Posts Tagged fMRI

[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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[WEB SITE] There’s a lot of junk fMRI research out there. Here’s what top neuroscientists want you to know.

John Greim/LightRocket via Getty Images

If you’ve followed the latest from the world of neuroscience, you might get the impression that the field is in deep trouble.

In July, a report in the Proceedings of the National Academy of Sciences declared that as many as 40,000 papers using the standard tool in neuroscience research, functional magnetic resonance imaging (fMRI), could suffer from a statistical flaw that rendered their results to be a false positive.

Headlines feared the worst:

These studies aren’t necessarily wrong, per se. It’s that the common statistical software they used is prone to wrongly guess the borders of “lit up” areas in the brain.

But authors of the PNAS paper quickly realized their estimate of 40,000 papers was too high. (The actual number of papers implicated is probably closer to 3,500, one of the authors wrote in a blog post. And PNAS has since amended the paper.)

Still, the results cast some doubt over the validity of fMRI. And they weren’t an anomaly. Over the past few years, doubt about fMRI research has been mounting. Researchers have found brain activity in dead fish. One 2009 paper found an epidemic of “puzzlingly high” correlations in fMRI research.

All this uncertainty has provoked a question: Is fMRI actually a faulty tool that should not be trusted?

Not at all, neuroscientists say. “It’s unfair to blame the tool,” Rebecca Saxe of MIT, who has been using fMRI since its earliest days, told me. “It’s like blaming a telescope when somebody’s wrong about [identifying] a planet. It’s not the telescope’s fault. The general problem is there are lots of ways to fool yourself with fMRI data.”

There are questions fMRI is good at answering and questions it is bad at answering. There are right ways to use fMRI, and there are irresponsible ways to use it.

I asked several experts in the field a simple question: What’s most important to know about fMRI and its limitations? Here’s what I learned.

1) An fMRI measures blood flow, not neural activity

When fMRI first became available as a scientific tool in the 1990s, it was a revelation.

Before then, scientists who wanted to learn about brain function had few options: They could wait for a patient with a brain injury to come along and test her mental abilities. Or they could inject people with radioactive dyes and then scan them with X-rays. The arrival of fMRI meant that a great many more scientists could study the brain. Neuroscience, as a field of research, exploded. In the past 20 years, 40,000 papers have been publishedusing fMRI.

“With fMRI, suddenly you could study the brain of a healthy person,” says Saxe. Better yet, fMRI wasn’t dangerous. You could repeat tests on the same person without fear of harming them. “There was a lot of hope,” she says. Finally, scientists thought they could peer into the brain and find the cause of autism.

And it was all thanks to magnets. An fMRI is conducted in a MRI machine equipped to scan people’s heads — it’s a giant doughnut-shaped device (people are loaded in through the center on a sliding table).The machine’s huge magnets can pick up on small changes in the brain; specifically, they’re looking for the presence of oxygenated blood.

When the brain region is activated, it calls out for more oxygen. The fMRI then follows oxygenated blood as it flows through the brain. On the printout of the scan, these oxygenated brain regions “light up.” Scientists can see areas smaller than a millimeter cubed (also called a voxel, which just means it’s a pixel in three dimensions) That blood flow is a sign that neural activity is happening in a given brain region.

 Wikipedia

From the 1990s onward, the use of the tool exploded. In 2015, there were more than 29,000 academic articles in the PubMed database mentioning “fMRI.”

2) fMRI studies go wrong not just because of the limitations of the tool. They go wrong because science, overall, has flaws.

With fMRI, scientists began to chart an atlas of the brain and its function. They confirmed that there was a specific area of the brain devoted to interpreting faces. And they found wonderfully simple things, like how wiggling of the fingers are controlled by a tiny area near the earlobes. (Alas, fMRI hasn’t revealed the secrets to disorders like autism or schizophrenia.)

But amid the successes, there was a quietly growing concern that labs were using the tool inappropriately and generating false positives with it.

The July PNAS paper was the latest in a string of papers casting doubt fMRI science. Here are two most famous ones:

  1. In 2009, a researcher showed brain activity in a dead salmon. Dead salmon aren’t thinking, and the paper revealed that neuroscientists need to be vigilant about separating out signal from noise. (The neuroscientists I spoke with said the field was largely aware of this problem and could correct for it when this paper came out.)
  2. Also in 2009, a paper in Perspectives in Psychological Science found an epidemic of “puzzlingly high” correlations in papers that tried to associate brain scans with personality types.

“The common thing people would do is say, ‘We found that activation in some area correlates with some aspect of people’s personality,’” Russell Poldrack, a researcher at Stanford’s Center for Reproducible Neuroscience, explains. They’d then focus their analysis on that specific area and find extremely high correlations. “This is problematic because it is basically double dipping.”

The Perspectives paper concluded: “[I]t is quite possible that a considerable number of relationships reported in this literature are entirely illusory.”

Each of these papers highlighted a big concern with fMRI methods, and for the most part, the field has corrected course. But problems sill remain.

There’s currently a bigger “crisis” in psychology and social science where researchers are realizing some of their most celebrated findings don’t replicate under stricter methods. “All the things that are wrong in psychology are clearly wrong in neuroimaging,” Poldrack says.

At Vox, we’ve discussed these problems in science at length. There’s publication bias — the trend that journals only publish positive, confirmatory results. That creates the file drawer effect: Because negative results are not published, the published literature may paint a too-rosy description of a theory. And then there’s p-hacking: the suite of methodological tweaks researchers can employ to ensure they land on a significant (i.e., publishable) result.

The problems make for an uneasy time for scientists: They’d like to charge ahead with new scientific questions, but also feel a nagging anxiety that they should recheck all the work their questions have been built upon.

There’s some reason to suspect these problems might be even be more problematic in neuroscience than in psychology at large. Here’s why: fMRIs are extremely expensive, costing hundreds of dollars an hour to operate. Money is pressure, and scientists don’t want to be left for nothing to show for a $20,000 study. The expense also makes it less likely for scientists to do replications of past work, and less likely to run large numbers of subjects through an experiment.

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.

 BSIP/UIG via Getty Images
Pyramidal neurons of a cat’s cerebral cortex.

“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.

 Sergey Nivens / Shutterstock

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.

Source: There’s a lot of junk fMRI research out there. Here’s what top neuroscientists want you to know. – Vox

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[Abstract] Music supported therapy promotes motor plasticity in individuals with chronic stroke

Abstract

Novel rehabilitation interventions have improved motor recovery by induction of neural plasticity in individuals with stroke. Of these, Music-supported therapy (MST) is based on music training designed to restore motor deficits.

Music training requires multimodal processing, involving the integration and co-operation of visual, motor, auditory, affective and cognitive systems. The main objective of this study was to assess, in a group of 20 individuals suffering from chronic stroke, the motor, cognitive, emotional and neuroplastic effects of MST.

Using functional magnetic resonance imaging (fMRI) we observed a clear restitution of both activity and connectivity among auditory-motor regions of the affected hemisphere. Importantly, no differences were observed in this functional network in a healthy control group, ruling out possible confounds such as repeated imaging testing. Moreover, this increase in activity and connectivity between auditory and motor regions was accompanied by a functional improvement of the paretic hand. The present results confirm MST as a viable intervention to improve motor function in chronic stroke individuals.

Source: Music supported therapy promotes motor plasticity in individuals with chronic stroke – Online First – Springer

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[ARTICLE] Functional versus non-functional rehabilitation in chronic ischemic stroke: evidences from a randomized functional MRI study – Full Text PDF

Abstract

Motor rehabilitation of stroke survivors is often based on different rehabilitation strategies that may include functional and/or non-functional exercises. Although functional strategies are largely used, there are still not enough evidences of its clinical efficacy, neither of its impact on functional neuroplasticity following rehabilitation, when compared with non-functional exercises.

The present study aimed to compare the effect of these strategies by means of clinical scales and functional Magnetic Resonance Imaging (fMRI). Twelve hemiparetic patients with a chronic stroke were selected. Patients were randomly assigned a non-functional (NFS) or functional (FS) rehabilitation scheme. Clinical scales (Fugl-Meyer, ARA Test and modified Barthel) and fMRI were applied at four moments: before rehabilitation (P1), immediately after (P2), 1 month (P3) and three months (P4) after the end of rehabilitation. The NFS group improved significantly their Fugl-Meyer scores at P2, P3, and P4, when compared to P1. On the other hand, the FS group increased significantly in Fugl-Meyer at P2, when compared to P1, and also in ARA and Barthel.

fMRI inspection at the individual level revealed that both rehabilitation schemes most often led to: decreased activation sparseness, decreased activity of contralesional M1, increased asymmetry of M1 activity to the ipsilesional side, decreased perilesional activity, and decreased SMA activity. Increased M1 asymmetry with rehabilitation was also confirmed by lateralization indexes. Nevertheless, no clear fMRI differences were found between groups.

Our analysis revealed similar clinical effects between FS and NFS, indicating that the strategy of choice may ultimately depend on the main goal of the individual rehabilitation program.

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[ARTICLE] Changes in cerebellar activation following onabotulinumtoxin A injections for spasticity after chronic stroke: A pilot fMRI study

Abstract

Objective: To investigate the effect of reducing spasticity via Obtx-A injection on cerebellar activation after chronic stroke during unilateral gripping.

Design: Pre-post, case series.

Setting: Outpatient spasticity clinic.

Participants: Four individuals with chronic spasticity.

Interventions: Upper limb Obtx-A injections

Outcome Measures: Functional magnetic resonance imaging (fMRI) was used to measure changes in cerebellar activation before and after upper limb Obtx-A injections. During fMRI testing, participants performed the same motor task before and after injection, which is 15% and 30% of maximum voluntary isometric gripping measured before Obtx-A injection.

Results: After Obtx-A injections, cerebellar activation increased bilaterally during gripping with the paretic hand and during rest. During both pre- and post-injection scans, the paretic hand showed larger cerebellar activation during gripping compared to the non-paretic hand. Cerebellar activation during gripping with the non-paretic hand did not change significantly after Obtx-A injections.

Conclusions: Reducing spasticity via Obtx-A injections may increase cerebellar activation both during gripping tasks with the paretic hand and during rest. To our knowledge, this is the first study that examines changes in cerebellar activation after spasticity treatment with Obtx-A.

Abbreviations: central nervous system (CNS), Onabotulinumtoxin A (Obtx-A), acetylcholine (Ach), Chedoke-McMaster Assessment (CMA), functional magnetic resonance imaging (fMRI), blood-oxygen-level dependent (BOLD), Modified Ashworth scores (MAS), echo planar imaging (EPI)

via Changes in cerebellar activation following onabotulinumtoxin A injections for spasticity after chronic stroke: A pilot fMRI study – Archives of Physical Medicine and Rehabilitation.

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[ARTICLE] A longitudinal study of brain activation during stroke recovery using BOLD-fMRI

Stroke recovery involves a battery of plastic changes in the brain. Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) provides brain activation information with exquisite spatial resolution as a powerful tool for investigating changes in brain plasticity.

In this paper, we performed a longitudinal study examining plasticity of functional activation by BOLD-fMRI following stroke. Data were collected from 11 patients with corticospinal tract (CST) damage at three stages of recovery, i.e., acute stage (3mons) post stroke. The evolution of cortical activations for both affected and unaffected hand motion tasks were studied. Quantitative activation measurements including the effective size and sum of t values were calculated and the correlations with patient Fugl-Meyer index were analyzed across all stages. Stroke patients showed a shift from bilateral activation in acute and early stage to the ipsilesional activation in chronic stage when performing a movement task with the affected hand, which suggests a compensation effect from the contralesional hemisphere during the recovery process. The correlation analysis showed a significantly negative correlation with cingulate cortex activity at early stage from both quantitative activation measurements, implying the special role of cingulate cortex in stroke recovery. Further investigations are in need to improve the understanding of brain plasticity in stroke patients.

via IEEE Xplore Abstract – A longitudinal study of brain activation during stroke recovery using BOLD-fMRI.

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