Posts Tagged neural activity

[NEWS] Brain-zapping implants that fight depression inch closer to reality | Science News

Researchers are resetting the part of the brain that can shift mood

BY LAURA SANDERS, FEBRUARY 10, 2019
neural activity

MOOD CHANGER  Neural activity in certain areas of the brain (brightly colored strands show connections emanating from those regions) can be measured to decode mood.

Like seismic sensors planted in quiet ground, hundreds of tiny electrodes rested in the outer layer of the 44-year-old woman’s brain. These sensors, each slightly larger than a sesame seed, had been implanted under her skull to listen for the first rumblings of epileptic seizures.

The electrodes gave researchers unprecedented access to the patient’s brain. With the woman’s permission, scientists at the University of California, San Francisco began using those electrodes to do more than listen; they kicked off tiny electrical earthquakes at different spots in her brain.

Most of the electrical pulses went completely unnoticed by the patient. But researchers finally got the effect they were hunting for by targeting the brain area just behind her eyes. Asked how she felt, the woman answered: “Calmer in my nerves.”

Zapping the same spot in other participants’ brains evoked similar responses: “I feel positive, relaxed,” said a 53-year-old woman. A 60-year-old man described “starting to feel a little more alive, a little more energy.” With stimulation to that one part of the brain, “participants would sit up a little straighter and seem a little bit more alert,” says UCSF neuroscientist Kristin Sellers.

Such positive mood changes in response to light neural jolts, described in the Dec. 17 Current Biology, bring researchers closer to an audacious goal: a device implanted into the brains of severely depressed people to detect a looming crisis coming on and zap the brain out of it.

It sounds farfetched, and it is. The project is “fundamental, pioneering, discovery neuroscience,” says Mark George, a psychiatrist and neurologist at the Medical University of South Carolina in Charleston. George has been studying depression for 30 years. “It’s like sending a spacecraft to the moon.”

This video shows the location of brain regions involved in emotion processing: the orbitofrontal cortex (green), cingulate (red), insula (purple), hippocampus (yellow) and amygdala (blue). The dots show where electrodes were placed to monitor seizures in patients with epilepsy.

Still, in the last several years, teams of scientists have made startling amounts of progress, both in their ability to spot the neural signatures that come with a low mood and to change a person’s feelings.

With powerful computational methods, scientists have recently zeroed in on some key features of depressed brains. Those hallmarks include certain types of brain waves in specific locations, like the one just behind and slightly above the eyes. Other researchers are focused on how to correct the faulty brain activity that underlies depression.

A small, implantable device capable of both learning the brain’s language and then tweaking the script when the story gets dark would be an immensely important clinical tool. Of the 16.2 million U.S. adults with severe depression, about a third don’t respond to conventional treatments. “That’s a huge number of people with a very disabling and probably underdiagnosed and underappreciated illness,” says neurologist Vikram Rao, who is working on the UCSF project with Sellers.

A disease of circuits

When George began studying depression decades ago, the field was still haunted by Sigmund Freud, who blamed the disorder on bad parenting and repressed anger. Soon after came the chemical imbalance concept, which held that the brain just needs a dash of the right chemical signal to fix itself. “It was the ‘brain is soup’ model,” George says. Toss in more of the crucial ingredient — serotonin, for instance — and the recipe would sing.

“We have a very different view now,” George says. Thanks to advances in brain imaging, scientists see depression as a disorder of neural circuits — altered connections between important brain regions can tip a person into a depressed state. “We’ve started to define the road map of depression,” George says.

Depression is a disorder, but one that’s tightly linked to emotion. It turns out that emotions span much of the brain. “Emotions are more widespread than we thought,” says cognitive neuroscientist Kevin LaBar. With his colleagues at Duke University, LaBar has used functional MRI scans to find signatures of certain emotions throughout the brain as people are feeling those emotions. He found the wide neural reach of sorrow, for instance, by prompting the emotion with gloomy songs and films.

Some electrical arrays that researchers at the University of California, San Francisco are testing sit on the surface of the brain (top); others penetrate deep into brain tissue (bottom).

Functional MRI allows scientists to see the entire scope of a working brain, but that wide view comes with the trade-off of lower resolution. And resolution is what’s needed to precisely and quickly sense — and change — brain activity. Implanting electrodes, like those used in the UCSF project, gives a more nuanced look into select brain areas. Those detailed recordings, taken from people undergoing epilepsy treatment, are what allowed neural engineer Maryam Shanechi to decode the brain’s emotions with precision.

As seven patients spent time in the hospital with electrodes monitoring brain activity, their emotions naturally changed. Every so often, the participants would answer mood-related questions on a tablet computer so that researchers could measure when the patients shifted between emotions. Then Shanechi, of the University of Southern California in Los Angeles, and her colleagues matched the brain activity data to the moods.

The task wasn’t simple. The implanted electrodes recorded an enormous pile of data, much of it irrelevant to mood. Shanechi and her team developed an algorithm to distill all that data into a few key predictive brain regions for each person. The resulting decoder could tell what mood a person was in based on brain activity alone, the team reported in the October Nature Biotechnology. “In every single individual, we can show how their mood changes in real time,” Shanechi says.[…]

more —-> Brain-zapping implants that fight depression inch closer to reality | Science News

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[WEB SITE] Half the brain encodes both arm movements

October 8, 2018, Society for Neuroscience
Half the brain encodes both arm movements

Patients implanted with electrocorticography arrays completed a 3D center-out reaching task. Electrode locations were based upon the clinical requirements of each patient and were localized to an atlas brain for display (A). B. Patients were seated in the semi-recumbent position and completed reaching movements from the center to the corners of a 50cm physical cube based upon cues from LED lights located at each target while hand positions and ECoG signals were simultaneously recorded. Each patient was implanted with electrodes in a single cortical hemisphere and performed the task with the arm contralateral (C) and ipsilateral (D) to the electrode array in separate recording sessions. Credit: Bundy et al., JNeuros(2018)

Individual arm movements are represented by neural activity in both the left and right hemispheres of the brain, according to a study of epilepsy patients published in JNeurosci. This finding suggests the unaffected hemisphere in stroke could be harnessed to restore limb function on the same side of the body by controlling a brain-computer interface.

The right side of the brain is understood to control the left side of the body, and vice versa. Recent evidence, however, supports a connection between the same side of the brain and body during .

Eric Leuthardt, David Bundy, and colleagues explored brain activity during such ipsilateral movements during a reaching task in four  whose condition enabled invasive monitoring of their brains through implanted electrodes. Using a machine learning algorithm, the researchers demonstrate successful decoding of speed, velocity, and position information of both left and right arm movements regardless of the location of the electrodes.

In addition to advancing our understanding of how the brain controls the body, these results could inform the development of more effective rehabilitation strategies following brain injury.

Half the brain encodes both arm movements

In the study a patient implanted with electrodes only on the left side of the brain was asked to make movements to 8 targets in 3D space with both their right and left arms. Using recordings from these electrodes, the authors were able to predict the hand speed, direction, and position for both arms showing that movements of both arms are encoded on one side of the brain. Credit: David Bundy and Eric Leuthardt

 Explore further: New research on the brain’s backup motor systems could open door to novel stroke therapies

More information: Unilateral, Three-dimensional Arm Movement Kinematics are Encoded in Ipsilateral Human Cortex, JNeurosci (2018). DOI: 10.1523/JNEUROSCI.0015-18.2018

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[Abstract] Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances – PM&R

Abstract

Recent technologic advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to track movement has emerged as a promising paradigm to enhance the care provided to patients with neurologic or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, telerehabilitation, and robotics. Moreover, the big data recorded across these applications serve as a pathway to personalized and precision medicine. This article presents state-of-the-art and next-generation wearable movement sensors, ranging from inertial measurement units to soft sensors. An overview of clinical applications is presented across a wide spectrum of conditions that have potential to benefit from wearable sensors, including stroke, movement disorders, knee osteoarthritis, and running injuries. Complementary applications enabled by next-generation sensors that will enable point-of-care monitoring of neural activity and muscle dynamics during movement also are discussed.

 

via Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances – PM&R

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[ARTICLE] Fatigue and Cognitive Fatigability in Mild Traumatic Brain Injury are Correlated with Altered Neural Activity during Vigilance Test Performance – Full Text

Introduction: Fatigue is the most frequently reported persistent symptom following a mild traumatic brain injury (mTBI), but the explanations for the persisting fatigue symptoms in mTBI remain controversial. In this study, we investigated the change of cerebral blood flow during the performance of a psychomotor vigilance task (PVT) by using pseudo-continuous arterial spin labeling (PCASL) MRI technique to better understand the relationship between fatigability and brain activity in mTBI.

Material and methods: Ten patients (mean age: 37.5 ± 11.2 years) with persistent complaints of fatigue after mTBI and 10 healthy controls (mean age 36.9 ± 11.0 years) were studied. Both groups completed a 20-min long PVT inside a clinical MRI scanner during simultaneous measurements of reaction time and regional cerebral blood flow (rCBF) with PCASL technique. Cognitive fatigability and neural activity during PVT were analyzed by dividing the performance and rCBF data into quintiles in addition to the assessment of self-rated fatigue before and after the PVT.

Results: The patients showed significant fatigability during the PVT while the controls had a stable performance. The variability in performance was also significantly higher among the patients, indicating monitoring difficulty. A three-way ANOVA, modeling of the rCBF data demonstrated that there was a significant interaction effect between the subject group and performance time during PVT in a mainly frontal/thalamic network, indicating that the pattern of rCBF change for the mTBI patients differed significantly from that of healthy controls. In the mTBI patients, fatigability at the end of the PVT was related to increased rCBF in the right middle frontal gyrus, while self-rated fatigue was related to increased rCBF in left medial frontal and anterior cingulate gyri and decreases of rCBF in a frontal/thalamic network during this period.

Discussion: This study demonstrates that PCASL is a useful technique to investigate neural correlates of fatigability and fatigue in mTBI patients. Patients suffering from fatigue after mTBI used different brain networks compared to healthy controls during a vigilance task and in mTBI, there was a distinction between rCBF changes related to fatigability vs. perceived fatigue. Whether networks for fatigability and self-rated fatigue are different, needs to be investigated in future studies.

Introduction

Fatigue is a frequently reported symptom after mild traumatic brain injury (mTBI) (13) and a major reason why patients fail to return to work (4). The subjective experience of fatigue may be concomitant with physiological fatigue or with deteriorating performance, but may also be a sole complaint (56). Research on the relationship between underlying neural correlates to fatigue in mTBI, and possible performance decrements is complicated by the fact that fatigue is still not a well-defined concept. It is multidimensional in its nature, involving both physiological and psychological components (79) and, therefore, a single explanatory mechanism is unlikely (310).

Kluger and coworkers (11) suggested distinguishing the self-rated fatigue measures from objective measures of fatigue by labeling the later as fatigability. Such distinction might encourage among others more focused correlational studies; such as fatigue in relation to the neural activity. Measuring performance during sustained cognitive process provides a method to evaluate fatigue/fatigability objectively (1214). For example, sustained attention during vigilance performance is a demanding cognitive task and performance induced fatigability has been demonstrated as increased error rate and reaction time (15). Our group has also found fatigability in mTBI on a higher order attention demanding task (16).

More recently, we studied the behavioral correlates of changes in resting-state functional connectivity before and after performing a 20-min psychomotor vigilance task (PVT) for mTBI patients with persistent post-concussion fatigue (17). Taking advantage of a quantitative data-driven analysis approach developed by us, we were able to demonstrate that there was a significant linear correlation between the self-rated fatigue and functional connectivity in the thalamus and middle frontal cortex. Furthermore, we found that the 20 min PVT was sufficiently sensitive to invoke significant mental fatigue and specific functional connectivity changes in mTBI patients. These findings indicate that resting-state functional MRI (fMRI) measurements before and after a 20 min PVT may serve as a useful method for objective assessment of fatigue level in the neural attention system. However, these measurements neither provide any information about the dynamic change of the neural activities in the involved functional networks during the performance of PVT nor can they answer whether other neural systems mediate the observed functional connectivity change in the attention network.

Arterial spin labeling (ASL) MRI technique has recently been used to examine the cerebral blood flow (CBF) in patients with amnestic mild cognitive impairment and cognitively normal healthy controls both at rest and during the active performance of a memory task (18). As compared to rest, CBF measurement during the task performance showed increased group difference between patients and healthy controls indicating that CBF measures during a cognitive task may increase the discriminatory ability and the sensitivity to detect subtle functional changes in neurological diseases. In another ASL MRI study, Lim et al. (19) investigated the neural correlates of cognitive fatigue effects in a group of healthy volunteers during a 20-min PVT (19). They observed progressively slower reaction times and significantly increased mental fatigue ratings after the task and reported that such persistent cognitive fatigue effect was significantly correlated with regional cerebral blood flow (rCBF) decline in the right fronto-parietal attention network in addition to the basal ganglia and sensorimotor cortices. They also found that the rCBF at rest in the thalamus and right middle frontal gyrus before the PVT task was predictive of subjects’ subsequent performance decline. Based on these findings, they claimed that the rCBF at rest in the attention network might be a useful indicator of performance potential and a marker of the level of fatigue in neural attention system. However, it remains to be clarified how the relationship between the neural activity in mTBI patients and their fatigability is dynamically influenced by the performance of a difficult cognitive task.

Pseudo-Continuous Arterial Spin Labeling (PCASL) can provide quantitative rCBF measurements with whole-brain coverage and high signal-to-noise ratio. Furthermore, it is non-invasive and repetitive experiments can be carried out. It has been shown that fMRI experiments based on PCASL perfusion measurements may have higher sensitivity than experimental designs based on blood oxygenation level-dependent (BOLD) fMRI, particularly when studying slow neural activity changes within a subject (2022) and useful as a biomarker of brain function (18). To shed light on the questions discussed above, in this study we used PCASL MRI technique to measure the rCBF changes during a 20 min PVT in a group of mTBI patients with chronic fatigue and matched healthy control subjects. The aims of the present study are the following: (1) evaluate the PVT induced fatigability over time by dividing the performance data (error rate and reaction time) into quintiles to verify if the change of fatigability for mTBI patients follows the same pattern as that for healthy controls; (2) estimate the dynamic change of neural activity during PVT in terms of rCBF measurements in each quintile to reveal brain activities significantly associated with the change of fatigability. (3) Voxel-wise assessment of the rCBF values pre- and post-PVT to detect brain activity associated with changes in self-rated fatigue level. […]

Continue —> Frontiers | Fatigue and Cognitive Fatigability in Mild Traumatic Brain Injury are Correlated with Altered Neural Activity during Vigilance Test Performance | Neurology

Figure 4. Summary of the F-score results from the three-way ANOVA modeling of the regional cerebral blood flow data acquired during a 20-min psychomotor vigilance task (PVT) performance to illustrate the brain regions of statistically significant differences (family-wise error rate, p ≤ 0.05) in neural activity associated with the two fixed factors (the PVT performance time and subject group) and their interaction. (A) The effect of PVT performance time; (B) the interaction effect between the PVT performance time and subject groups. The color bar indicates the F-score of the three-way ANOVA results.

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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.

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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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