Posts Tagged fMRI

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.[…]

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

Roland 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.

References

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2. Eklund A, Andersson M, Josephson C, Johannesson M, Knutsson H. Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets. Neuroimage (2012) 61(3):565–78. doi:10.1016/j.neuroimage.2012.03.093

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4. Eickhoff SB, Laird AR, Fox PM, Lancaster JL, Fox PT. Implementation errors in the GingerALE Software: description and recommendations. Hum Brain Mapp (2017) 38(1):7–11. doi:10.1002/hbm.23342

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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. […]

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

[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.

[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

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. 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

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.

3) fMRI is good at mapping broad regions of brain activity. But it’s not good at specifics.

We know fMRI measures blood flow and not neural activity directly. And the assumption (that’s been validated in studies) is that blood flow correlates with neural activity. The blood flow can reveal changes in brain areas as small as a millimeter cubed.

But still, at best, that’s just a crude view of things. There can be hundreds of thousands of neurons in a tiny voxel.

“The analogy is it’s like flying over a city and seeing where the lights are on,” Tal Yarkoni, who studies neuroscience research methods at University of Texas Austin, says. If you’re in an airplane, you might look the window and identify a patch of land as a residential area. But it’s impossible to know what people are doing in their homes. And it’s difficult to understand how people in their homes interact with the city’s center.

And fMRI is like that. You can learn what broad areas of the brain are working. But figuring out what, exactly, those brain areas are doing is a totally different problem.

4) The results of a study can be correct, but the interpretation of those results can be very, very wrong

The frustrating part about fMRI research is that it can be really hard to interpret what a brain region “lighting up” means.

“For a while anytime people saw activation in the anterior cingulate cortex, they would say, that must mean the subject is experiencing conflict,” Poldrack says. “What we found is that the anterior cingulate lights up in almost a third of all papers. The fact that it is active tells you almost nothing about what is going on.”

It’s too easy to look at what “lights up” in the brain, and then craft a story around it. Here’s an example from 2007, after a group of scientists put 20 voters in an fMRI and asked them questions about politicians:

When viewing images of [Hillary Clinton], these voters exhibited significant activity in the anterior cingulate cortex, an emotional center of the brain that is aroused when a person feels compelled to act in two different ways but must choose one. It looked as if they were battling unacknowledged impulses to like Mrs. Clinton.

Did the voters’ anterior cingulates “light up”?

Yes.

Does it mean they’re conflicted about her as a candidate?

You can’t really say.

“It’s crazy to think of a situation where there’s not an association between some brain region and some behavior,” Yarkoni says. “Pick any brain region you like, and pick behavior you’d like — and the correlation between the two is not going to be zero. It can’t be zero. There are hundreds of millions of neurons in that brain region. And somehow, some way, there’s going to be a path between activation in that region to the behavior you care about.”

To try to infer “what the subject is thinking or what their preferences are by the activity of one part of the brain is extremely difficult, ” says Peter Bandettini, chief of the department on fMRI methods at the National Institute of Mental Health.

Here’s another, more recent example.

You’ve might have seen a headline like “Dogs understand both words and intonation of human speech.” The story was based off a Science paper that found words of praise raised activation in the left hemisphere of dogs’ brains while being scanned in an fMRI. That result was interpreted in many articles to mean the dogs understood the words, because in humans, we respond to words we understand with a similar pattern of activation.

But if it’s hard to draw conclusions from human fMRI data, dog data may be even a bigger stretch.

“What they’ve shown is that the left hemisphere is more active than the right for auditory processing,” Gregory Berns, a neurobiologist not involved in the study, writes me in an email. “But this is a far reach from lexical processing, which is what they claim.”

(Attila Andics, one of the authors of the dog fMRI paper wrote me, disagreeing with this assessment. “The left hemisphere was more active for lexically marked sounds — meaningful words — but not for … meaningless sound sequences,” he writes. “This is why it was fair to call it lexical processing.” But Andics agrees that a lot of the articles about his study were exaggerated: “We made no claims about what dogs actually understand,” he says.)

If a headline based on an fMRI study feels a bit too incredible, it’s possible that the journalist (perhaps egged on by the university’s press department and the scientists themselves) is reading too much into the results.

5) fMRI isn’t great at establishing the order of brain activity

The brain is complexly interwoven. All of its structures work with one another to complete thoughts. In order to understand how it works, we need to understand how activation in one region impacts activation in another.

Surprisingly, fMRI isn’t very good at creating a fine-grained time series of brain activity.

Brain regions “turn on” within milliseconds of one another. But “the hemodynamic response, just due to the blood vessels, is about two seconds. so that certainty is down to about two seconds on how much you can discern when something turns on thoughts,” Bandettini says. “You just can’t discern one part of the brain turning on first before the other.”

6) Ultimately, progress will be made: fMRI researchers keep using the machine in new, intriguing ways

The classic fMRI studies are kind of simple. Place a participant in the machine, have him complete as task, and then see which regions of the brain are active during that task. The goal is to create a map of the functioning brain — what regions matter for which tasks and why.

For the reasons mentioned above, this approach has many limitations. To overcome them, fMRI researchers have come up with a new, radical-sounding approach for conducting studies: They’ve stopped caring what the brain is actually doing.

fMRI produces patterns of activity. These patterns are more complicated in ways that we may never be able to understand. But a machine might.

Recently, studies have been employing the following design: Scientists put people in an MRI, have them do a task, and then, using machine-learning software, ask the computers to look for patterns between the brain activation and the task the participant is completing.

The scientists, in effect, train the computer to brain-read. That is: They can take guesses about what a participant is doing just by looking at brain data. “You might not care about the brain at all; you might just be viewing the brain as a tool for trying to predict some outcome of interest,” Yarkoni says.

In a way, the prediction makes fMRI a cleaner science: Either a prediction is true or it is not. There’s less ambiguity in interpreting results.

This has some practical applications. A computer can look to see what different levels of pain look like in an fMRI scan. Then when a new person enters the scanner, the computer can better predict what level of pain she’s feeling.

“The next phase of fMRI is … huge databases of thousands or tens of thousands of subjects, and having very well-curated data along with a whole list of behavioral measures for each subject,” Bandettini says. “You can then go back and start using this more clinically.” You can put a patient in an fMRI and ask the computer to see if his brain is exhibiting the complicated pattern that suggests it’s schizophrenic.

This approach can also help us learn about brain structures as well.

Recently I reported on a study where scientists used fMRI and machine learning to reconstruct images the participants saw in the scanner. The regions targeted in the scan have been long known to be related to vivid memories. Because the artificial intelligence was able to make the connection between faces and brain activity, that suggests that region of the brain has something to do with remembering faces. Which shows how this machine learning approach can help confirm the insights gained in more traditional fMRI studies.

7) And remember: Researchers have more than one tool to study the brain

Also know that fMRI isn’t the only tool researchers have to peer into the brain. There are electroencephalograms (a.k.a. EEG — think of those caps with dozens of electrodes), which are good at seeing broad patterns of brain activity in a time series (like states of sleep). There’s magnetoencephalography, MEG, which uses magnets to record electrical currents in the brain. And there are new exciting tools like optogenetics, a technique for activating and studying neural pathways with light (this has only been used in lab animals). Like fMRI, each tool has its advantages and flaws for scientists to grapple with.

There will never be one ultimate tool to understand the brain — besides, perhaps, for the brain itself.

The scientists I spoke to all agreed: Researchers have published sloppy work based on fMRI data in the past. Sadly, that’s true of any tool in science. But here’s the good news: Researchers want to get better.

Correction: This post originally misstated how MRI machines operate. They don’t “spin magnets.” The magnets remain stationary and cause atoms inside the scanner to spin aligned in the same direction.