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[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).

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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

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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

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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

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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

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via Frontiers | Editorial: Investigating Brain Activity After Acquired and Traumatic Brain Injury: Applications of Functional MRI | Neurology

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[Systematic review] The potential of real-time fMRI neurofeedback for stroke rehabilitation – Full Text

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.

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.[…]

Fig. 1

Fig. 1. Real-time fMRI neurofeedback is a closed-loop system that can be used to voluntarily modulate brain-activity through the principle of operant conditioning. (A) The participants use self-generated or prior instructed strategies to attempt to change their brain activity. (B) fMRI data are acquired and (C) processed in real-time. Computer programs select the relevant signals and (D) return these to the participants after varied degrees of pre-processing to allow them to adjust their control strategies.

Continue —>  The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review – ScienceDirect

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[OPINION ARTICLE] Can Functional Magnetic Resonance Imaging Generate Valid Clinical Neuroimaging Reports? – Full Text

imageRoland Beisteiner* Study Group Clinical fMRI, High Field MR Center, Department of Neurology, Medical University of Vienna, Vienna, Austria

A highly critical issue for applied neuroimaging in neurology—and particularly for functional neuroimaging—concerns the question of validity of the final clinical result. Within a clinical context, the question of “validity” often equals the question of “instantaneous repeatability,” because clinical functional neuroimaging is done within a specific pathophysiological framework. Here, not only every brain is different but also every pathology is different, and most importantly, individual pathological brains may rapidly change in short time.

Within the brain mapping community, the problem of validity and repeatability of functional neuroimaging results has recently become a major issue. In 2016, the Committee on Best Practice in Data Analysis and Sharing from the Organization for Human Brain Mapping (OHBM) created recommendations for replicable research in neuroimaging, focused on magnetic resonance imaging and functional magnetic resonance imaging (fMRI). Here, “replication” is defined as “Independent researchers use independent data and … methods to arrive at the same original conclusion.” “Repeatability” is defined as repeated investigations performed “with the same method on identical test/measurement items in the same test or measuring facility by the same operator using the same equipment within short intervals of time” (ISO 3534-2:2006 3.3.5). An intermediate position between replication and repeatability is defined for “reproducibility”: repeated investigations performed “with the same method on identical test/measurement items in different test or measurement facilities with different operators using different equipment” (ISO 3534-2:2006 3.3.10). Further definitions vary depending on the focus, be it the “measurement stability,” the “analytical stability,” or the “generalizability” over subjects, labs, methods, or populations.

The whole discussion was recently fueled by an PNAS article published by Eklund et al. (1), which claims that certain results achieved with widely used fMRI software packages may generate false-positive results, i.e., show brain activation where is none. More specifically, when looking at activation clusters defined by the software as being significant (clusterwise inference), the probability of a false-positive brain activation is not 5% but up to 70%. This was true for group as well as single subject data (2). The reason lies in an “imperfect” model assumption about the distribution of the spatial autocorrelation of functional signals over the brain. A squared exponential distribution was assumed but found not to be correct for the empirical data. This article received heavy attention and discussion in scientific and public media and a major Austrian newspaper titled “Doubts about thousands of brain research studies.” A recent PubMed analysis indicates already 69 publications citing the Eklund work. Critical comments by Cox et al. (3)—with focusing on the AFNI software results—criticize the authors for “their emphasis on reporting the single worst result from thousands of simulation cases,” which “greatly exaggerated the scale of the problem.” Other groups extended the work. With regard to the fact that “replicability of individual studies is an acknowledged limitation,” Eickhoff et al. (4) suggest that “Coordinate-based meta-analysis offers a practical solution to this limitation.” They claim that meta-analyses allow “filtering and consolidating the enormous corpus of functional and structural neuroimaging results” but also describe “errors in multiple-comparison corrections” in GingerALE, a software package for coordinate-based meta-analysis. One of their goals is to “exemplify and promote an open approach to error management.” More generally and probably also triggered by the Eklund paper, Nissen et al. (5) discuss the current situation that “Science is facing a ‘replication crisis’.” They focus on the publicability of negative results and model “the community’s confidence in a claim as a Markov process with successive published results shifting the degree of belief.” Important findings are, that “unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact” and “Should negative results become easier to publish … true and false claims would be more readily distinguished.”

As a consequence of this discussion, public skepticism about the validity of clinical functional neuroimaging arose. At first sight, this seems to be really bad news for clinicians. However, at closer inspection, it turns out that particularly the clinical neuroimaging community has already long been aware of the problems with standard (“black box”) analyses of functional data recorded from compromised patients with largely variable pathological brains. Quite evidently, methodological assumptions as developed for healthy subjects and implemented in standard software packages may not always be valid for distorted and physiologically altered brains. There are specific problems for clinical populations and particularly for defining the functional status of an individual brain (as opposed to a “group brain” in group studies). With task-based fMRI—the most important clinical application—the major problems may be categorized in “patient problems” and “methodological problems.”

Critical patient problems concern:

  • – Patient compliance may change quickly and considerably.
  • – The patient may “change” from 1 day to the other (altered vigilance, effects of pathology and medication, mood changes—depression, exhaustion).
  • – The clinical state may “change” considerably from patient to patient (despite all having the same diagnosis). This is primarily due to location and extent of brain pathology and compliance capabilities.

Critical methodological problems concern:

  • – Selection of clinically adequate experimental paradigms (note paresis, neglect, aphasia).
  • – Performance control (particularly important in compromised patients).
  • – Restriction of head motion (in patients artifacts may be very large).
  • – Clarification of the signal source (microvascular versus remote large vessel effects).
  • – Large variability of the contrast to noise ratio from run to run.
  • – Errors with inter-image registration of brains with large pathologies.
  • – Effects of data smoothing, definition of adequate functional regions of interest, and definition of essential brain activations.
  • – Difficult data interpretation requires specific clinical fMRI expertise and independent validation of the local hardware and software performance (preferably with electrocortical stimulation).

All these problems have to be recognized and specific solutions have to be developed depending on the question at hand—generation of an individual functional diagnosis or performance of a clinical group study. To discuss such problems and define solutions, clinical functional neuroimagers have already assembled early (Austrian Society for fMRI,1 American Society of Functional Neuroradiology2) and just recently the Alpine Chapter from the OHBM3 was established with a dedicated focus on applied neuroimaging. Starting in the 1990s (6), this community published a considerable number of clinical methodological investigations focused on the improvement of individual patient results and including studies on replication, repeatability, and reproducibility [compare (7)]. Early examples comprise investigations on fMRI signal sources (8), clinical paradigms (9), reduction of head motion artifacts (10), and fMRI validation studies (11, 12). Of course the primary goal of this clinical research is improvement of the validity of the final clinical result. One of the suggested clinical procedures focuses particularly on instantaneous replicability as a measure of validity [Risk Map Technique (1315); see Figure 1] with successful long-term clinical use. This procedure was developed for presurgical fMRI and minimizes methodological assumptions to stay as close to the original data as possible. This is done by avoiding data smoothing and normalization procedures and minimization of head motion artifacts by helmet fixation (avoiding artifacts instead of correcting them). It is interesting to note that in the Eklund et al. (1) analysis it was also the method with minimal assumptions (a non-parametric permutation), which was the only one that achieved correct (nominal) results. The two general ideas of the risk map technique are (a) to use voxel replicability as a criterion for functionally most important voxels (extracting activation foci = voxels with largest risk for a functional deficit when lesioned) and (b) to consider regional variability of brain conditions (e.g., close to tumor) by variation of the hemodynamic response functions (step function/HRF/variable onset latencies) and thresholds. The technique consists only of few steps, which can easily be realized by in house programming: (i) Record up to 20 short runs of the same task type to allow checking of repeatability. (ii) Define a reference function (e.g., step function with a latency of 1 TR). (iii) Calculate Pearson correlation r for every voxel and every run. (iv) Color code voxels according to their reliability at a given correlation threshold (e.g., r > 0.5): yellow voxels >75%, orange voxels >50%, red voxels >25% of runs need to be active. (v) Repeat (i)–(iv) with different reference functions (to our experience, a classical HRF and two step functions with different latencies are sufficient to evaluate most patients) and at different correlation thresholds (e.g., r > 0.2 to r > 0.9). The clinical fMRI expert performs a comprehensive evaluation of all functional maps with consideration of patient pathology, patient performance, and the distribution and level of artifacts [compare descriptions in Ref. (13, 15)]. The final clinical result is illustrated in Figure 1, and a typical interpretation would be: most reliable activation of the Wernicke area is found with a step function of 1 TR latency and shown at Pearson correlation r > 0.5. It is important to note that risk maps extract the most active voxel(s) within a given brain area and judgment of a “true” activation extent is not possible. However, due to the underlying neurophysiological principles [gradients of functional representations (16)], it is questionable whether “true” activation extents of fMRI activations can be defined with any technique.

 

Figure 1. Example for a missing language activation (Wernicke activity, white arrow) with a “black box” standard analysis (right, SPM12 applying motion regression and smoothing, voxelwise inference FWE <0.05, standard k = 25) using an overt language design [described in Ref. (17)]. Wernicke activity is detectable with the clinical risk map analysis (left) based on activation replicability (yellow = most reliabel voxels). Patient with left temporoparietal tumor.

The importance to check individual patient data from various perspectives instead of relying on a “standard statistical significance value,” which may not correctly reflect the individual patients signal situation, has also been stressed by other authors [e.g., Ref. (18)]. Of course, clinical fMRI—as all other applied neuroimaging techniques—requires clinical fMRI expertise and particularly pathophysiological expertise to be able to conceptualize where to find what, depending on the pathologies of the given brain. One should be aware that full automatization is currently not possible neither for a comparatively simple analysis of a chest X-ray nor for applied neuroimaging. In a clinical context, error estimations still need to be supported by the fMRI expert and cannot be done by an algorithm alone. As a consequence, the international community started early with offering dedicated clinical methodological courses (compare http://oegfmrt.org or http://ohbmbrainmappingblog.com/blog/archives/12-2016). Meanwhile, there are enough methodological studies that enable an experienced clinical fMRI expert to safely judge the possibilities and limitations for a valid functional report in a given patient with his/her specific pathologies and compliance situation. Of course, this also requires adequate consideration of the local hard- and software. Therefore and particularly when considering the various validation studies, neither for patients nor for doctors there is a need to raise “doubts about clinical fMRI studies” but instead good reason to “keep calm and scan on.”4

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The methodological developments have been supported by the Austrian Science Fund (KLI455, KLI453, P23611) and Cluster Grants of the Medical University of Vienna and the University of Vienna, Austria.

Footnotes

References

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Source: Frontiers | Can Functional Magnetic Resonance Imaging Generate Valid Clinical Neuroimaging Reports? | Neurology

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[ARTICLE] Kinematics in the brain: unmasking motor control strategies? – Full Text

 

Abstract

In rhythmical movement performance, our brain has to sustain movement while correcting for biological noise-induced variability. Here, we explored the functional anatomy of brain networks during voluntary rhythmical elbow flexion/extension using kinematic movement regressors in fMRI analysis to verify the interest of method to address motor control in a neurological population. We found the expected systematic activation of the primary sensorimotor network that is suggested to generate the rhythmical movement. By adding the kinematic regressors to the model, we demonstrated the potential involvement of cerebellar–frontal circuits as a function of the irregularity of the variability of the movement and the primary sensory cortex in relation to the trajectory length during task execution. We suggested that different functional brain networks were related to two different aspects of rhythmical performance: rhythmicity and error control. Concerning the latter, the partitioning between more automatic control involving cerebellar–frontal circuits versus less automatic control involving the sensory cortex seemed thereby crucial for optimal performance. Our results highlight the potential of using co-registered fine-grained kinematics and fMRI measures to interpret functional MRI activations and to potentially unmask the organisation of neural correlates during motor control.

Introduction

During rhythmical movement, sensory and motor systems need to interact closely to sustain the rhythm and to meet task requirements. Understanding how our system controls such a basic, all day movement is a prerequisite to improve motor (re)learning models to ameliorate rehabilitation in case of neurological movement disorders, like stroke. Mathematically, the simplest way to model rhythmicity is by means of a continuous oscillator (e.g. Haken et al. 1985). Biological noise interfering with planning and execution makes human movements unavoidably variable, which asks for correction processes (Franklin and Wolpert 2011). One of the principles governing human motor control states that optimised control is characterised by a maximum efficiency, e.g. minimal costs (Guigon et al. 2007). Minimal cost is dependent on the varying interaction between different system characteristics, including anatomical constraints, force generating capacities, and biological noise inducing the intra and interpersonal variability that is inherent to our system’s output (van Galen and Hueygevoort 2000).

Current knowledge about the neural correlates of rhythmical upper limb movement is based on standard finger and wrist movement paradigms that compare different movement conditions within people (high frequency versus low frequency, Kelso et al. 1998; rhythmic versus discrete movements, Schaal et al. 2004). Using this paradigm, simple unilateral rhythmical movements have been shown to elicit contralateral activations of the primary sensorimotor cortex (S1 + M1) and of the supplementary motor area (SMA), complemented by an ipsilateral activation of the anterior cerebellum (Allison et al. 2000; Ball et al. 1999; Schaal et al. 2004). Bilateral movements are associated with a symmetric facilitation of neural activity in the sensorimotor network, with additional frontal activations to ensure coordination between limbs. It is mediated by increased intrahemispheric connectivity and enhanced transcallosal coupling of SMA and M1 (Grefkes et al. 2008; Jäncke et al. 2000).

The activation pattern is comparable between dominant and non-dominant sided movements in extension and intensity when people move at their preferred frequency (Lutz et al. 2005; Jäncke et al. 2000; Koeneke et al. 2004). However, when movement frequency is imposed, activations during non-dominant sided movements increase in intensity compared to those of the dominant side (Lutz et al. 2005). Second, activation increases and expands for both uni and bilateral movements when movement frequency is increased above the preferred frequency (e.g. Kelso et al. 1998; Rao et al. 1996). Together, this demonstrates that moving at a non-preferred frequency is marked by an increase in costs. Therefore, imposing a fixed frequency may lead to different task-induced cost levels between participants and thus lead to biased results when comparing rhythmical motion and its neural correlates between people.

Over the time course of the movement, fine-grained kinematic variables capture the outcome of the interaction between the planned movement and the noise-dependent variability (Newell and Corcos 1993). Here, we explored whether kinematics may additionally provide information on the underlying control system, when the kinematic outcome is linked directly to brain activity. We simultaneously recorded brain activation (fMRI) and movement kinematics during a sensorimotor task that consisted of a self-paced continuous flexion/extension of the elbow. We focused on uni as well as bilateral movements, as many daily living tasks involve bilateral coordination. The task is evaluated as a simple well-known movement that does not require complex motor learning.

Based on the described theoretical model of motor control, we hypothesised that rhythmic voluntary flexion of the elbow is modulated by neural networks involved in (1) the sustained execution of the basic oscillatory rhythmical component and (2) correction processes in reaction to the variability resulting from biological noise. Sustaining the movement in rhythmical motion has been shown to involve the primary sensorimotor network, whereas discrete movements solicit additional higher cortical planning areas (Schaal et al. 2004). First, we expected to confirm the role of the sensorimotor network by performing a standard general linear-model analysis. Second, because task costs were as much equalised over participants as possible, we expected that correlating natural variation in movement execution with variation in BOLD-activation might unmask different brain regions involved in the secondary correction processes that could be (partly) separated from the primary sensorimotor network. […]

Continue —> Kinematics in the brain: unmasking motor control strategies? | SpringerLink

Fig. 2 Functional basis network: the main effect of task (flexion/extension of the elbow), FWE corrected, p < 0.05 at voxel level and the condition-specific activations p < 0.001, FWE corrected at cluster level, 22 degrees of freedom. R right sided, L left sided, B bilateral, U unilateral movement, RH right hemisphere, LH left hemisphere

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[Abstract] Effects of low-frequency repetitive transcranial magnetic stimulation and neuromuscular electrical stimulation on upper extremity motor recovery in the early period after stroke: a preliminary study

 

Objective: To assess the efficacy of inhibitory repetitive transcranial magnetic stimulation (rTMS) and neuromuscular electrical stimulation (NMES) on upper extremity motor function in patients with acute/subacute ischemic stroke.

Methods: Twenty-five ischemic acute/subacute stroke subjects were enrolled in this randomized controlled trial. Experimental group 1 received low frequency (LF) rTMS to the primary motor cortex of the unaffected side + physical therapy (PT) including activities to improve strength, flexibility, transfers, posture, balance, coordination, and activities of daily living, mainly focusing on upper limb movements; experimental group 2 received the same protocol combined with NMES to hand extensor muscles; and the control group received only PT. Functional magnetic resonance imaging (fMRI) scan was used to evaluate the activation or inhibition of the affected and unaffected primary motor cortex.

Results: No adverse effect was reported. Most of the clinical outcome scores improved significantly in all groups, however no statistically significant difference was found between groups due to the small sample sizes. The highest percent improvement scores were observed in TMS + NMES group (varying between 48 and 99.3%) and the lowest scores in control group (varying between 13.1 and 28.1%). Hand motor recovery was significant in both experimental groups while it did not change in control group. Some motor cortex excitability changes were also observed in fMRI.

Conclusion: LF-rTMS with or without NMES seems to facilitate the motor recovery in the paretic hand of patients with acute/subacute ischemic stroke. TMS or the combination of TMS + NMES may be a promising additional therapy in upper limb motor training. Further studies with larger numbers of patients are needed to establish their effectiveness in upper limb motor rehabilitation of stroke.

Source: Effects of low-frequency repetitive transcranial magnetic stimulation and neuromuscular electrical stimulation on upper extremity motor recovery in the early period after stroke: a preliminary study: Topics in Stroke Rehabilitation: Vol 24, No 5

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[WEB SITE] Understanding the Human Brain – Neuroscience News

Functional magnetic resonance images reflect input signals of nerve cells.

The development of magnetic resonance imaging (MRI) is a success story for basic research. Today medical diagnostics would be inconceivable without it. But the research took time to reach fruition: it has been nearly half a century since physicists first began their investigations that ultimately led to what became known as nuclear magnetic resonance. In 2001, Nikos K. Logothetis and his colleagues at the Max Planck Institute for Biological Cybernetics in Tübingen devised a new methodological approach that greatly deepened our understanding of the principles of functional MRI.

The great advantage of functional magnetic resonance imaging (fMRI) is that it requires no major interventions in the body. In fMRI, the human body is exposed to the action of electromagnetic waves. As far as we know today, the process is completely harmless, despite the fact that fMRI equipment generates magnetic fields that are about a million times stronger than the natural magnetic field of the earth.

The physical phenomenon underlying fMRI is known as nuclear magnetic resonance, and the path to its discovery was paved with several Nobel prizes. The story begins in the first half of the 20th century with the description of the properties of atoms. The idea of using nuclear magnetic resonance as a diagnostic tool was mooted as early as the 1950s. But the method had to be refined before finally being realised in the form of magnetic resonance imaging.

Today, MRI not only produces images of the inside of our bodies; it also provides information on the functional state of certain tissues. The breakthrough for fMRI came in the 1980s when researchers discovered that MRI can also be used to detect changes in the oxygen saturation of blood, a principle known as BOLD (blood oxygen level dependent) imaging. There is a 20 percent difference between the magnetic sensitivity of oxygenated arterial blood and that of deoxygenated venous blood. Unlike oxygenated haemoglobin, deoxygenated haemoglobin amplifies the strength of a magnetic field in its vicinity. This difference can be seen on an MRI image.

Resuscitation of the brain after a 15-minute cardiac arrest in fMRI: The pictorial representation provides information about the degree of damage of the brain as well as a detailed analysis of the recovery curve. The top three rows are examples of successful and the bottom row for an unsuccessful resuscitation. The comparison with the concentration images of ATP, glucose and lactate shows that the MR images are in fact closely related to the biochemical changes. Based on such studies, the course of cerebral infarction and the success of various therapeutic measures can be documented. Credit Max Planck Institute.

fMRI has given us new insights into the brain, especially in neurobiology. However, the initial phase of euphoria was followed by a wave of scepticism among scientists, who questioned how informative the “coloured images” really are. Although fMRI can in fact generate huge volumes of data, there is often a lack of background information or basic understanding to permit a meaningful interpretation. As a result, there is a yawning gap between fMRI measurements of brain activity and findings in animals based on electrophysiological recordings.

This is due mainly to technical considerations: interactions between the strong MRI field and currents being measured at the electrodes made it impossible to apply the two methods simultaneously to bridge the gap between animal experiments and findings in humans.

fMRT shows input signals

In 2001, Nikos Logothetis and his colleagues at the Max Planck Institute for Biological Cybernetics in Tübingen were the first to overcome this barrier. With the help of special electrodes and sophisticated data processing, they showed unambiguously that BOLD fMRI actually does measure changes in the activity of nerve cells. They also discovered that BOLD signals correlate to the arrival and local processing of data in an area of the brain rather than to output signals that are transmitted to other areas of the brain. Their paper was a milestone in our understanding of MRI and has been cited over 2500 times worldwide.

Their novel experimental setup enabled the Tübingen scientists to study various aspects of nerve cell activity and to distinguish between action potentials and local field potentials. Action potentials are electrical signals that originate from single nerve cells or a relatively small group of nerve cells. They are all-or-nothing signals that occur only if the triggering stimulus exceeds a certain threshold. Action potentials therefore reflect output signals. These signals are detected by electrodes located in the immediate vicinity of the nerve cells. By contrast, local field potentials generate slowly varying electrical potentials that reflect signals entering and being processed in a larger group of nerve cells.

Applying these three methods simultaneously, the Max Planck researchers examined the responses to a visual stimulus in the visual cortex of anaesthetized monkeys. Comparison of the measurements showed that fMRI data relate more to local field potentials than to single-cell and multi-unit potentials. This means that changes in blood oxygen saturation are not necessarily associated with output signals from nerve cells; instead, they reflect the arrival and processing of signals received from other areas of the brain.

Another important discovery the Tübingen researchers made was that, because of the large variability of vascular reactions, BOLD fMRI data have a much lower signal-to-noise ratio than electrophysiological recordings. Because of this, conventional statistical analyses of human fMRI data underestimate the extent of activity in the brain. In other words, the absence of an fMRI signal in an area of the brain does not necessarily mean that no information is being processed there. Doctors need to take this into account when interpreting fMRI data.

NOTES ABOUT THIS NEUROIMAGING RESEARCH

Contact: Christina Beck – Max Planck Institute
Source: Max Planck Institute press release
Image Source: The image is credited to Max Planck Institute and is adapted from the press release

Source: Understanding the Human Brain – Neuroscience News

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

Abstract

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

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

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

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

Fig. 2

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

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

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[WEB SITE] Much of what we know about the brain may be wrong: The problem with fMRI

The past decade has brought us jaw-dropping insights about the hidden workings of our brains, in part thanks to a popular brain scan technique called fMRI. But a major new study has revealed that fMRI interpretation has a serious flaw, one that could mean that much of what we’ve learned about our brains this way might need a second look.

On TV and in movies, we’ve all seen doctors stick an X-ray up on the lightbox and play out a dramatic scene: “What’s that dark spot, doctor?” “Hm…”

In reality, though, a modern medical scan contains so much data, no single pair of doctor’s eyes could possibly interpret it. The brain scan known as fMRI, for functional magnetic resonance imaging, produces a massive data set that can only be understood by custom data analysis software. Armed with this analysis, neuroscientists have used the fMRI scan to produce a series of paradigm-shifting discoveries about our brains.

Now, an unsettling new report, which is causing waves in the neuroscience community, suggests that fMRI’s custom software can be deeply flawed — calling into question many of the most exciting findings in recent neuroscience.

The problem researchers have uncovered is simple: the computer programs designed to sift through the images produced by fMRI scans have a tendency to suggest differences in brain activity where none exist. For instance, humans who are resting, not thinking about anything in particular, not doing anything interesting, can deliver spurious results of differences in brain activity. It’s even been shown to indicate brain activity in a dead salmon, whose stilled brain lit up an MRI as if it were somehow still dreaming of a spawning run.

The report throws into question the results of some portion of the more than 40,000 studies that have been conducted using fMRI, studies that plumb the brainy depths of everything from free will to fear. And scientists are not quite sure how to recover.

“It’s impossible to know how many fMRI studies are wrong, since we do not have access to the original data,” says computer scientist Anders Eklund of Linkoping University in Sweden, who conducted the analysis.

How it should have worked: Start by signing up subjects. Scan their brains while they rest inside an MRI machine. Then scan their brains again when exposed to pictures of spiders, say. Those subjects who are afraid of spiders will have blood rush to those regions of the brain involved in thinking and feeling fear, because such thoughts or feelings are suspected to require more oxygen. With the help of a computer program, the MRI machine then registers differences in hemoglobin, the iron-rich molecule that makes blood red and carries oxygen from place to place. (That’s the functional in fMRI.) The scan then looks at whether those hemoglobin molecules are still carrying oxygen to a given place in the brain, or not, based on how the molecules respond to the powerful magnetic fields. Scan enough brains and see how the fearful differ from the fearless, and perhaps you can identify the brain regions or structures associated with thinking or feeling fear.

That’s the theory, anyway. In order to detect such differences in brain activity, it would be best to scan a large number of brains, but the difficulty and expense often make this impossible. A single MRI scan can cost around $2,600, according to a 2014 NerdWallet analysis. Further, the differences in the blood flow are often tiny. And then there’s the fact that computer programs have to sift the through images of the 1,200 or so cubic centimeters of gelatinous tissue that make up each individual brain and compare them to others, a big data analysis challenge.

Eklund’s report shows that the assumptions behind the main computer programs used to sift such big fMRI data have flaws, as turned up by nearly 3 million random evaluations of the resting brain scans of 499 volunteers from Cambridge, Massachusetts; Beijing; and Oulu, Finland. One program turned out to have a 15-year-old coding error (which has now been fixed) that caused it to detect too much brain activity. This highlights the challenge of researchers working with computer code that they are not capable of checking themselves, a challenge not confined just to neuroscience.

An FMRI scan during working memory tasks.

The brain is even more complicated than we thought.Worse, Eklund and his colleagues found that all the programs assume that brains at rest have the same response to the jet-engine roar of the MRI machine itself as well as whatever random thoughts and feelings occur in the brain. Those assumptions appear to be wrong. The brain at rest is “actually a bit more complex,” Eklund says.

More specifically, the white matter of the brain appears to be underrepresented in fMRI analyses while another specific part of the brain — the posterior cingulate, a region in the middle of the brain that connects to many other parts — shows up as a “hot spot” of activity. As a result, the programs are more likely to single it out as showing extra activity even when there is no difference. “The reason for this is still unknown,” Eklund says.

Overall, the programs had a false positive rate — detecting a difference where none actually existed — of as much as 70 percent.

Unknown unknowns: This does not mean all fMRI studies are wrong. Co-author and statistician Thomas Nichols of the University of Warwick calculates that some 3,500 studies may be affected by such false positives, and such false positives can never be eliminated entirely. But a survey of 241 recent fMRI papers found 96 that could have even worse false-positive rates than those found in this analysis.

“The paper makes an important criticism,” says Nancy Kanwisher, a neuroscientist at MIT (TED Talk: A neural portrait of the human mind), though she points out that it does not undermine those fMRI studies that do not rely on these computer programs.

Nonetheless, it is worrying. “I think the fallout has yet to be fully evaluated. It appears to apply to quite a few studies, certainly the studies done in a generic way that is the bread-and-butter of fMRI,” says Douglas Greve, a neuroimaging specialist at Massachusetts General Hospital. What’s needed is more scrutiny, Greve suggests.

Another argument for open data. Eklund and his colleagues were only able to discover this methodological flaw thanks to the open sharing of group brain scan data by the 1,000 Functional Connectomes Project. Unfortunately, such sharing of brain scan data is more the exception than the norm, which hinders other researchers attempting to re-create the experiment and replicate the results. Such replication is a cornerstone of the scientific method, ensuring that findings are robust. Eklund, for one, therefore encourages neuroimagers to “share their fMRI data, so that other researchers can replicate their findings and re-analyze the data several years later.” Only then can scientists be sure that the undiscovered activity of the human brain is truly revealed … and that dead salmon are not still dreaming.

ABOUT THE AUTHOR

David Biello is an award-winning journalist writing most often about the environment and energy. His book “The Unnatural World” publishes November 2016. It’s about whether the planet has entered a new geologic age as a result of people’s impacts and, if so, what we should do about this Anthropocene. He also hosts documentaries, such as “Beyond the Light Switch” and the forthcoming “The Ethanol Effect” for PBS. He is the science curator for TED.

Source: The problem with fMRI |

 

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[ARTICLE] Neural Network Underlying Intermanual Skill Transfer in Humans- Full Text

 

Highlights

 

Unimanual training also enhances performance in the untrained hand (cross-education)

Real-time manipulation of visual feedback enhances magnitude of cross-education
Yoking movement of untrained to trained hand further increases cross-education
Functional connectivity with SPL during training predicts cross-education

Summary

Physical practice with one hand results in performance gains of the other (un-practiced) hand, yet the role of sensory feedback and underlying neurophysiology is unclear. Healthy subjects learned sequences of finger movements by physical training with their right hand while receiving real-time movement-based visual feedback via 3D virtual reality devices as if their immobile left hand was training. This manipulation resulted in significantly enhanced performance gain with the immobile hand, which was further increased when left-hand fingers were yoked to passively follow right-hand voluntary movements. Neuroimaging data show that, during training with manipulated visual feedback, activity in the left and right superior parietal lobule and their degree of coupling with motor and visual cortex, respectively, correlate with subsequent left-hand performance gain. These results point to a neural network subserving short-term motor skill learning and may have implications for developing new approaches for learning and rehabilitation in patients with unilateral motor deficits.

Introduction

It is common wisdom that “practice makes perfect”; however, what constitutes an optimal practice regime when learning a new skill is not clear. In the domain of motor skills, for example, when learning to dribble a basketball, physical training with the relevant effector obviously plays a crucial role. Nonetheless, research over the past decades has recognized that sensory feedback and mental imagery play a significant role in the learning process (Nyberg et al., 2006, Sigrist et al., 2013, Wolpert et al., 2011). In the case of vision, it has been shown that even in the absence of physical training, mere observation of someone else performing a motor task is sufficient to introduce significant gains in subsequent performance of the observer (Bird et al., 2005, Cross et al., 2009, Kelly et al., 2003, Mattar and Gribble, 2005, Nojima et al., 2015, Vogt and Thomaschke, 2007, Ossmy and Mukamel, 2016). Furthermore, passive limb movement has also been shown to facilitate learning (Beets et al., 2012, Darainy et al., 2013, Vahdat et al., 2014, Wong et al., 2012). Finally, physical training with one hand is known to result in significant performance gains in the opposite (untrained) hand—a phenomenon termed intermanual transfer or cross-education (Ruddy and Carson, 2013). Intermanual transfer has been reported as early as 1894, showing that unilateral strength training of a single limb increases the strength of the contralateral (untrained) homologous muscle group (Scripture et al., 1894). Since then, this effect has been demonstrated across multiple motor tasks (Anguera et al., 2007, Brass et al., 2001, Camus et al., 2009, Carroll et al., 2006, Criscimagna-Hemminger et al., 2003, Farthing et al., 2007, Lee et al., 2010, Malfait and Ostry, 2004, Perez and Cohen, 2008, Perez et al., 2007, Sainburg and Wang, 2002) and is suggested to occur through plastic changes in the brain that are not confined to the specific neural networks controlling the physically trained effector (e.g., plastic changes also in motor cortex ipsilateral to the active hand [Duque et al., 2008, Hortobágyi et al., 2003, Muellbacher et al., 2000, Obayashi, 2004]). Enhancing the behavioral effect of intermanual transfer and elucidating its underlying neural mechanism has important implications for rehabilitation of patients with unimanual deficits (Hendy et al., 2012, Ramachandran and Altschuler, 2009) in which direct training of the affected hand is difficult.

Given that visual input, physical training, and passive movement play a significant role in performance and intermanual transfer of motor skills, research in recent years examined the behavioral and neural consequences of training with manipulated visual feedback (Halsband and Lange, 2006). In particular, unimanual training with mirrored visual feedback (as if the opposite, passive hand, is training) has been shown to enhance transfer to the opposite hand and increase excitability of primary motor cortex (M1) ipsilateral to the physically trained hand (Garry et al., 2005, Hamzei et al., 2012, Nojima et al., 2012). Nonetheless, much less is known at the whole-brain network level and how inter-regional coupling during such training correlates with subsequent behavioral changes in performance. Additionally, at the behavioral level, the interaction between manipulated visual feedback and passive movement during training is unknown.

In the present study, we examined intermanual transfer using a novel setup employing 3D virtual reality (VR) devices to control visual feedback of finger movements during unimanual training of healthy adults (experiment 1). By using a novel device, we also examined whether the addition of passive finger movement of the non-physically training hand further enhances the intermanual transfer effect (experiment 2). Finally, we used whole-brain functional magnetic resonance imaging (fMRI) to probe the relevant brain regions engaged during such training and examined their degree of inter-regional coupling with respect to subsequent behavioral changes in performance of individual subjects (experiment 3).

Continue —>  Neural Network Underlying Intermanual Skill Transfer in Humans: Cell Reports

(A) Schematic illustration of one experimental condition. A unique sequence of five digits was presented together with a sketch of the mapped fingers (instructions). Subjects performed the sequence as accurately and rapidly as possible using their right hand (RH) and their left hand (LH) separately for initial evaluation of performance. Next, subjects were trained under a specific training type and finally repeated the evaluation test again. (B) Subjects wore a headset and motion sensitive gloves and received visual feedback of virtual hands. The VR devices allowed visual manipulation of online visual feedback. A camera mounted on the headset allowed embedding the virtual hands and subject’s view inside a natural environment. (C) Experiment 1 results. Physical training with the right hand while receiving online visual feedback as if the left hand is moving (RH-LH) resulted in highest left-hand performance gains relative to all other training conditions. Error bars indicate SEM across subjects. For condition acronyms, see Table 1.

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

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

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