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Immobilization of an arm to favor the use of the other arm is a major component of Constrained-Induced Therapy (CIT). In stroke patients, this therapy improves the motor function of the affected hand. However, the neurological effect of this therapy has not been studied in non-patients populations.

In a recent paper published in the journal Neurology, Swiss scientists took advantage of arm immobilization after arm injury as a proxy for constrained-induced therapy in non-stroke patients. They investigated the effect of limb immobilization on brain structure, especially on gray and white matter plasticity.
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or Memory interference at the single neuron level

Could Roger Federer become a table tennis champion?
Could Roger Federer be a world champion at tennis and table tennis at the same time? A new study suggests that it depends on the motor cortex neurons encoding those skills. If the same neurons are involved in tennis and table tennis, then the two tasks will interfere one with the other. If different sets of neurons are used, then Roger Federer could become a tennis table champion while maintaining his tennis ranking.
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The primary motor cortex (M1) plays a major role in the control of movements. Monkeys are able to control a prosthetic arm by modulating the neuronal activity of M1 without any overt movements. However, the neural mechanisms of learning such ability (abstract skill learning) has remained unexplored so far. In their study, Koralek and his colleagues from Berkeley investigated this learning process in rodents. They show that abstract skill learning follows a similar time course to physical skill learning (involving movements of the limbs). In addition, they also demonstrate the importance of corticostriatal pathways for this process.

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How reward affects motor behavior has been the focus of the motor control field for decades. For instance, it has been shown that monkeys make faster saccades towards rewarded target than non-rewarded ones (Takikawa et al. 2002). It has been suggested that the higher velocity of the saccades, which is linked to a decrease in movement time, was due to temporal discount of reward (Shadmehr et al. 2010). Namely, if movement time to get to the target is larger, the target is less rewarding. In a paper published recently in the Journal of Neuroscience, Joshua and Lisberger (2012) investigated the effect of reward on smooth pursuit eye movements. Smooth pursuit consists in a smooth motion of the eyes that is triggered by the motion of a target in the environment. During smooth pursuit initiation, the eyes smoothly accelerate until eye velocity matches target velocity.
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This text was written by Frédéric Crevecoeur and myself and was submitted to Journal of Neuroscience as a Journal Club article. Unfortunately, it was rejected. So, here it is...

Transcranial magnetic stimulation (TMS) has been a powerful tool to understand the function of the motor cortex. It has been used to investigate its role in decision-making (Michelet et al., 2010), movement preparation (Soto et al., 2010) and, in a recent study by Gritsenko and colleagues in the Journal of  Neuroscience, knowledge of limb dynamics (Gritsenko et al., 2011).

Knowledge of limb dynamics is essential for the control of reaching movements. Indeed, shoulder motion produces interaction torques at the elbow and movements of the elbow generate torques at the shoulder joint. This complex inter-segmental dynamics must clearly be taken into account during movement planning and execution in order to produce the typical straight paths that characterizes natural reaching movements (Hollerbach and Flash, 1982). The source of interaction torque compensation has been a matter of debate for years. Some argue that compensation takes place at the level of the spinal cord (Bizzi et al., 2000) whereas others argue for a more direct control of muscle activation mediated by the primary motor cortex (M1) (Todorov, 2000; Scott, 2004). Whether the descending motor commands accounts for inter-segmental dynamics remains a significant open question at the centre of spinal versus cortical control hypotheses.
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Sketches of spinal and cortical control hypothese in the extreme case. qS,E represent the shoulder and elbow angles and tS,E represent the shoulder and elbow torques. According to the spinal control hypothesis, the descending motor command (u1, right panel) encodes high-level features of the intended movement such as the location of the reach target. The control command that compensates for interaction torques (u2) could be mediated by sub-cortical pathways including the spinal cord. Alternatively, the descending motor command (u, left panel) could account for the location of the reach target as well as shoulder and elbow torques that result from interaction dynamics.
Gritsenko and colleagues address whether M1 accounts for inter-segmental dynamics by using TMS over M1 at different points in time during reaching movements. The approach is based on the assumption that stimulation of M1 interacts with the descending motor commands and generates Motor Evoked Potentials (MEPs) that reflect M1 excitability (Palmer and Ashby, 1992). Indeed, MEPs are known to interact with the overall background activity of the motoneurons as well as with the excitability of M1. In order to extract the contribution of M1, MEPs are usually normalized to the background EMG, which factors out the dependency on motoneuron activity. The variations in MEPs that persist after normalization are assumed to reflect the excitability of M1 through the descending motor command. This approach has been used to investigate the typical triphasic pattern of activation associated with single joint movements (e.g. MacKinnon and Rothwell, 2000). The supra linear scaling of MEPs relative to background EMG mostly occurred at the beginning of the first agonist burst, which was interpreted as a reduced contribution of M1 in controlling the subsequent bursts of activity. Following the same logic, Gritsenko and colleagues used single pulse TMS over primary motor cortex to study the influence of limb dynamics on descending motor commands. In their experiment, Gritsenko and collaborators asked their subjects to reach to one of two possible targets that were chosen such that the interaction torque elicited by shoulder motion during the movement was either assisting or resisting motion at the elbow joint. Interaction torques at the elbow were cleverly matched across movements to the two targets. In each of the two conditions, assistive or resistive, movement kinematics followed the usual bell-shaped velocity profile. Arm motion resulted from muscle torques and the laws of motion, from which interaction torques could be computed. Both motor evoked potentials (MEPs) and EMG activity presented time varying profile across movement execution. However, even when the background level of EMG was taken into account, the normalized MEP (or MEP gain as named in the paper) still varied over the course of the movement. In particular, changes in MEP gains correlated with interaction torques.
 From these interaction effects, Gritsenko and colleagues suggest that the correlation between M1 excitability (inferred from increased MEPs after normalization) and interaction torques reflects the integration of intersegmental dynamics in the descending motor commands. For the shoulder muscles, the MEP gain was well correlated with interaction torque at the shoulder joint but not with the level of co-contraction or total torque. Interestingly, a similar correlation was found between the MEP gain of the bi-articular muscles and interaction torque at the elbow but not for the monoarticular muscles. The modulation of the MEP gain with interaction torques was present both before and during the movements.

Most TMS studies that investigated modulation of MEPs during movement used single-joint motor tasks (e.g. MacKinnon and Rothwell, 2000). In those studies, the proportional relationship between EMG activity and MEP size was only broken around movement onset. The study by Gritsenko and colleagues demonstrates that such modest changes in cortical excitability observed during movement execution may be specific to single-joint movements. For multi-joint movements, the MEP gain clearly varied beyond movement onset. Interaction effects were observed from the start to the end of the movements, highlighting that M1 may be more involved for controlling complex arm dynamics. In addition, the presence of this modulation before the movement suggests that movement planning at the level of M1 incorporates knowledge of limb dynamics.

These results parallel recent findings on feedback responses to mechanical perturbations. Rapid motor responses are tuned to the actual underlying torque in as little as 50 ms relative to perturbation onset, which reflects the processing of multi-joint afferent feedback via an internal model of arm dynamics (Kurtzer et al., 2008). Single unit recordings in the monkey coupled with a TMS study recently demonstrated that M1 is causally involved in fine-tuning these fast corrective responses to the actual underlying torque (Pruszynski et al., 2011)(Pruszynski et al., 2011). Therefore, internal models in M1 appear as a general cortical function driving planning and execution of voluntary movements as well as rapid feedback corrections for mechanical perturbations.

            The work of Gritsenko and colleagues raises some significant questions. First, these authors suggest that spanning the shoulder and elbow joints confers the biarticular muscles an advantageous anatomical situation to compensate for interaction torques. However, the preferred torque directions of both monoarticular and biarticular muscles systematically differ from their theoretical actions: monoarticular muscles show preference for multi-joint torques and biarticulars preferred in some cases parallel single joint torques (Kurtzer et al., 2006). Therefore, all upper limb muscles are potentially involved in the control of multi-joint dynamics and how the compensation for interaction torques is distributed across them is still unresolved.

            Second, it is proposed that M1 excitability is not responsible for modulation of joints stiffness, which was inferred from co-contraction levels. Stiffness control is known to be a determinant strategy to stabilize motor control. However, there are many different factors that influence joint stiffness (Hasan, 1992)(Hasan, 1992), and modest changes in co-contraction have only a limited impact on (Pruszynski et al., 2009)stiffness (Pruszynski et al., 2009)(Pruszynski et al., 2009)(Pruszynski et al., 2009)(Pruszynski et al., 2009). The control process underlying stiffness control and its possible contribution in stabilizing interaction dynamics remains to be investigated.

Finally, it is known that other brain areas are important for the control of voluntary movements. For instance, cerebellar ataxia patients have a reduced ability to take interaction torques into account during movement planning (Bastian et al., 1996; Bastian et al., 2000; Cooper et al., 2000). Similarly, Parkinson Disease patients compensate for interaction torques differently than control subjects (Leiguarda et al., 2000; Seidler et al., 2001). Consistent with this study, hemiparetic stroke patients have trouble with inter-joint coordination as well (Beer et al., 2000). Therefore, although Gritsenko et al. focused on the primary motor cortex, the specific contribution of M1 and of other brain areas in shaping the descending motor command remains to be investigated.

In all, these results reflect that the nonlinear arm dynamics, often presented as a difficulty for experimental design or modeling studies, is actually a strong asset to address brain functions associated with motor planning and execution as well as feedback control.
Bastian AJ, Zackowski KM, Thach WT (2000) Cerebellar ataxia: torque deficiency or torque mismatch between joints? J Neurophysiol 83:3019-3030.

Bastian AJ, Martin TA, Keating JG, Thach WT (1996) Cerebellar ataxia: abnormal control of interaction torques across multiple joints. J Neurophysiol 76:492-509.

Beer RF, Dewald JP, Rymer WZ (2000) Deficits in the coordination of multijoint arm movements in patients with hemiparesis: evidence for disturbed control of limb dynamics. Exp Brain Res 131:305-319.

Bizzi E, Tresch MC, Saltiel P, d'Avella A (2000) New perspectives on spinal motor systems. Nat Rev Neurosci 1:101-108.

Cooper SE, Martin JH, Ghez C (2000) Effects of inactivation of the anterior interpositus nucleus on the kinematic and dynamic control of multijoint movement [In Process Citation]. J Neurophysiol 84:1988-2000.

Gritsenko V, Kalaska JF, Cisek P (2011) Descending corticospinal control of intersegmental dynamics. J Neurosci 31:11968-11979.

Hasan Z (1992) IS STIFFNESS THE MAINSPRING OF POSTURE AND MOVEMENT. Behavioral and Brain Sciences 15:756-758.

Hollerbach MJ, Flash T (1982) Dynamic interactions between limb segments during planar arm movement. Biol Cybern 44:67-77.

Kurtzer I, Pruszynski JA, Herter TM, Scott SH (2006) Primate upper limb muscles exhibit activity patterns that differ from their anatomical action during a postural task. J Neurophysiol 95:493-504.

Kurtzer IL, Pruszynski JA, Scott SH (2008) Long-latency reflexes of the human arm reflect an internal model of limb dynamics. Curr Biol 18:449-453.

Leiguarda R, Merello M, Balej J, Starkstein S, Nogues M, Marsden CD (2000) Disruption of spatial organization and interjoint coordination in Parkinson's disease, progressive supranuclear palsy, and multiple system atrophy. Mov Disord 15:627-640.

MacKinnon CD, Rothwell JC (2000) Time-varying changes in corticospinal excitability accompanying the triphasic EMG pattern in humans. J Physiol 528:633-645.

Michelet T, Duncan GH, Cisek P (2010) Response competition in the primary motor cortex: corticospinal excitability reflects response replacement during simple decisions. J Neurophysiol 104:119-127.

Palmer E, Ashby P (1992) Evidence that a long latency stretch reflex in humans is transcortical. J Physiol 449:429-440.

Scott SH (2004) Optimal feedback control and the neural basis of volitional motor control. Nat Rev Neurosci 5:532-546.

Seidler RD, Alberts JL, Stelmach GE (2001) Multijoint movement control in Parkinson's disease. Exp Brain Res 140:335-344.

Soto O, Valls-Solé J, Kumru H (2010) Paired-pulse transcranial magnetic stimulation during preparation for simple and choice reaction time tasks. J Neurophysiol 104:1392-1400.

Todorov E (2000) Direct cortical control of muscle activation in voluntary arm movements: a model. Nat Neurosci 3:391-398.
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If you had the choice of reaching to a cookie 10cm away from your hand or another cookie 15 cm away, which cookie would you choose?

Humans tend to be lazy so most of them would pick the cookie 10cm away. So, scientists concluded that humans more likely choose the option that minimizes the energy cost. Clearly, it takes less energy to grab the 10cm away cookie than the 15cm away one. However, a new study in the Journal of Neurophysiology demonstrates that the energy cost is not the only factor we take into account before making decisions (Cos, Belanger and Cisek, JNP, 2011).
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Here is my pick of seven papers that had/have/will have a lasting impact on the tDCS field.

Learning
1. Reis J, Schambra HM, Cohen LG, et al. Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(5):1590-5
 In this paper, Reis and colleagues demonstrate that anodal tDCS over the primary motor cortex improves motor skill learning by increasing offline improvements (from one session to the next one) but not online improvements (within a session). Using tDCS over the cerebellum, Galea and colleagues demonstrated that cerebellar stimulation speeds up learning in a visuomotor rotation task. Those two studies highlight a dissocation between the role of the cerebellum (online learning) and the role of the primary motor cortex (retention/ offline learning).

2. Vines BW, Cerruti C, Schlaug G. Dual-hemisphere tDCS facilitates greater improvements for healthy subjectsʼ non-dominant hand compared to uni-hemisphere stimulation. BMC neuroscience. 2008;9:103.
In this study, Vines demonstrate that dual-hemisphere tDCS is more efficient than uni-hemispheric tDCS. This important for stroke rehabilitation where people either increase excitability of the lesioned hemisphere with anodal tDCS or decrease excitability of the contralateral hemisphere with cathodal tDCS to improve recovery (see #5). This study shows that the combination of both could be interesting to study.

Neural basis
3. Stagg CJ, Best JG, Stephenson MC, et al. Polarity-sensitive modulation of cortical neurotransmitters by transcranial stimulation. The Journal of neuroscience. 2009;29(16):5202-6.
In this study, Stagg and colleagues the impact of tDCS on the concentration of GABA and glutamate neurotransmitters. Using MRS spectroscopy, they show that both anodal and cathodal tDCS decrease GABA concentration below the stimulating electrode (primary motor cortex). In addition, cathodal stimulation also decreased glutamate concentration. Their follow-up study (about which I wrote here) demonstrates that the responsiveness of the motor cortex to tDCS is correlated with the speed of learning in a given skill learning task. Finally, this study might also explain some of the effect of tDCS on motor learning that I found in my paper.

4. Fritsch B, Reis J, Martinowich K, et al. Direct Current Stimulation Promotes BDNF-Dependent Synaptic Plasticity: Potential Implications for Motor Learning. Neuron. 2010;66(2):198-204.
This is the first in vitro model of tDCS that shed light on the neural basis of tDCS. It highlights the importance of the genotype for the response to tDCS and to motor skill learning in general.

Clinical studies
5. Hummel FC, Celnik P a, Giraux P, et al. Effects of non-invasive cortical stimulation on skilled motor function in chronic stroke. Brain. 2005;128(Pt 3):490-9.
In this study, the authors demonstrate that anodal tDCS used on the lesioned hemisphere increases motor cortex excitability of chronic stroke patients as assessed by TMS measurements and their motor function as assessed by the Jebsen-Taylor Hand Function Test. This same test was used by Fregni and colleagues to study the influence of cathodal tDCS on the contralesional hemisphere. They found similar improvements than in the Hummel study. Together, those studies show that decreasing the excitability of the contralesional hemisphere or increasing the excitability of the ipsilesional hemisphere improve recovery of hand function of chronic stroke patients. Modulating the excitability of contralesional or ipsilesional hemisphere can also be performed with repetitive TMS protocols (Grefkes and Fink 2011).

6. Benninger DH, Lomarev MP, Lopez G, et al. Transcranial direct current stimulation for the treatment of Parkinsonʼs disease. Journal of Neurology, Neurosurgery & Psychiatry. 2010;81(10):1105-11.
This is one of the first clinical trial involving tDCS and Parkinson Disease patients. The results of the trial show some prolonged improvements of tDCS on motor funciton of PD patients. However, we need to better understand what are the optimal tDCS paremeters in order to optimize the influence of tDCS on improving motor function in patients (montage, intensity, during or before practice, etc...)

Review
There are plenty of reviews discussing transcranial direct current stimulation. I picked this one for its historical perspective... Here is another one that I like...

7. Priori A. Brain polarization in humans: a reappraisal of an old tool for prolonged non-invasive modulation of brain excitability. Clinical Neurophysiology. 2003;114(4):589-595.  This is a really old tool...
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GABA is a neurotransmitter, i.e. a messenger that transmits signal from one neuron to another. GABA is inhibitory in the sense that it will drive the excitability of the targeted neuron down. Therefore, it plays a huge role in regulating the excitability in the cortex throughout the nervous system.

Motor learning has been associated with changes in GABA concentration in the motor cortex (M1) (Floyer-Lea et al. 2006) and GABAergic medication can disrupt the learning process (Donchin et al. 2002; Bütefisch et al. 2000). Indeed, a decrease in GABA concentration appears to be crucial for motor cortex reorganization elicited by motor learning (Jacobs & Donoghue 1991).

In the paper that I'll write about in this post, Charlotte Stagg and her colleagues from Oxford tested the hypothesis that the responsiveness of the GABA system correlated with the amount of motor learning across individuals (Stagg et al. 2011). In other words, if your brain can easily drive your GABA concentration down, can you learn faster or better a new motor task?
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This is how I currently do it
Here are a few links:
http://apps.isiknowledge.com/
http://www.scopus.com/
http://www.sciencedirect.com/
http://www.biomedexperts.com/
http://f1000biology.com/
http://www.sciencedaily.com/news/mind_brain/
http://researchblogging.org/
http://scientopia.org/
http://scienceblogging.org/
http://scienceblogs.com/

Any suggestions for improvement?
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Skill leaning elicits changes in the brain (Yarrow et al. 2009). In humans and rats, it leads to a massive increase in the connections between neurons in the primary motor cortex, the area that is responsible for the control of movements. Over time and training, the more useful connections are reinforced while the weaker ones are discarded. Such a profound reorganization of the area underlying the control of movements, should impact a large part of the movement repertoire or at least the part that encompasses the use of the same limbs or effectors as those required for the skill. Gentner et al. (2010) investigated how musical practice reorganizes the primary motor cortex and impacts daily life tasks such as grasping.
Gentner et al investigated how musical practice reorganizes the primary motor cortex and impacts daily life tasks such as grasping
Professional musicians practice their skills several dozens of hours per week. This extensive practice is known to produce lasting changes in the primary motor cortex (Pascual-Leone et al. 1995; Elbert et al. 1995), the area that controls our movements. Does this practice influence how they use their fingers to grasp a glass of wine or throw a ball?

In their study, Gentner and colleagues demonstrate that brain reorganization due to intensive musical practice leads to different patterns of hand movements. More impressively, the more the patterns of hand movements were biased towards musical practice, the less similar they were to the patterns of hand movements used for daily life activities.
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