Posts Tagged Variability

[Abstract] Sham tDCS: A hidden source of variability? Reflections for further blinded, controlled trials

Abstract

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique increasingly used to modulate neural activity in the living brain. In order to establish the neurophysiological, cognitive or clinical effects of tDCS,tDCS most studies compare the effects of active tDCS to those observed with a sham tDCS intervention. In most cases, sham tDCS consists in delivering an active stimulation for a few seconds to mimic the sensations observed with active tDCS and keep participants blind to the intervention. However, to date, sham-controlled tDCS studies yield inconsistent results, which might arise in part from sham inconsistencies. Indeed, a multiplicity of sham stimulation protocols is being used in the tDCS research field and might have different biological effects beyond the intended transient sensations. Here, we seek to enlighten the scientific community to this possible confounding factor in order to increase reproducibility of neurophysiological, cognitive and clinical tDCS studies.

via Sham tDCS: A hidden source of variability? Reflections for further blinded, controlled trials – Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation

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[Abstract] Longitudinal Recovery of Executive Control Functions After Moderate-Severe Traumatic Brain Injury: Examining Trajectories of Variability and Ex-Gaussian Parameters

Background. Executive control deficits are deleterious and enduring consequences of moderate-severe traumatic brain injury (TBI) that disrupt everyday functioning. Clinically, such impairments can manifest as behavioural inconsistency, measurable experimentally by the degree of variability across trials of a reaction time (RT) task (also known as intraindividual variability [IIV]). Growing research on cognition after TBI points to cognitive deterioration in the chronic stages postinjury. Objective. To examine the longitudinal recovery of RT characteristics (IIV and more detailed ex-Gaussian components, as well as the number of impulsively quick responses) following moderate-severe TBI. Methods. Seventy moderate-severe TBI patients were assessed at 2, 5, 12, and 24+ months postinjury on a go/no-go RT task. RT indices (ex-Gaussian parameters mu and sigma [mean and variability of the normal distribution component], and tau [extremely slow responses]; mean, intraindividual coefficient of variation [ICV], and intraindividual standard deviation [ISD]) were analyzed with repeated-measures multivariate analysis of variance. Results. ICV, ISD, and ex-Gaussian tau significantly decreased (ie, improved) over time in the first year of injury, but worsened from 1 to 2+ years, as did the frequency of extremely fast responses. These quadratic patterns were accentuated by age and shown primarily in tau (extremely slow) and extremely fast (impulsive) responses. Conclusions. The pattern of early recovery followed by decline in executive control function is consistent with growing evidence that moderate-severe TBI is a progressive and degenerative disorder. Given the responsiveness to treatment of executive control deficits, elucidating the trajectory and underpinnings of inconsistent behavioral responding may reveal novel prognostic and clinical management opportunities.

1. Azouvi, P, Couillet, J, Leclercq, M, Martin, Y, Asloun, S, Rousseaux, M. Divided attention and mental effort after severe traumatic brain injury. Neuropsychologia. 2004;42:12601268. doi:10.1016/j.neuropsychologia.2004.01.001. Google ScholarCrossrefMedline
2. Dockree, PM, Bellgrove, MA, O’Keeffe, FM. Sustained attention in traumatic brain injury (TBI) and healthy controls: enhanced sensitivity with dual-task load. Exp Brain Res. 2006;168:218229. doi:10.1007/s00221-005-0079-x. Google ScholarCrossrefMedline
3. Ponsford, JL, Downing, MG, Olver, J. Longitudinal follow-up of patients with traumatic brain injury: outcome at two, five, and ten years post-injury. J Neurotrauma. 2014;31:6477. doi:10.1089/neu.2013.2997. Google ScholarCrossrefMedline
4. Green, RE, Colella, B, Christensen, B. Examining moderators of cognitive recovery trajectories after moderate to severe traumatic brain injury. Arch Phys Med Rehabil. 2008;89(12 suppl):S16S24. doi:10.1016/j.apmr.2008.09.551. Google ScholarCrossrefMedline
5. Stuss, DT, Pogue, J, Buckle, L, Bondar, J. Characterization of stability of performance in patients with traumatic brain injury: variability and consistency on reaction time tests. Neuropsychology. 1994;8:316324. doi:10.1037/0894-4105.8.3.316. Google ScholarCrossref
6. West, R, Murphy, KJ, Armilio, ML, Craik, FIM, Stuss, DT. Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control. Brain Cogn. 2002;49:402419. doi:10.1006/brcg.2001.1507. Google ScholarCrossrefMedlineISI
7. Segalowitz, SJ, Dywan, J, Unsal, A. Attentional factors in response time variability after traumatic brain injury: an ERP study. J Int Neuropsychol Soc. 1997;3:95107Google ScholarMedline
8. Sinclair, KL, Ponsford, JL, Rajaratnam, SMW, Anderson, C. Sustained attention following traumatic brain injury: use of the Psychomotor Vigilance Task. J Clin Exp Neuropsychol. 2013;35:210224. doi:10.1080/13803395.2012.762340. Google ScholarCrossrefMedline
9. Slovarp, L, Azuma, T, Lapointe, L. The effect of traumatic brain injury on sustained attention and working memory. Brain Inj. 2012;26:4857. doi:10.3109/02699052.2011.635355. Google ScholarCrossrefMedline
10. Stuss, DT, Stethem, LL, Hugenholtz, H, Picton, T, Pivik, J, Richard, MT. Reaction time after head injury: fatigue, divided and focused attention, and consistency of performance. J Neurol Neurosurg Psychiatry. 1989;52:742748Google ScholarCrossrefMedline
11. Whyte, J, Polansky, M, Fleming, M, Coslett, HB, Cavallucci, C. Sustained arousal and attention after traumatic brain injury. Neuropsychologia. 1995;33:797813Google ScholarCrossrefMedlineISI
12. Collins, LF, Long, CJ. Visual reaction time and its relationship to neuropsychological test performance. Arch Clin Neuropsychol. 1996;11:613623Google ScholarCrossrefMedline
13. Vasquez, BP, Binns, MA, Anderson, ND. Staying on task: age-related changes in the relationship between executive functioning and response time consistency. J Gerontol B Psychol Sci Soc Sci. 2016;71:189200. doi:10.1093/geronb/gbu140. Google ScholarCrossrefMedline
14. Lacouture, Y, Cousineau, D. How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times. Tutorials Quant Methods Psychol. 2008;4:3545. doi:10.20982/tqmp.04.1.p035. Google ScholarCrossref
15. Cicerone, KD, Maestas, KL. Rehabilitation of attention and executive function impairments. In: Sherer, M, Sanders, AM, eds. Handbook on the Neuropsychology of Traumatic Brain Injury. New York, NYSpringer2014:191211. doi:10.1007/978-1-4939-0784-7_10. Google ScholarCrossref
16. Christensen, BK, Colella, B, Inness, E. Recovery of cognitive function after traumatic brain injury: a multilevel modeling analysis of Canadian outcomes. Arch Phys Med Rehabil. 2008;89(12 suppl):S3S15. doi:10.1016/j.apmr.2008.10.002. Google ScholarCrossrefMedline
17. Masel, BE, DeWitt, DS. Traumatic brain injury: a disease process, not an event. J Neurotrauma. 2010;27:15291540. doi:10.1089/neu.2010.1358. Google ScholarCrossrefMedlineISI
18. Till, C, Colella, B, Verwegen, J, Green, RE. Postrecovery cognitive decline in adults with traumatic brain injury. Arch Phys Med Rehabil. 2008;89(12 suppl):S25S34. doi:10.1016/j.apmr.2008.07.004. Google ScholarCrossrefMedline
19. Millis, SR, Rosenthal, M, Novack, TA. Long-term neuropsychological outcome after traumatic brain injury. J Head Trauma Rehabil. 2001;16:343355Google ScholarCrossrefMedlineISI
20. Ruff, RM, Young, D, Gautille, T. Verbal learning deficits following severe head injury: heterogeneity in recovery over 1 year. J Neurosurg. 1991;75(1s):S50S58Google Scholar
21. Himanen, L, Portin, R, Isoniemi, H, Helenius, H, Kurki, T, Tenovuo, O. Longitudinal cognitive changes in traumatic brain injury: a 30-year follow-up study. Neurology. 2006;66:187192. doi:10.1212/01.wnl.0000194264.60150.d3. Google ScholarCrossrefMedline
22. Hetherington, CR, Stuss, DT, Finlayson, MA. Reaction time and variability 5 and 10 years after traumatic brain injury. Brain Inj. 1996;10:473486Google ScholarCrossrefMedlineISI
23. MacDonald, SWS, Hultsch, DF, Dixon, RA. Performance variability is related to change in cognition: evidence from the Victoria Longitudinal Study. Psychol Aging. 2003;18:510523. doi:10.1037/0882-7974.18.3.510. Google ScholarCrossrefMedlineISI
24. Bielak, AM, Cherbuin, N, Bunce, D, Anstey, KJ. Intraindividual variability is a fundamental phenomenon of aging: evidence from an 8-year longitudinal study across young, middle, and older adulthood. Dev Psychol. 2014;50:143151. doi:10.1037/a0032650. Google ScholarCrossrefMedline
25. Levin, HS, O’Donnell, VM, Grossman, RG. The Galveston orientation and amnesia test. A practical scale to assess cognition after head injury. J Nerv Ment Dis. 1979;167:675684Google ScholarCrossrefMedlineISI
26. Green REA. Editorial: brain injury as a neurodegenerative disorder. Front Hum Neurosci. 2016;9:615. doi:10.3389/fnhum.2015.00615. Google ScholarCrossref
27. Robertson, IH, Manly, T, Andrade, J, Baddeley, BT, Yiend, J. “Oops!”: performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia. 1997;35:747758Google ScholarCrossrefMedlineISI
28. Hultsch, DF, Strauss, E, Hunter, MA, MacDonald, SWS. Intraindividual variability, cognition, and aging. In: Craik, FIM, Salthouse, TA, eds. The Handbook of Aging and Cognition. 3rd ed. New York, NYPsychology Press2008:491556Google Scholar
29. Dixon, RA, Garrett, DD, Lentz, TL, MacDonald, SWS, Strauss, E, Hultsch, DF. Neurocognitive markers of cognitive impairment: exploring the roles of speed and inconsistency. Neuropsychology. 2007;21:381399. doi:10.1037/0894-4105.21.3.381. Google ScholarCrossrefMedlineISI
30. MacDonald, SWS, Hultsch, DF, Bunce, D. Intraindividual variability in vigilance performance : does degrading visual stimuli mimic age-related “neural noise?“ J Clin Exp Neuropsychol. 2006;28:655675. doi:10.1080/13803390590954245. Google ScholarCrossrefMedline
31. Garrett, DD, MacDonald, SWS, Craik, FIM. Intraindividual reaction time variability is malleable: feedback- and education-related reductions in variability with age. Front Hum Neurosci. 2012;6:101. doi:10.3389/fnhum.2012.00101. Google ScholarCrossrefMedline
32. Beharelle, AR, Kovačević, N, McIntosh, AR, Levine, B. Brain signal variability relates to stability of behavior after recovery from diffuse brain injury. Neuroimage. 2012;60:15281537. doi:10.1016/j.neuroimage.2012.01.037. Google ScholarCrossrefMedline
33. Gmehlin, D, Fuermaier, ABM, Walther, S. Intraindividual variability in inhibitory function in adults with ADHD—an ex-Gaussian approach. PLoS One. 2014;9:e112298. doi:10.1371/journal.pone.0112298.Google ScholarCrossrefMedline
34. MacFlynn, G, Montgomery, EA, Fenton, GW, Rutherford, W. Measurement of reaction time following minor head injury. J Neurol Neurosurg Psychiatry. 1984;47:13261331Google ScholarCrossrefMedline
35. Dykiert, D, Der, G, Starr, JM, Deary, IJ. Sex differences in reaction time mean and intraindividual variability across the life span. Dev Psychol. 2012;48:12621276. doi:10.1037/a0027550. Google ScholarCrossrefMedline
36. Williams, BR, Hultsch, DF, Strauss, EH, Hunter, MA, Tannock, R. Inconsistency in reaction time across the life span. Neuropsychology. 2005;19:8896. doi:10.1037/0894-4105.19.1.88. Google ScholarCrossrefMedline
37. Farbota, KDM, Sodhi, A, Bendlin, BB. Longitudinal volumetric changes following traumatic brain injury: a tensor-based morphometry study. J Int Neuropsychol Soc. 2012;18:10061018. doi:10.1017/S1355617712000835. Google ScholarCrossrefMedline
38. Green, RE, Colella, B, Maller, JJ, Bayley, M, Glazer, J, Mikulis, DJ. Scale and pattern of atrophy in the chronic stages of moderate-severe TBI. Front Hum Neurosci. 2014;8:67. doi:10.3389/fnhum.2014.00067.Google ScholarCrossrefMedline

via Longitudinal Recovery of Executive Control Functions After Moderate-Severe Traumatic Brain Injury: Examining Trajectories of Variability and Ex-Gaussian Parameters – Brandon P. Vasquez, Jennifer C. Tomaszczyk, Bhanu Sharma, Brenda Colella, Robin E. A. Green, 2018

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[ARTICLE] Plasticity induced by non-invasive transcranial brain stimulation: A position paper – Full Text

Abstract

Several techniques and protocols of non-invasive transcranial brain stimulation (NIBS), including transcranial magnetic and electrical stimuli, have been developed in the past decades. Non-invasive transcranial brain stimulation may modulate cortical excitability outlasting the period of non-invasive transcranial brain stimulation itself from several minutes to more than one hour. Quite a few lines of evidence, including pharmacological, physiological and behavioral studies in humans and animals, suggest that the effects of non-invasive transcranial brain stimulation are produced through effects on synaptic plasticity. However, there is still a need for more direct and conclusive evidence. The fragility and variability of the effects are the major challenges that non-invasive transcranial brain stimulation currently faces. A variety of factors, including biological variation, measurement reproducibility and the neuronal state of the stimulated area, which can be affected by factors such as past and present physical activity, may influence the response to non-invasive transcranial brain stimulation. Work is ongoing to test whether the reliability and consistency of non-invasive transcranial brain stimulation can be improved by controlling or monitoring neuronal state and by optimizing the protocol and timing of stimulation.

1. Introduction

Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are the most commonly used methods of non-invasive transcranial brain stimulation that has been abbreviated by previous authors as either as NIBS or NTBS. Here we use NIBS since it seems to be the most common term at the present time. When it was first introduced in 1985, TMS was employed primarily as a tool to investigate the integrity and function of the human corticospinal system (Barker et al., 1985). Single pulse stimulation was used to elicit motor evoked potentials (MEPs) that were easily evoked and measured in contralateral muscles (Rothwell et al., 1999). The robustness and repeatability of measures of conduction time, stimulation threshold and “hot spot” location allowed TMS to be developed into a standard tool in clinical neurophysiology.

As we review below, a number of NIBS protocols can lead to effects on brain excitability that outlast the period of stimulation. These may reflect basic synaptic mechanisms involving long-term potentiation (LTP)- or long-term depression (LTD)-like plasticity, and because of this there has been great interest in using the methods as therapeutic interventions in neurological and psychiatric diseases. Furthermore, recently they are more frequently applied to modify memory processes and to enhance cognitive function in healthy individuals. However, apart from success in treating some patients with depression (Lefaucheur et al., 2014; Padberg et al., 2002, 1999), there is little consensus that they have improved outcomes in a clinically meaningful fashion in any other conditions. The reason for this is probably linked to the reason why many other protocols failed to reach routine clinical neurophysiology: they are too variable both within and between individuals to make them practically useful in a health service setting (Goldsworthy et al., 2014; Hamada et al., 2013; Lopez-Alonso et al., 2014, 2015).

Below we review the evidence for the mechanisms underlying the “neuroplastic” effects of NIBS, and then consider the problems in reproducibility and offer some potential ways forward in research. […]

Continue —> Plasticity induced by non-invasive transcranial brain stimulation: A position paper – ScienceDirect

There are three major lines of evidence supporting NIBS produces effects…

Fig. 1. There are three major lines of evidence supporting NIBS produces effects through mechanisms of synaptic plasticity: (1) Drugs that modulate the function of critical receptors/channels for plasticity, e.g. Ca2+ channels and NMDA receptors, alter the effect of NIBS; (2) NIBS mainly changes I-waves rather than the D-wave in the epidural recording of descending volleys evoked by TMS, suggesting the effect of NIBS occurs trans-synaptically; and (3) NIBS interacts between protocols and with motor practice and cognitive learning processes, suggesting the effect of NIBS is involves in plasticity-related motor and psychological processes.

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[ARTICLE] Reorganization of finger coordination patterns through motor exploration in individuals after stroke – Full Text

 

Abstract

Background

Impairment of hand and finger function after stroke is common and affects the ability to perform activities of daily living. Even though many of these coordination deficits such as finger individuation have been well characterized, it is critical to understand how stroke survivors learn to explore and reorganize their finger coordination patterns for optimizing rehabilitation. In this study, I examine the use of a body-machine interface to assess how participants explore their movement repertoire, and how this changes with continued practice.

Methods

Ten participants with chronic stroke wore a data glove and the finger joint angles were mapped on to the position of a cursor on a screen. The task of the participants was to move the cursor back and forth between two specified targets on a screen. Critically, the map between the finger movements and cursor motion was altered so that participants sometimes had to generate coordination patterns that required finger individuation. There were two phases to the experiment – an initial assessment phase on day 1, followed by a learning phase (days 2–5) where participants trained to reorganize their coordination patterns.

Results

Participants showed difficulty in performing tasks which had maps that required finger individuation, and the degree to which they explored their movement repertoire was directly related to clinical tests of hand function. However, over four sessions of practice, participants were able to learn to reorganize their finger movement coordination pattern and improve their performance. Moreover, training also resulted in improvements in movement repertoire outside of the context of the specific task during free exploration.

Conclusions

Stroke survivors show deficits in movement repertoire in their paretic hand, but facilitating movement exploration during training can increase the movement repertoire. This suggests that exploration may be an important element of rehabilitation to regain optimal function.

Background

Stroke often results in impairments of upper extremity, including hand and finger function, with 75% of stroke survivors facing difficulties performing activities of daily living [12]. Critically, impairments after stroke not only include muscle- and joint-specific deficits such as weakness, and changes in the kinetic and kinematic workspace of the fingers [34], but also coordination deficits such as reduced independent joint control [5] and impairments in finger individuation and enslaving [6789]. Therefore, understanding how to address these coordination deficits is critical for improving hand rehabilitation.

Typical approaches to hand rehabilitation emphasize repetition [10] and functional practice based on evidence that such experience can cause reorganization in the brain [11]. Although this has proven to be reasonably successful, functional practice (such as repetitive grasping of objects) does not specify the coordination pattern to be used when performing the tasks. As a result, because of the redundancy in the human body, there is a risk that stroke survivors may adopt atypical compensatory movements to perform tasks [12]. These compensatory movements have been mainly identified during reaching [1314], but there is evidence that they are also present in finger coordination patterns during grasping [15]. Although there is still debate over the role of compensatory movements in rehabilitation [16], there is at least some evidence both in animal and humans that continued use of these compensatory patterns may be detrimental to true recovery [171819].

To address this issue, there has been a greater focus on directly facilitating the learning of new coordination patterns. Specifically, in hand rehabilitation, virtual tasks (such as playing a virtual piano) have been examined as a way to train finger individuation [2021]. In these protocols, individuation is encouraged by asking participants to press a particular key with a finger, while keeping other fingers stationary. A similar approach to improve hand dexterity was also adopted by developing a glove that could be used as a controller for a popular guitar-playing video game [22]. However, directly instructing desired coordination patterns to be produced becomes challenging as the number of degrees of freedom involved in the coordination pattern increase. For example, the hand has approximately 20 kinematic degrees of freedom, and providing verbal, visual or auditory feedback for simultaneously controlling all these degrees of freedom would be a major challenge. A potential solution that has been suggested is not to directly instruct the coordination pattern itself, but rather let participants explore different coordination patterns [23]. This idea of motor exploration is based on dynamical systems theory that suggests that variability and exploration may help participants escape sub-optimal pre-existing coordination patterns and potentially settle in more optimal coordination patterns [24252627]. Such exploration has been shown to be important in adapting existing movement repertoire [28], and has also been shown to be associated with faster rates of learning [29].

In order to test the hypothesis that exploration of novel coordination patterns can improve overall movement repertoire, I used a body-machine interface [3031] to examine how stroke survivors explore and reorganize finger coordination patterns with practice. A body-machine interface maps body movements (in this case finger movements) to the control of a real or virtual object (in this case a screen cursor), which can provide a way to elicit different coordination patterns in the context of an intuitive task. Specifically I examined: (i) how stroke survivors reorganize their finger coordination patterns, (ii) how training to explore novel coordination patterns affects their ability to reorganize their coordination pattern, and (iii) if training to explore novel coordination patterns has an effect on their overall movement repertoire. In this context, I use the term “novel” to indicate coordination patterns that require finger individuation. This assumption is motivated by the finding that stroke survivors have difficulty producing finger individuation even under explicit instruction [69], and therefore it is highly likely that they would not use coordination patterns requiring finger individuation frequently in activities of daily living.[…]

Continue —>  Reorganization of finger coordination patterns through motor exploration in individuals after stroke | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 a Experimental setup – participants wore a data glove and moved their fingers to control a screen cursor b Schematic of task – participants moved a cursor between two targets using movements of four fingers (thumb excluded). c Experimental protocol. Participants came in for 5 total sessions – an initial assessment phase, followed by a learning phase. d Weighting coefficients of the index and middle (blue), and ring and little (red) fingers during the initial assessment phase, and e weighting coefficients during the learning phase

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[Abstract] Test-retest reliability of prefrontal transcranial Direct Current Stimulation (tDCS) effects on functional MRI connectivity in healthy subjects

Highlights

• Prefrontal non-invasive brain stimulation targeting specific brain circuits has the potential to be applied in therapeutic settings but reliability, validity and generalisability have to be evaluated.

 

• This is the first study investigating the test-retest reliability of prefrontal tDCS-induced resting-state functional-connectivity (RS fcMRI) modulations.

 

• Analyses of individual RS-fcMRI responses to active tDCS across three single sessions revealed no to low reliability, whereas reliability of RS-fcMRI baselines and RS-fcMRI responses to sham tDCS was low to moderate.

 

• Our pilot data can be used to plan future imaging studies investigating rs-fcMRI effects of prefrontal tDCS.

Abstract

Transcranial Direct Current Stimulation (tDCS) of the prefrontal cortex (PFC) can be used for probing functional brain connectivity and meets general interest as novel therapeutic intervention in psychiatric and neurological disorders. Along with a more extensive use, it is important to understand the interplay between neural systems and stimulation protocols requiring basic methodological work. Here, we examined the test-retest (TRT) characteristics of tDCS-induced modulations in resting-state functional-connectivity MRI (RS fcMRI). Twenty healthy subjects received 20 minutes of either active or sham tDCS of the dorsolateral PFC (2 mA, anode over F3 and cathode over F4, international 10–20 system), preceded and ensued by a RS fcMRI (10 minutes each). All subject underwent three tDCS sessions with one-week intervals in between. Effects of tDCS on RS fcMRI were determined at an individual as well as at a group level using both ROI-based and independent-component analyses (ICA). To evaluate the TRT reliability of individual active-tDCS and sham effects on RS fcMRI, voxel-wise intra-class correlation coefficients (ICC) of post-tDCS maps between testing sessions were calculated. For both approaches, results revealed low reliability of RS fcMRI after active tDCS (ICC(2,1) = −0.09 – 0.16). Reliability of RS fcMRI (baselines only) was low to moderate for ROI-derived (ICC(2,1) = 0.13 – 0.50) and low for ICA-derived connectivity (ICC(2,1) = 0.19 – 0.34). Thus, for ROI-based analyses, the distribution of voxel-wise ICC was shifted to lower TRT reliability after active, but not after sham tDCS, for which the distribution was similar to baseline. The intra-individual variation observed here resembles variability of tDCS effects in motor regions and may be one reason why in this study robust tDCS effects at a group level were missing. The data can be used for appropriately designing large scale studies investigating methodological issues such as sources of variability and localisation of tDCS effects.

 

Source: Test-retest reliability of prefrontal transcranial Direct Current Stimulation (tDCS) effects on functional MRI connectivity in healthy subjects

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