This research was supported by an International Reintegration Grant (n. 14847) from the European Commission to SDB, and by a grant from Polish Narodowe Centrum Nauki (decision No. Dec-2011/01/N/HS6/04092) to JS.

1. Synchronization Tasks
<ol>
	<li>Preparation of Instruments:
	<ol>
		<li>Connect a standard MIDI percussion instrument to the computer via a conventional MIDI interface. 
		NOTE: Data acquisition is realized via a MIDI electronic percussion instrument. The device captures the exact timing of the finger taps during the motor synchronization tasks.</li>
		<li>Open the dedicated software for stimulus presentation and response recording. 
		NOTE: The synchronization task is implemented using standard software for the presentation of audio material and recording of data from a digital MIDI musical instrument (with 1 msec precision).</li>
	</ol>
	</li>
	<li>Sound Material and Procedure:
	<ol>
		<li>From the software interface, select the pacing stimulus to be used in the synchronization task from among three choices (isochronous sequence, music, and amplitude-modulated noise obtained from the wave envelope of the musical stimulus). 
		NOTE: The isochronous sequence is composed of 96 isochronously presented tones (duration = 30 msec). The musical stimulus is a computer-generated piano version of a fragment of the Radetzky March (Opus 228) by Johann Strauss that includes 96 beats (beat = quarter note). Excerpts of the three stimuli are provided as Additional Materials to this manuscript.</li>
		<li>Select the appropriate tempo for the selected pacing stimulus (450, 600, or 750 msec Inter-onset-interval (IOI) / Inter-beat-interval (IBI)) as indicated in the software interface. Ensure that the stimuli are delivered at a comfortable volume level over the headphones.</li>
		<li>Ask the participant to sit in a quiet room in front of the computer monitor.</li>
		<li>Ask the participant to tap on the MIDI percussion instrument using the index finger of her or his dominant hand in synchrony with the tones of the isochronous sequence or with the musical beats for more complex stimuli (music or noise). Instruct the participant to tap as regularly as possible, without changing the tapping rate, while synchronizing with the pacing stimulus.</li>
		<li>Start the stimulus presentation and recording of taps.</li>
		<li>End recording of taps after presenting the last tone or musical beat.</li>
	</ol>
	</li>
	<li>Data Analysis: 
	NOTE: Analyze the data from the synchronized tapping tasks using circular statistics38,39. This method is particularly well-suited for the analysis of synchronization data40,41; moreover, circular statistics are sensitive to individual differences in timing abilities and are therefore able to uncover cases of poor synchronization2,40. The analysis procedure outlined below is implemented using Matlab software (using the CircStat toolbox39).
	<ol>
		<li>Transform the time of the taps relative to the pacing stimuli into angles on the unit circle (from 0-360&#176;) following the procedure indicated by Berens39. 0&#176; (which is equal to 360&#176;) corresponds to the time of the occurrence of the pacing stimulus (i.e., the sounds or musical beats). Use the following formula to obtain the angle for each tap time: [angle(radians) = 2 &#215; &#960; &#215; (time of the tap / IOI)]. Convert radians into degrees with the circ_rad2ang function39.</li>
		<li>Plot the angles obtained in the tapping trial as a distribution of dots on the unit circle. Do this using the circ_plot function39. Provide angles in radians as the argument for the function to display the plot (see example in Figure 1).</li>
		<li>For each tapping trial, use the angles (dots on the circle) to compute the mean resultant vector R 38,39,42 (see Figure 1). Use the circ_mean39 and circ_r39 functions, which allow for the computation of synchronization accuracy and consistency, respectively.</li>
		<li>Compute the synchronization accuracy (i.e., on average, how far from the pacing stimulus the participant taps in a synchronized tapping trial), which corresponds to the angle &#952; of vector R. Use the circ_mean function39. Provide angles in radians as the argument for the function.</li>
		<li>Submit the tapping data to the Rayleigh test43 to assess whether the distribution of the dots around the circle is random, using the circ_rtest function39. Provide angles in radians as the argument for the function. 
		NOTE: In the Rayleigh test, reject the null hypothesis (i.e., circular uniformity, randomly distributed dots around the circle) if the R vector length is large enough (e.g., greater than 0.4), indicating that participants tapped at a given phase relationship with respect to the pacing stimulus above chance. Only when Rayleigh test is significant (i.e., when the distribution of dots around the circle is not random) synchronization accuracy can be properly interpreted.</li>
		<li>Compute the synchronization consistency (i.e., the variability in the discrepancy between the time of the taps and the pacing stimuli), which corresponds to the length of the vector R (from 0 to 1). Use the circ_r function39. Provide angles in radians as the argument for the function. 
		NOTE: The consistency is 1 when all of the taps occur at exactly the same time interval before or after the pacing stimuli; the consistency is 0 when the taps are randomly distributed around the circle.</li>
	</ol>
	</li>
	<li>Evaluation of Individual Results: 
	NOTE: Compare the performance of a participant to a normative group or to a control group to uncover cases of poor synchronization accuracy or poor consistency. To perform this comparison, run a corrected t-test44 implemented in the singlims computer program (<a href="http://homepages.abdn.ac.uk/j.crawford/pages/dept/SingleCaseMethodsComputerPrograms.htm" target="_blank">http://homepages.abdn.ac.uk/j.crawford/pages/dept/SingleCaseMethodsComputerPrograms.htm</a>).
	<ol>
		<li>Open the singlims computer program. Enter the mean and SD of the synchronization accuracy, and the sample size of the normative or control group. Provide the synchronization accuracy for the participant to be compared to the normative or control group. Click on the &#8220;Compute&#8221; button to obtain the results of the corrected t-test. 
		NOTE: The participant performed significantly poorer than the normative or control group when the two-tailed probability of the corrected t-test is below 0.05.</li>
		<li>Enter the mean and SD of the synchronization consistency and sample size of the normative or control group. Provide the synchronization consistency for the participant who is to be compared to the normative or control group.</li>
	</ol>
	</li>
</ol>
2. Rhythm Perception Tasks (Anisochrony Detection)
<ol>
	<li>Preparation of Instruments:
	<ol>
		<li>Open the computer program used for implementing the anisochrony detection tasks. Ensure that the keys of the computer keyboard are properly set to record participants&#8217; answers. 
		NOTE: The rhythm perception tasks are implemented using standard software for running behavioral experiments (i.e., stimulus presentation and recording of behavioral responses).</li>
	</ol>
	</li>
	<li>Sound Material and Procedure:
	<ol>
		<li>Select the stimulus (either isochronous stimulus or music) as indicated by the software interface. Choose the appropriate tempo (450, 600, or 750 msec IOI/IBI) of the selected stimulus. Ensure that the stimuli are delivered over the headphones at a comfortable volume level. 
		NOTE: Stimuli are based on the same auditory material used in the Synchronization tasks. Each stimulus includes only 8 isochronously presented tones or musical beats instead of 96. For each stimulus type, there is a &#8220;change&#8221; version (50% of the trials, n = 24) and a &#8220;no-change&#8221; version (50% of the trials, n = 24). In the change stimuli, the penultimate sound or musical beat occurs earlier or later than expected (by 8, 12, or 16% of the sequence IOI/IBI) based on the previous IOIs/IBIs. In the no-change stimulus, the IOIs/IBIs are completely isochronous.</li>
		<li>Instruct the participant to sit in a quiet room in front of the computer monitor, listen to the stimulus and then judge, after its presentation, whether a change in the interval between the stimuli or beats (i.e., anisochrony) is present or not. Encourage the participant to pay attention to the entire sequence.</li>
		<li>Start the stimulus presentation. Ask the participant to respond by pressing one of two keys on the computer keyboard (i.e., one key for &#8220;change&#8221; or the other key for &#8220;no-change&#8221; responses) after the presentation of the stimulus.</li>
	</ol>
	</li>
	<li>Data Analysis: 
	NOTE: Analyze the data obtained from the rhythm perception task by calculating the discriminability index (d&#8217;) at each level of change (at 8, 12, 16% of the IOI/IBI) and for each IOI/IBI. The higher the d&#8217; value, the greater the sensitivity to anisochronies.
	<ol>
		<li>Consider the responses (n = 48) yielded by each participant for a given stimulus, recorded in the output file by the software used to run the behavioral experiment. Count the number of responses when the anisochrony present in the stimulus has been correctly detected. Compute the Hits rate (i.e., number of Hits / number of change stimuli).</li>
		<li>Count the number of responses when the participant reported a change in the interval between stimuli or beats when there was no change. Compute the False-Alarm (FA) rate (i.e., number of FAs/number of no-change stimuli).</li>
		<li>Calculate the z-score for the Hits rate and FA rate, using the norminv Matlab function (z-score = norminv(Hits rate or FA rate)). Subtract the z-score for the FA rate from the z-score for the Hits rate to obtain d&#8217;.</li>
	</ol>
	</li>
	<li>Evaluation of Individual Results: 
	NOTE: Compare the performance of a participant to a normative or control group to uncover cases of poor rhythm perception. Regarding the results of the synchronization tasks, perform a corrected t-test using the singlims computer program.
	<ol>
		<li>Open the singlims computer program. Enter the mean and SD of d&#8217; and the sample size of the normative or control group. Provide the d&#8217; value for the participant who is to be compared to the normative or control group. 
		NOTE: The participant performed significantly poorer than the normative or control group when the two-tailed probability of the corrected t-test is below 0.05.</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

The goal of the described method is to provide a set of tasks and analysis strategies to characterize the timing abilities of the majority of individuals and detect cases of beat deafness or poor synchronization. The critical steps of the protocol involve 1) the setup of the instruments used for stimulus presentation and collection of finger tapping data and subjects&#8217; responses, 2) data collection using two sets of tasks (synchronization and rhythm perception), 3) analysis of synchronization data with circular stat...

A correction to the Acknowledgements section was made in: Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks.
The Acknowledgements section has been updated from:
This research was supported by an International Reintegration Grant (n. 14847) from the European Commission to SDB and a grant from Polish Ministry for Science and Education to JS.
to:
This research was supported by an International Reintegration Grant (n. 14847) from the European Commission to SDB, and by a grant from Polish Narodowe Centrum Nauki (decision No. Dec-2011/01/N/HS6/04092) to JS.

A correction to the Acknowledgements section was made in: Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks.

Erratum: Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Humans are particularly efficient at processing the duration of events occurring in their environment1. In particular, the ability to perceive the beat of music or the regular ticking of a clock and the ability to move along with it (e.g., in dance or synchronized sports) is widespread in the general population (i.e., in individuals who have not received musical training)2,3. These abilities are underpinned by a complex neuronal network involving cortical brain regions (e.g., the premotor cortex and the supplementary motor area) and subcortical structures, such as the basal ganglia and the cerebellum4-7.
Disruption of this network and consequent poor temporal processing can result from brain damage8-10 or neuronal degeneration, as observed in patients with Parkinson&#8217;s disease11. However, poor perception of duration and poor synchronization to the beat of music can also manifest in healthy individuals in the absence of brain damage. In spite of the fact that the majority can perceive auditory rhythms and synchronize the movement to the beat (e.g., in music), there are notable exceptions. Some individuals have major difficulties in synchronizing their body movements or finger tapping to the beat of music and can exhibit poor beat perception, showing difficulties in discriminating melodies with notes of different duration. This condition has been referred to as &#8220;beat deafness&#8221; or &#8220;dysrhythmia&#8221; 2,12-14. For example, beat deafness was described in a recent study13, in which the case of a patient named Mathieu was reported. Mathieu was particularly inaccurate at bouncing to the beat of rhythmical songs (e.g., a Merengue song). Synchronization was still possible, but only to the sounds of a simple isochronous sequence (e.g., a metronome). Poor synchronization was associated with poor beat perception, as revealed by the Montreal Battery of Evaluation of Amusia (MBEA)15. In an additional task, Mathieu was asked to match the movements of a dancer to the music; interestingly, Mathieu exhibited unimpaired pitch perception.
Poor rhythm perception and poor synchronization, in beat-deaf individuals with spared pitch perception, were observed in further studies2,12,14, thus providing compelling evidence that rhythm disorders can occur in isolation. Beat deafness is therefore distinct from the typical description of congenital amusia (i.e., tone deafness), a neurodevelopmental disorder affecting pitch perception and production16-19. Interestingly, poor rhythm perception and production can co-occur with poor pitch processing in congenital amusia12,16,20. Nevertheless, poor rhythm perception in this case depends on the ability of an individual to perceive pitch variation. When pitch variations in melodies are removed, congenital amusics can successfully discriminate rhythm differences21.
Important individual differences have been observed in beat deafness; this fact deserves particular attention. In most cases, both rhythm perception and synchronization to the beat of music are deficient2,12-14; however, poor synchronization can also occur when rhythm perception is spared2. This dissociation between perception and action in the timing domain has been shown using synchronized tapping tasks with a variety of rhythmic auditory stimuli (e.g., a metronome and music) and using different rhythm perception tasks (e.g., the discrimination of melodies based on different note durations and the detection of deviations from isochrony in rhythmic sequences). This finding is particular relevant because it points to the possible separation of perception and action with regard to timing mechanisms, as previously observed in pitch processing17,22-25. Further dissociations were highlighted depending on the stimulus complexity2. Most poor synchronizers exhibited selective difficulties with complex stimuli (e.g., music or amplitude-modulated noise derived from music), while they still showed accurate and consistent synchronization with simple isochronous sequences; other poor synchronizers showed the opposite pattern. In summary, these results converge in indicating that there are a variety of phenotypes of timing disorders in the general population (as observed in other domains of musical processing such as pitch25,26), which require a sensitive set of tasks to be detected. Characterizing the patterns of rhythm disorders is particularly relevant to shed light on the specific mechanisms that are malfunctioning in the timing system.
The goal of the method illustrated here is to provide a set of tasks that can be used to uncover cases of beat deafness in the general population and detect different subtypes of timing disorders (e.g., affecting perceptual vs. sensorimotor timing or a particular class of rhythmic stimuli). Sensorimotor timing abilities have mostly been examined using finger tapping tasks with auditory material. Participants are asked to tap their index finger in synchrony with auditory stimuli, such as to a sequence of tones equally spaced in time or to music (i.e., in a synchronized or paced tapping task27-29). Another popular paradigm, which has been the source of considerable modeling efforts29-32, is the synchronization-continuation paradigm, in which the participant continues tapping at the rate provided by a metronome after the sound has stopped. Rhythm perception is studied with a variety of tasks ranging from duration discrimination, estimation, bisection (i.e., comparing durations to &#39;short&#39; and &#39;long&#39; standards), and detection of anisochrony (i.e., determining whether there is a deviant interval within an isochronous sequence) to the beat alignment task (i.e., detecting whether a metronome superimposed onto music is aligned with the beat)1,2,20,33,34. Most studies have focused on time perception, beat production or sensorimotor timing, which were tested in isolation. However, it is likely that such different tasks refer to somewhat different abilities (e.g., interval timing vs. beat-based timing, perceptual vs. sensorimotor timing) and do not reflect the functioning of the same timing mechanisms and the associated neuronal circuitry. This issue can be circumvented by using recently proposed batteries of tasks that assess both perceptual and sensorimotor timing abilities. These batteries allow researchers to obtain an exhaustive profile of an individual&#8217;s timing abilities. Examples of such batteries are the beat alignment test (BAT)34, the Battery for the Assessment of Auditory Sensorimotor Timing Abilities (BAASTA)35, and the Harvard Beat Assessment Test (H-BAT)36. These batteries consist of tapping tasks with a variety of rhythmic auditory stimuli ranging from music to isochronous sequences as well as perceptual tasks (e.g., duration discrimination, detection of the alignment of a metronome to the beat of music, and anisochrony detection). In all cases, the same set of musical excerpts was used in perceptual and sensorimotor tasks.
In this paper, we illustrate a set of tasks that are particularly efficient at revealing patterns of rhythm disorders in beat-deaf individuals and poor synchronizers, as shown in previous studies2. These tasks are part of a larger battery of tests, the BAASTA35. Sensorimotor timing abilities are tested by asking participants to tap their finger to the beat of simple and complex auditory stimuli (e.g., isochronous sequences, music, and rhythmic noise derived from musical stimuli)27,28. Perceptual timing is tested with an anisochrony detection task2,20,33,37. A set of isochronous tones is presented. In some cases, one of the tones (e.g., the penultimate) is presented sooner or later than expected based on the isochronous structure of the auditory sequence. Participants are asked to detect deviations from isochrony. The advantage of these sensorimotor and rhythm perception tasks is that they both involve sequences of stimuli (instead of single durations) and stimuli of different complexity. Thus, based on previous evidence, these tasks provide the optimal conditions to uncover different phenotypes of beat deafness and poor synchronization. Particular attention is paid to the technique adopted in the analysis of synchronization data. This technique is based on circular statistics, an approach that is particularly well-suited for examining inaccurate and inconsistent synchronization to the beat.

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uncovering beat deafness: detecting rhythm disorders with synchronized finger tapping and perceptual timing tasks

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here with the goal of uncovering rhythm disorders, such as beat deafness. Beat deafness is characterized by poor performance in perceiving durations in auditory rhythmic patterns or poor synchronization of movement with auditory rhythms (e.g., with musical beats). These tasks include the synchronization of finger tapping to the beat of simple and complex auditory stimuli and the detection of rhythmic irregularities (anisochrony detection task) embedded in the same stimuli. These tests, which are easy to administer, include an assessment of both perceptual and sensorimotor timing abilities under different conditions (e.g., beat rates and types of auditory material) and are based on the same auditory stimuli, ranging from a simple metronome to a complex musical excerpt. The analysis of synchronized tapping data is performed with circular statistics, which provide reliable measures of synchronization accuracy (e.g., the difference between the timing of the taps and the timing of the pacing stimuli) and consistency. Circular statistics on tapping data are particularly well-suited for detecting individual differences in the general population. Synchronized tapping and anisochrony detection are sensitive measures for identifying profiles of rhythm disorders and have been used with success to uncover cases of poor synchronization with spared perceptual timing. This systematic assessment of perceptual and sensorimotor timing can be extended to populations of patients with brain damage, neurodegenerative diseases (e.g., Parkinson&#8217;s disease), and developmental disorders (e.g., Attention Deficit Hyperactivity Disorder).

The tasks described above have been used with success to characterize the timing abilities of individuals without musical training2,34-36. In a recent representative study on beat-deafness2, a group of 99 non-musicians (university students) were screened using two simple synchronization tasks. Participants synchronized their finger tapping with an isochronous sequence and a musical excerpt at a comfortable tempo (with an IOI/IBI of 600 msec). Ten of the participants showed particularly poor synchron...

Watch this Scientific Journal Video about Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks at JoVE.com

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Behavioral tasks that allow for the assessment of perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) are presented. Synchronization of finger tapping to the beat of an auditory stimuli and detecting rhythmic irregularities provides a means of uncovering rhythm disorders.

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here ...

uncovering-beat-deafness-detecting-rhythm-disorders-with-synchronized

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JoVE Journal

Behavior

12.7K Views.  University of Montpellier. Behavioral tasks that allow for the assessment of perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) are presented. Synchronization of finger tapping to the beat of an auditory stimuli and detecting rhythmic irregularities provides a means of uncovering rhythm disorders.

Video: Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated characters) can evoke negative or positive affect (valence) depending on the object's degree of visual and behavioral realism along a dimension of human likeness (DHL) (Figure 1). But studies of affective valence of subjective responses to variously realistic non-human characters have produced inconsistent findings 3, 4, 5, 6. One of a number of reasons for this is that human likeness is not perceived as the hypothesis assumes. While the DHL can be defined following Mori's description as a smooth linear change in the degree of physical humanlike similarity, subjective perception of objects along the DHL can be understood in terms of the psychological effects of categorical perception (CP) 7. Further behavioral and neuroimaging investigations of category processing and CP along the DHL and of the potential influence of the dimension's underlying category structure on affective experience are needed. This protocol therefore focuses on the DHL and allows examination of CP. Based on the protocol presented in the video as an example, issues surrounding the methodology in the protocol and the use in "uncanny" research of stimuli drawn from morph continua to represent the DHL are discussed in the article that accompanies the video. The use of neuroimaging and morph stimuli to represent the DHL in order to disentangle brain regions neurally responsive to physical human-like similarity from those responsive to category change and category processing is briefly illustrated.

Perceptual and Category Processing of the Uncanny Valley Hypothesis&#039; Dimension of Human Likeness: Some Methodological Issues

Investigation of the Uncanny Valley Hypothesis and affective experience requires an understanding of the hypothesis' dimension of human likeness (DHL). This protocol allows representation of the DHL and examination of categorical perception. Use of the same stimuli and fMRI to distinguish brain regions responsive to physical and category change is illustrated.

Mori's Uncanny Valley Hypothesis1,2 proposes that the perception of humanlike characters such as robots and, by extension, avatars (computer-generated ...

Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and retrieve food rewards positioned at a specific location, their motor performance (defined as accuracy and speed) improves progressively over time, and plateaus after persistent training. Once such reaching skill is mastered, its further maintenance does not require constant practice. Here we introduce a single-pellet reaching task to study the acquisition and maintenance of skilled forelimb movements in mice. In this video, we first describe the behaviors of mice that are commonly encountered in this learning and memory paradigm, and then discuss how to categorize these behaviors and quantify the observed results. Combined with mouse genetics, this paradigm can be utilized as a behavioral platform to explore the anatomical underpinnings, physiological properties, and molecular mechanisms of learning and memory.

Persistent practice improves the precision of coordinated movements.&#160;Here we introduce a single-pellet reaching task, which is designed to assess the learning and memory of forelimb skill in mice.&#160;

Reaching for and retrieving objects require precise and coordinated motor movements in the forelimb. When mice are repeatedly trained to grasp and ...

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Transcranial magnetic stimulation (TMS) is a safe, non-invasive brain stimulation technique that uses a strong electromagnet in order to temporarily disrupt information processing in a brain region, generating a short-lived &#8220;virtual lesion.&#8221; Stimulation that interferes with task performance indicates that the affected brain region is necessary to perform the task normally. In other words, unlike neuroimaging methods such as functional magnetic resonance imaging (fMRI) that indicate correlations between brain and behavior, TMS can be used to demonstrate causal brain-behavior relations. Furthermore, by varying the duration and onset of the virtual lesion, TMS can also reveal the time course of normal processing. As a result, TMS has become an important tool in cognitive neuroscience. Advantages of the technique over lesion-deficit studies include better spatial-temporal precision of the disruption effect, the ability to use participants as their own control subjects, and the accessibility of participants. Limitations include concurrent auditory and somatosensory stimulation that may influence task performance, limited access to structures more than a few centimeters from the surface of the scalp, and the relatively large space of free parameters that need to be optimized in order for the experiment to work. Experimental designs that give careful consideration to appropriate control conditions help to address these concerns. This article illustrates these issues with TMS results that investigate the spatial and temporal contributions of the left supramarginal gyrus (SMG) to reading.

Transcranial magnetic stimulation (TMS) is a technique for non-invasively disrupting neural information processing and measuring its effect on behavior. When TMS interferes with a task, it indicates that the stimulated brain region is necessary for normal task performance, allowing one to systematically relate brain regions to cognitive functions.

Transcranial magnetic stimulation (TMS) is a safe, non-invasive brain stimulation technique that uses a strong electromagnet in order to temporarily ...

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in different areas of their brain for the localization of their seizure focus and eloquent areas. After implantation, the patient must remain in the hospital until the pathological area of brain is found and possibly resected. During this time, these patients offer a unique opportunity to the research community because any number of behavioral paradigms can be performed to uncover the neural correlates that guide behavior. Here we present a method for recording brain activity from intracranial implants as subjects perform a behavioral task designed to assess decision-making and reward encoding. All electrophysiological data from the intracranial electrodes are recorded during the behavioral task, allowing for the examination of the many brain areas involved in a single function at time scales relevant to behavior. Moreover, and unlike animal studies, human patients can learn a wide variety of behavioral tasks quickly, allowing for the ability to perform more than one task in the same subject or for performing controls. Despite the many advantages of this technique for understanding human brain function, there are also methodological limitations that we discuss, including environmental factors, analgesic effects, time constraints and recordings from diseased tissue. This method may be easily implemented by any institution that performs intracranial assessments; providing the opportunity to directly examine human brain function during behavior.

Patients implanted with intracranial electrodes provide a unique opportunity to record neurological data from multiple areas of the brain while the patient performs behavioral tasks. Here, we present a method of recording from implanted patients that can be reproducible at other institutions with access to this patient population.

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in ...

Practical Methodology of Cognitive Tasks Within a Navigational Assessment

This paper describes an approach for measuring navigation accuracy relative to cognitive skills. The methodology behind the assessment will thus be clearly outlined in a step-by-step manner. Navigational skills are important when trying to find symbols within a speech-generating device (SGD) that has a dynamic screen and taxonomical organization. The following skills have been found to impact children&#8217;s ability to find symbols when navigating within the levels of an SGD: sustained attention, categorization, cognitive flexibility, and fluid reasoning1,2. According to past studies, working memory was not correlated with navigation1,2.
The materials needed for this method include a computerized tablet, an augmentative and alternative communication application, a booklet of symbols, and the Leiter International Performance Scale-Revised (Leiter-R)3. This method has been used in two previous studies. Robillard, Mayer-Crittenden, Roy-Charland, Minor-Corriveau and B&#233;langer1 assessed typically developing children, while Rondeau, Robillard and Roy-Charland2 assessed children and adolescents with a diagnosis of Autism Spectrum Disorder. The direct observation of this method will facilitate the replication of this study for researchers. It will also help clinicians that work with children who have complex communication needs to determine the children&#8217;s ability to navigate an SGD with taxonomical categorization.

Children who have complex communication needs can benefit from the use of a speech-generating device (SGD). Cognitive skills were identified as having an impact on the ability to navigate between levels of SGDs. This protocol describes the steps needed for Speech-language Pathologists to assess cognitive skills and navigational abilities.

This paper describes an approach for measuring navigation accuracy relative to cognitive skills. The methodology behind the assessment will thus be ...

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is relatively flexible with respect to participant positioning and allows for some head movements during tasks. Additionally, it is inexpensive, light weight, and portable, with very few contraindications to its use. This presents a unique opportunity to study functional brain activity during motor tasks in individuals who are typically developing, as well as those with movement disorders, such as cerebral palsy. An additional consideration when studying movement disorders, however, is the quality of actual movements performed and the potential for additional, unintended movements. Therefore, concurrent monitoring of both blood flow changes in the brain and actual movements of the body during testing is required for appropriate interpretation of fNIRS results. Here, we show a protocol for the combination of fNIRS with muscle and kinematic monitoring during motor tasks. We explore gait, a unilateral multi-joint movement (cycling), and two unilateral single-joint movements (isolated ankle dorsiflexion, and isolated hand squeezing). The techniques presented can be useful in studying both typical and atypical motor control, and can be modified to investigate a broad range of tasks and scientific questions.

Monitoring brain activity during upright motor tasks is of great value when investigating the neural source of movement disorders. Here, we demonstrate a protocol that combines functional near infrared spectroscopy with continuous monitoring of muscle and kinematic activity during 4 types of motor tasks.

There are several advantages that functional near-infrared spectroscopy (fNIRS) presents in the study of the neural control of human movement. It is ...

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular coupling, fNIRS is able to measure the haemoglobin concentration changes secondary to neuronal activity. Compared to other neuroimaging techniques, fNIRS represents a good compromise in terms of spatial and temporal resolution. Moreover, it is portable, lightweight, less sensitive to motion artifacts and does not impose significant physical restraints. It is therefore appropriate to monitor a wide range of cognitive tasks (e.g., auditory, gait analysis, social interaction) and different age populations (e.g., new-borns, adults, elderly people). The recent development of fiberless fNIRS devices has opened the way to new applications in neuroscience research. This represents a unique opportunity to study functional activity during real-world tests, which can be more sensitive and accurate in assessing cognitive function and dysfunction than lab-based tests. This study explored the use of fiberless fNIRS to monitor brain activity during a real-world prospective memory task. This protocol is performed outside the lab and brain haemoglobin concentration changes are continuously measured over the prefrontal cortex while the subject walks around in order to accomplish several different tasks.

Monitoring brain activity outside the lab without physical constraints presents methodological challenges. A fiberless, wearable functional Near Infrared Spectroscopy (fNIRS) system was used to measure brain activity during an ecological prospective memory task. It was demonstrated that this system could be used to monitor brain activity during non-lab based experiments.

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular ...

Event-related Potentials During Target-response Tasks to Study Cognitive Processes of Upper Limb Use in Children with Unilateral Cerebral Palsy

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral motor impairments that are frequently dominated in the upper limb. In addition to a reduced movement capacity of the affected upper limb, several children with unilateral CP show a reduced awareness of the remaining movement capacity of that limb. This phenomenon of disregarding the preserved capacity of the affected upper limb is regularly referred to as Developmental Disregard (DD). Different theories have been postulated to explain DD, each suggesting slightly different guidelines for therapy. Still, cognitive processes that might additionally contribute to DD in children with unilateral CP have never been directly studied. The current protocol was developed to study cognitive aspects involved in upper limb control in children with unilateral CP with and without DD. This was done by recording event-related potentials (ERPs) extracted from the ongoing EEG during target-response tasks asking for a hand-movement response. ERPs consist of several components, each of them associated with a well-defined cognitive process (e.g., the N1 with early attention processes, the N2 with cognitive control and the P3 with cognitive load and mental effort). Due to its excellent temporal resolution, the ERP technique enables to study several covert cognitive processes preceding overt motor responses and thus allows insight into the cognitive processes that might contribute to the phenomenon of DD. Using this protocol adds a new level of explanation to existing behavioral studies and opens new avenues to the broader implementation of research on cognitive aspects of developmental movement restrictions in children.

Several children with unilateral Cerebral Palsy seem to disregard the preserved capacity of their affected upper limb. This Developmental Disregard is extensively described in the literature but the involved cognitive processes have not been studied. To study underlying cognitive factors of upper limb control, an event-related potential protocol was developed.

Unilateral Cerebral Palsy (CP) is a neurodevelopmental disorder that is a very common cause of disability in childhood. It is characterized by unilateral ...

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. Usually, only one of those tasks is chosen to examine lexical access in a study. Using two exemplar experiments, this paper introduces an approach to include both the lexical decision task and the naming task in a study. In the first experiment, the stimuli were Chinese characters with frequency and regularity manipulated. In the second experiment, the stimuli were switched to Chinese two-character words, in which the word frequency and the regularity of the leading character were manipulated. The logic of these two exemplar experiments was to explore some important issues such as the role of phonology on recognition by comparing the frequency effect between both the tasks. The results revealed different patterns of lexical access from those reported in the alphabetic systems. The results of Experiment 1 manifested a larger frequency effect in the naming task as compared to the LDT, when the stimuli were Chinese characters. And it is noteworthy that, in Experiment 1, when the stimuli were regular Chinese characters, the frequency effect observed in the naming task was roughly equivalent to that in the LDT. However, a smaller frequency effect was shown in the naming task as compared to the LDT, when the stimuli were switched to Chinese two-character words in Experiment 2. Taking advantage of the respective demands and characteristics in both tasks, researchers can obtain a more complete and precise picture of character/word recognition.

Researchers adopt both the lexical decision task and the naming task to investigate some important topics such as character/word recognition by comparing the frequency effect between these two tasks. This article introduces this approach through two exemplar experiments and elaborates on the underlying logic.

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. ...

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors

Diagnostic ultrasound imaging has been a common tool in medical practice for several decades. It provides a safe and effective method for imaging structures internal to the body. There has been a recent increase in the use of ultrasound technology to visualize the shape and movements of the tongue during speech, both in typical speakers and in clinical populations. Ultrasound imaging of speech has greatly expanded our understanding of how sounds articulated with the tongue (lingual sounds) are produced. Such information can be particularly valuable for speech-language pathologists. Among other advantages, ultrasound images can be used during speech therapy to provide (1) illustrative models of typical (i.e.&#160;&#34;correct&#34;) tongue configurations for speech sounds, and (2) a source of insight into the articulatory nature of deviant productions. The images can also be used as an additional source of feedback for clinical populations learning to distinguish their better productions from their incorrect productions, en route to establishing more effective articulatory habits.
Ultrasound feedback is increasingly used by scientists and clinicians as both the expertise of the users increases and as the expense of the equipment declines. In this tutorial, procedures are presented for collecting ultrasound images of the tongue in a clinical context. We illustrate these procedures in an extended example featuring one common error sound, American English /r/. Images of correct and distorted /r/ are used to demonstrate (1) how to interpret ultrasound images, (2) how to assess tongue shape during production of speech sounds, (3), how to categorize tongue shape errors, and (4), how to provide visual feedback to elicit a more appropriate and functional tongue shape. We present a sample protocol for using real-time ultrasound images of the tongue for visual feedback to remediate speech sound errors. Additionally, example data are shown to illustrate outcomes with the procedure.

Ultrasound imaging can be used to display the shape and movements of the tongue in real time during speech. The images can be used to determine the nature of speech sound errors. Visual feedback of the tongue can be used to facilitate improvements in speech sound production in clinical populations.