Posts Tagged brain imaging

[ARTICLE] Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report – Full Text

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Introduction

Worldwide, stroke is a leading cause of adult long-term disability (Mozaffarian et al., 2015). From those who survive, an increased number is suffering with severe cognitive and motor impairments, resulting in loss of independence in their daily life such as self-care tasks and participation in social activities (Miller et al., 2010). Rehabilitation following stroke is a multidisciplinary approach to disability which focuses on recovery of independence. There is increasing evidence that chronic stoke patients maintain brain plasticity, meaning that there is still potential for additional recovery (Page et al., 2004). Traditional motor rehabilitation is applied through physical therapy and/or occupational therapy. Current approaches of motor rehabilitation include functional training, strengthening exercises, and range of movement exercises. In addition, techniques based on postural control, stages of motor learning, and movement patterns have been proposed such as in the Bobath concept and Bunnstrom approach (amongst others) (Bobath, 1990). After patients complete subacute rehabilitation programs, many still show significant upper limb motor impairment. This has important functional implications that ultimately reduce their quality of life. Therefore, alternative methods to maximize brain plasticity after stroke need to be developed.

So far, there is growing evidence that action observation (AO) (Celnik et al., 2008) and motor imagery (MI) improve motor function (Mizuguchi and Kanosue, 2017) but techniques based on this paradigm are not widespread in clinical settings. As motor recovery is a learning process, the potential of MI as a training paradigm relies on the availability of an efficient feedback system. To date, a number of studies have demonstrated the positive impact of virtual-reality (VR) based on neuroscientific grounds on recovery, with proven effectiveness in the stroke population (Bermúdez i Badia et al., 2016). However, patients with no active movement cannot benefit from current VR tools due to low range of motion, pain, fatigue, etc. (Trompetto et al., 2014). Consequently, the idea of directly training the central nervous system was promoted by establishing an alternative pathway between the user’s brain and a computer system.

This is possible by using electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), since they can provide an alternative non-muscular channel for communication and control to the external world (Wolpaw et al., 2002), while they could also provide a cost-effective solution for training (Vourvopoulos and Bermúdez, 2016b). In rehabilitation, BCIs could offer a unique tool for rehabilitation since they can stimulate neural networks through the activation of mirror neurons (Rizzolatti and Craighero, 2004) by means of action-observation (Kim et al., 2016), motor-intent and motor-imagery (Neuper et al., 2009), that could potentially lead to post-stroke motor recovery. Thus, BCIs could provide a backdoor to the activation of motor neural circuits that are not stimulated through traditional rehabilitation techniques.

In EEG-based BCI systems for motor rehabilitation, Alpha (8–12 Hz) and Beta (12–30 Hz) EEG rhythms are utilized since they are related to motor planning and execution (McFarland et al., 2000). During a motor attempt or motor imagery, the temporal pattern of the Alpha rhythms desynchronizes. This rhythm is also named Rolandic Mu-rhythm or the sensorimotor rhythm (SMR) because of its localization over the sensorimotor cortices. Mu-rhythms are considered indirect indications of functioning of the mirror neuron system and general sensorimotor activity (Kropotov, 2016). These are often detected together with Beta rhythm changes in the form of an event-related desynchronization (ERD) when a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG patterns are primarily detected during task-based EEG (e.g., when the participant is actively moving or imagining movement) and they are of high importance in MI-BCIs for motor rehabilitation.

A meta-analysis of nine studies (combined N = 235, sample size variation 14 to 47) evaluated the clinical effectiveness of BCI-based rehabilitation of patients with post-stroke hemiparesis/hemiplegia and concluded that BCI technology could be effective compared to conventional treatment (Cervera et al., 2018). This included ischemic and hemorrhagic stroke in both subacute and chronic stages of stoke, between 2 to 8 weeks. Moreover, there is evidence that BCI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis (Ramos-Murguialday et al., 2019), while overall BCI’s are starting to prove their efficacy as rehabilitative technologies in patients with severe motor impairments (Chaudhary et al., 2016).

The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional tariffs on a screen (Prasad et al., 2010Mihara et al., 2013), embodied avatar representation of the patient on a screen or with augmented reality (Holper et al., 2010Pichiorri et al., 2015), neuromuscular electrical stimulation (NMES) (Kim et al., 2016Biasiucci et al., 2018). and robotic exoskeletal orthotic movement facilitation (Ramos-Murguialday et al., 2013Várkuti et al., 2013Ang et al., 2015). In addition, it has been shown that multimodal feedback lead to a significantly better performance in motor-imagery (Sollfrank et al., 2016) but also multimodal feedback combined with motor-priming, (Vourvopoulos and Bermúdez, 2016a). However, there is no evidence which modalities are more efficient in stroke rehabilitation are.

Taking into account all previous findings in the effects of multimodal feedback in MI training, the purpose of this case study is to examine the effect of the MI paradigm as a treatment for post-stroke upper limb motor dysfunction using the NeuRow BCI-VR system. This is achieved through the acquisition of clinical scales, dynamics of EEG during the BCI treatment, and brain activation as measured by functional MRI (fMRI). NeuRow is an immersive VR environment for MI-BCI training that uses an embodied avatar representation of the patient arms and haptic feedback. The combination of MI-BCIs with VR can reinforce activation of motor brain areas, by promoting the illusion of physical movement and the sense of embodiment in VR (Slater, 2017), and hence further engaging specific neural networks and mobilizing the desired neuroplastic changes. Virtual representation of body parts paves the way to include action observation during treatment. Moreover, haptic feedback is added since a combination of feedback modalities could prove to be more effective in terms of motor-learning (Sigrist et al., 2013). Therefore, the target of this system is to be used by patients with low or no levels of motor control. With this integrated BCI-VR approach, severe cases of stroke survivors may be admitted to a VR rehabilitation program, complementing traditional treatment.

Methodology

Patient Profile

In this pilot study we recruited a 60 years old male patient with left hemiparesis following cerebral infarct in the right temporoparietal region 10 months before. The participant had corrected vision through eyewear, he had 4 years of schooling and his experience with computers was reported as low. Moreover, the patient was on a low dose of diazepam (5 mg at night to help sleep), dual antiplatelet therapy, anti-hypertensive drug and metformin. Hemiparesis was associated with reduced dexterity and fine motor function; however, sensitivity was not affected. Other sequelae of the stroke included hemiparetic gait and dysarthria. Moreover, a mild cognitive impairment was identified which did not interfere with his ability to perform the BCI-VR training. The patient had no other relevant comorbidities. Finally, the patient was undergoing physiotherapy and occupational therapy at the time of recruitment and had been treated with botulinum toxin infiltration 2 months before due to focal spasticity of the biceps brachii.

Intervention Protocol

The patient underwent a 3-weeks intervention with NeuRow, resulting in 10 BCI sessions of a 15 min of exposure in VR training per session. Clinical scales, motor imagery capability assessment, and functional -together with structural- MRI data had been gathered in three time-periods: (1) before (serving as baseline), (2) shortly after the intervention and (3) one-month after the intervention (to assess the presence of long-term changes). Finally, electroencephalographic (EEG) data had been gathered during all sessions, resulting in more than 20 datasets of brain electrical activity.

The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM) and approved by the scientific and ethic committees of the Central Hospital of Funchal. Finally, written informed consent was obtained from the participant upon recruitment for participating to the study but also for the publication of the case report in accordance with the 1964 Declaration of Helsinki.

Assessment Tools

A set of clinical scales were acquired including the following:

1. Montreal Cognitive Assessment (MoCA). MoCA is a cognitive screening tool, with a score range between 0 and 30 (a score greater than 26 is considered to be normal) validated also for the Portuguese population, (Nasreddine et al., 2005).

2. Modified Ashworth scale (MAS). MAS is a 6-point rating scale for measuring spasticity. The score range is 0, 1, 1+, 2, 3, and 4 (Ansari et al., 2008).

3. Fugl-Meyer Assessment (FMA). FMA is a stroke specific scale that assesses motor function, sensation, balance, joint range of motion and joint pain. The motor domain for the upper limb has a maximum score of 66 (Fugl-Meyer et al., 1975).

4. Stroke Impact Scale (SIS). SIS is a subjective scale of the perceived stroke impact and recovery as reported by the patient, validated for the Portuguese population. The score of each domain of the questionnaire ranges from 0 to 100 (Duncan et al., 1999).

5. Vividness of Movement Imagery Questionnaire (VMIQ2). VMIQ2 is an instrument that assess the capability of the participant to perform imagined movements from external perspective (EVI), internal perspective imagined movements (IVI) and finally, kinesthetic imagery (KI) (Roberts et al., 2008).

NeuRow BCI-VR System

EEG Acquisition

For EEG data acquisition, the Enobio 8 (Neuroelectrics, Barcelona, Spain) system was used. Enobio is a wearable wireless EEG sensor with 8 EEG channels for the recording and visualization of 24-bit EEG data at 500 Hz and a triaxial accelerometer. The spatial distribution of the electrodes followed the 10–20 system configuration (Klem et al., 1999) with the following electrodes over the somatosensory and motor areas: Frontal-Central (FC5, FC6), Central (C1, C2, C3, C4), and Central-Parietal (CP5, CP6) (Figure 1A). The EEG system was connected via Bluetooth to a dedicated desktop computer, responsible for the EEG signal processing and classification, streaming the data via UDP through the Reh@Panel (RehabNet Control Panel) for controlling the virtual environment. The Reh@Panel is a free tool that acts as a middleware between multiple interfaces and virtual environments (Vourvopoulos et al., 2013).

FIGURE 1

Figure 1. Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.

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[WEB SITE] How Doctors Are Using Brain Imaging to Treat Depression

Typically, depression is diagnosed based on what a patient describes about their emotional and mental state. People who suffer from depression often state that they’re sad more often than not and that things they used to enjoy are no longer enjoyable.

The biggest hurdle in diagnosing depression is overcoming the stigma and embarrassment of possibly having a mental health disorder. It’s hard to talk about such raw, emotional, and personal details. Another issue is the fact that depression manifests itself in different ways. Some patients stop eating, others gain weight and suffer from anxiety. There’s no one-size-fits-all when it comes to depression symptoms.

While there aren’t many biological indicators that can be used to diagnose someone with depression, brain imaging has proven to be useful in diagnosing and helping to shape a treatment plan.

What Does Brain Imaging Show?

A recent study that was published in Nature Medicine discuss biological markers that can be used to distinguish different types of depression. To get a better look at the brain, functional magnetic resonance imaging was used to measure the connection strength between the brain and neural circuits. From these images researchers were able to pinpoint four types of depression.

While further research is needed to confirm initial findings, the potential of using biological indicators paves the way for clearer diagnoses and more personalized and effective therapies that treat the brain.

Based on the research, it was observed that certain patients experienced higher levels of fatigue while others discussed a lack of pleasure. In the future there is hope that certain treatment types can be matched to a type of depression. For example, those who report a lack of pleasure may benefit from a treatment known as transcranial magnetic stimulation (TMS). Because TMS uses a magnet to create small electric currents in the brain, the under-functioning reasons can be restored through TMS therapy.

The Next Steps

Though several studies have been conducted to compare depressed brains to those who don’t have the condition, it will take some time before brain imaging becomes a fool-proof way of diagnosing depression. Doctors and researchers will need to find common ground and patterns between the various types of depression so there is one unified method of determining if a patient has depression and the type.

In the future, it’s hoped that brain imaging can not only be used to diagnose depression but also to:

  • Determine treatment options
  • Determine the success rate of treatment
  • Understand other mental health disorders
  • Diagnose other conditions that may impact depression symptoms

While there is still a way to go in using brain imaging to diagnose and treat depression, the future is bright in this health arena.

Treatment Options

There are several forms of brain treatment that can be used to treat depression. The top two options include electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS).

Electroconvulsive Therapy (ECT)

The use of ECT dates back hundreds of years. In fact, ECT is the most commonly used brain treatment for those who suffer from depression. When undergoing ECT treatment, an electric current is formed in the brain that creates a spurt of energy. This causes the patient to have a seizure. Though seizures can be quite scary to experience and even scarier to watch, patients are given anesthesia and a muscle relaxant to avoid the convulsions that are often seen in someone who is having a seizure.

The biggest drawback to ECT is memory loss. Patients often have a hard time remembering past memories so doctors encourage people to create new memories to get that functionality in the brain back up and running.

Transcranial Magnetic Stimulation (TMS)

While electroconvulsive therapy (ECT) is often the go-to procedure for those with severe, long-term, or treatment resistant depression, TMS has proven to be an effective brain treatment for depression. As we better understand how depression impacts regions of the brain, especially the prefrontal cortex, doctors will be able to pinpoint which treatment of combination thereof will produce the best results for a patient.

TMS is beneficial in that it is safe, non-invasive, has minimal side effects, and is designed to target and restore those abnormal connections in the brain. Unlike ECT and other forms of brain treatment options, TMS typically produces minimal to no side effects. Some patients have complained of headache and scalp discomfort but nothing as serious as the memory loss that is often found in those who undergo ECT.

Conclusion

As it stands physical symptoms are the best indicators of whether or not someone has depression. But, with the continued research of using brain imaging to diagnose and determine treatment brings new hope and ideas into the mental health realm.

via How Doctors Are Using Brain Imaging to Treat Depression

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[TED Talk] Nancy Kanwisher: A neural portrait of the human mind – TED.com

Brain imaging pioneer Nancy Kanwisher, who uses fMRI scans to see activity in brain regions (often her own), shares what she and her colleagues have learned: The brain is made up of both highly specialized components and general-purpose “machinery.” Another surprise: There’s so much left to learn.

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[WEB SITE] fNIRS brain imaging

The fNIRS allows a participant to engage in meaningful, everyday social interactions, computer-based interactions or various movements.

BRAIN IMAGING

Photo by Evan Krape

Functional near-infrared spectroscopy adds new research capability

New brain imaging equipment is now available to scientists at the University of Delaware’s Science, Technology and Advanced Research (STAR) Campus.

The technology is called functional near-infrared spectroscopy, or just fNIRS for short. It gathers brain activity, including cortical activation, during real-world tasks.

Most importantly for research participants, it is non-invasive, allowing them to be more at ease during studies.

The system was recently installed at UD’s STAR Health Sciences Complex.

The fNIRS system comes to the University via a National Institutes of Health shared instrumentation grant.

Anjana Bhat, associate professor in the Department of Physical Therapy, is the principal investigator on the grant and played a key role in acquiring the equipment.

The collaboration already includes 11 faculty members from three departments — Physical Therapy, Kinesiology and Applied Physiology, and Psychological and Brain Sciences. But, the equipment can help researchers in lots of areas, both inside and outside of the University.

In addition to Bhat, the UD advisory committee includes Tom Buchanan, George W. Laird Professor of Mechanical Engineering and director of the Delaware Rehabilitation Institute, and Stuart Binder-Macleod, Edward L. Ratledge Professor of Physical Therapy.

The group is offering open time for researchers around the University to explore possibilities for studies.

“We’d love anyone conducting research in, for example, neuroscience or behavioral research to come use the fNIRS system,” said Bhat.

Bhat has studied motor and social development in children along the autism spectrum. She saw the fNIRS system as a tool to dive deeper into brain mechanisms.

The fNIRS system provides distinct advantages over the traditional magnetic resonance imaging (MRI) to study cortical mechanisms of various human behaviors.

“Kids have a tough time laying still in an MRI scanner,” said Bhat. “MRI machines/rooms emit high sounds and lots of lights. It is an unusual environment and since children with autism can have aversion to lights and sounds, they may struggle following through on instructions during an MRI scan.”

In contrast, the fNIRS system only requires the participant to wear a cap. It allows a participant to engage in meaningful, everyday social interactions, computer-based interactions or various movements. They can play, reach or walk on a treadmill.

So how does it work? Well, the cap has several probes that emit near-infrared light. This light passes through the skull and brain. Depending on how much blood supply is in the brain tissue, the light is then absorbed. Each emitter has a corresponding detector, which picks up the reflected light.

“The difference in light emitted versus absorbed tells us how much blood supply reaches the underlying brain tissue. This gives us an indirect measure of how active the brain tissue is during a particular function or behavior,” Bhat said.

Samuel Lee, associate professor of physical therapy, uses neuromuscular electrical stimulation to enhance function in individuals with cerebral palsy. His lab might use stimulation to assist muscles in activities like walking or cycling.

Lee said, “fNIRS will allow us an objective and quantitative measure to substantiate the theories behind our investigations.”

Scheduling is managed through an fNIRS group calendar. To sign up for time with the fNIRS system, interested parties can contact Anjana Bhat. Training opportunities and operational assistance are also available through Delaware Rehabilitation Institute’s Kevin McGinnis.

Source: fNIRS brain imaging | UDaily

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[ARTICLE] Insights and Perspectives on Sensory-Motor Integration and Rehabilitation – Full Text HTML/PDF

Abstract

The present review focuses on the flow and interaction of somatosensory-motor signals in the central and peripheral nervous system.

Specifically, where incoming sensory signals from the periphery are processed and interpreted to initiate behaviors, and how ongoing behaviors produce sensory consequences encoded and used to fine-tune subsequent actions. We describe the structure–function relations of this loop, how these relations can be modeled and aspects of somatosensory-motor rehabilitation.

The work reviewed here shows that it is imperative to understand the fundamental mechanisms of the somatosensory-motor system to restore accurate motor abilities and appropriate somatosensory feedback. Knowledge of the salient neural mechanisms of sensory-motor integration has begun to generate innovative approaches to improve rehabilitation training following neurological impairments such as stroke.

The present work supports the integration of basic science principles of sensory-motor integration into rehabilitation procedures to create new solutions for sensory-motor disorders.

Presently, we aim to unravel the nature and mechanisms responsible for the ability to organize sensory perceptions and motor routines, in order to open a window onto the organization of the somatosensory-motor loop. We will focus on somatosensory input and how this is processed and integrated centrally, to determine motor behavior. The fine balance between sensory input and motor output is essential for efficient interactions within the environment, and also includes the integration of incoming multisensory signals (e.g., vision, hearing, touch). Somatosensory feedback is pertinent for the fine tuning of dexterous movements. If this is impaired due to trauma or injury, due to, e.g., stroke or spinal cord injury, the incoming somatosensory signals are degraded and the effects can be very detrimental. In these cases, the absence of precise somatosensory feedback can render patients unable to perform movements despite the fact that basic motor function is relatively preserved (Ionta , 2016). In recent years, there have been important updates on the basic mechanisms, anatomo-functional neural basis, and rehabilitation procedures of such sensory-motor integration. Furthering the understanding of healthy and pathological somatosensory-motor integration mechanisms is essential and supports a theoretical model as general reference framework, with direct clinical outcomes.

In the next section we will provide a broad overview of state-of-the-art functional neuroimaging evidence on the interaction between somatosensory afferent information and efferent movement control, with a particular focus on touch. The third section will summarize the relationship between structural neuroimaging data and clinical phenotypes of sensory-motor disorders. In the fourth section we will discuss behavioral data within the framework of theoretical generalizations and modeling of the sensory-motor loop. The last section will discuss mechatronic tactile stimulation platforms developed in order to enable human touch studies with psychophysical and electrophysiological methods.

 Full Text PDF

Continue —> Insights and Perspectives on Sensory-Motor Integration and Rehabilitation  »  Brill Online

image of Figure 1.

Figure 1. Overview of touch pathways from the periphery to brain. Once a tactile stimulus has been registered by mechanoreceptors in the skin, the information is sent to the primary somatosensory cortex (S1) or the insula, which are both somatosensory regions with different multisensory influences.

 

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[Review] Insights and Perspectives on Sensory-Motor Integration and Rehabilitation – Full Text

ABSTRACT

The present review focuses on the flow and interaction of somatosensory-motor signals in the central and peripheral nervous system. Specifically, where incoming sensory signals from the periphery are processed and interpreted to initiate behaviors, and how ongoing behaviors produce sensory consequences encoded and used to fine-tune subsequent actions.

We describe the structure–function relations of this loop, how these relations can be modeled and aspects of somatosensory-motor rehabilitation. The work reviewed here shows that it is imperative to understand the fundamental mechanisms of the somatosensory-motor system to restore accurate motor abilities and appropriate somatosensory feedback.

Knowledge of the salient neural mechanisms of sensory-motor integration has begun to generate innovative approaches to improve rehabilitation training following neurological impairments such as stroke. The present work supports the integration of basic science principles of sensory-motor integration into rehabilitation procedures to create new solutions for sensory-motor disorders.

 

1. Introduction
 Presently, we aim to unravel the nature and mechanisms responsible for the ability to organize sensory perceptions and motor routines, in order to open a window onto the organization of the somatosensory-motor loop. We will focus on somatosensory input and how this is processed and integrated centrally, to determine motor behavior. The fine balance between sensory input and motor output is essential for efficient interactions within the environment, and also includes the integration of incoming multisensory signals (e.g., vision, hearing, touch). Somatosensory feedback is pertinent for the fine tuning of dexterous movements. If this is impaired due to trauma or injury, due to, e.g., stroke or spinal cord injury, the incoming somatosensory signals are degraded and the effects can be very detrimental. In these cases, the absence of precise somatosensory feedback can render patients unable to perform movements despite the fact that basic motor function is relatively preserved (Ionta , 2016). In recent years, there have been important updates on the basic mechanisms, anatomo-functional neural basis, and rehabilitation procedures of such sensory-motor integration. Furthering the understanding of healthy and pathological somatosensory-motor integration mechanisms is essential and supports a theoretical model as general reference framework, with direct clinical outcomes.

In the next section we will provide a broad overview of state-of-the-art functional neuroimaging evidence on the interaction between somatosensory afferent information and efferent movement control, with a particular focus on touch. The third section will summarize the relationship between structural neuroimaging data and clinical phenotypes of sensory-motor disorders. In the fourth section we will discuss behavioral data within the framework of theoretical generalizations and modeling of the sensory-motor loop. The last section will discuss mechatronic tactile stimulation platforms developed in order to enable human touch studies with psychophysical and electrophysiological methods.


               Figure 1.
            Figure 1.
Overview of touch pathways from the periphery to brain. Once a tactile stimulus has been registered by mechanoreceptors in the skin, the information is sent to the primary somatosensory cortex (S1) or the insula, which are both somatosensory regions with different multisensory influences.

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