Posts Tagged sensorimotor

[ARTICLE] Effects of dual-task and walking speed on gait variability in people with chronic ankle instability: a cross-sectional study – Full Text

Abstract

Background

Recent evidence suggests that impaired central sensorimotor integration may contribute to deficits in movement control experienced by people with chronic ankle instability (CAI). This study compared the effects of dual-task and walking speed on gait variability in individuals with and without CAI.

Methods

Sixteen subjects with CAI and 16 age- and gender-matched, able-bodied controls participated in this study. Stride time variability and stride length variability were measured on a treadmill under four different conditions: self-paced walking, self-paced walking with dual-task, fast walking, and fast walking with dual-task.

Results

Under self-paced walking (without dual-task) there was no difference in stride time variability between CAI and control groups (P = 0.346). In the control group, compared to self-paced walking, stride time variability decreased in all conditions: self-paced walking with dual-task, fast speed, and fast speed with dual-task (P = 0.011, P = 0.016, P = 0.001, respectively). However, in the CAI group, compared to self-paced walking, decreased stride time variability was demonstrated only in the fast speed with dual-task condition (P = 1.000, P = 0.471, P = 0.008; respectively). Stride length variability did not change under any condition in either group.

Conclusions

Subjects with CAI and healthy controls reduced their stride time variability in response to challenging walking conditions; however, the pattern of change was different. A higher level of gait disturbance was required to cause a change in walking in the CAI group compared to healthy individuals, which may indicate lower adaptability of the sensorimotor system. Clinicians may use this information and employ activities to enhance sensorimotor control during gait, when designing intervention programs for people with CAI.

The study was registered with the Clinical Trials network (registration NCT02745834, registration date 15/3/2016).

Background

Recurrent ankle sprains occur in up to 40% of individuals who have previously experienced a lateral ankle sprain [1, 2]. Individuals who report residual symptoms, which include repetitive episodes of ‘giving way’ and subjective feeling of ankle joint instability are termed as having chronic ankle instability (CAI) [3]. The cause of these symptoms and the high frequency of recurrent ankle sprain is not fully understood [4]. It has been suggested that the residual joint instability and the high reoccurrence rates can be attributed to loss of sensory input from articular mechano-receptors, decreased muscle strength, mechanical instability of the ankle joint, and reduced ankle range of motion [5, 6].

Recent evidence suggests that deficits in central neural sensorimotor integration can contribute to impaired movement control in people with CAI [7, 8, 9, 10, 11, 12, 13, 14]. For example, Springer et al. [8] assessed the correlation between single-limb stance postural control (Overall Stability Index) and shoulder position sense (Absolute Error Score) among people with CAI and healthy controls. Correlations between the lower and upper limbs were observed only in the healthy controls, indicating altered sensorimotor integration in the CAI group. Several studies have observed altered gait mechanism in people with CAI, which was explained by compromised central nervous system (CNS) control [9, 14, 15, 16]. It was shown that people with CAI have a typical gait pattern of increased inversion kinematics and kinetics, lateral shift of body weight, increased hip flexion during terminal swing to mid stance, reduced hip extension and increased knee flexion during terminal stance to initial swing, and slow weight transfer at the beginning and end of the stance [15, 16, 17]. Altered biomechanical strategies during gait initiation and termination tasks (e.g., reduced center of pressure displacement), have also been demonstrated in this population [9, 14]. Studies that assessed movement variability, such as knee and hip joint motions during single leg jump landing, identified differences between individuals with and without CAI, which may also indicate central motor programming deficits [10, 11, 12, 13]. Hence, further investigation of motor control adaptations may contribute to understanding the underlying neurophysiologic mechanisms of CAI.

Gait speed and other spatio-temporal parameters during daily activities should reflect behavioral goals and environmental conditions [18]. Studies revealed that walking speed has a significant effect on joint coordination pattern and gait variability [18, 19, 20]. Therefore, assessing gait variability under challenging situations such as walking at different speeds might test CNS flexibility in controlling gait [19, 20]. Moreover, based on the understanding that for many daily activities even a fully intact motor control system requires attention and cognitive resources [21], the dual-task paradigm has been used to provide insight into the demands of postural control and gait on attention. Performance of a cognitive task has been shown to decrease postural control in participants with CAI as compared to healthy controls [7, 22]. However, no previous study examined the impact of cognitive task and walking speed on gait performance in subjects with CAI.

Balance during walking is reflected by precise spatial and temporal control of foot placement. Stride to stride fluctuations in time and length are related to control of the rhythmic walking mechanism. Thus, previous research has suggested that studying gait variability is a reliable way to quantify locomotion [23]. The mechanism of adjusting movement variability is considered beneficial for coping with changes, maintaining stability, preventing injury, and attaining higher motor skills [24]. Performing a cognitive task while walking or while altering self-paced walking speed has been related to changes in gait variability in populations with neurological and musculoskeletal pathologies, as well in healthy young individuals [25, 26, 27, 28]. Yet, there is no consensus in the literature as to how to interpret these changes. Decreased variability while performing demanding gait tasks may reflect voluntary gait adaptation toward a more conservative gait pattern [26]. Alternatively, it has been suggested that increased variability may indicate CNS flexibility and adaptability to changes in task demands [29]. A possible central sensorimotor control deficit in people with CAI may constrain the ability of the CNS to adjust to different task demands; thus, affecting central control over gait variability and reducing the ability to cope with varied tasks. Consequently, testing the mechanism of adjusting gait variability as a response to complex walking conditions in people with CAI compared to healthy controls may provide more information on sensorimotor control in this population.

The present study was designed to compare the effects of dual-task and walking speed on gait variability in individuals with and without CAI. Previous reports, including a meta-analysis, indicated that simple postural tasks do not always discriminate between participants with CAI and those without [6, 8, 30]. Consequently, we hypothesized that gait variability among individuals with and without CAI will be similar during “normal” self-paced walking, whereas gait will vary under complex walking conditions.[…]

Continue —> Effects of dual-task and walking speed on gait variability in people with chronic ankle instability: a cross-sectional study | BMC Musculoskeletal Disorders | Full Text

Fig. 1 Stride time variability results of the two groups under all gait conditions. CAI- chronic ankle instability, SP- self-paced, DT- dual task

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[ARTICLE] Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable – Full Text 

Finding, testing and demonstrating efficacy of new treatments for stroke recovery is a multifaceted challenge. We believe that to advance the field, neurorehabilitation trials need a conceptually rigorous starting framework. An essential first step is to agree on definitions of sensorimotor recovery and on measures consistent with these definitions. Such standardization would allow pooling of participant data across studies and institutions aiding meta-analyses of completed trials, more detailed exploration of recovery profiles of our patients and the generation of new hypotheses. Here, we present the results of a consensus meeting about measurement standards and patient characteristics that we suggest should be collected in all future stroke recovery trials. Recommendations are made considering time post stroke and are aligned with the international classification of functioning and disability. A strong case is made for addition of kinematic and kinetic movement quantification. Further work is being undertaken by our group to form consensus on clinical predictors and pre-stroke clinical data that should be collected, as well as recommendations for additional outcome measurement tools. To improve stroke recovery trials, we urge the research community to consider adopting our recommendations in their trial design.

Lack of a standardized approach to measurement in stroke recovery research hampers our ability to advance understanding of recovery mechanisms, devise better treatments and consolidate knowledge from a body of research using meta-analyses.1 As examples, examination of a recent Cochrane Overview of interventions to improve upper limb function after stroke identified 208 unique assessment tools from 243 trials2; another review found more than 100 measures of activities of daily living (ADLs).3 Furthermore, in most motor rehabilitation trials, measures are taken at arbitrary time points relative to stroke onset, e.g. time of admission to, or discharge from, rehabilitation rather than at standard time points aligned with underlying recovery processes.4

We must challenge the common assumption that most sensorimotor therapies are universally applicable and will achieve the same benefit for all people with stroke. The magnitude of change and likelihood of achieving clinically meaningful improvement in response to specific therapies will depend on age, stroke severity, and other factors including pre-existing comorbid conditions (e.g. diabetes, cognitive impairment, depression)5 and pre-stroke lifestyle factors (e.g. social engagement, exercise).6 The respective contributions of these factors have yet to be fully understood. Going forward, we need to identify the determinants that may help predict responders and non-responders to interventions.

The measurement working group of the Stroke Recovery and Rehabilitation Roundtable (SRRR)7 was established to develop recommendations for standardized assessment time points and measures to be included in all adult trials of sensorimotor recovery after stroke. Given the current lack of standards for data collection and heterogeneous reports in stroke recovery trials, our expert group also considered pre-stroke clinical, demographic and stroke-related data that should be collected to improve clinical prediction of recovery and characterization of patient cohorts.

The decision to focus on sensorimotor recovery reflects the volume of existing trials in this area, the range of outcomes currently in use across these trials, and the gap in current research that known international initiatives has not addressed (e.g. Core Outcome Measures in Effectiveness Trials Initiative (COMET), National Institute of Neurological Disorders and Stroke Common Data Elements (NINDS CDE), The International Consortium for Health Outcomes Measurement (ICHOM),8 Improving Research Outcome Measurement in Aphasia (ROMA)9 and Standardization of Measures in Arm Rehabilitation Trials after Stroke (SMART), Supplementary Table 1). Acknowledging that clinical measures cannot distinguish between true neurological repair (behavioral restitution) and use of compensatory strategies,10 a second objective was to consider whether we could recommend specific kinetic and/or kinematic outcomes that reflect quality of motor performance in order to better understand the neurophysiological changes that occur when patients improve.11,12 Our overall objective of the roundtable was to provide recommendations that, if applied, could improve the methodology of rehabilitation and recovery trials, help build our understanding of the trajectory of stroke recovery and aid discovery of new and more targeted treatments.

Continue —>  Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation RoundtableInternational Journal of Stroke – Gert Kwakkel, Natasha A Lannin, Karen Borschmann, Coralie English, Myzoon Ali, Leonid Churilov, Gustavo Saposnik, Carolee Winstein, Erwin EH van Wegen, Steven L Wolf, John W Krakauer, Julie Bernhardt, 2017

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[Abstract] Gross Motor AbiLity predictS Response to upper extremity rehabilitation in chronic stroke  

Abstract

The majority of rehabilitation research focuses on the comparative effectiveness of different interventions in groups of patients, while much less is currently known regarding individual factors that predict response to rehabilitation. In a recent article, authors presented a prognostic model to identify the sensorimotor characteristics predictive of the extent of motor recovery after Constraint-Induced Movement (CI) therapy amongst individuals with chronic mild-to-moderate motor deficit using the enhanced probabilistic neural network (EPNN). This follow-up paper examines which participant characteristics are robust predictors of rehabilitation response irrespective of the training modality. To accomplish this, EPNN was first applied to predict treatment response amongst individuals who received a virtual-reality gaming intervention (utilizing the same enrollment criteria as the prior study). The combinations of predictors that yield high predictive validity for both therapies, using their respective datasets, were then identified. High predictive classification accuracy was achieved for both the gaming (94.7%) and combined datasets (94.5%). Though CI therapy employed primarily fine-motor training tasks and the gaming intervention emphasized gross-motor practice, larger improvements in gross motor function were observed within both datasets. Poorer gross motor ability at pre-treatment predicted better rehabilitation response in both the gaming and combined datasets. The conclusion of this research is that for individuals with chronic mild-to-moderate upper extremity hemiparesis, residual deficits in gross motor function are highly responsive to motor restorative interventions, irrespective of the modality of training.

Source: Gross Motor AbiLity predictS Response to upper extremity rehabilitation in chronic stroke – ScienceDirect

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[Abstract] Gross Motor AbiLity predictS Response to upper extremity rehabilitation in chronic stroke

Abstract

The majority of rehabilitation research focuses on the comparative effectiveness of different interventions in groups of patients, while much less is currently known regarding individual factors that predict response to rehabilitation. In a recent article, authors presented a prognostic model to identify the sensorimotor characteristics predictive of the extent of motor recovery after Constraint-Induced Movement (CI) therapy amongst individuals with chronic mild-to-moderate motor deficit using the enhanced probabilistic neural network (EPNN). This follow-up paper examines which participant characteristics are robust predictors of rehabilitation response irrespective of the training modality. To accomplish this, EPNN was first applied to predict treatment response amongst individuals who received a virtual-reality gaming intervention (utilizing the same enrollment criteria as the prior study). The combinations of predictors that yield high predictive validity for both therapies, using their respective datasets, were then identified. High predictive classification accuracy was achieved for both the gaming (94.7%) and combined datasets (94.5%). Though CI therapy employed primarily fine-motor training tasks and the gaming intervention emphasized gross-motor practice, larger improvements in gross motor function were observed within both datasets. Poorer gross motor ability at pre-treatment predicted better rehabilitation response in both the gaming and combined datasets. The conclusion of this research is that for individuals with chronic mild-to-moderate upper extremity hemiparesis, residual deficits in gross motor function are highly responsive to motor restorative interventions, irrespective of the modality of training.

Source: Gross Motor AbiLity predictS Response to upper extremity rehabilitation in chronic stroke – ScienceDirect

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[ARTICLE] Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power – Full Text

Abstract

Background

Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation.

Methods

A new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field. The effect of current density (0, 0.02, 0.04 or 0.06 mA/cm2) on electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on 5 healthy volunteers for different stimulation modalities: 1) 10-minutes anodal tDCS before EEG acquisition over right-hand or 2) feet motor cortical area, and 3) 4-seconds anodal tDCS during EEG acquisition either on right-hand or feet cortical areas before each time right-hand or feet motor imagery is performed. For each subject and tDCS modality, analysis of variance and Tukey-Kramer multiple comparisons tests (p <0.001) are used to detect significant differences between classification accuracies that are obtained with different current densities. For tDCS modalities that improved accuracy, t-tests (p <0.05) are used to compare μ and βband power when a specific current density is provided against the case of supplying no stimulation.

Results

The proposed montage improved the classification of right-hand motor imagery for 4 out of 5 subjects when the highest current was applied for 10 minutes over the right-hand motor area. Although EEG band power changes could not be related directly to classification improvement, tDCS appears to affect variably different motor areas on μ and/or β band.

Conclusions

The proposed montage seems capable of enhancing right-hand motor imagery detection when the right-hand motor area is stimulated. Future research should be focused on applying higher currents over the feet motor cortex, which is deeper in the brain compared to the hand motor cortex, since it may allow observation of effects due to tDCS. Also, strategies for improving analysis of EEG respect to accuracy changes should be implemented.

Background

Transcranial direct current stimulation (tDCS) is a noninvasive technique for brain stimulation where direct current is supplied through two or more electrodes in order to modulate temporally brain excitability [12]. This technique has shown potential to improve motor performance and motor learning [345]. Hence, it could be applied in motor neurorehabilitacion [1]. However, tDCS effects vary depending on several factors, such as the size or position of the stimulation electrodes and the current intensity that is applied [6] or the mental state of the user [7]. Therefore, it should be considered that outcomes of tDCS studies are the result of different affected brain networks that may be involved in attention and movements, among other processes.

Volitional locomotion requires automatic control of movement while the cerebral cortex provides commands that are transmitted by neural projections toward the brainstem and the spinal cord. This control involves predictive motor operations that link activity from the cerebral cortex, cerebellum, basal ganglia and brainstem in order to modify actions at the spinal cord level [8]. In general, this set of structures can be considered to form a motor network that allow voluntary movement.

Different parts of the cerebral cortex participate in the performance of self-initiated movement, like the supplementary motor (SMA), the primary motor (M1) and premotor (PM) areas. It is known that M1 is activated during motor execution. Excitatory effects of M1 have been studied with anodal stimulation [6], finding that activation of this region is related to higher motor evoked potentials (MEPs) and an increment of force movement on its associated body part area [910]. Moreover, M1 seems to be critical in the early phase of consolidation of motor skills during procedural motor learning [11], i.e., the implicit skill acquisition through the repeated practice of a task [12].

In addition, the SMA and PM influence M1 in order to program opportune precise motor commands when movement pattern is modified intentionally, based on information from temporoparietal cortices regarding to the body’s state [8]. The SMA contributes in the generation of anticipatory postural adjustments [13]. Consequently, its facilitatory stimulation seems to increase anticipatory postural adjustments amplitudes, to reduce the time required to perform movements during the learning task of sequential movements, and to produce early initiation of motor responses [141516]. These studies suggest the possibility of using SMA excitation during treatments for motor disorders, since hemiparesis after stroke involves the impairment of anticipatory motor control at the affected limb [17]. In addition, some studies propose the participation of the SMA in motor memory and both implicit and explicit motor learning [18192021], i.e, when new information is acquired without intending to do so and when acquisition of skill is conscious [22], respectively. Complimentary to the role of SMA, the PM is crucial for sensory-guided movement initiation and the consolidation of motor sequence learning during sleep [823], while its facilitation with anodal tDCS seems to enhance the excitability from the ipsilateral M1 [24], which may be useful for treatment of PM disorders.

As previously mentioned, the cerebellum is also involved in locomotion through the regulation of motor processes by influencing the cerebral cortex, among other neural structures. During adaptive control of movement, as in the gait process, it seems that loops that interconnect reciprocally motor cortical areas to the basal ganglia and cerebellum allow predictive control of locomotion and they exhibit correlation with movement parameters [825]. Regarding to studies about cerebellar stimulation, there is still not enough knowledge about the effects of tDCS on different neuronal populations and the afferent pathways, so results are variable among studies and their interpretation is more complex than for cerebral tDCS [26]. Furthermore, the topographical motor organization of the cerebellum is not clear yet [27]. Nevertheless, most studies base their experimental procedure on the existence of decussating cerebello-cerebral connections, even if there are also ipsilateral cerebello-cerebral tracts or inter-hemispheric cerebellar connections [28]. Hence, a cerebellar hemisphere is stimulated to affect cerebellar brain inhibition (CBI), which refers to the inherent suppression of cerebellum over the contralateral M1 [29]. For example, the supply of anodal and cathodal stimulation over the right cerebellum in [30] resulted in incremental and decremental CBI on the left M1, respectively. In contrast, there are some studies that suggest this expectation may be not always appropriate. In [31] it was shown that inhibitory transcranial magnetic stimulation (a stimulation technique that provides magnetic field pulses on the brain [32]) over the lateral right cerebellum led to procedural learning decrement for tasks performed with either the right or left hand, whereas inhibition of lateral left cerebellar hemisphere decreased learning only with the left hand. In addition, results from [33] showed that cathodal cerebellar tDCS worsened locomotor adaptation ipsilaterally. These two studies may provide a reference for using cerebellar inhibition for avoiding undesired brain activity changes during motor rehabilitation, such as compensatory movement habits that might contribute to maladaptative plasticity and hamper the goal of achieving a normal movement pattern [34]. […]

Continue —> Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1 tDCS montage. Scheme of tDCS electrodes position in reference to EEG electrodes and inion (left), and placement of tDCS electrodes on the EEG cap (right). Electrodes 1,2 and 3 are highlighted in red, green and blue, respectively

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[ARTICLE] Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke – Full Text

Abstract

While motor recovery following mild stroke has been extensively studied with neuroimaging, mechanisms of recovery after moderate to severe strokes of the types that are often the focus for novel restorative therapies remain obscure. We used fMRI to: 1) characterize reorganization occurring after moderate to severe subacute stroke, 2) identify brain regions associated with motor recovery and 3) to test whether brain activity associated with passive movement measured in the subacute period could predict motor outcome six months later.

Because many patients with large strokes involving sensorimotor regions cannot engage in voluntary movement, we used passive flexion-extension of the paretic wrist to compare 21 patients with subacute ischemic stroke to 24 healthy controls one month after stroke. Clinical motor outcome was assessed with Fugl-Meyer motor scores (motor-FMS) six months later. Multiple regression, with predictors including baseline (one-month) motor-FMS and sensorimotor network regional activity (ROI) measures, was used to determine optimal variable selection for motor outcome prediction. Sensorimotor network ROIs were derived from a meta-analysis of arm voluntary movement tasks. Bootstrapping with 1000 replications was used for internal model validation.

During passive movement, both control and patient groups exhibited activity increases in multiple bilateral sensorimotor network regions, including the primary motor (MI), premotor and supplementary motor areas (SMA), cerebellar cortex, putamen, thalamus, insula, Brodmann area (BA) 44 and parietal operculum (OP1-OP4). Compared to controls, patients showed: 1) lower task-related activity in ipsilesional MI, SMA and contralesional cerebellum (lobules V-VI) and 2) higher activity in contralesional MI, superior temporal gyrus and OP1-OP4. Using multiple regression, we found that the combination of baseline motor-FMS, activity in ipsilesional MI (BA4a), putamen and ipsilesional OP1 predicted motor outcome measured 6 months later (adjusted-R2 = 0.85; bootstrap p < 0.001). Baseline motor-FMS alone predicted only 54% of the variance. When baseline motor-FMS was removed, the combination of increased activity in ipsilesional MI-BA4a, ipsilesional thalamus, contralesional mid-cingulum, contralesional OP4 and decreased activity in ipsilesional OP1, predicted better motor outcome (djusted-R2 = 0.96; bootstrap p < 0.001).

In subacute stroke, fMRI brain activity related to passive movement measured in a sensorimotor network defined by activity during voluntary movement predicted motor recovery better than baseline motor-FMS alone. Furthermore, fMRI sensorimotor network activity measures considered alone allowed excellent clinical recovery prediction and may provide reliable biomarkers for assessing new therapies in clinical trial contexts. Our findings suggest that neural reorganization related to motor recovery from moderate to severe stroke results from balanced changes in ipsilesional MI (BA4a) and a set of phylogenetically more archaic sensorimotor regions in the ventral sensorimotor trend. OP1 and OP4 processes may complement the ipsilesional dorsal motor cortex in achieving compensatory sensorimotor recovery.

Fig. 2

Fig. 2. Four axial slices representative showing stroke lesion extent in 21 patients (FLAIR images).

Continue —> Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke

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[VIDEO] Bryan Baxter – Sensorimotor Rhythm BCI with TDCS Alters Task Performance – YouTube

Δημοσιεύτηκε στις 25 Οκτ 2016

This talk was given at the BCI Meeting 2016 at Asilomar Conference Grounds on May 31st, 2016.

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[Letter to the Editor] Brief history of transcranial direct current stimulation (tDCS): from electric fishes to microcontrollers | Cambridge Core

Electrical stimulation to treat medical conditions is not a new therapy; it has been used to treat diseases for centuries. The first electricity sources used for electrical stimulation were produced by animal electricity. Antique Egyptians knew about the electrical proprieties of Nile catfish, but it is unclear if (and how) they experimented with them for clinical purposes. The first reported evidence of electrical stimulation arrives some centuries later in antique Greece times, when Plato and Aristotle described the ability of the torpedo fish to generate curative effects by its electric discharges (Althaus, 1873; Rockwell, 1896; Harris, 1908).

The first evidence of transcranial stimulation in history comes in Roman Empire times, when Scribonius Largus (the physician of the Roman Emperor Tiberius Claudius Nero Caesar) described how placing a live torpedo fish over the scalp could relieve headache in a patient (Scribonius Largus, 1529). Perhaps the first person known to have been cured by torpedo fish electricity was Anthero, a freed slave of Tiberius Caesar, who suffered gutta (probably gout) (Cambridge, 1977). In the late 11th century, the Muslim physician in Persia, Ibn-Sidah also suggested the use of torpedo fishes to treat epilepsy (Priori, 2003), placing the live catfish on the brows of the patients (Delbourgo, 2006). The use of electric fish stimulation also spread to Africa, where Jesuit missionaries in early modern Abyssinia (Ethiopia) reported that locals used catfishes as a method of expelling ‘Devils out of the human body’ (Delbourgo, 2006). Fish electricity was maybe the most popular type of electrical stimulation for more than 10 centuries though it is not clear how the effects were measured.

In 1660 the German scientist Otto von Guericke invented the first electrostatic generator (Comroe & Dripps, 1976), a frictional crank-controlled machine. This device could be considered the first stimulator device and its variations were used later by scientists like the Italian anatomist Leopoldo Marco Antonio Caldani in 1756 to stimulate muscles in sheep and frogs (Caldani, 1760). The Middlesex Hospital (England) was probably the first hospital to purchase an electrostatic machine in 1767 (Cambridge, 1977).

In 1745 Ewald Georg von Kleist invented the Leyden jar, the first capacitor in history (Keithley, 1999). This device could store electric charge produced from an electrostatic generator. Experimenters, like Anton de Haen in 1755 (Priestley, 1767) and Benjamin Franklin in 1757 (Franklin, 1757), were able to combine electrostatic generators and the Leyden jar for therapeutic electrification (McWhirter et al. 2015).

In 1773 the anatomist and physiologist John Hunter studied the torpedo fish thoroughly. These investigations were undertaken at the request of John Walsh, who showed that the ‘shocks’ produced by the torpedo fish were caused by the generation of electricity (Walsh, 1773). These kind of animals or fishes have an electric organ that, on brain command, generate a three-dimensional dipole field around their bodies, discharging single-cycle pulses from below 1 Hz to about 65 Hz at rest (Hopkins, 2009). Electric fish electricity is not direct current (DC); nevertheless it is the first reported kind of stimulation in history.

Unlike fish electricity and electrostatic electricity, DC is the flow of electrical charge that does not vary with time, generating a constant signal (Belove & Drossman, 1976). The birth of the DC generator was in the 1st century BC with the so-called Baghdad battery, attributed to the ancient Persian civilization (Scrosati, 2011), but other references attribute the invention to the Parthians, calling it the Parthian galvanic cell (Keyser, 1993). This invention remained forgotten until the 20th century, when the archeologist Wilhelm Köning discovered it in Iraq and it was possibly used for medical purposes. In the 18th century Luigi Galvani invented a DC battery (galvanic battery) and his nephew, Giovanni Aldini, was one of the first persons to utilize DC for clinical applications. Aldini’s most detailed account of DC clinical treatment concerns Luigi Lanzarini, a 27-year-old farmer suffering from melancholy madness (major depression), who had been committed to Santo Orsola Hospital, in Bologna, Italy on 17 May 1801, but first assessing the effects of galvanic currents on his own head (Fitzgerald, 2014). The patient’s mood progressively improved so that Lazarini was apparently completely cured several weeks after the beginning of the treatment (Parent, 2004).

Aldini’s work was the milestone which began the era of DC stimulation for neurological and psychiatric conditions. Later in 1802, Hellwag and Jacobi reported the use of transcranial DC, also reporting the first evidence of phosphenes using DC (Hellwag & Jacobi, 1802; Paulus, 2010). Around 1880 the application of brain stimulation treatments on patients was particularly popular among German psychiatrists, pioneers in electrotherapy, an early tDCS method. Wilhelm Tigges, Rudolph Gottfried Arndt (Steinberg, 2013a ) and Erb (1883) tried to establish clear rules on the most beneficial application methods and doses in order to investigate which results it may produce and under what circumstances (Steinberg, 2014). The experimental designs with larger groups in electric therapy research protocols were a common factor in this age. For example, Arndt used 12 psychotic patients in his 1870 experiment. Despite being very detailed, Arndt’s reports do not provide exact data about the strength of the applied current. Due to controversial reports (some with positive results and others with negative) and the lack of understanding of operating principles, electrotherapy was repeatedly suspected of attaining result through suggestion only (Steinberg, 2013a ). Many other researchers used DC for the treatment of mental disorders during the 19th century and the early part of the 20th century, but the variation of procedures, unclear descriptions, few qualitative details and the misunderstood effect of polarization led to variable and/or inconclusive results. The use of DC stimulation was abandoned from the 1930s (Steinberg, 2013b ).

In 1957 DC reappeared in electrosleep therapy and around 1960–1963 electro-anesthesia research incorporated DC bias. In 1964, motivated by animal studies that reported lasting changes in excitability using prolonged scalp DC stimulation, Lippold and Redfearn used 50–500 µA DC currents in 32 healthy subjects, and reported that anodal current induced an increase in alertness, mood and motor activity, whereas cathodal polarization induced quietness and apathy (Lippold & Redfearn, 1964; Guleyupoglu et al. 2013). Despite several follow-up research works, from the 1970s DC stimulation was once again abandoned, probably due to the introduction of new psychiatric drugs (Dubljević et al. 2014).

It was not until 1998 when the usage of DC was advocated and the modern tDCS was born, when Priori and his colleagues investigated the influence of DC in the brain by testing its effects on cerebral cortex excitability using transcranial magnetic stimulation (Brunoni et al. 2012). Characteristics of tDCS, such as the fact that it is non-invasive, mostly well tolerated and its mild adverse effects, have sparked great interest and increase in clinical studies recently (Brunoni et al. 2012).

The transcranial DC stimulators have evolved from a simple galvanic battery in the 18th and 19th centuries, passing from vacuum tubes and transistors to microprocessors and microcontroller technologies in the 20th century. Progress in microcontroller technology has enabled electronic and biomedical engineers to build precise tDCS devices with a better control of stimulation parameters (Paulus & Opitz, 2013) at reduced costs. The future tDCS designs will focus on obtaining simple systems by way of reducing size, power consumption, weight and enhanced portability (Kouzani et al. 2016).

The tDCS is a promising tool for basic neuroscientists, clinical neurologists and psychiatrists in their quest to causally probe cortical representations of sensorimotor and cognitive functions, to facilitate the treatment of various neuropsychiatric disorders (Schlaug & Renga, 2008) and enhance neurological functions in healthy humans (Dubljević et al. 2014). Although tDCS has demonstrated benefits, the scientific community must communicate carefully about their findings, by providing neutral data to the media and public, since the popular media sometimes consider tDCS as a ‘miracle device’ (Riggall et al. 2015). Such sensationalistic news about the benefits of tDCS leads people to self-administer stimulation, as we can see in some Internet do-it-yourself tDCS forums (Wexler, 2015).

Linked references

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

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Source: Letter to the Editor: Brief history of transcranial direct current stimulation (tDCS): from electric fishes to microcontrollers | Cambridge Core

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[ARTICLE] Paired Associative Stimulation using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot study – Full Text PDF

Abstract

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses braincomputer interface (BCI) technology to monitor patients’ movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar feedback for complete sensorimotor closed loop. To realize this approach, we introduce the recoveriX system, a hardware and software platform for PAS. After 10 sessions of recoveriX training, one stroke patient partially regained control of dorsiflexion in her paretic wrist. A controlled group study is planned with a new version of the recoveriX system, which will use a new FES system and an avatar instead of bar feedback.

I. INTRODUCTION

In conventional rehabilitation therapy, patients are often asked to try to move the paretic limb, or imagine its movement, while a functional electrical stimulator (FES), physiotherapist, or robotic device helps them perform the desired movement. However, if patients cannot perform the movement without help, there is no objective way to determine whether each patient is actually performing the desired motor imagery task. This dissociation between motor commands and sensory feedback may explain why the therapy does not significantly induce the reorganization of the patients’ brain around their lesioned area. To close the feedback loop for paralyzed patients, we used bar feedback and FES based on their motor imagery (MI) [1]–[3]. This paired associative stimulation (PAS) is an important factor for motor recovery [4]–[10]. Neural networks are facilitated when the presynaptic and postsynaptic neurons are both active…

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[Abstract] “How Did I Make It?”: Uncertainty about Own Motor Performance after Inhibition of the Premotor Cortex – Journal of Cognitive Neuroscience.

Journal of Cognitive NeuroscienceAbstract

Optimal motor performance requires the monitoring of sensorimotor input to ensure that the motor output matches current intentions. The brain is thought to be equipped with a “comparator” system, which monitors and detects the congruence between intended and actual movement; results of such a comparison can reach awareness.

This study explored in healthy participants whether the cathodal transcranial direct current stimulation (tDCS) of the right premotor cortex (PM) and right posterior parietal cortex (PPC) can disrupt performance monitoring in a skilled motor task.

Before and after tDCS, participants underwent a two-digit sequence motor task; in post-tDCS session, single-pulse TMS (sTMS) was applied to the right motor cortex, contralateral to the performing hand, with the aim of interfering with motor execution. Then, participants rated on a five-item questionnaire their performance at the motor task. Cathodal tDCS of PM (but not sham or PPC tDCS) impaired the participants’ ability to evaluate their motor performance reliably, making them unconfident about their judgments. Congruently with the worsened motor performance induced by sTMS, participants reported to have committed more errors after sham and PPC tDCS; such a correlation was not significant after PM tDCS.

In line with current computational and neuropsychological models of motor control and awareness, the present results show that a mechanism in the PM monitors and compare intended versus actual movements, evaluating their congruence. Cathodal tDCS of the PM impairs the activity of such a “comparator,” disrupting self-confidence about own motor performance.

Source: MIT Press Journals – Journal of Cognitive Neuroscience – Early Access – Abstract

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