# Posts Tagged fMRI

### [WEB PAGE] fMRI vs. SPECT Scan for the Brain: Know Your Options

By: Dr. Mark Allen PhD on February 3rd, 2020

If you’re struggling to recover after a brain injury, dealing with healthcare providers is often a frustrating process. Unless you have a clear, severe injury, they might be dismissive of your symptoms or just may not have enough treatment options to help you. Oftentimes, they’ll order an MRI or a CT scan.

But MRI and CT scans will only show structural damage. So, they’re helpful if you have a severe traumatic brain injury, but if you’ve had a mild traumatic brain injury (mTBI, aka concussion), they likely won’t show anything. If your structural MRI scan comes back as normal, many doctors won’t do much in the way of follow-up.

However, that doesn’t mean you’re out of options. If you’re here, you’ve probably heard of SPECT scans and functional MRI. These imaging tests can show dysfunction resulting from an mTBI.

But which one is right for you? In this post, we’ll explain:

• What SPECT imaging and fMRIs actually are
• What it’s like to get a SPECT scan or fMRI
• What they can show, and what that means for your diagnosis.

Note: If you’re experiencing symptoms that won’t go away after a concussion, we can help. On average, our patients improve by 75% after treatment. To learn about diagnosis and treatment options, sign up for a free consultation.

## All About Brain SPECT Scans

### What is a SPECT Scan?

SPECT stands for “single photon emission computed tomography.” That’s a mouthful, so here’s what it translates to:

Patients are injected with a radioactive isotope that emits gamma rays (electromagnetic radiation emitted by a decaying atom). As the isotope (also known as a radioactive tracer) travels through the patient’s bloodstream, it continues to emit radiation. (We know how that sounds, but it really is a safe level of radiation).

A special “gamma camera” is positioned above the patient and able to detect the gamma rays emitted by the isotope. A computer then triangulates in 3-dimensional space where the gamma rays are coming from.

In other words, it looks at multiple data points to figure out where the isotope was in your body when the radiation was emitted.

Over time, collecting multiple “images” can show physicians if blood flow in your body has been impacted by your condition.

Note: You may be more familiar with a PET scan (positron emission tomography). The imaging technique is very similar to SPECT scanning, since both use radioactive tracers to investigate blood flow. PET has greater resolution and fewer image artifacts, but it has other drawbacks.

### What Can a SPECT Scan Show?

SPECT scans can be used to look at the heart, brain, and a few other things. In the brain, it can be used in evaluating conditions such as neurodegenerative diseases, stroke, seizures, tumors, and other brain trauma. In the heart, it’s most commonly used to view how well the heart is moving the blood that comes through it.

Because of the delay between when the isotope emits gamma rays and when the camera records them, combined with the inaccuracy of having to triangulate their position, SPECT images are an average over time rather than an instantaneous picture. In many cases, it can take 10-15 minutes to get that average image.

Because of that, it can identify certain conditions better than others. If you’ve had a concussion, brain SPECT imaging can confirm whether you have or have not sustained brain dysfunction after the injury. Unfortunately, it often can’t provide more detailed information about specific regions and how they were affected.

### How Long Does a SPECT Scan Take?

The time you spend on a SPECT scan depends somewhat on where you’re getting the scan and why you’re getting the scan. They generally range from 1-2 hours (including the time needed for the injection).

The actual scan itself can take as few as 30 minutes.

### How Much Does a SPECT Scan Cost?

SPECT scans are one of the more affordable ways to image the brain, but prices can fluctuate a lot based on location, the purpose of the scan, and what additional interpretation is involved.

According to MDsave, brain SPECT scans range from $1,300 to over$3,500.

### Does a SPECT Scan Have Any Side Effects?

The main possible side effect of a SPECT scan is having an allergic reaction to the injected isotope. Some people can have bruising and soreness around the site of the injection as well.

If you’re nervous about the radiation, that’s understandable. As far as Western medicine is concerned, however, it’s perfectly harmless. Most people get more radiation from being on a plane at 30,000 feet in the air than they would from a SPECT brain scan.

## All About Brain fMRI Scans

### What is an fMRI?

fMRI scans (functional nuclear magnetic resonance imaging) work through a combination of radio waves and magnets. Engineers have figured out how to magnetize soft tissues — such as the brain — very precisely. When you send radio waves through those magnetized tissues, the magnetic field changes the radio wave.

Sensors detect even minimal changes in the radio waves to form a 3D image of the scanned tissue. The only limits to how fine the imaging can become are human ingenuity and engineering skill.

In a structural MRI, that information is used to examine the physical integrity of the brain (or any other organ being imaged). It should show any physical brain damage you’ve sustained. A functional MRI is used to observe blood flow. Since increased cerebral blood flow is tied to increased brain activity, fMRI can show how the brain calls for resources during a given task.

If you’re trying to understand the difference between a structural MRI and a functional MRI, in terms of what it means for patients, this article will help.

### What Can an fMRI Show?

fMRIs can show detailed images of blood flow in internal organs. In brain imaging, this means doctors and researchers can see how the brain is managing its oxygen supply and whether the right regions respond in the right way when given a certain task.

For example, we found that patients who have post-concussion syndrome (the condition in which symptoms don’t go away after a concussion) will show tell-tale signs of hyperactivity and hypoactivity in the affected brain regions. Thanks to fMRI, we’re able to pinpoint for post-concussion patients which areas of the brain are dysfunctional and in what way.

### How Long Does an fMRI Take?

Because fMRI is used more in the research setting than the clinical setting as yet, scan times can vary dramatically. The more you need to know, the longer the scan will take.

At Cognitive FX, an fNCI takes about 45 minutes. During that time, patients take six different cognitive tests while we image their brain to learn how it responds to that stimulation.

### How Much Does fMRI Cost?

That depends on who you’re asking — researchers might pay hundreds of dollars per hour for access to one, but if you’re part of a clinical trial, it might be free. That said, if you’re looking to get a brain fMRI for diagnostic purposes, you’ll be charged for both the scan and whatever diagnostic analysis is performed.

At Cognitive FX, charges for an fNCI can run from $3,500 to$5,250, depending on several factors (such as whether you pay in full at the visit or via a payment plan, get the scan as part of a treatment package, etc.).

### Does an fMRI Have Any Side Effects?

fMRI does not have any side effects per se, but there are situations in which you might not be able to use it. Some types of foreign metal objects in your body (such as surgical implants, braces, or even permanent eye-liner) may prohibit you from entering the MRI scanner. However, the imaging facility will provide you with full details before you commit to undergoing the scan.

If you have extreme anxiety or fear of enclosed spaces, that would also pose a challenge. fMRI is completed in an enclosed space and is very loud (you are given earplugs, headphones, and cushioning to make the noise more tolerable).

## SPECT vs. fMRI: Which is Better?

fMRI is a higher quality test than SPECT, for a few reasons. However, which functional neuroimaging test you need depends on your situation.

The spatial and temporal resolution of fMRI is significantly better: fMRI can see things down to a few millimeters, whereas SPECT resolution is on the centimeter scale.

In other words, fMRI has at least 10x better spatial resolution.

When it comes to temporal (time) resolution, there’s no comparison. SPECT gives an image from 10-15 minutes of activity at a time. fMRI, on the other hand, can give a second-by-second picture of how your brain reacts to given stimuli.

While both methods can show if your brain has been affected by a concussion, fMRI can tell you which parts of your brain were affected (Thalamus? Basal ganglia? Prefrontal cortex?) and how (hyperactive or hypoactive). The latter information is far more useful: If we know which areas of the brain are affected, we can tailor treatment to target those regions. This insight into how your brain function has been impacted by injury is invaluable during treatment.

SPECT makes more sense than fMRI in the case of easier-to-see conditions such as stroke and seizure. Since a SPECT scan is typically cheaper than fMRI, there’s no reason not to use it when it will do the job. But for concussion diagnosis, fMRI provides much more robust, clinically useful data.

## fMRI for Concussion Diagnosis

All that being said, it’s important to mention that neither fMRI nor SPECT can be used to diagnose a concussion unless the doctor reading the scans has the right information and tools available.

At Cognitive FX, we do a type of fMRI called fNCI, or functional neurocognitive imaging. It’s what allows us to pinpoint which brain regions were affected (as mentioned above).

fNCI uses the same technology of an fMRI, but the imaging process involves having patients perform standardized tasks while in the MRI machine. Over years of research, we built a database of healthy and unhealthy brains performing these tasks. We know which areas of the brain are supposed to be active during these tasks, and how much or little these areas should respond to each separate task.

When we give a patient an fNCI, we analyze the images of their brain to see which areas of their brain are not working optimally. This process allows for accurate diagnosis of injuries that hinder brain function (such as concussion, but sometimes other conditions like brain dysfunction from carbon monoxide poisoning.)

After the test, we meet with patients to discuss their results and how that will affect their treatment. Patients get an overall score and several pages breaking down how each brain region we scanned performed. Here’s an example that one of our patients agreed to share:

From Olivia’s story: “The fNCI showed that my thalamus was hypoactive and my basal ganglia was completely out to lunch. 3 standard deviations from normal basically means that there was no activation seen in that area on the fMRI. All the work was being routed around it — causing fatigue and stress on the rest of my brain. The inferior frontal gyrus was trending toward hyperactive (using too many resources for given tasks).”

Because fNCI is a kind of fMRI, the test is noninvasive and harmless. There is no radiation, however, the same restrictions around metal apply.

### What You Can Do About Concussion Recovery

For many patients, concussion symptoms resolve after about two weeks. But for others, those symptoms just won’t seem to go away. If that describes your situation, you may have post-concussion syndrome. We’ve listed some of the symptoms in the chart below:

Your best bet is to seek treatment from a doctor or clinic that specializes in post-concussion treatment. If you’d like to know more about how we can help you, sign up for a free consultation.

Or, if you’d like to learn about choosing a clinic, here’s our post on the best concussion clinics in the U.S.

### Active Recovery

In the meantime, there are things you can do to improve your chances at a good recovery. Do your best to fit these into every day:

1. Plenty of rest (if you’re having difficulty, this post on post-concussion sleep may help).
2. Light physical activity for 30 minutes per day (at whatever level you can tolerate without causing symptoms). Here’s our guide to exercising safely after a concussion.
3. Cognitive activities like reading or logic puzzles, as tolerated.
4. Work or school activities, as tolerated.

## Conclusion

Knowing whether you need an fMRI or SPECT scan comes down to a matter of how much you need to know in order to receive effective treatment. If you’re suffering from post-concussion syndrome, a SPECT scan is better than nothing, but an fMRI is significantly more useful than a SPECT scan.

If you’ve been suffering from lingering symptoms after a concussion and haven’t found relief, you’re not imagining things: 10-20% of people who have had a concussion endure lingering symptoms that do not go away without treatment. We’ve written at length about some of the more common issues our patients face, such as headachesmemory problemsrelentless fatigue, and more.

### [Abstract] Neural Correlates of Passive Position Finger Sense After Stroke

Background. Proprioception of fingers is essential for motor control. Reduced proprioception is common after stroke and is associated with longer hospitalization and reduced quality of life. Neural correlates of proprioception deficits after stroke remain incompletely understood, partly because of weaknesses of clinical proprioception assessments.

Objective. To examine the neural basis of finger proprioception deficits after stroke. We hypothesized that a model incorporating both neural injury and neural function of the somatosensory system is necessary for delineating proprioception deficits poststroke.

Methods. Finger proprioception was measured using a robot in 27 individuals with chronic unilateral stroke; measures of neural injury (damage to gray and white matter, including corticospinal and thalamocortical sensory tracts), neural function (activation of and connectivity of cortical sensorimotor areas), and clinical status (demographics and behavioral measures) were also assessed.

Results. Impairment in finger proprioception was present contralesionally in 67% and bilaterally in 56%. Robotic measures of proprioception deficits were more sensitive than standard scales and were specific to proprioception. Multivariable modeling found that contralesional proprioception deficits were best explained (r2 = 0.63; P = .0006) by a combination of neural function (connectivity between ipsilesional secondary somatosensory cortex and ipsilesional primary motor cortex) and neural injury (total sensory system injury).

Conclusions. Impairment of finger proprioception occurs frequently after stroke and is best measured using a quantitative device such as a robot. A model containing a measure of neural function plus a measure of neural injury best explained proprioception performance. These measurements might be useful in the development of novel neurorehabilitation therapies.

### [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.

[…]

### [Editorial] Functional brain mapping of epilepsy networks: methods and applications – Neuroscience

This multidisciplinary research topic is a collection of contemporary advances in neuroimaging applied to mapping functional brain networks in epilepsy. With technology such as simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) now more readily available, it is possible to non-invasively map epileptiform activity throughout the entire brain at millimetre resolution. This research topic includes original research studies, technical notes and reviews of the field. Due to the multidisciplinary nature of the domain, the topic spans two journals: Frontiers in Neurology (Section: Epilepsy) and Frontiers in Neuroscience (Section: Brain Imaging Methods).
In this editorial we consider the outcomes of the multidisciplinary work presented in the topic. With the benefit of time elapsed since the original papers were published, we can see that the works are making a substantial impact in the field. At the time of writing, this topic had well over 27,000 full-paper downloads (including over 18,000 for the 15 papers in the Epilepsy section, and over 9,000 for the 8 papers in the Brain Imaging Methods section). Several papers in the topic have climbed the tier in Frontiers and received an associated invited commentary, demonstrating there is substantial interest in this research area.
Reviews
The topic’s review papers set the scene for the original research papers and synthesise contemporary thinking in epilepsy research and neuroimaging methods. We see that Epilepsy, whether of a “generalised” or “focal” origin, is increasingly recognised as a disorder of large-scale brain networks. At one level it is self-evident that otherwise healthy functional networks are recruited during epileptic activity, as this is what generates patient perceptions of their epileptic aura. For example, the epileptic aura of mesial temporal lobe epilepsy can include an intense sensation of familiarity (déjà vu) associated with involvement of the hippocampus, and unpleasant olfactory auras which may reflect involvement of adjacent olfactory cortex. As seizures spread more widely throughout the brain, presumably along pre-existing neural pathways, patients lose control of certain functions; for example, their motor system in the case of generalised convulsions, or aspects of awareness in seizures that remain localised to non-motor brain regions. Yet these functions return when the seizure abates, implying involved brain regions are also responsible for normal brain function. What has been less clear, and difficult to investigate until the advent of functional neuroimaging, is precisely which brain networks are involved (especially in ‘generalised’ epilepsy syndromes), and the extent to which functional networks are perturbed during seizures, inter-ictal activity, and at other times.
Functional imaging evidence of brain abnormalities in temporal lobe epilepsy is explored in (Caciagli et al., 2014), including evidence of dysfunction in limbic and other specific brain networks, as well as global changes in network topography derived from resting-state fMRI. Archer et al systematically review the functional neuroimaging of a particularly severe epilepsy phenotype, Lennox-Gastaut Syndrome (LGS), illustrating well how different forms of brain pathology can manifest in a similar clinical phenotype, simply by the nature of the healthy networks that the underlying pathology perturbs (Archer et al., 2014). Similarly, the mechanisms of absence seizure generation are reviewed by (Carney and Jackson, 2014), revealing that it too has a signature pattern of large-scale functional brain network perturbation. The ability to make such observations has considerable clinical significance, as highlighted in the review by (Pittau et al., 2014).
The tantalising proposition that there may be a common treatment target for all focal epilepsy phenotypes is also explored in a review of the piriform cortex by (Vaughan and Jackson, 2014). The piriform cortex was first implicated as a common brain region associated with spread of interictal discharges in focal epilepsy in an experiment that analysed the spatially normalised functional imaging data of a heterogeneous group of focal epilepsy patients (Laufs et al., 2011). This finding, since replicated (Flanagan et al., 2014), led Vaughan & Jackson to explore in detail what is known of the piriform cortex. Their findings reveal the piriform has several features that likely predispose it to involvement in focal epilepsy, and features that also explain many of the peculiar symptoms experienced by patients, from olfactory auras to the characteristic nose-wiping that many patients perform postictally. This work points to the need for future studies to determine whether the piriform might be an effective target for deep brain stimulation or other targeted therapy to prevent the spread of epileptiform activity.
Original research
Temporal lobe epilepsy is investigated in several papers in this topic. One of these studies also introduces a new exploratory method, Shared and specific independent component analysis (SSICA), that builds upon independent component analysis to perform between-group network comparison (Maneshi et al., 2014). In application to mesial temporal lobe epilepsy (MTLE) and healthy controls, three distinct reliable networks were revealed: two that exhibited increased activity in patients (a network including hippocampus and amygdala bilaterally, and a network including postcentral gyri and temporal poles), and a network identified as specific to healthy controls (i.e. effectively decreased in patients, consisting of bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus). These finding give mechanistic clues to the cognitive impairments often reported in patients with MTLE. Further clues are revealed in a study of the dynamics of fMRI and its functional connectivity (Laufs et al., 2014). Compared to healthy controls, temporal variance of fMRI was seen to be most increased in the hippocampi of TLE patients, and variance of functional connectivity to this region was increased mainly in the precuneus, the supplementary and sensorimotor, and the frontal cortices. More severe disruption of connectivity in these networks during seizures may explain patients’ cognitive dysfunction (Laufs et al., 2014). Yang and colleagues also show that it may be possible to use fMRI functional connectivity to lateralise TLE (Yang et al., 2015), which could be a useful clinical tool.
Mechanistic explanations of symptomatology beyond the seizure onset zone can also be revealed with conventional nuclear medicine techniques such as 18F-FDG-PET. This is demonstrated in a study of Occipital Lobe Epilepsy by Wong and colleagues, who observed that patients with automatisms have metabolic changes extending from the epileptogenic occipital lobe into the ipsilateral temporal lobe, whereas in patients without automatisms the 18F-FDG-PET was abnormal only in the occipital lobe (Wong et al., 2014).
The clinical significance of the ability to non-invasively study functional brain networks extends to understanding the impact of surgery on brain networks. This Frontiers research topic includes an investigation by Doucet and colleagues revealing that temporal lobe epilepsy and surgery selectively alter the dorsal, rather than the ventral, default-mode network (Doucet et al., 2014).
Another approach to better understand the mechanisms of seizure onset and broader symptomatology is computational modelling. It can track aspects of neurophysiology than cannot be readily measured: for example effective connectivity and mean membrane potential dynamics are shown by (Freestone et al., 2014) to be estimable using model inversion. In a proof-of-principle experiment with simulated data, they demonstrate that by tailoring the model to subject-specific data, it may be possible for the framework to identify a seizure onset site and the mechanism for seizure initiation and termination. Also in this topic, Petkov and colleagues utilise a computational model of the transition into seizure dynamics to explore how conditions favourable for seizures relate to changes in functional networks. They find that networks with higher mean node degree are more prone to generating seizure dynamics in the model, thus providing a mathematical mechanistic explanation for increasing node degree causing increased ictogenicity (Petkov et al., 2014).
Seizure prediction is an area of considerable research, and in this topic Cook and colleagues reveal intriguing characteristics in the long-term temporal pattern of seizure onset. They confirmed that human inter-seizure intervals follow a power law, and they found evidence of long-range dependence. Specifically, the dynamics that led to the generation of a seizure in most patients appeared to be affected by events that took place much earlier (as little as 30 minutes prior and up to 40 days prior in some patients) (Cook et al., 2014). The authors rightly note that this information could be valuable for individually-tuned seizure prediction algorithms.
Several methodological papers in this Frontiers Topic prove there remains considerable potential to improve neuroimaging methods as applied to the study of epilepsy. For example, (Mullinger et al., 2014) reveal the critical importance of the accuracy of physical models if one is to optimise lead positioning in functional MRI with simultaneous EEG. Confirming with computer modelling and phantom measurements that lead positioning can have a substantial effect on the amplitude of the MRI gradient artefact present on the EEG, they optimised the positions in a novel cap design. However, whilst this substantially reduced gradient artefact amplitude on the phantom, it made things worse when used on human subjects. Thus, improvement is required in model accuracy if one is to make accurate predictions for the human context.
Reduction of artefact, particularly cardioballistic and non-periodic motion artefact, remains a challenge for off-the-shelf MRI-compatible EEG systems. However, for over a decade, the Jackson group in Melbourne has dealt well with this issue using insulated carbon-fibre artefact detectors, physically but not electrically attached to the scalp (Masterton et al., 2007). In the present topic, they provide detailed instructions for building such detectors and interfacing them with a commercially available MRI-compatible EEG system (Abbott et al., 2015). This team also previously developed event-related ICA (eICA), to map fMRI activity associated with inter-ictal events observed on EEG (Masterton et al., 2013b). The method is capable of distinguishing separate sub-networks characterised by differences in spatio-temporal response (Masterton et al., 2013a). The eICA approach frees one from assumptions regarding the shape of the time-course of the neuronal and haemodynamic response associated with inter-ictal activity (which can vary according to spike type, can vary from conventional models and may include pre-spike activity (Masterton et al., 2010); issues explored further in the present topic by (Faizo et al., 2014) and (Jacobs et al., 2014)). However, the effectiveness of eICA can be affected by fMRI noise or artefact. In the present topic we see that application of a fully automated de-noising algorithm (SOCK) is now recommended, as it can substantially improve the quality of eICA results (Bhaganagarapu et al., 2014).
The ability to detect activity associated with inter-ictal events can also be improved with faster image acquisition. Magnetic Resonance Encephalography (MREG) is a particularly fast fMRI acquisition method (TR=100ms) that achieves its speed using an under-sampled k-space trajectory (Assländer et al., 2013; Zahneisen et al., 2012). This has now been applied in conjunction with simultaneous EEG, to reveal that the negative fMRI response in the default-mode network is larger in temporal compared to extra-temporal epileptic spikes (Jacobs et al., 2014).
The default mode network and its relationship to epileptiform activity is also examined in several other papers in this topic. In a pilot fMRI connectivity study of Genetic Generalised Epilepsy and Temporal Lobe Epilepsy patients, (Lopes et al., 2014) observed that intrinsic connectivity in portions of the default mode network appears to increase several seconds prior to the onset of inter-ictal discharges. The authors suggest that the default mode network connectivity may facilitate IED generation. This is plausible, although causality is difficult to establish and it is possible that something else drives both the connectivity and EEG changes (Abbott, 2015).
Complicating matters further is the question of what connectivity means. There are many ways in which connectivity can be assessed. Jones and colleagues have discovered that some of these do not necessarily correlate well with each other. They examined connectivity between measurements made with intracranial electrodes, connectivity assessed using simultaneous BOLD fMRI and intracranial electrode stimulation, connectivity between low-frequency voxel measures of fMRI activity, and a diffusion MRI measure of connectivity – an integrated diffusivity measure along a connecting pathway (Jones et al., 2014). They found only mild correlation between these four measures, implying they assess quite different features of brain networks. More research in this domain would therefore be valuable.
Whatever the measure of connectivity utilised, most evidence of alterations in connectivity in epilepsy has been obtained from comparison of a group of patients with a group of healthy controls. However, a new method called Detection of Abnormal Networks in Individuals (DANI) is now proposed by (Dansereau et al., 2014). This method is designed to detect the organisation of brain activity in stable networks, which the authors call modularity. The conventional definition of modularity refers to the degree to which networks can be segregated into distinct communities, usually estimated by maximising within-group nodal links, and minimising between group links (Girvan and Newman, 2002; Rubinov and Sporns, 2010). Dansereau take a novel approach to this concept, instead evaluating the stability of each resting state network across replications of a bootstrapped clustering method (Bellec et al., 2010). In the DANI approach, the degree to which an individual’s functional connectivity modular pattern deviates from a population of controls is quantified. Whilst application of the method to epilepsy patients is preliminary, significant changes were reported likely related to the epileptogenic focus in 5 of the 6 selected focal epilepsy patients studied. In several patients, modularity changes in regions distant from the focus were also observed, adding further evidence that the pervasive network effects of focal epilepsy can extend well beyond the seizure onset zone.
When it comes to application of EEG-fMRI to detect the seizure onset zone, there is typically a trade-off between specificity and sensitivity, with the added complication that activity or network changes may also occur in brain regions other than the ictal onset zone. The distant activity may be due to activity propagation from the onset zone, pervasive changes in functional networks creating a ‘permissive state’, or in some cases might be the brain’s attempt to prevent seizures. Specificity and sensitivity of EEG-fMRI to detect the ictal onset zone is explored by (Tousseyn et al., 2014). They determined how rates of true and false positives and negatives varied with voxel height and cluster size thresholds, both for the full statistical parametric map, and for the single cluster that contained the voxel of maximum statistical significance. The latter conferred the advantage of reducing positives remote from the seizure onset zone. As a result, it appeared to be more robust to variations in statistical threshold than analysis of the entire map. One needs to be cautious however, given the small numbers of patients studied, and the fact that the “optimal” settings were determined using receiver operator characteristic curves of the same study data. It remains to be seen how well this might generalise to a different study.
Perhaps the greatest potential for future advancement in EEG-fMRI is in methods to make the most of the all the information captured by each modality. This is highlighted by the work of Deligianni et al, demonstrating with a novel analysis framework the potential to obtain more information on the human functional connectome by utilising EEG and fMRI together (Abbott, 2016; Deligianni et al., 2014).
We hope that you enjoy this collection of papers providing a broad snapshot of advances in brain mapping methods and application to better understand epilepsy.

### [ARTICLE] Effort and Fatigue-Related Functional Connectivity in Mild Traumatic Brain Injury – Full Text

Mental fatigue in healthy individuals is typically observed under conditions of high cognitive demand, particularly when effort is required to perform a task for a long period of time—thus the concepts of fatigue and effort are closely related. In brain injured individuals, mental fatigue can be a persistent and debilitating symptom. Presence of fatigue after brain injury is prognostic for return to work/school and engagement in activities of daily life. As such, it should be a high priority for treatment in this population, but because there is little understanding of its behavioral and neural underpinnings, the target for such treatment is unknown. Here, the neural underpinnings of fatigue and effort are investigated in active duty military service members with mild traumatic brain injury (mTBI) and demographically-matched orthopedic controls. Participants performed a Constant Effort task for which they were to hold a pre-defined effort level constant for long durations during fMRI scanning. The task allowed for investigation of the neural systems underlying fatigue and their relationship with sense of effort. While brain activation associated with effort and fatigue did not differentiate the mTBI and controls, functional connectivity amongst active brain regions did. The mTBI group demonstrated immediate hyper-connectivity that increased with effort level but diminished quickly when there was a need to maintain effort. Controls, in contrast, demonstrated a similar pattern of hyper-connectivity, but only when maintaining effort over time. Connectivity, particularly between the left anterior insula, rostral anterior cingulate cortex, and right-sided inferior frontal regions, correlated with effort-level and state fatigue in mTBI participants. These connections also correlated with effort level in the Control group, but only the connection between the left insula and superior medial frontal gyrus correlated with fatigue, suggesting a differing pattern of connectivity. These findings align, in part, with the dopamine imbalance, and neural efficiency hypotheses that pose key roles for medial frontal connections with insular or striatal regions in motivating or optimizing performance. Sense of effort and fatigue are closely related. As people fatigue, sense of effort increases systematically. The data propose a complex link between sense of effort, fatigue, and mTBI that is centered in what may be an inefficient neural system due to brain trauma that warrants further investigation.

## Introduction

A signature injury of service members deployed during the conflicts in Iraq and Afghanistan is traumatic brain injury (TBI). Of the approximately 360,000 service members who suffer from TBI, 70% are classified as mild injuries (mTBI; DVBIC Quarterly Reports). At least 19% of the service members with mTBI have persistent symptoms that contribute to difficulty engaging in social and work activities. The consequences of persistent fatigue in mTBI pose a real challenge to rehabilitation (1). High levels of mental fatigue commonly persist and relate to failure to return to work and loss of productivity (23). In fact, presence of fatigue is the strongest predictive factor of poor outcomes following TBI (1). Despite the prevalence of fatigue in TBI, our understanding of its behavioral and neural underpinnings is lacking.

Mental fatigue is a complex process that is operationally defined by time on task and increased mental effort. When performance suffers (reaction time, accuracy, etc.) over time, presumably from fatigue, there tends to be fairly diffusely increased brain activity (4). Simultaneously, there may also be decreased motivation under high effort (5). According to Kahneman’s “resource capacity theory,” the amount of effort needed to perform a task is related to the complexity of the task and an individual’s limited general capacity to perform mental work [i.e., resource capacity, (67)]. When a task is difficult, the demand for resources is high, and performance suffers when resources near depletion. When a person recognizes that performance is suffering, tasks are perceived as more difficult, and require greater effort, which Kahneman equates with the experience of mental fatigue.

Brain imaging in mTBI indicates an increase in brain activity with increased time on task regardless of the type or demand requirements of the task (8). In contrast, healthy individuals have decreased activation over time without a serious decrement in performance, and without reporting significant fatigue. This brain response in TBI may suggest a perception of higher levels of effort when the task is long, or that individuals with TBI inefficiently regulate cognitive control and exert more mental effort to maintain a high-level of performance, resulting in fatigue.

While there is a plethora of literature reporting that task demand causes degradation of performance in mTBI, few have investigated whether task demand results in fatigue more so than in healthy controls, or how this fatigue manifests in behavior or in neural function. The few available studies have small sample sizes [e.g., (9)] limiting their generalizability. The brain networks implicated in effort and fatigue include frontostriatal circuitry, or the ventromedial prefrontal cortex more specifically. Damage to these brain regions is thought to diminish resource capacity and impair allocation of resources, resulting in an increased perception of expended effort (1012). Additionally, fatigue related to lack of motivation to engage and maintain performance on a task, or to predict and manage change in performance based on feedback about performance, is associated with the integrity of the ventromedial prefrontal cortical. That is, individuals with larger lesions of this brain region report more fatigue and apathy (1314). The frontostriatal network is involved in coding the incentive value for an expected outcome (15), and is mediated by dopaminergic frontostriatal networks (131619). Breakdowns in ventromedial prefrontal cortex-related network connectivity may disrupt the ability to appropriately detect, monitor, and self-correct errors or to adequately motivate behavior (2021). For example, the anterior cingulate cortex is associated with monitoring and detecting errors, the pre-supplementary motor area with engaging in task, and the connectivity amongst these two regions is related to fatigue (22).

One gap in the existing literature on fatigue is that paradigms infer “probable” fatigue [exception is Wylie et al. (22)], rather than directly measuring it. In the present study, we investigate brain activity and network connectivity in mTBI participants while they perform a task explicitly designed to study the relationship between task-related effort and fatigue. We assess fatigue with a questionnaire about fatigue over the week prior to scanning (trait) as well as with task manipulation during brain imaging [state, Constant Effort Task [CE]]. For Constant Effort, subjects are asked to squeeze a bulb to a prescribed effort level and hold it constant for a discrete period of time. The task is considered a general index of central fatigue as it is not specific to motor system engagement (2324). Varying effort levels result in predictable changes in the ability to maintain pressure on the bulb such that the time it takes to fatigue is slower at low effort levels than at higher effort levels. Performance on the CE task during functional fMRI allowed for identification of the neural systems underlying effort and fatigue as well as the differences in these systems in mTBI relative to control. We hypothesize that fatigue in mTBI arises when there is an altered perception of the amount of effort needed to perform the task, either because there is a failure to:

a) update the amount of effort given to the task based on internal feedback about performance, which is assessed by contrasting performance across effort levels,

b) sustain a given effort level, which is assessed via time on task, or

c) both.

Because estimating and maintaining effort are likely a result of a complex network of interacting brain regions, we examined not only brain activation during task performance, but also functional connectivity (FC) amongst the regions active during the task. We predict that mTBI participants will demonstrate increased pre-frontal and anterior cingulate cortex activation, as well as increased connectivity of these regions to ventral-striatal regions relative to Control participants.[…]

Figure 1. Effort and Fatigue in the Constant Effort task demonstrated differing regional effects with effort associated with caudal, medial prefrontal cortex (red) while fatigue was associated with rostral prefrontal cortex as well as postcentral and posterior cingulate cortex (blue). Controls demonstrated significantly higher activity than mTBI in a small area of the right medial prefrontal cortex (green) while mTBI had more activity in the posterior occipital cortex, but there were no other significant group effects. When these regions were used in computing functional connectivity, it was only the connectivity amongst the regions of the effort effect (red) that demonstrated group differences in connection strength. For example, the connection between the left insula (A) and the right inferior frontal gyrus (B, pars orbitalis) was significantly stronger in the TBI group for time on task at 75% effort.

### [Systematic Review] Neural Correlates of Familiarity in Music Listening: A Systematic Review and a Neuroimaging Meta-Analysis – Full Text

Familiarity in music has been reported as an important factor modulating emotional and hedonic responses in the brain. Familiarity and repetition may increase the liking of a piece of music, thus inducing positive emotions. Neuroimaging studies have focused on identifying the brain regions involved in the processing of familiar and unfamiliar musical stimuli. However, the use of different modalities and experimental designs has led to discrepant results and it is not clear which areas of the brain are most reliably engaged when listening to familiar and unfamiliar musical excerpts. In the present study, we conducted a systematic review from three databases (Medline, PsychoINFO, and Embase) using the keywords (recognition OR familiar OR familiarity OR exposure effect OR repetition) AND (music OR song) AND (brain OR brains OR neuroimaging OR functional Magnetic Resonance Imaging OR Position Emission Tomography OR Electroencephalography OR Event Related Potential OR Magnetoencephalography). Of the 704 titles identified, 23 neuroimaging studies met our inclusion criteria for the systematic review. After removing studies providing insufficient information or contrasts, 11 studies (involving 212 participants) qualified for the meta-analysis using the activation likelihood estimation (ALE) approach. Our results did not find significant peak activations consistently across included studies. Using a less conservative approach (p < 0.001, uncorrected for multiple comparisons) we found that the left superior frontal gyrus, the ventral lateral (VL) nucleus of the left thalamus, and the left medial surface of the superior frontal gyrus had the highest likelihood of being activated by familiar music. On the other hand, the left insula, and the right anterior cingulate cortex had the highest likelihood of being activated by unfamiliar music. We had expected limbic structures as top clusters when listening to familiar music. But, instead, music familiarity had a motor pattern of activation. This could reflect an audio-motor synchronization to the rhythm which is more engaging for familiar tunes, and/or a sing-along response in one’s mind, anticipating melodic, harmonic progressions, rhythms, timbres, and lyric events in the familiar songs. These data provide evidence for the need for larger neuroimaging studies to understand the neural correlates of music familiarity.

## Introduction

Music is ubiquitous in human culture and has been present since prehistorical times (Conard et al., 2009). Music does not appear to have a survival value, yet most of the current literature has pinpointed it as a fundamental aspect of human life, describing it as a “universal reward” (Trehub et al., 2005). People often value music for the emotions it generates (Juslin and Laukka, 2004Brattico and Pearce, 2013), and listening to music can help to regulate mood and increase well-being (Hills and Argyle, 1998Kawakami et al., 2014). This might explain the use of music in people’s everyday lives (Schäfer and Sedlmeier, 2010).

Familiarity or repeated exposure in music has been reported as an important factor modulating emotional and hedonic responses in the brain (Pereira et al., 2011). The familiarity principle, also known as the “mere exposure effect,” was first described by Zajonc (1968). It is a psychological phenomenon which suggests that the more exposed we are to someone or something, the more we like it. Repetition in music can be of different types: within a piece, across pieces, or across multiple hearings (Margulis, 2013). Both familiarity and repetition may increase the liking of a piece of music, thus inducing positive emotions (Witviliet and Vrana, 2007Omar Ali and Peynircioglu, 2010).

Long before its description in 1968, the phenomenon of familiarity had been known by social psychologists and applied to the music field (King and Prior, 2013). The first person who documented it was Meyer in 1903. He presented his subjects with a dozen repetitions of unfamiliar music that he had composed. After listening to the last repetition, most subjects asserted that “the aesthetic effect was improved by hearing the music repeatedly” (Meyer, 1903). Moreover, Meyer showed that melodies which ended on the frequency ratio symbol 2 (the Lipps-Meyer Law) was preferred to all other melodies. However, this law was later on disputed by Paul Farnsworth, his student, who argued that interval ending preferences could be altered by training. Therefore, repetition and familiarity with a specific ratio ending could increase preference for that specific ending. This effect, explaining the perception of music closure, was called the “habit principle” (Farnsworth, 1926). Overall, it seems familiarity deepens the understanding of music and engagement with music listening (King and Prior, 2013).

However, according to numerous studies, the relationship between exposure and enjoyment is non-linear, following an inverted-U shape preference response. Repeated exposure to music can increase pleasure (“hedonic value”) for a certain period, but ultimately gives rise to increasing displeasure (Jakobovits, 1966Berlyne, 1971Szpunar et al., 2004Schellenberg, 2008).

There are different explanations for the inverted U-shape preference response. One is the perceptual fluency model (Bornstein and D’Agostino, 1994) which explains that people incorrectly assume that the facilitated processing of a familiar stimulus is associated to some positive attribute of the stimulus itself. However, as the conscious recognition of fluency processing increases, they stop misattributing this effect to the stimulus but to repeated exposure, and therefore pleasure decreases. Another explanation proposed by Berlyne (1971) states that the inverted U reflects the “interaction of two opposing impulses:” the ascending part arises from an evolutionary conditioned preference for the familiar (positive learned safety effect), and the subsequent decline of the U favors for novelty seeking (aversion to boredom). Moreover, the complexity of the stimulus also influences the timescale of satiation effect. According to Szpunar et al. (2004), despite initial increases in liking, after the stimulus complexity has been absorbed, boredom intercedes, and satiation reduces likability.

Peretz et al. reported that familiarity is best conceptualized as an “implicit memory phenomenon,” in which previous experience aids the performance of a task without conscious awareness of these previous episodes (Peretz et al., 1998). The ability to recognize familiar melodies appeared to be dependent on the integrity of pitch and rhythm perception. Of these two factors, pitch is thought to play a more important role (Hébert and Peretz, 1997). The authors noted that “although the mere exposure effect is simple to define and to reproduce experimentally, it is more complicated to explain.”

Familiarity is a complex subject and the neural mechanisms underlying this memory phenomenon toward music listening are still not very clear or consistent. Some authors define familiarity as a semantic memory process, which is a declarative knowledge (e.g., words, colors, faces, or music) acquired over a lifetime. Musical semantic memory is defined as the long-term storage of songs or musical excerpts, which enables us to have a strong feeling of familiarity when we listen to music (Groussard et al., 2010a). Brain lesion studies showed that music semantic memory appears to involve both hemispheres; however, the integrity of the left hemisphere is critical, suggesting functional asymmetry favoring the left hemisphere for semantic memory (Platel et al., 2003). Neuroimaging studies featuring musical semantic memory have reported the involvement of the anterior part of the temporal lobes, either in the left hemisphere or bilaterally, and the activation of the left inferior frontal gyrus (Brodmann area (BA) 47) (Plailly et al., 2007). Groussard and her co-workers also found activation of the superior temporal gyri (BA 22). The right superior temporal gyrus is mostly involved in the retrieval of perceptual memory traces (information about rhythm and pitch), which are useful for deciding whether or not a melody is familiar. The left superior temporal gyrus seems to be involved in distinguishing between familiar and unfamiliar melodies (Groussard et al., 2010a).

Plailly et al. (2007) also addressed the neural correlates of familiarity and its multimodal nature by studying odors and musical excerpts stimuli. These were used to investigate the feeling of familiarity and unfamiliarity. Results for the feeling of familiarity indicated a bimodal activation pattern in the left hemisphere, specifically the superior and inferior frontal gyri, the precuneus, the angular gyrus, the parahippocampal gyrus, and the hippocampus. On the other hand, the feeling of unfamiliarity (impression of novelty) of odors and music was related to the activation of the right anterior insula (Plailly et al., 2007). Janata (2009) studied the neural correlates of music-evoked autobiographical memories in healthy individuals and those with Alzheimer disease. His findings showed that familiar songs from our own past can trigger emotionally salient episodic memories and that this process is mediated by the medial prefrontal cortex (MPFC). In the same study, hearing familiar songs also activated the pre-supplementary motor area (SMA), left inferior frontal gyrus, bilateral thalamus, and the right cerebellar hemisphere (Janata, 2009).

Brain imaging studies in the neurobiology of reward during music listening demonstrated the involvement of mesolimbic striatal areas, especially the nucleus accumbens (NAcc) in the ventral striatum. This structure is connected with subcortical limbic areas such as the amygdala and hippocampus, insula and anterior cingulate cortex, and also integrated with cortical areas including the orbital cortex and ventromedial prefrontal cortex. These limbic and paralimbic structures are considered the core structures of emotional and reward processing (Koelsch, 2010Salimpoor et al., 2013Zatorre and Salimpoor, 2013). Recently, Pereira et al. (2011) investigated familiarity and music preference effects in determining the emotional involvement of the listeners and showed that familiarity with the music contributed more to the recruitment of the limbic and reward centers of the brain.

Electroencephalography (EEG) is another neuroimaging technique that enabled us to address the brain’s response to stimuli. It provides a real-time picture of neural activity, recording how it varies millisecond by millisecond. Time-locked EEG activity or event-related potential (ERP) are small voltages generated in the brain structures in response to specific sensory, cognitive or motor event (Luck, 2005). With regards to auditory stimuli—and, more specifically, to music listening and recognition—the N1, P200, P300, and N400 waves have been found to be particularly important. N1, a negative component found 80–110 ms after stimulus onset, is thought to represent the detection of a sound and its features, as well as detection of change of any kind (pitch, loudness, source location etc.) (Näätänen and Picton, 1987Seppänen et al., 2012). It originates in the temporal lobe, predominantly in or near the primary auditory cortex, suggesting that it is involved in early phases of information processing (Hyde, 1997). Secondly, P2 is a positive component that arises 160–200 ms after the onset of the stimulus (Seppänen et al., 2012) and is localized in the parieto-occipital region (Rozynski and Chen, 2015). It is involved in evaluation and classification of the stimulus (Seppänen et al., 2012) as well as other related cognitive processes, such as working memory and semantic processing (Freunberger et al., 2007). P3, instead, is considered to be more related to selective attention and information processing, such as recognition and memory processes. It is traditionally divided into P3a, arising in the frontal region, and P3b, arising in the temporal and parietal regions; it appears 300–400 ms after the stimulus and lasts 300–600 ms (Patel and Azzam, 2005). However, its timing can vary widely, so it is often described as the late positive complex (LPC), a definition which also includes later deflections, such as P500 and P600 (Finnigan et al., 2002). Finally, N400 arises 200–600 ms after the stimulus, but its anatomical localization has not been well defined since it does not seem to be related to a specific mental operation only. Indeed, it seems to be connected to the processing of meaning at all levels, since it is influenced by factors acting both at lower and at higher levels of these cognitive processes (Kutas and Federmeier, 2011).

Advances in brain imaging techniques have facilitated the examination of music familiarity processing in the human brain. Nevertheless, the use of different modalities and experimental designs has led to differing results. Over the years, studies have used varying music stimuli such as melodies, songs with and without lyrics, with diverse acoustic complexity. Due to this heterogeneity, it is not clear which areas are most reliably engaged when listening to familiar and unfamiliar songs and melodies.

To our knowledge, no systematic review or meta-analysis has been conducted to resolve the inconsistencies in the literature. The present study systematically reviews the existing literature to establish the neural correlates of music familiarity, in healthy population using different neuroimaging methods, including fMRI, PET, EEG, ERP, and MEG. Finally, we used the activation likelihood estimation (ALE) method (Eickhoff et al., 2009) to conduct a series of coordinate-based meta-analyses for fMRI and PET studies. We expected to find brain areas related to emotion or reward as the most active regions when listening to familiar music, as familiarity is positively correlated with likeability and pleasure, at least to a certain number of exposures.[…]

### [ARTICLE] Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback – Full Text

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants’ preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.

## Introduction

Neurofeedback (NFB) is a novel application of brain-computer interfaces that aids real-time voluntarily regulation of brain activity. Mounting evidence shows that NFB has promising effects to enhance behavior, cognitive and emotional processes in normative samples (1–5). NFB has also been preliminary used to restore aberrant neurobiology and symptoms in neurological conditions (e.g., stroke, traumatic brain injury) and in psychopathology (e.g., ADHD, autism, depression, addiction) (1–7). Real-time functional magnetic resonance imaging (rtfMRI) based NFB has the potential to provide insight in understanding the mechanisms of psychological states (8–10). These include affiliative emotions (11) underpinned by deep brain nuclei (12, 13) the activity of which is unlikely to be robustly measured via surface electroencephalography.

rtfMRI NFB tools can be used to study the causal mechanisms of complex emotions and to inform evidence-based personalized interventions to enhance and recover aberrant emotional states (and their neural substrates) in normative and clinical samples. One key practical human challenge of fMRI studies includes participants being distracted and experiencing difficulties to feel valid psychological states in the scanner environment, particularly when trying to sustain complex emotions.

Recent studies have combined immersive virtual environments with multiple sensory modalities to deliver psychological/cognitive interventions, and to enhance their effectiveness via engaging and motivating individuals to practice (14–16).

Only two proof of concept studies have combined rt-NFB with virtual environments as brain computer interfaces (BCI). An electroencephalography-based NFB study computed brain activity from about 500 participants collectively, during an interactive game of relaxation and concentration over one night (16), where individual’s level of brain activity could not be discerned. A separate rtfMRI-NFB paradigm used a virtual fire interface to up-regulate and down-regulate brain activity in eight healthy participants—but this was devoid of any emotional states and far from multimodal and immersive (17).

It remains untested whether rt-NFB platforms integrating multisensory virtual environments can successfully recruit complex emotions and sustain these emotions long and strong enough to probe their underlying neural correlates. Such a platform can advance NFB applications, via (i) increasing the ecological validity of rtfMRI-NFB experiments, and their relevance for the daily experiences of emotions outside of experimental settings, (ii) adapting NFB interfaces to the individual and target population so these are more relatable, engaging and effective in generating and sustaining complex emotions that maximize the success of rtfMRI-NFB interventions (18–20).

This study aims to demonstrate the feasibility of an engaging rtfMRI-NFB interface that can be individually tailored and, specifically, to provide a proof of concept for a rtfMRI-NFB integrating a virtual environment as a BCI and musical stimuli using both local (region of interest, ROI) and distributed (support vector machine, SVM) analyses. The FRIEND Engine Framework system (21) was enhanced and adapted for this aim. We recruited healthy young adults performing rtfMRI-NFB during complex emotion experiences, including tenderness—a positive affiliative emotion – and anguish—a self-reflective negative emotion (11, 13, 22–25).

We also aimed to validate the functional anatomy of these complex emotions during rtfMRI-NFB. After the real-time data was collected, we ran offline fMRI data analyses to verify the effects of the real-time neurofeedback task on brain activity using standard preprocessing and statistical analysis methods.

We hypothesized that participants would voluntary change the color of a virtual environment in the BCI during rtfMRI-NFB using the activity of the following regions: (i) for the tenderness condition, the septo-hypothalamic area (when using ROI-based rtfMRI-NFB method) and other brain areas ascribed to positive affiliative emotions i.e., medial orbitofrontal areas (when using SVM-based rtfMRI-NFB method) (11, 25–27); and (ii) for the anguish condition, the amygdala (during the ROI-based fMRI-NFB method) and also lateral prefrontal cortices implicated in negative affect (e.g., anguish, fear, anxiety, negative mood, stress, psychological pain), and in psychopathologies where negative affect is a feature [e.g., depression and generalized anxiety disorder (28–32)] (during SVM-based rtfMRI-NFB).

## Materials and Methods

### Participants

We used a single subject, repeated measures design with two identical assessments on two consecutive days, counterbalanced by rtfMRI-NFB method (i.e., ROI and SVM). We recruited eight psychiatrically and neurologically healthy postgraduate research students, free of psychoactive medication and with normal or corrected-to-normal vision. Four participants were recruited from the D’Or Institute for Research and Education (IDOR) in Rio de Janeiro, Brazil (approved by the Ethics and Scientific committees of the Copa D’Or Hospital, Rio de Janeiro, Brazil – No 922.218). To validate the protocol in a different scanner and institution, we also recruited four participants from the Monash Biomedical Imaging (MBI) at Monash University in Melbourne, Australia (MUHREC CF15/1756 – 2015000893). All volunteers provided written informed consent prior to study participation.

### Design of the Neurofeedback BCI

Supplementary video 1 and Figure 1 show the BCI used for the rt-fMRI NFB. The BCI comprised a virtual environment as a medium to convey sensory feedback to participants in real time, in association with ongoing tenderness, anguish and neutral emotional states. The virtual environment was created by editing the Unity 3D asset Autumnal Nature Pack (Unity 3D, https://assetstore.unity.com/packages/3d/environments/autumnal-nature-pack-3649) and displayed a first-person navigation at walking speed through hills and cornfields, with a total duration of 10′8″ (Supplementary Video 1). The virtual environment was prepared to alternate between different trial types: neutral (30″), tenderness (46″) and anguish (46″).

The trial types were displayed via changes in the base color hues of the virtual environment and via specific music excerpts. Music excerpts were fixed for each trial type, and not influenced by current neural/psychological states (no music for Neutral, mild, gentle music for Tenderness and eerie, distorted music for Anguish). Music excerpts were selected from 20 audio tracks, all normalized using the root mean square feature of Audacity software (Audacity, http://www.audacityteam.org). The audio tracks were previously rated to have comparable volume, pace, and rhythm. For the rtfMRI-NFB task runs, four excerpts for tenderness and four excerpts for anguish were played.

Neutral trials were characterized by a normal colored virtual landscape displayed in the BCI with no background music. Tenderness trials were characterized by a change in the color of the virtual landscape to orange and were accompanied by tenderness music excerpts. Anguish trials commenced when the color of the environment turned to purple hues and were accompanied by anguish music excerpts.

#### Task Practice Outside the MRI

For training purposes, we recorded a video showing a sample of the virtual environment. The video lasted as long as one run of the rtfMRI-NFB task (10′ 8″) and was used by participants to practice tenderness, anguish and neutral states before the MRI. With this practice, participants could learn which music tracks and VR color changes in the BCI corresponded to tenderness, anguish and neutral trials.

#### Neurofeedback Interface

As shown in Figure 1, instead of a classic thermometer, the color of the virtual environment was used as BCI changed in real time with increased engagement of the neural activity/pattern corresponding to distinct target emotional states—orange for tenderness trials, purple for anguish trials and natural light tones for neutral trials. Participants were instructed to experience tenderness or anguish as intensely as possible in the respective trials and to increase the intensity of their emotion to turn in real time, the color of the virtual environment BCI to as orange as possible during tenderness trials, and as purple as possible during anguish trials, which increased in turn the corresponding neural activity/pattern.

FIGURE 1

Figure 1. Color hue modulation of the virtual environment during rtfMRI-NFB. The color hue changes from baseline neutral trials to a more intense orange and purple as participants increasingly engage target brain regions for tenderness and anguish trials.

[…]

## Abstract

Following advances in robotic rehabilitation, there have been many efforts to investigate the recovery process and effectiveness of robotic rehabilitation procedures through monitoring the activation status of the brain. This work presents the development of a two degree-of-freedom (DoF) magnetic resonance (MR)-compatible hand device that can perform robotic rehabilitation procedures inside an fMRI scanner. The device is capable of providing real-time monitoring of the joint angle, angular velocity, and joint force produced by the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints of four fingers. For force measurement, a custom reflective optical force sensor was developed and characterized in terms of accuracy error, hysteresis, and repeatability in the MR environment. The proposed device consists of two non-magnetic ultrasonic motors to provide assistive and resistive forces to the MCP and PIP joints. With actuation and sensing capabilities, both non-voluntary–passive movements and active–voluntary movements can be implemented. The MR compatibility of the device was verified via the analysis of the signal-to-noise ratio (SNR) of MR images of phantoms. SNR drops of 0.25, 2.94, and 11.82% were observed when the device was present but not activated, when only the custom force sensor was activated, and when both the custom force sensor and actuators were activated, respectively.

## References

1. 1.
Buryanov A, Kotiuk V (2010) Proportions of hand segments. Int J Morphol 28(3):755–758
2. 2.
Carey JR, Kimberley TJ, Lewis SM, Auerbach EJ, Dorsey L, Rundquist P, Ugurbil K (2002) Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain 125:773–788
3. 3.
Chapuis D, Gassert R, Sache L, Burdet E, Bleuler H (2004) Design of a simple MRI/fMRI compatible force/torque sensor. In: IEEE/RSJ international conference on intelligent robots and systems, 2004, pp 2593–2599Google Scholar
4. 4.
Determination of signal-to-noise ratio (SNR) in diagnostic magnetic resonance imaging. In: national electrical manufacturers association NEMA MS 1, 2008, pp 1–19Google Scholar
5. 5.
El Bannan K, Handler WB, Wyenberg C, Chronik BA, Salisbury SP (2013) Prediction of force and image artifacts under MRI for metals used in medical devices. IEEE/ASME Trans Mechatron 18(3):954–962
6. 6.
Erwin A, Malley MKO, Ress D, Sergi F (2015) Development, control, and MRI-compatibility of the MR-SoftWrist. In: IEEE international conference on rehabilitation robotics (ICORR), 2015, pp 187–192Google Scholar
7. 7.
Gassert R, Moser R, Burdet E, Bleuler H (2006) MRI/fMRI-compatible robotic system with force feedback for interaction with human motion. IEEE/ASME Trans Mechatron 11(2):216–224
8. 8.
Gassert R, Dovat L, Lambercy O, Ruffieux Y, Chapuis D, Ganesh G, et.al. (2006) A 2-DOF fMRI compatible haptic interface to investigate the neural control of arm movements. In: IEEE international conference on robotics and automation, ICRA, 2006, pp 3825–3831Google Scholar
9. 9.
Gu GM, Shin YK, Son J, Kim J (2012) Design and characterization of a photo-sensor based force measurement unit (FMU). Sens Actuators, A 182:49–56
10. 10.
Heo P, Gu GM, Lee SJ, Rhee K, Kim J (2012) Current hand exoskeleton technologies for rehabilitation and assistive engineering. Int J Precis Eng Manuf 13(5):807–824
11. 11.
Heuer H, Lüttgen J (2015) Robot assistance of motor learning: a neuro-cognitive perspective. Neurosci Biobehav Rev 56:222–240
12. 12.
Jacq C, Lüthi B, Maeder T, Lambercy O, Gassert R, Ryser P (2010) Thick-film multi-DOF force/torque sensor for wrist rehabilitation. Sens Actuators, A 162:361–366
13. 13.
Jones CL, Wang F, Morrison R, Sarkar N, Kamper DG (2014) Design and development of the cable actuated finger exoskeleton for hand rehabilitation following stroke. IEEE/ASME Trans Mechatron 19:131–140
14. 14.
Khanicheh A, Mintzopoulos D, Weinberg B, Tzika A, Mavroidis C (2008) MR_CHIROD v. 2: magnetic resonance compatible smart hand rehabilitation device for brain imaging. IEEE Trans Neural Syst Rehabil Eng 16(1):91–98
15. 15.
Kim HM, Kim GS (2013) Development of a finger-rehabilitation robot for fingers’ flexibility rehabilitation exercise. Int J Precis Eng Manuf 14(4):535–541
16. 16.
Kim YH, Park JW, Ko MH, Jang SH, Lee PKW (2004) Plastic changes of motor network after constraint-induced movement therapy. Yonsei Med J 45(2):241–246
17. 17.
Komi ER, Roberts JR, Rothberg SJ (2007) Evaluation of thin, flexible sensors for time-resolved grip force measurement. J Mech Eng Sci 221:1687–1699
18. 18.
Lee SJ, Kim YJ, Jeong GH, Yoon BR, Jho JY, Kim DM, Rhee K (2012) Computational analyses of pinching dynamics of a finger exoskeleton composed of IPMC actuators. Int J Precis Eng Manuf 13(12):2135–2141
19. 19.
Li Z (2003) Using robotic hand technology for the rehabilitation of recovering stroke patients with loss of hand powerGoogle Scholar
20. 20.
Li G, Li B, Sun J, Zhang W, Sun Z, Chen Q (2013) Development of a directly self-adaptive robot hand with pulley-belt mechanism. Int J Precis Eng Manuf 14(8):1361–1368
21. 21.
Lin J, Wu Y, Huang TS (2000) Modeling the constraints of human hand motion. In: proceedings work human motion, pp 21–126Google Scholar
22. 22.
Monfaredi R, Seifabadi R, Fichtinger G, Iordachita I (2013) Design of a decoupled MRI-compatible force sensor using fiber bragg grating sensors for robot-assisted prostate interventions. 8671:1–9Google Scholar
23. 23.
Richer E, Hurmuzlu Y (2016) Force actuator system : part II—Nonlinear controller design vol. 122, no. Sept 2000Google Scholar
24. 24.
Shellock FG (2000) Radiofrequency energy-induced heating during MR procedures: a review. J Magn Reson Imag 12:30–36
25. 25.
Taffoni F, Formica D, Saccomandi P, Di Pino G, Schena E (2013) Optical fiber-based MR-compatible sensors for medical applications: an overview. Sensors 13(10):14105–14120
26. 26.
Takahashi CD, Der-Yeghiaian L, Le V, Motiwala RR, Cramer SC (2008) Robot-based hand motor therapy after stroke. Brain 131:425–437
27. 27.
Tan U, Yang B, Gullapalli R, Desai JP (2011) Triaxial MRI-compatible fiber-optic force sensor. IEEE Trans Robot 27(1):65–74
28. 28.
Tang Z, Iwata H, Shigeki S (2015) An fMRI pilot study evaluating brain activation during different finger training exercises. In: IEEE international conference on rehabilitation robotics (ICORR), 2015, pp 967–972Google Scholar
29. 29.
Tsekos NV, Khanicheh A, Christoforou E, Mavroidis C (2007) Magnetic resonance-compatible robotic and mechatronics systems for image-guided interventions and rehabilitation: a review study. Annu Rev Biomed Eng 9:351–387
30. 30.
Yap HK, Lim JH, Nasrallah F, Low FZ, Goh JCH, Yeow RCH (2015) MRC-Glove : a fMRI compatible soft robotic glove for hand rehabilitation application. In: IEEE international conference on rehabilitation robotics (ICORR), 2015, pp 735–740Google Scholar
31. 31.
Yu N, Estévez N, Hepp-Reymond MC, Kollias SS, Riener R (2011) FMRI assessment of upper extremity related brain activation with an MRI-compatible manipulandum. Int J Comput Assist Radiol Surg 6:447–455

### [Editorial] Investigating Brain Activity After Acquired and Traumatic Brain Injury: Applications of Functional MRI

• 1Stroke, Kessler Foundation, West Orange, NJ, United States
• 2Neuropsychology and Neuroscience, Kessler Foundation, West Orange, NJ, United States
• 3Traumatic Brain Injury, Kessler Foundation, West Orange, NJ, United States

Editorial on the Research Topic

Investigating Brain Activity After Acquired and Traumatic Brain Injury: Applications of Functional MRI

Every year, approximately 795,000 people in the United States are affected by stroke and 2.8 million lives are impacted by traumatic brain injury (TBI) (1). Stroke and TBI are also major causes of serious long-term disability, reducing mobility, and impacting thinking, memory, sensation, and emotional functioning. Neuroscience holds great promise in addressing the needs of persons with a history of stroke or TBI by improving the current understanding of brain injury and recovery mechanisms. This is the first step in working to inform and improve the available treatments.

While a great many functional neuroimaging methods exist for studying the healthy brain, such methods have not received widespread acceptance in characterizing patient groups. Several methodological barriers may explain why. First, patient populations can be diverse in terms of injury location and stages of recovery. Accurate measurement and interpretation of functional neuroimaging signal in the damaged brain can also pose a challenge, because stroke and TBI can dramatically alter cerebral blood flow, even in areas that are not affected by a structural lesion (23). Finally, correct interpretation of findings in light of impaired and/or changing behavioral function depends on careful experimental design and precise a priorioperational definitions of the anticipated effects.

Despite these challenges, or, perhaps, because of them, functional neuroimaging is a promising area of investigation in TBI and stroke. This Research Topic is a collection of original research and review articles focused on functional neuroimaging in persons with TBI and stroke. Below, we highlight a few of the most notable findings and ideas from this collection of articles. Readers are encouraged to access the full text articles for more details.

In one of the two review articles, Medaglia provides an overview of fMRI methodology, analyses, and the caveats of applying these analyses to the injured brain. This includes methods, such as seed-based and voxel-based functional connectivity, effective connectivity, including psychophysiological interactions, causal connectivity, and graph analyses. Medaglia discusses the concept of functional re-organization. The term is sometimes used to describe a change in the magnitude of activation or of functional connectivity. It is also used to refer to a re-allocation of function to new brain areas following injury. Medaglia suggests that to improve clarity a precise description of the effect should be provided. Formal tests of re-organization should include a search for areas with activity profile closely resembling that of a damaged area, and with corresponding evidence of recovered behavioral function. Distinguishing different innate recovery mechanisms is especially important in intervention studies, because failing to understand which process may be at work when introducing an intervention, may lead to inadvertent interference with endogenous repair mechanisms.

Nair et al. studied brain activation in acute stroke and healthy older controls participants during a covert verbal fluency task. They controlled for the blood oxygen level dependent (BOLD) response variability across participants using resting state fluctuation amplitude (RSFA) (4). RSFA calibration is thought to eliminate any inter-subject variability due to vascular factors and retain any differences due to neuronal activation factors. They found that after scaling, the BOLD response differences between stroke patients and healthy controls were eliminated. This finding suggests that some of the group differences were due to vascular variables. Additionally, some fine-tuning may be required when scaling with RSFA, perhaps scaling by brain region, rather than across the whole brain.

Bernier et al. applied graph theory to a data set of healthy and TBI subjects with moderate/severe TBI. Their aim was to determine if loss of network differentiation accounts for changes in brain connectivity, specifically hyperconnectivity. This hypothesis was examined within the default mode (DMN) and the task positive network. Supporting other results in the field, they observed hyperconnectivity within the DMN and task positive networks. DMN hyperconnectivity was found to be associated with higher scores on the standardized working memory measure. Thus, the work of these authors demonstrates how fMRI and connectivity analyses can inform the cognitive profile observed following TBI.

The second review in the Research Topics explores a common deficit in TBI. Namely, cognitive control, an executive function that is generally necessary for switching between habitual and goal-directed behavior. In his review, Scheibel talks about functional neuroimaging studies of cognitive control in mild TBI (mTBI). The review draws attention to how the fMRI findings are mixed, with reports of decreased as well as increased brain activation in mTBI, and urges for future studies to aim at recruiting more homogenous samples, as the mixed findings might be explained by the presence of comorbidies in TBI samples.

The original research article by Saleh et al. explored how different approaches to rehabilitation of hand function after stroke can alter brain activity across the sensorimotor brain networks and demonstrates network re-organization discussed in the Medaglia review. Both treatment approaches tested in the study improved hand function. However, only the robot-assisted virtual reality group showed reduction of activity and re-lateralization of activation to ipsilesional cortex, a pattern associated with better arm function in this study and with positive recovery in other studies (5).

A contribution by Möller et al. used arterial spin labeling (ASL) fMRI to examine fatigue in mTBI during psychomotor vigilance task performance. The mTBI participants showed different patterns of brain activation compared to healthy controls, in addition to higher self-reported fatigue and reductions in performance as the task progressed (fatigability). Together with the self-reported fatigue ratings and task performance, the ASL results suggested the engagement of disparate functional networks compared in mTBI.

fMRI research in stroke and TBI poses a unique set of challenges to researchers. The articles assembled in this Research Topic address some of these challenges. Using methods designed to work in patients with brain lesions, using appropriate controls, and applying network neuroscience tools are a few of the promising solutions. This topic is an important frontier in neuroscience research today offering tangible benefits for public health and is a potential area of growth in the coming years

## References

1. Centers for Disease Control and Prevention (CDC). U. Centers for Disease con [WWW Document]. (2018). Available from: https://www.cdc.gov/ (Accessed: February 22, 2018).

2. Brumm KP, Perthen JE, Liu TT, Haist F, Ayalon L, Love T. An arterial spin labeling investigation of cerebral blood flow deficits in chronic stroke survivors. Neuroimage (2010) 51:995–1005. doi:10.1016/j.neuroimage.2010.03.008

3. Hillis AE. Magnetic resonance perfusion imaging in the study of language. Brain Lang (2007) 102:165–75. doi:10.1016/j.bandl.2006.04.016

4. Kannurpatti SS, Motes MA, Rypma B, Biswal BB. Increasing measurement accuracy of age-related BOLD signal change: minimizing vascular contributions by resting-state-fluctuation-of-amplitude scaling. Hum Brain Mapp (2011) 32:1125–40. doi:10.1002/hbm.21097

5. Cramer SC. Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery. Ann Neurol (2008) 63:272–87. doi:10.1002/ana.21393

## 1. Introduction

The number of stroke survivors is continuously increasing with the ageing of the population: about 15 million people worldwide suffer from stroke every year, of whom 5 million die, whereas another 5 million become chronically disabled (WHO, 2012). Behavioural deficits in cognitive and motor domains are highly prevalent and persistent in stroke survivors (Bickerton et al., 2014; Demeyere, Riddoch, Slavkova, Bickerton, & Humphreys, 2015; Demeyere et al., 2016; Jaillard, Naegele, Trabucco-Miguel, LeBas, & Hommel, 2009; Planton et al., 2012; Verstraeten, Mark, & Sitskoorn, 2016). Neurophysiological and neuroimaging studies suggested that stroke causes network-wide changes across structurally intact regions, directly or indirectly connected to the site of infarction (Carrera & Tononi, 2014; Carter et al., 2010; Gillebert & Mantini, 2013; Grefkes et al., 2008; Ward & Cohen, 2004). Disruptions in even one of the many networks or brain regions implicated in the different aspects of motor function and cognition can have a major impact on quality of life (Achten, Visser-Meily, Post, & Schepers, 2012; Hochstenbach, Mulder, Limbeek, Donders, & Schoonderwaldt, 1998). Accordingly, both local tissue damage and secondary changes in brain function should be considered when developing rehabilitation strategies to improve the recovery rate and generally increase the quality of life in stroke survivors (Chechlacz, Mantini, Gillebert, & Humphreys, 2015; Chechlacz et al., 2013; Corbetta et al., 2015; Gillebert & Mantini, 2013). In this regard, the use of neurofeedback may be a promising approach.

### 1.1. Neurofeedback

Neurofeedback works as a closed loop system that provides real-time information regarding the participant’s own brain activity and/or connectivity, which can be used to develop self-learning strategies to modulate these brain signals (Weiskopf, Mathiak, et al., 2004). It follows the principle of operant conditioning, a method of learning that occurs through reinforcing specific behaviour with rewards and punishments (Skinner, 1938). If the participant learns to control activity of the brain areas targeted through neurofeedback, this may ultimately lead to a measurable behavioural change that is related to the function of those areas (DeCharms et al., 2005; Haller, Birbaumer, & Veit, 2010; Hartwell et al., 2016).

The origins of neurofeedback are rooted in electroencephalography (EEG), which measures dynamic changes of electrical potentials over the participant’s scalp (Nowlis & Kamiya, 1970). This technique is portable and inexpensive, and provides estimates of brain activity at high temporal resolution. EEG neurofeedback has been widely used over the years to induce long-lasting behavioural changes, both in healthy volunteers and in patients (Gruzelier, 2014; Nelson, 2007). However, because of the low spatial resolution associated with this technique, it is very challenging to selectively target brain areas of interest. As such, the effects of EEG neurofeedback are often not specific (Rogala et al., 2016; Scharnowski & Weiskopf, 2015). Other neuroimaging techniques used for neurofeedback include magnetoencephalography (MEG) (Buch et al., 2012; Okazaki et al., 2015) and functional near-infrared spectroscopy (fNIRS) (Kober et al., 2014; Mihara et al., 2013). However, as also for EEG, their spatial resolution is relatively limited and they do not permit to target precise brain regions.

The field of neurofeedback has rapidly developed and delved into new avenues by the introduction of real-time functional magnetic resonance imaging (rt-fMRI) technology (Cox, Jesmanowicz, & Hyde, 1995). Accordingly, in the past years there has been a steady increase of studies focussing on rt-fMRI neurofeedback applications to induce behavioural changes (Sulzer et al., 2013). Rt-fMRI neurofeedback uses the blood-oxygenation level-dependent (BOLD) signal to present contingent feedback to the participant and to enable modulation of brain activity (Fig. 1). Various acquisition parameters are available, and chosen based on a trade-off between spatial and temporal resolution, and signal-to-noise ratio (Weiskopf, Scharnowski, et al., 2004). The analysis is performed almost immediately or with a delay of a few seconds depending on the available computational resources. With a much higher spatial resolution than EEG, fMRI allows for a refined delineation of both cortical and subcortical target regions. These properties can be valuable for neurofeedback applications (Stoeckel et al., 2014). Recent studies suggest that rt-fMRI is a mature technology to use in the context of neurofeedback training (for a review, see e.g., Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014; Weiskopf, 2012). As a result, doors are being opened to the application of rt-fMRI neurofeedback in ameliorating disrupted brain functions in stroke survivors.[…]