (Chambers et al., 2008). In the case of drug testing in pregnancy, this requires that physicians monitor and report rare adverse effects. Brain stimulation is similarly associated with rare and subtle side-effects, although in this case the patient may not be aware of or able to report these adverse effects. I propose that a clear system be developed for recording adverse effects in people with limited capacity to report these effects. Wider ethical concerns We have seen how incomplete knowledge of the effects of brain stimulation in adults and in children may entail risks when applied to children, and have seen that TMS and tCS are likely to be of use in treating neurally-mediated disorders. In younger patients, the most promising treatment targets are epileptic disorders, depression and chronic pain, where some benefits have been shown in adults (Eldaief et al., 2013). There is at present a small number of publications that support the use of brain stimulation in developmental cognitive conditions including autism (Oberman et al., 2013; Enticott et al., 2014), attention deficit-hyperactivity disorder (e.g., Bloch et al., 2010) or developmental dyslexia (e.g., Costanzo et al., 2013; Vicario and Nitsche, 2013b). Recently researchers have suggested that brain stimulation might enhance performance, in domains such as mathematical ability (Snowball et al., 2013), sport (Davis, 2013), moral reasoning (Young et al., 2010) and vigilance (Nelson et al., 2014). The possibility exists that a child might take a dose of stimulation before sitting an exam or a driving test. As access to brain stimulation becomes more widespread, in particular an internet-based do-it-yourself movement (“DIY-tDCS”), it is increasingly likely that people will take the findings reported in scientific reports and in the press, and attempt to apply the same stimulation parameters without the safeguards of the lab or clinic (Fitz and Reiner, 2013). Researchers and clinicians therefore have an increased duty of caution in presenting our findings to a wider audience. Conclusion I have so far presented a somewhat negative view of the use of brain stimulation in younger people. In balance, I would add that based on the published literature, amounting to around 1000 pediatric cases, the protocols do not appear to expose patients to significantly enhanced risk of serious adverse effects. Adverse reactions have occurred, although generally these have been in patients who have an increased risk, such as in a case of rTMS leading to a seizure in a patient with elevated blood alcohol levels (Chiramberro et al., 2013). Sessions of TMS and tDCS are reasonably well tolerated in studies that have reported subjective experience. Rajpakse and Kirton (2013) and Krause and Cohen Kadosh (2013) give comprehensive recent overviews of brain stimulation studies in children. When used with care, brain stimulation in children appears to be safe and well tolerated, at least over the range of expected effects that occur following stimulation. I therefore hope to offer a positive conclusion. Transcranial stimulation will almost certainly play a large role in future treatment options for neurological disorders in children, including the developmental cognitive disorders listed above, for which there is some theoretical justification for optimism. Many of the disorders discussed here are disorders of plasticity; the hope is that maladapted communication between or within brain areas might be adjusted through the use of externally-applied stimulation. Certainly in adults TMS and tDCS are likely to be associated with fewer and less unpleasant side-effects than the neuroactive drugs that they are intended to replace, and brain stimulation is thought to be safe when used within known safety parameters (e.g., Green et al., 1997; Bikson et al., 2009; Rossi et al., 2009; Davis et al., 2013). It is clear that a large amount still remains to be done in establishing safe use of brain stimulation for children. The major practical problems that remain are: safe dosing of stimulation for individual children; developing a framework for establishing informed consent in children and their guardians; and an efficient system for monitoring and reporting adverse effects during and following brain stimulation in minors. Researchers and clinicians should also be conscious that children and parents are increasingly technologically aware, and that headline-grabbing news related to brain stimulation could lead people to self-administer stimulation; this is already occurring, as a brief search of internet forums will reveal. Brain stimulation is a powerful tool, and it is our duty to ensure that it is used responsibly in people who are most vulnerable. With scientific and practical developments, we can be confident that brain stimulation offers an opportunity to help those who have most to benefit. Conflict of interest statement The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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          Most cited references36

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          Physiological basis of transcranial direct current stimulation.

          Since the rediscovery of transcranial direct current stimulation (tDCS) about 10 years ago, interest in tDCS has grown exponentially. A noninvasive stimulation technique that induces robust excitability changes within the stimulated cortex, tDCS is increasingly being used in proof-of-principle and stage IIa clinical trials in a wide range of neurological and psychiatric disorders. Alongside these clinical studies, detailed work has been performed to elucidate the mechanisms underlying the observed effects. In this review, the authors bring together the results from these pharmacological, neurophysiological, and imaging studies to describe their current knowledge of the physiological effects of tDCS. In addition, the theoretical framework for how tDCS affects motor learning is proposed.
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            White matter development during childhood and adolescence: a cross-sectional diffusion tensor imaging study.

            Maturation of brain white matter pathways is an important factor in cognitive, behavioral, emotional and motor development during childhood and adolescence. In this study, we investigate white matter maturation as reflected by changes in anisotropy and white matter density with age. Thirty-four children and adolescents aged 6-19 years received diffusion-weighted magnetic resonance imaging scans. Among these, 30 children and adolescents also received high-resolution T1-weighed anatomical scans. A linear regression model was used to correlate fractional anisotropy (FA) values with age on a voxel-by-voxel basis. Within the regions that showed significant FA changes with age, a post hoc analysis was performed to investigate white matter density changes. With increasing age, FA values increased in prefrontal regions, in the internal capsule as well as in basal ganglia and thalamic pathways, the ventral visual pathways, and the corpus callosum. The posterior limb of the internal capsule, intrathalamic connections, and the corpus callosum showed the most significant overlaps between white matter density and FA changes with age. This study demonstrates that during childhood and adolescence, white matter anisotropy changes in brain regions that are important for attention, motor skills, cognitive ability, and memory. This typical developmental trajectory may be altered in individuals with disorders of development, cognition and behavior.
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              Long-Term Enhancement of Brain Function and Cognition Using Cognitive Training and Brain Stimulation

              Results Behavioral Effect of TRNS We assessed two distinct learning regimes: (1) deep-level cognitive processing and (2) shallow-level cognitive processing [6]. In the current context of mathematical cognition, deep- and shallow-level processing have been defined as calculation and drill learning, respectively (Figure 1). Drill learning is characterized by the ability to recall arithmetic “facts” (e.g., 4 × 8 = 32) from memory (rote learning). Extensive repetition of the association between numerical operands and answers is employed as subjects “learn by remembering,” but no knowledge of the arithmetic/mathematical relationship between operands and answers is required [8]. Calculation learning is characterized by the manipulation of numbers according to set procedures or algorithms involving one or several mathematical operations (e.g., 32 − 17 + 5 = 20). It is distinct from drill learning in that an understanding of basic mathematical principles is necessary for effective performance [8]. For each participant, error rates (ERs) and median reaction times (RTs; correct trials only) were calculated for drill and calculation problems presented over the 5 days of training. These data are provided in the Supplemental Information (Figure S1 available online). Assessing Short-Term Neuroplasticity For assessment of skill acquisition, it is recommended that calculation and drill learning be modeled by fitting of RT data to a power law function [9] (Supplemental Experimental Procedures). This modeling allows one to quantify both initial performance (B) and learning rates (α) for each learning regime. For both calculation and drill arithmetic, there were no significant differences between the groups with regard to initial performance, indicating similar proficiency at the beginning of training (p values > 0.27). In contrast, calculation and drill learning rates were significantly higher for the transcranial random noise stimulation (TRNS) group relative to sham controls [calculation, F(1,22) = 6.75, p = 0.016; drill, F(1,22) = 10.24, p = 0.004, using initial performance as a covariate [10]; Figures 2A and 2B]. This result indicates that TRNS facilitated the speed of learning for both calculation and drill regimes. Potential confounds arising from our behavioral data were examined, and all further analyses confirmed the robustness of the above findings (Supplemental Results and Figures S1 and S2). The specificity of the effect of TRNS for arithmetic enhancements was assessed by quantification of the influence of brain stimulation on a selection of cognitive faculties subserved by the dorsolateral prefrontal cortex (DLPFC). Participants’ mental rotation and attentional capacities were tested before and after training. TRNS did not influence performance in these domains, indicating that the effects of stimulation were specific to the trained material, at least in the context of the control tasks employed here (Supplemental Results). Assessing Long-Term Neuroplasticity For assessment of the long-term effects of cognitive training and TRNS, participants’ performance on old (presented during training) and new (not presented during training) calculation and drill problems was tested 6 months after training. Subjects were contacted without prior notice and asked to solve problems while near-infrared spectroscopy (NIRS) recordings were taken in the absence of further TRNS. No significant interaction existed between calculation problem novelty (old, new) and group (sham, TRNS) (F  0.17). Physiological Effect of TRNS NIRS exploits the relative transparency of biological tissue in the NIR region of the electromagnetic spectrum (700–1000 nm) to measure changes in the concentrations of oxyhemoglobin (HbO2), deoxyhemoglobin (HHb), and total hemoglobin (HbT), the summation of HbO2 and HHb. Functional NIRS describes the measurement of hemodynamic changes specifically associated with brain activation in response to a given stimulus [11]. Assessing Short-Term Neuroplasticity A cushioned plate embedded with six receiving and two transmitting optodes was placed on the scalp of each participant to extract NIRS data from the prefrontal cortex (PFC) on the first and last days of training (Figure 3A). In order to differentiate meaningful hemodynamic responses from type I error [12], we first identified the NIRS recording channels that displayed functional activation during cognitive training. Functional activation is characterized by a significant increase in the concentrations of HbO2 and HbT, coupled with a decrease in the HHb concentration [13]. In line with previous functional magnetic resonance imaging studies (for a meta-analysis, see 5]), greater arithmetic-induced functional activation was observed for channels over the lateral PFC (LPFC) than the medial PFC [F(1,24) = 6.26, p = 0.02]. Subsequent analysis was therefore focused on the amplitude and latency of peak changes in HbO2, HHb, and HbT concentrations within the LPFC that occurred as a function of TRNS. These parameters will henceforth be referred to as “peak amplitude” and “peak latency,” respectively. A mixed-model analysis of variance was run with hemodynamic measure (HbO2, HHb, HbT peak amplitude/latency), learning regime, and day of training (first day, last day) as within-subject factors and group as a between-subject factor. Note that this analysis is completely independent of that used to define activated channels, and thus circular inference is avoided [14]. The interaction between hemodynamic measure (peak amplitude), day, and group was significant [F(2,40) = 4.22, p = 0.02; Figure 3B]. This interaction indicated similar peak amplitudes in both groups at the beginning of training [first day, group X hemodynamic measure interaction, F(2,40) = 0.37, p = 0.69] that evolved into reduced peak amplitudes for HbO2 and HbT in the TRNS group relative to sham controls by the end of training [fifth day, group X hemodynamic measure interaction, F(2,40) = 6.59, p = 0.003; due to group effects on the last day for HbO2, F(1,20) = 8.64, p = 0.008; HbT, F(1,20) = 4.89, p = 0.04; but not HHb, F(1,20) = 0.21, p = 0.65]. A main effect of group was also found for peak latency, indicating a decrease in peak latency in the TRNS group compared to sham controls [F(1,20) = 6.67, p = 0.02, across HbO2, HHb, and HbT; Figure 3C]. Notably, these hemodynamic response effects were specific to the left LPFC and were not observed in the right LPFC (p values > 0.2; Figure S3; Supplemental Discussion). Assessing Long-Term Neuroplasticity Peak amplitude/latency hemodynamic responses were also assessed during testing 6 months later. The only significant effect was a learning regime X group interaction for peak latency in the left LPFC [F(1,10) = 5.22, p = 0.04; Figure 4A], a finding that mirrors the behavioral results after 6 months. For calculation problems, the TRNS group showed a significant decrease in peak latency compared to sham controls [t(10) = −3.4, p = 0.007]. The groups did not differ for drill problems [t(10) = 0.7, p = 0.5]. To investigate the relationship between our observed behavioral and physiological responses, we correlated calculation RTs with changes in peak latency during testing. The results highlighted significant correlations between the physiological and behavioral parameters (across HbO2, HHb, and HbT, r = 0.89, p = 0.00009; HbO2, r = 0.83, p = 0.0007; HHb, r = 0.78, p = 0.002; HbT, r = 0.8, p = 0.001; Figures 4B and S4A). Notably, no significant correlations existed when similar analyses assessed the relationship between behavioral performance and peak latency differences on the last day of training (across HbO2, HHb, and HbT, r = 0.12, p = 0.7; HbO2, r = 0.2, p = 0.52; HHb, r = 0.08, p = 0.8; HbT, r = 0.07, p = 0.82; Figure S4B). The physiological-behavioral correlations with hemodynamic data extracted on the last day of training and those extracted after 6 months differed significantly (p values < 0.05). Assessing the Specificity of Brain Stimulation In order to examine whether the current results were specific to DLPFC stimulation, we performed a control experiment in which TRNS was applied to the bilateral parietal cortex. The results of this control experiment indicated that both the cognitive enhancement and TRNS-induced hemodynamic responses described above were specific for DLPFC stimulation (Supplemental Results). Discussion The results presented here indicate that TRNS of the bilateral DLPFC can enhance learning with respect to high-level cognitive functions, namely algorithmic manipulation and factual recall in mental arithmetic. When this learning is based on deep-level cognitive processing, as is the case for calculation arithmetic, such enhancements are extremely long-lived both behaviorally and physiologically. Arithmetic struggles are a characteristic feature of developmental dyscalculia, a learning disorder affecting approximately 5%–7% of the population [15]. In addition, they are present in up to 20% of otherwise healthy children and adults [16] and in a large number of individuals suffering from neurodegenerative disease or stroke [17]. Techniques that can assuage the decline in, or even enhance, cognitive learning and processing are thus highly sought after for both educational and therapeutic purposes [18]. The current results support TRNS as a noninvasive cognitive enhancement tool capable of improving learning in one of the most complex human faculties, mental arithmetic. One key discovery in the current study is that TRNS-induced changes in calculation performance are maintained for at least 6 months after training. This shows that relatively short stimulation sessions of suitable brain areas can induce long-term learning improvements when coupled with an appropriate training regime. The current results also support the use of learning regimes based on deep-level cognitive processing over those involving shallow-level processing, as the former not only results in long-lasting performance improvements, but also generalization to new, unlearned material. Such generalization is rarely observed in cognitive training studies [19, 20], yet together with long-term performance modulation it will be essential if transcranial electrical stimulation (TES) techniques are to successfully progress to the clinic or classrooms. Improvements that manifest only during the period of stimulation and only for learned material, while scientifically interesting, are less useful in an educational or therapeutic context [21]. TRNS did not improve long-term drill performance. The specificity of the long-term effects of TRNS for calculation arithmetic can be explained by the level of cognitive processing involved in the learning [6]. The strength and longevity of memory formation depends on the depth of processing during the encoding stage [22]. Deep-level processing contributes to the generation of elaborate memory traces better integrated with organized knowledge structures. This allows calculation problems to be solved with reconstructive retrieval processes absent from shallower, drill-type processing. The transfer to new problems in the calculation task supports the proposition that deep-level learning processes modify underlying cognitive systems [22], which are further influenced by concurrent TRNS (Supplemental Discussion). It can be argued that superior calculation performance in the TRNS group during testing arises not from long-term enhancement of arithmetic abilities per se, but other cognitive processes associated with DLPFC function. While the failure to observe similar long-term effects in the drill condition excludes some possibilities such as long-term memory enhancement, mental arithmetic is a complex faculty based on a variety of cognitive abilities [23]. As such, the current enhancement might stem from the “boosting” of more general DLPFC-associated cognition that is not necessarily specific to arithmetic. TRNS modulated both the peak amplitude and peak latency of hemodynamic responses to functional activation. At the end of training, the peak amplitudes of HbO2 and HbT concentrations in the left LPFC were smaller in the TRNS group than in sham controls. Changes in local HbO2 and HbT concentrations are representative of alterations in regional cerebral blood flow (rCBF) and oxygen delivery [24–26], while changes in the local HHb concentration, responses for which were not modified by TRNS, are more sensitive to alterations in the regional cerebral metabolic rate of oxygen consumption (rCMRO2) [27]. Our results suggest, therefore, that TRNS elicited changes in corticoexcitability within the left LPFC that significantly reduced rCBF without affecting rCMRO2. TRNS, via its amplification of subthreshold oscillatory activity by stochastic resonance, may increase neural firing synchronization within stimulated regions [28]. This could reduce the amount of endogenous electrical noise within such areas, meaning that smaller rCBF responses are required to maintain neural activity. The absence of alterations in rCMRO2 with significant changes in neural excitability is well described in the literature [29, 30] (Supplemental Discussion). That identical metabolic demands (compared to sham controls) were supported by smaller rCBF responses is consistent with a TRNS-induced enhancement of neurovascular coupling efficiency within the left LPFC, a region heavily implicated in arithmetic processing [4, 5]. Peak latency responses further support this proposal. An earlier peak time existed for all three hemoglobin parameters in the TRNS group relative to sham controls on the first and last days of training, and this was maintained, for the calculation task, until the testing phase 6 months later. Previous work has demonstrated that just 4 min of TRNS can modify corticoexcitability [28]. While such rapid modifications would allow TRNS to directly influence peak latency responses in the short-term (training days 1 and 5), long-term (after 6 months) responses occurring in the absence of further stimulation must have arisen via a more indirect mechanism. One possibility is that of structural alterations to the cerebrovasculature. Specific hemodynamic events induced during the training phase could act as precursors to long-term angioplastic modifications. If these were to increase cerebrovascular innervation of neural networks involved in mental arithmetic, faster hemodynamic responses might accompany calculation-induced functional activation, as was observed during the testing phase of the current work. This theory is consistent with animal studies demonstrating significant angiogenesis, and upregulation of the angiogenic vascular endothelial growth factor, after electrical brain stimulation [31]. For the testing phase, we observed strong physiological-behavioral correlations between calculation RTs and peak latencies, which explained up to 79% of the variance. These results serve as good evidence that peak latency responses within the left LPFC are reliable indicators of calculation performance, with earlier peak times indicative of better performance. Notably, despite both superior calculation performance and reduced peak latencies in the TRNS group on day 5 of training, the significant correlations observed after 6 months were not present at this stage. The delayed development of this significant correlation suggests that the behavioral and hemodynamic changes observed on the last day of training are not unrelated, as one might assume [32], but rather act as a scaffold for a more meaningful relationship that manifests in the 6-month training-testing interval. We have demonstrated that five consecutive days of TRNS-accompanied arithmetic training can markedly improve learning as assessed with both a deep-level cognitive processing calculation task and a shallow-level drill task. Such improvements were accompanied by defined hemodynamic responses consistent with more efficient neurovascular coupling in brain regions associated with mental arithmetic. Both the behavioral and physiological changes displayed extreme longevity, spanning a period of 6 months, but only when learning involved deep-level cognitive processing. By its demonstration of such longevity and, for the calculation task, generalization to new, unlearned material, the present study highlights TRNS as a promising tool for enhancing high-level cognition and facilitating learning. These findings have significant scientific and translational implications for cognitive enhancement in both healthy individuals and patients suffering from disorders characterized by arithmetic deficits [17, 33, 34]. Experimental Procedures Detailed experimental procedures are provided in the Supplemental Information. Participants Twenty-five participants were matched for age and gender and randomly assigned to either the TRNS or sham group (TRNS, six males and seven females, mean age = 20.92, SD = 2.10; sham, six males and six females, mean age = 21.42, SD = 3.23). All participants had normal or corrected-to-normal vision and no history of neurological or psychiatric illness. Informed consent was obtained, and volunteers received £60 for their participation. This research was approved by the Berkshire Ethics Committee. Arithmetic Tasks: Training Participants were required to perform two types of learning task: calculation and drill [7, 35]. The tasks are summarized in Figure 1. Arithmetic Tasks: Testing The testing phase included four blocks each of old calculation, new calculation, old drill, and new drill problems. Feedback was not provided, and participants progressed to subsequent problems regardless of whether their previous answer was correct or not. Control Tasks To assess whether TRNS influenced other cognitive domains outside mental arithmetic (perhaps even in a detrimental manner [36]), immediately before (day 1) and after (day 5) training participants completed two control tasks: a mental rotation task and an attention network test (Supplemental Experimental Procedures). TRNS Subjects received TRNS while performing the learning task each day. Two electrodes (5 cm × 5 cm) were positioned over areas of scalp corresponding to the DLPFC (F3 and F4, identified in accordance with the international 10-20 EEG procedure; Figure 3A). Electrodes were encased in saline-soaked synthetic sponges to improve contact with the scalp and avoid skin irritation. Stimulation was delivered by a DC-Stimulator-Plus device (DC-Stimulator-Plus, neuroConn). Noise in the high-frequency band (100–600Hz) was chosen as it elicits greater neural excitation than lower frequency stimulation [37]. For the TRNS group, current was administered for 20 min, with 15 s increasing and decreasing ramps at the beginning and end, respectively, of each session of stimulation. In the sham group current was applied for 30 s after upward ramping and then terminated. NIRS The current study employed a continuous wave (CW) NIRS system (Oxymon MK III, Artinis Medical Systems). This device measures changes in light attenuation at two wavelengths, 764 nm and 858 nm, and utilizes the modified Beer-Lambert law [38] with an age-dependent differential pathlength factor [39] to resolve changes in HbO2, HHb, and HbT concentrations within cortical brain tissue.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                05 August 2014
                2014
                : 8
                Affiliations
                Department of Psychology, Swansea University Swansea, UK
                Author notes

                This article was submitted to the journal Frontiers in Human Neuroscience.

                Edited by: Peter G. Enticott, Deakin University, Australia

                Reviewed by: Lindsay M. Oberman, University of California San Diego, USA; Brian D. Earp, University of Oxford, UK

                Article
                10.3389/fnhum.2014.00600
                4122183
                787a89b3-83c3-487b-86f6-d5f47aed2b29
                Copyright © 2014 Davis.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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                Categories
                Neuroscience
                Opinion Article

                Neurosciences
                tms,tdcs,non-invasive,neuroethics,safety,paediatric
                Neurosciences
                tms, tdcs, non-invasive, neuroethics, safety, paediatric

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