Name: brain infarct segmentation and registration on mri or ct for lesion-symptom mapping
Uploaded: 2019-09-25T13:00:00
Description: Watch this Scientific Journal Video about Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping at JoVE.com

The work of Dr. Biesbroek is supported by a Young Talent Fellowship from the Brain Center Rudolf Magnus of the University Medical Center Utrecht. This work and the Meta VCI Map consortium are supported by Vici Grant 918.16.616 from ZonMw, The Netherlands, Organisation for Health Research and Development, to Geert Jan Biessels. The authors would like to thank Dr. Tanja C.W. Nijboer for sharing scans that were used in one of the figures.

This protocol follows the guidelines of our institutions human research ethics committee.
1. Collection of Scans and Clinical Data
<ol>
	<li>Collect brain CT or MRI scans of patients with ischemic stroke. Most scanners save the scans as DICOM (Digital Imaging and Communications in Medicine) files that can be copied to a hard disk or server. 
	NOTE: Scans from every scanner type, scan protocol, and MRI field strength can be used, as long as 1) the time window requirement...

LSM is a powerful tool to study the functional architecture of the human brain. A crucial step in any lesion-symptom mapping study is the preprocessing of imaging data, segmentation of the lesion and registration to a brain template. Here, we report a standardized pipeline for lesion segmentation and registration for the purpose of lesion-symptom mapping. This method can be performed with freely available image processing tools, can be used to process both CT and structural MRI scans, and covers the entire process of pre...

Lesion-symptom mapping (LSM), also called lesion-behavior mapping, is an important tool for studying the functional architecture of the human brain1. In lesion studies, brain function is inferred and localized by studying patients with acquired brain lesions. The first case studies linking neurological symptoms to specific brain locations performed in the nineteenth century&#160;already provided fundamental insights into the anatomical correlates of language and several other cognitive processes2. Yet, the neuroanatomical correlates of many aspects of cognition and&#160;other brain functions remained elusive. In the past decades, improved structural brain imaging methods and technical advances have enabled large-scale in vivo LSM studies with high spatial resolution (i.e., at the level of individual voxels or specific cortical/subcortical regions of interest)1,2. With these methodological advances, LSM has become an increasingly popular method in cognitive neuroscience and continues to offer new insights into the neuroanatomy of cognition and neurological symptoms3. A crucial step in any LSM study is the accurate segmentation of lesions and registration to a brain template. However, a comprehensive tutorial for the preprocessing of brain imaging data for the purpose of LSM is lacking.
Provided here is a complete tutorial for a standardized lesion segmentation and registration method. This method provides researchers with a pipeline for&#160;standardized brain image processing and an overview of potential pitfalls that must be avoided. The presented image processing pipeline was developed through international collaborations4 and is part of the framework of the recently founded Meta VCI map consortium, whose purpose is performing multicenter lesion-symptom mapping studies in vascular cognitive impairment &#60;www.metavcimap.org&#62;5. This method has been designed to process both CT and MRI scans from multiple vendors and heterogeneous scan protocols to allow combined processing of imaging datasets from different sources. The required RegLSM software and all other software needed for this protocol is freely available except for MATLAB, which requires a license. This tutorial focuses on the segmentation and registration of brain infarcts, but this image processing pipeline can also be used for other lesions, such as white matter hyperintensities6.
Prior to initiating an LSM study, a basic understanding of the general concepts and pitfalls is required. Several detailed guidelines and a hitchhiker&#39;s guide are available1,3,6. However, these reviews do not provide a detailed hands-on tutorial for the practical steps involved in gathering and converting brain scans to a proper format, segmenting the brain infarct, and registering the scans to a brain template. The present paper provides such a tutorial. General concepts of LSM are provided in the introduction with references for further reading on the subject.
General aim of lesion-symptom mapping studies
From the perspective of cognitive neuropsychology, brain injury can be used as a model condition to better understand the neuronal underpinnings of certain cognitive processes and to obtain a more complete picture of the cognitive architecture of the brain1. This is a classic approach in neuropsychology that was first applied in post-mortem studies in the nineteenth century by pioneers like Broca and Wernicke2. In the era of functional brain imaging, the lesion approach has remained a crucial tool in neuroscience because it provides proof that lesions in a specific brain region disrupt task performance, while functional imaging studies demonstrate brain regions that are activated during the task performance. As such, these approaches provide complementary information1.
From the perspective of clinical neurology, LSM studies can clarify the relationship between the lesion location and cognitive functioning in patients with acute symptomatic infarcts, white matter hyperintensities, lacunes, or other lesion types (e.g., tumors). Recent studies have shown that such lesions in strategic brain regions are more relevant in explaining cognitive performance than global lesion burden2,5,7,8. This approach has the potential to improve understanding of the pathophysiology of complex disorders (in this example, vascular cognitive impairment) and may provide opportunities for developing new diagnostic and prognostic tools or supporting treatment strategies2.
LSM also has applications beyond the field of cognition. In fact, any variable can be related to lesion location, including clinical symptoms, biomarkers, and functional outcome. For example, a recent study determined infarct locations that were predictive of functional outcome after ischemic stroke10.
Voxel-based versus region of interest-based lesion-symptom mapping
To perform lesion-symptom mapping, lesions need to be segmented and registered to a brain template. During the registration procedure, each patient&#39;s brain is spatially aligned (i.e., normalized or registered to a common template) to correct for differences in brain size, shape, and orientation so that each voxel in the lesion map represents the same anatomical structure for all patients7. In standard space, several types of analyses can be performed, which are briefly summarized here.
A crude lesion-subtraction analysis can be performed to show the difference in lesion distribution in patients with deficits compared to patients without deficits. The resulting subtraction map show regions that are more often damaged in patients with deficits and spared in patients without deficits1. Though a lesion-subtraction analysis can provide some insights into correlates of a specific function, it provides no statistical proof and is now mostly used when the sample size is too low to provide enough statistical power for voxel-based lesion-symptom mapping.
In voxel-based lesion-symptom mapping, an association between the presence of a lesion and cognitive performance is determined at the level of each individual voxel in the brain (Figure 1). The main advantage of this method is the high spatial resolution. Traditionally, these analyses have been performed in a mass-univariate approach, which warrants correction for multiple testing and introduces a spatial bias caused by inter-voxel correlations that are not taken into account1,10,11. Recently developed approaches that do take inter-voxel correlations into account (usually referred to as multivariate lesion-symptom mapping methods, such as Bayesian analysis13, support vector regression4,14, or other machine learning algorithms15) show promising results and appear to improve the sensitivity and specificity of findings from voxel-wise LSM analyses compared to traditional methods. Further improvement and validation of multivariate methods for voxel-wise LSM is an ongoing process. The best method choice for specific lesion-symptom mapping depends on many factors, including the distribution of lesions, outcome variable, and underlying statistical assumptions of the methods.
In the region of interest (ROI)-based lesion-symptom mapping, an association between the lesion burden within a specific brain region and cognitive performance is determined (see Figure 1 in Biesbroek et al.2 for an illustration). The main advantage of this method is that it considers the cumulative lesion burden within an anatomical structure, which in some cases may be more informative than a lesion in a single voxel. On the other hand, ROI-based analyses have limited power for detecting patterns that are only present in a subset of voxels in the region16. Traditionally, ROI-based lesion-symptom mapping is performed using logistic or linear regression. Recently, multivariate methods that deal better with collinearity have been introduced (e.g., Bayesian network analysis17, support vector regression4,18, or other machine learning algorithms19), which may improve the specificity of findings from lesion-symptom mapping studies.
Patient selection
In LSM studies, patients are usually selected based on a specific lesion type (e.g., brain infarcts or white matter hyperintensities) and the time interval between diagnosis and neuropsychological assessment (e.g., acute vs. chronic stroke). The optimal study design depends on the research question. For example, when studying the functional architecture of the human brain, acute stroke patients are ideally included because functional reorganization has not yet occurred in this stage, whereas chronic stroke patients should be included when studying the long-term effects of stroke on cognition. A detailed description of considerations and pitfalls in patient selection is provided elsewhere7.
Brain image preprocessing for the purpose of lesion-symptom mapping
Accurate lesion segmentation and registration to a common brain template are crucial steps in lesion-symptom mapping. Manual segmentation of lesions remains the gold standard for many lesion types, including infarcts7. Provided is a detailed tutorial on criteria for manual infarct segmentation on CT scans, diffusion weighted imaging (DWI), and fluid-attenuated inversion recovery (FLAIR) MRI sequences in both acute and chronic stages. The segmented infarcts (i.e., the 3D binary lesion maps) need to be registered before any across-subject analyses are performed. This protocol uses the registration method RegLSM, which was developed in a multicenter setting4. RegLSM applies linear and non-linear registration algorithms based on elastix20 for both CT and MRI, with an additional CT processing step specifically designed to enhance registration quality of CT scans21. Furthermore, RegLSM allows for using different target brain templates and an (optional) intermediate registration step to an age-specific CT/MRI template22. The possibility of processing both CT and MRI scans and its customizability regarding intermediate and target brain templates makes RegLSM a highly suitable image processing tool for LSM. The entire process of preparing and segmenting CT/MRI scans, registration to a brain template, and manual corrections (if required) are described in the next section.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59653/59653fig1.jpg" /> 
Figure 1: Schematic illustration of the concept of voxel-based lesion-symptom mapping. The upper part shows the brain image pre-processing steps consisting of segmenting the lesion (an acute infarct in this case) followed by registration to a brain template (the MNI-152 template in this case). Below, a part of the registered binary lesion map of the same patient is shown as a 3D grid, where each cube represents a voxel. Taken together with the lesion maps of 99 other patients, a lesion overlay map is generated. For each voxel, a statistical test is performed to determine the association between lesion status and cognitive performance. The chi-squared test shown here is just an example, any statistical test could be used. Typically, hundreds of thousands of voxels are tested throughout the brain, followed by a correction for multiple comparisons. <a href="https://www.jove.com/files/ftp_upload/59653/59653fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="0" cellpadding="0" cellspacing="0" style="width:611px;" width="609">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">dcm2niix</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td style="width:172px;">free download https://github.com/rordenlab/dcm2niix</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">ITK-SNAP</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download www.itksnap.org</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:22px;width:223px;">MATLAB</td>
			<td style="width:94px;">MathWorks</td>
			<td style="width:123px;">N/A</td>
			<td>Version 2015a or higher</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">MRIcron</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download https://www.nitrc.org/projects/mricron</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">RegLSM</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download www.metavcimap.org/support/software-tools</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:223px;">SPM12b</td>
			<td style="width:94px;">N/A</td>
			<td style="width:123px;">N/A</td>
			<td>free download https://www.fil.ion.ucl.ac.uk/spm/</td>
		</tr>
	</tbody>
</table>

brain infarct segmentation and registration on mri or ct for lesion-symptom mapping

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a group of patients. With recent advances in brain imaging and image processing, LSM has become a popular tool in cognitive neuroscience. LSM can provide fundamental insights into the functional architecture of the human brain for a variety of cognitive and non-cognitive functions. A crucial step in performing LSM studies is the segmentation of lesions on brains scans of a large group of patients and registration of each scan to a common stereotaxic space (also called standard space or a standardized brain template). Described here is an open-access, standardized method for infarct segmentation and registration for the purpose of LSM, as well as a detailed and hands-on walkthrough based on exemplary cases. A comprehensive tutorial for the manual segmentation of brain infarcts on CT scans and DWI or FLAIR MRI sequences is provided, including criteria for infarct identification and pitfalls for different scan types. The registration software provides multiple registration schemes that can be used for processing of CT and MRI data with heterogeneous acquisition parameters. A tutorial on using this registration software and performing visual quality checks and manual corrections (which are needed in some cases) is provided. This approach provides researchers with a framework for the entire process of brain image processing required to perform an LSM study, from gathering of the data to final quality checks of the results.

Exemplar cases of brain infarct segmentations on CT (Figure 3), DWI (Figure 5), and FLAIR (Figure 6) images, and subsequent registration to the MNI-152 template are provided here. The registration results shown in Figure 3B and Figure 5C were not entirely successful, as there was misalignment near the frontal horn of the ventricle. The regis...

Watch this Scientific Journal Video about Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping at JoVE.com

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a ...

Lesion-symptom mapping is a powerful tool to localize brain functions by studying patients with brain lesions. An important step involves image processing, including lesion segmentation and registration to standard space. This protocol describes a unified framework that works with all structural imaging modalities, providing a consistent output of lesions in standard space for use in lesion-symptom mapping studies.The lesions must be transformed to standard space to allow for comparisons across subjects. Each patient's brain must be spatially aligned to correct for differences in brain size and shape. While using this method, it helps to see how each step should be performed, rather than following written instructions alone.It also requires a good understanding of brain images, which makes a visual demonstration useful. This protocol describes how to perform the processing steps required for lesion-symptom mapping. Once the lesion maps are registered to standard space, lesion-symptom mapping can be performed on large groups of patients.When all the lesion maps are transformed to standard space, each voxel represents the same brain region across subjects. This allows for statistical analyses to be performed, for example, to test if the presence of a lesion in a particular voxel is associated with a cognitive deficit. Begin by collecting brain CT or MRI scans of patients with ischemic stroke.Most scanners have the scans as DICOM files that can be copied to a hard disk or server. Collect the clinical variables in a data file by making separate rows for each case and columns for each clinical variable. For infarct segmentation, include at least the date of stroke and the date of imaging or a variable that indicates the time interval between stroke and imaging.To convert the DICOM images to uncompressed NIfTI files, using the DICOM-to-NIfTI tool, type the command seen on-screen here in the command prompt, using the folder path to the DICOM files. An example of the command with the folder paths inserted can be seen here. This command will run the executable, convert the DICOM images in the selected folder, and save the NIfTI files in this folder.Finally, organize the NIfTI files in a convenient folder structure, with a subfolder for each case, before beginning infarct segmentation. First, assure that the scan was performed at least 24 hours after stroke symptom onset. Otherwise, the acute infarct will not be visible on CT or will only be partially visible, and the scan cannot be used for segmentation.Open the native CT using ITK-SNAP software by selecting File, then Open Main Image from the dropdown menu. Then click Browse, and select the file to open the scan. Now identify the infarct based on imaging characteristics.First, note that infarcts have a low signal compared to normal brain tissue. In the acute stage, large infarcts can cause mass effect, resulting in displacement of surrounding tissues, compression of ventricles, midline shift, and obliteration of sulci. There can be hemorrhagic transformation, which is visible as regions with high signal within the infarct.In the chronic stage, the infarct will consist of a hypodense, cavitated center, with a similar density as the cerebrospinal fluid, and a less hypodense rim, which represents damaged brain tissue. In case of large infarcts, there can be ex vacuo enlargement of adjacent sulci or ventricles. Segment the infarcted brain tissue using the Paintbrush Mode from the main toolbar, using left-click to draw and right-click to erase.Alternatively, use the Polygon Mode to place anchor points at the borders of the lesion, and hold the left mouse button while moving the mouse over the borders of the lesion. Once all points are connected, click accept to fill the delineated area. After finishing the segmentation, save it as a binary NIfTI file in the same folder as the scan by clicking Segmentation and Save Segmentation Image from the dropdown menu, then save the segmentation by giving it the exact same name as the segmented scan, with the extension of lesion.First, assure that the DWI scan was performed within seven days of stroke onset. Infarcts are visible on DWI within several hours after stroke onset, and their visibility on DWI gradually decreases after approximately seven days. Next, identify and annotate the infarcted brain tissue based on the high signal on DWI and low signal on the ADC map.Do not mistake a high diffusion signal near interfaces between air and either tissue or bone, which are a commonly observed artifact on DWI. For FLAIR imaging, first check that the scan was performed 48 hours after stroke symptom onset. In the hyperacute stage, the infarct is usually not visible, or the exact boundaries are unclear.Next, open the FLAIR image in ITK-SNAP, as well as the T1-weighted scan in a separate window for reference, if available. In the acute stage, the infarct is visible as a somewhat homogeneous hyperintense lesion, with or without swelling and mass effect. If available, use the DWI to differentiate between the acute infarct and chronic lesions, such as white matter hyperintensities.To register images, first download RegLSM, and use this tool to process CT scans or any kind of MRI sequence. The registration procedure can be seen in the figure shown here. Next, open MATLAB Version 2015a or higher, set the current folder to RegLSM, then enable SPM by typing addpath followed by the folder name for SPM.Next, type RegLSM to open the graphic interface. To perform the registration for a single case, select Test Mode in the Registration dropdown menu. Then use the Open Image button to select the segmented scan, the annotation, and optionally the T1, and select the desired registration scheme.Alternatively, Batch Mode can be used to register all cases in the selected folder. Ensure that RegLSM saved the resulting registration parameters, the registered scans, and the registered lesion map in the automatically generated subfolders. Now, review the registration results by selecting Check Results in the RegLSM interface, and browse to the main folder with these results.Scroll through the registered scan, and use the crosshair to check the alignment to the MNI152 template, paying attention to recognizable anatomical landmarks. Be sure to mark all failed registrations in a separate column in the previously created data file for subsequent manual correction. For any lesion maps that do need correction, open the MNI152 T1 template in ITK-SNAP, then select Open Segmentation from the Segmentation menu, and choose the registered lesion map, which will overlay onto the template.Also, open the registration results in a separate window for reference. Correct the registered lesion map in ITK-SNAP for any type of misalignment, using the brush function to add voxels with left-click or remove voxels with right-click. Finally, save the corrected lesion map as a NIfTI file in the same folder as the uncorrected lesion map, then save the segmentation by giving it the exact same name as the uncorrected lesion map, with the extension of corrected.Here, we see CT scans for a single patient. The initial scan cannot be used for segmentation because the infarct is not yet visible, even though the CT perfusion maps show ischemia. The CT on day six shows swelling of the infarcted brain tissue with slight midline shift and hemorrhagic transformation.The scan after four months shows brain tissue loss with ex vacuo enlargement of ventricles and nearby sulci. The result of registration of the CT on day six to the MNI152 template is seen here. The registration algorithm insufficiently compensated for the midline shift and compression of the left ventricle, which required a manual correction.After correction, the registered lesion map is an accurate representation of the infarct in native space and can be used for lesion-symptom mapping. Here, we see comparison of the registered DWI sequence and the corresponding registered infarct with the MNI152 template. Note the slight error at the head of the right caudate nucleus.This required a manual correction of the segmentation in standard space. Finally, this figure shows the results of the registration of the MRI FLAIR sequence on day three, which is adequate and requires no manual correction. This protocol covers the process of preparing imaging data for lesion-symptom mapping, including practical guidelines on how to perform accurate infarct segmentation and registration to a brain template.The lesion maps can be used in lesion-symptom mapping studies to localize a specific cognitive function or to use lesion location to predict cognitive outcome in stroke patients. This protocol accelerates and harmonizes the workflow for lesion-symptom mapping studies, enabling researchers to perform large multicenter studies including hundreds to thousands of subjects.

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Research

JoVE Journal

Behavior

Flat-floored Air-lifted Platform: A New Method for Combining Behavior with Microscopy or Electrophysiology on Awake Freely Moving Rodents

It is widely acknowledged that the use of general anesthetics can undermine the relevance of electrophysiological or microscopical data obtained from a living animal&#8217;s brain. Moreover, the lengthy recovery from anesthesia limits the frequency of repeated recording/imaging episodes in longitudinal studies. Hence, new methods that would allow stable recordings from non-anesthetized behaving mice are expected to advance the fields of cellular and cognitive neurosciences. Existing solutions range from mere physical restraint to more sophisticated approaches, such as linear and spherical treadmills used in combination with computer-generated virtual reality. Here, a novel method is described where a head-fixed mouse can move around an air-lifted mobile homecage and explore its environment under stress-free conditions. This method allows researchers to perform behavioral tests (e.g., learning, habituation or novel object recognition) simultaneously with two-photon microscopic imaging and/or patch-clamp recordings, all combined in a single experiment. This video-article describes the use of the awake animal head fixation device (mobile homecage), demonstrates the procedures of animal habituation, and exemplifies a number of possible applications of the method.

This method creates a tangible, familiar environment for the mouse to navigate and explore during microscopic imaging or single-cell electrophysiological recordings, which require firm fixation of the animal&#8217;s head.

It is widely acknowledged that the use of general anesthetics can undermine the relevance of electrophysiological or microscopical data obtained from a ...

A General Method for Evaluating Deep Brain Stimulation Effects on Intravenous Methamphetamine Self-Administration

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people. There is a need for novel, effective and practical treatment strategies that are validated in animal models. Neuromodulation, including deep brain stimulation (DBS) therapy, refers to the use of electricity to influence pathological neuronal activity and has shown promise for psychiatric disorders, including drug dependence. DBS in clinical practice involves the continuous delivery of stimulation into brain structures using an implantable pacemaker-like system that is programmed externally by a physician to alleviate symptoms. This treatment will be limited in methamphetamine users due to challenging psychosocial situations. Electrical treatments that can be delivered intermittently, non-invasively and remotely from the drug-use setting will be more realistic. This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence. Methamphetamine dependence is rapidly developed in rodents using an operant paradigm of intravenous (IV) self-administration that incorporates a period of extended access to drug and demonstrates both escalation of use and high motivation to obtain drug.

This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence.

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people.  There is a need ...

A Method for Evaluating the Reinforcing Properties of Ethanol in Rats without Water Deprivation, Saccharin Fading or Extended Access Training

Operant oral self-administration methods are commonly used to study the reinforcing properties of ethanol in animals. However, the standard methods require saccharin/sucrose fading, water deprivation and/or extended training to initiate operant responding in rats. This paper describes a novel and efficient method to quickly initiate operant responding for ethanol that is convenient for experimenters and does not require water deprivation or saccharin/sucrose fading, thus eliminating the potential confound of using sweeteners in ethanol operant self-administration studies. With this method, Wistar rats typically acquire and maintain self-administration of a 20% ethanol solution in less than two weeks of training. Furthermore, blood ethanol concentrations and rewards are positively correlated for a 30 min self-administration session. Moreover, naltrexone, an FDA-approved medication for alcohol dependence that has been shown to suppress ethanol self-administration in rodents, dose-dependently decreases alcohol intake and motivation to consume alcohol for rats self-administering 20% ethanol, thus validating the use of this new method to study the reinforcing properties of alcohol in rats.

This protocol describes a novel and efficient method to quickly initiate operant responding for ethanol in rats that, contrary to standard methods, does not require water deprivation or saccharin/sucrose fading to initiate responding.

Operant oral self-administration methods are commonly used to study the reinforcing properties of ethanol in animals. However, the standard methods ...

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Proprioceptive drift, which is a perceptual shift in body-part position from the unseen real body to a visible body-like image, has been measured as the behavioral correlate for the sense of ownership. Previously, the estimation of proprioceptive drift was limited to one spatial dimension, such as height, width, or depth. As the hand can move freely in 3D, measuring proprioceptive drift in only one dimension is not sufficient for the estimation of the drift in real life situations. In this article, we provide a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion by combining an objective behavioral measurement (hand position tracking) and subjective, phenomenological assessment (subjective assessment of hand position and questionnaire) with a sophisticated machine learning approach. This technique permits not only an investigation of the underlying mechanisms of the sense of ownership and agency but also assists in the rehabilitation of a missing or paralyzed limb and in the design rules of real-time control systems with a self-body-like usability, in which the operator controls the system as if it were part of his/her own body.

This article describes a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion and combining a psychophysical procedure with an analysis using machine learning.

Proprioceptive drift, which is a perceptual shift in body-part position from the unseen real body to a visible body-like image, has been measured as the ...

fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance production. The most straightforward manner of musical production is singing, but, from an imaging perspective, it still presents more challenges than listening because it involves motor activity. The accurate singing of musical intervals requires integration between auditory feedback processing and vocal motor control in order to correctly produce each note. This protocol presents a method that permits the monitoring of neural activations associated with the vocal production of consonant and dissonant intervals. Four musical intervals, two consonant and two dissonant, are used as stimuli, both for an auditory discrimination test and a task that involves first listening to and then reproducing given intervals. Participants, all female vocal students at the conservatory level, were studied using functional Magnetic Resonance Imaging (fMRI) during the performance of the singing task, with the listening task serving as a control condition. In this manner, the activity of both the motor and auditory systems was observed, and a measure of vocal accuracy during the singing task was also obtained. Thus, the protocol can also be used to track activations associated with singing different types of intervals or with singing the required notes more accurately. The results indicate that singing dissonant intervals requires greater participation of the neural mechanisms responsible for the integration of external feedback from the auditory and sensorimotor systems than does singing consonant intervals.

The neural correlates of listening to consonant and dissonant intervals have been widely studied, but the neural mechanisms associated with production of consonant and dissonant intervals are less well known. In this article, behavioral tests and fMRI are combined with interval identification and singing tasks to describe these mechanisms.

The neural correlates of consonance and dissonance perception have been widely studied, but not the neural correlates of consonance and dissonance ...

Key Elements of Photo Attraction Bioassay for Insect Studies or Monitoring Programs

Optimized visual attractants will increase insect trapping efficiency by using the target insect&#39;s innate behaviors (positive photo-taxis) as a means to lure the insect into a population control or monitoring trap. Light emitting diodes (LEDs) have created customizable lighting options with specific wavelengths (colors), intensities, and bandwidths, all of which can be customized to the target insects. Photo-attraction behavioral bioassays can use LEDs to optimize the attractive color(s) for an insect species down to specific life history stages or behaviors (mating, feeding, or seeking shelter). Researchers must then confirm the bioassay results in the field and understand the limited attractive distance of the visual attractants.
The cloverleaf bioassay arena is a flexible method to assess photo attraction while also assessing a range of natural insect behaviors such as escape and feeding responses. The arena can be used for terrestrial or aerial insect experiments, as well as diurnal, and nocturnal insects. Data collection techniques with the arena are videotaping, counting contact with the lights, or physically collecting the insects as they are attracted towards the lights. The assay accounts for insects that make no-choice and the arenas can be single (noncompetitive) color or multiple (competitive) colors. The cloverleaf design causes insects with strong thigmotaxis to return to the center of the arena where they can view all the options in a competitive LED tests. The cloverleaf arena presented here has been used with mosquitoes, bed bugs, Hessian fly, house flies, biting midges, red flour beetles, and psocids. Bioassays are used to develop accurate and effective insect traps to guide the development and optimization of insect traps used to monitor pest population fluctuations for disease vector risk assessments, the introduction of invasive species, and/or be used for population suppression.

Photo-attraction bioassay arenas are used to determine the optimal light color(s) to maximize insect attraction; however bioassays and methods are specific to target insect behaviors and habitats. Customizable equipment and modifications are explained for nocturnal or diurnal and terrestrial or aerial insects.

Optimized visual attractants will increase insect trapping efficiency by using the target insect&#39;s innate behaviors (positive photo-taxis) as a means ...

The Unpredictable Chronic Mild Stress Protocol for Inducing Anhedonia in Mice

Depression is a highly prevalent and debilitating condition, only partially addressed by current pharmacotherapies. The lack of response to treatment by many patients prompts the need to develop new therapeutic alternatives and to better understand the etiology of the disorder. Pre-clinical models with translational merits are rudimentary for this task. Here we present a protocol for the unpredictable chronic mild stress (UCMS) method in mice. In this protocol, adolescent mice are chronically exposed to interchanging unpredictable mild stressors. Resembling the pathogenesis of depression in humans, stress exposure during the sensitive period of mice adolescence instigates a depressive-like phenotype evident in adulthood. UCMS can be used for screenings of antidepressants on the variety of depressive-like behaviors and neuromolecular indices. Among the more prominent tests to assess depressive-like behavior in rodents is the sucrose preference test (SPT), which reflects anhedonia (core symptom of depression). The SPT will also be presented in this protocol. The ability of UCMS to induce anhedonia, instigate long-term behavioral deficits and enable reversal of these deficits via chronic (but not acute) treatment with antidepressants strengthens the protocol&#39;s validity compared to other animal protocols for inducing depressive-like behaviors.

Here we present the unpredictable chronic mild stress protocol in mice. This protocol induces a long-term depressive-like phenotype and enables to assess the efficacy of putative antidepressants in reversing the behavioral and neuromolecular depressive-like deficits.

Depression is a highly prevalent and debilitating condition, only partially addressed by current pharmacotherapies. The lack of response to treatment by ...

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various physiological or pathological changes. The object location task (OLT) and the novel object recognition task (NORT) are two effective behavioral tasks commonly used to reveal the function and relative health of specific brain regions involved in memory. While both of these tests exploit the inherent preference of mice for the novelty to reveal memory for previously encountered objects, the OLT primarily evaluates spatial learning, which relies heavily on hippocampal activity. The NORT, in contrast, evaluates non-spatial learning of object identity, which relies on multiple brain regions. Both tasks require an open-field-testing arena, objects with equivalent intrinsic value to mice, appropriate environmental cues, and video recording equipment and the software. Commercially available systems, while convenient, can be costly. This manuscript details a simple, cost-effective method for building the arenas and setting up the equipment necessary to perform the OLT and NORT. Furthermore, the manuscript describes an efficient testing protocol that incorporates both OLT and NORT and provides typical methods for data acquisition and analysis, as well as representative results. Successful completion of these tests can provide valuable insight into the memory function of various mouse model systems and appraise the underlying neural regions that support these functions.

Here we provide a protocol which includes comprehensive instructions for the economical establishment of murine object location and novel object recognition behavioral testing, including the design, cost, and construction of required equipment as well as execution of behavioral testing, data collection, and analysis.

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various ...

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

Prior research demonstrated that learning information via retrieval practice, which entails studying and taking practice tests, resulted in less memory impairment under stress than learning information via repeated studying. The present experiment combined three experimental procedures to further examine the memory mechanisms underlying the efficacy of retrieval practice in the context of stress. A list-discrimination task was implemented, in which participants learned two distinct wordlists. This was combined with a retrieval-practice manipulation, as half of the participants engaged in practice testing and half engaged in conventional studying during learning. A week later, participants underwent stress induction, using the Trier Social Stress Test. Before and after stress induction, participants completed tests of item and source memory (i.e., list discrimination). The combination of these three procedures yielded informative results: retrieval practice, in the context of stress, improved item memory but not source memory relative to conventional studying. Limitations and future directions for the use of this methodology are discussed.

The present experiment combined three experimental procedures &#8212; a retrieval-practice learning manipulation, a list-discrimination task, and a stress-induction technique &#8212; to examine the influences of different learning strategies and acute stress on multiple measures of episodic memory.

Prior research demonstrated that learning information via retrieval practice, which entails studying and taking practice tests, resulted in less memory ...

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

We present a canine functional Magnetic Resonance Imaging (fMRI) training protocol that can be done in a cost-effective manner, with high-energy dogs, for acquisition of functional and structural data. This method of training dogs for awake, unrestrained fMRI employs a generalization procedure of stationing in several dissimilar locations to facilitate transfer of the stationing behavior to the real MRI scan environment; it does so without the need for extensive training time in the MRI scan environment, which can be expensive. Further, this method splits the training of a stationing (i.e., chin rest) behavior from desensitization to the MRI environment (i.e., 100+ decibel scan audio), the latter accomplished during dedicated Auditory Exposure conditioning sessions. The complete training and testing protocol required 14 hours and resulted in immediate transfer to novel locations. We also present examples of canine fMRI data that have been acquired from visual face processing and olfactory discrimination paradigms.

Magnetic resonance imaging (MRI) on unrestrained awake dogs is a new method with several advantages over imaging with physical or chemical restraint. This protocol introduces a cost-effective training method that minimizes training in the MRI environment, which can be expensive, and maximizes the subject pool available for canine functional MRI.

We present a canine functional Magnetic Resonance Imaging (fMRI) training protocol that can be done in a cost-effective manner, with high-energy dogs, for ...