Tuesday, February 28, 2017

Neurofeedback Training For Insomnia No Better Than Sham



Neurofeedback training (NFT) is a procedure that tries to shape a participant's pattern of brain activity by providing real-time feedback, often in the form of a video game combined with other sensory stimuli that provide rewards when the “correct” state is achieved. The most common form of NFT uses EEG (brainwave) activity recorded non-invasively from the scalp. The EEG is a complex mixture of neural oscillations of different frequencies. Specific frequency bands are targeted for enhancement or reduction, so the participant can learn to modulate their own brain activity.

An overview of the neurofeedback process is shown below. Signals are recorded from sensors, processed, and classified. The calculated signal is then presented to the subject via feedback in one or more sensory modalities. Participants can learn to modulate their neural function, and complete the loop when the feedback is processed.

- click on image for a larger view -

modified from Fig 1 (Sitaram et al., 2016). The methods included here are electroencephalography (EEG), magnetoencephalography (MEG) and invasive electrocorticography (ECoG). MVPA, multivariate patterns of activity. FFT, fast Fourier transformation.


In one study, participants were trained to reduce the amplitude of alpha oscillations, with a goal of increasing long-range temporal correlations (Ros et al., 2016). Here's a description of the training procedure:
For online training, the EEG signal was ... band-pass filtered to extract alpha (8–12 Hz) amplitude with an epoch size of 0.5 s. Here, subjects were rewarded upon reduction of their absolute alpha amplitude... Visual feedback was clearly displayed on a monitor via 1) a dynamic bar graph at the center of the screen whose height was proportional to real-time alpha fluctuations and 2) a “Space Race” game, where a spaceship advanced through space when amplitude was below threshold, and became stationary when above threshold. No explicit instructions were given on how to achieve control over the spaceship, and all participants were told to be guided by the visual feedback process.

In a sham condition, the participants were shown feedback recorded from another subject in an earlier session (Ros et al., 2016). Having a sham (or placebo) condition is critical for demonstrating that any gains in performance (or increases in long-range temporal correlations, in this example) are due to self-regulation of specific EEG features learned during training, and not from some generic aspect of the procedure.

In academic articles, neurofeedback is often called closed-loop brain training, perhaps to distinguish it from neurofeedback therapy (also NFT). A recent paper in Nature Reviews Neuroscience discussed experimental applications of NFT1 and theories of the underlying mechanisms. Animal studies have demonstrated that rats and monkeys are capable of modulating the firing rates of small groups of neurons. Models of neurofeedback learning include instrumental (operant) conditioning, motor learning, global workspace theory, and skill learning. Exciting and important research ventures that capitalize on NFT are applications to brain-computer interfaces (BCI) and brain-machine interfaces (BMI), which have allowed paralyzed individuals to type and move prosthetic hands.

Psychiatric applications of NFT have been more problematic. First, you have to correctly identify the frequency band(s) that are abnormal in a clinical population. Then you must have a principled method for selecting the NFT protocol. Finally, you must demonstrate that your specific NFT protocol is superior to sham feedback (in a randomized, controlled trial). Unfortunately, this is rarely done.

Neurofeedback therapy has received critical coverage from the press in recent weeks. The new U.S. Secretary of Education, billionaire Betsy DeVos, has a major financial stake in an NFT company called Neurocore. The New York Times ran two articles critical of both DeVoss's conflict of interest and of the supposed benefits of NFT.

Betsy DeVos Won’t Shed Stake in Biofeedback Company, Filings Show
. . .

Ms. DeVos and her husband promote Neurocore heavily on the website for Windquest Group, a family office the couple use to manage some of their many investments...

But the claims that Neurocore’s methods can help children improve their performance in school could present a conflict for Ms. DeVos if she is confirmed as education secretary — especially given that the company is moving to expand its national reach.

Betsy DeVos Invests in a Therapy Under Scrutiny
. . .

Neurocore has not published its results in peer-reviewed medical literature. Its techniques — including mapping brain waves to diagnose problems and using neurofeedback, a form of biofeedback, to treat them — are not considered standards of care for the majority of the disorders it treats, including autism. Social workers, not doctors, perform assessments, and low-paid technicians with little training apply the methods to patients, including children with complex problems.

And Neurocore is in no way unique. Hundreds of Neurofeedback Centers offer cures for everything from A to T by merely wearing a few electrodes and playing a computer game for 20-30 sessions (and $2,000-3,000).

ADD / ADHD
Addiction
Alzheimer’s Disease
Anger Management
Anxiety
Attachment Disorders
Autism
Bipolar Disorder
Borderline Personality Disorder
Chronic Pain
Conduct Disorders
Depression
Dyslexia
Epilepsy / Seizures
Fibromyalgia
Insomnia / Sleep Disorders
Learning Disorders
Lyme Disease
Memory Loss
Migraines
Obsessive-Compulsive Disorder
OCD / Tourrette’s
Parkinson’s
Pre-Menstrual Syndrome
Stress / PTSD
Schizophrenia
Sleep Disorders
Stroke
Substance Abuse
Tourette’s Syndrome
Traumatic Brain Injury

Pitches are often targeted to concerned parents, but there is little to no evidence that the therapy offered at most of these centers is based on sound scientific research. As mentioned, double-blind, placebo controlled clinical trials are rarely conducted. Thibault and Raz (2017) have been particularly vocal about the lack of rigor in published studies, as well as the inflated claims of successful treatment.2 
Advocates of neurofeedback make bold claims concerning brain regulation, treatment of disorders, and mental health. Decades of research and thousands of peer-reviewed publications support neurofeedback using electroencephalography (EEG-nf); yet, few experiments isolate the act of receiving feedback from a specific brain signal as a necessary precursor to obtain the purported benefits. Moreover, while psychosocial parameters including participant motivation and expectation, rather than neurobiological substrates, seem to fuel clinical improvement across a wide range of disorders, for-profit clinics continue to sprout across North America and Europe. 

Here's how Neurocore describes its Natural Sleep Disorder Therapy for insomnia:

Neurofeedback. Natural treatment for sleep disorders & insomnia.

Neurocore’s approach to treating insomnia and sleeping disorders starts by looking at the brain. Using advanced qEEG technology, we measure your brainwaves to help identify the cause of the problem. We also monitor your heart rate and evaluate how in sync it is with your breathing pattern. Your unique neurometrics yield a customized neurofeedback training program that will teach your brain to self-regulate. The result is a brain that’s calibrated for better ongoing recovery, which means better sleep for you.

But the Neurocore “neurometrics” are not obtained from a clinical sleep study (polysomnography) which measures not only brain and heart activity, but also muscle activity, eye movements, and respiration. They haven't identified the “cause of the problem”. It could be sleep apnea or another medical condition.

Brand new evidence indicates that targeted, sensorimotor-rhythm (SMR) NFT for insomnia is no better than sham feedback. Earlier work had suggested that training to increase 12-15 Hz activity over the sensorimotor cortex could improve sleep by enhancing sleep spindles, which are in the same 12-15 Hz frequency range. In the new study, Schabus et al. (2017) took 25 patients with insomnia and administered 12 sessions of real neurofeedback and 12 sessions of sham neurofeedback, also called placebo feedback training (PFT):
Importantly, during the NFT condition, participants had to enhance EEG amplitudes in the SMR range between 12 and 15 Hz, whereas during the PFT sessions participants had to enhance random frequency ranges between 7 and 20 Hz (but not the 12–15 Hz SMR range); importantly within a PFT session only one frequency was trained and rewarded. The reason for choosing this kind of placebo or sham protocol was to involve patients to a similar degree as in NFT, yet with no specific frequency being rewarded systematically. Rewarding another frequency systematically could have resulted in undesired effects on EEG and behaviour that would render the PFT control condition suboptimal.

Outcome variables were objective (EEG) and subjective measures of sleep quality. A forthcoming commentary from Thibault et al. (2017) summarizes the results in a nifty cartoon.


As expected, when participants received genuine neurofeedback, they were able to significantly increase power in the SMR frequency band. This was not the case during sham neurofeedback sessions. Genuine neurofeedback did not alter objective measures of sleep quality (nor did sham). The most important result came in the patient ratings of subjective sleep quality. Genuine SMR neurofeedback improved subjective sleep measures, BUT SO DID SHAM NEUROFEEDBACK. This suggests that any benefit obtained from NFT was due to a placebo effect. Although this was a small study with some complications (e.g., nine of the 25 patients were “misperception” insomniacs with no objective indicators of insomnia), the results were informative about the cause of subjective improvements — they were non-specific in nature and did not rely on training SMR activity.

In their commentary, Thibault et al. (2017) refer to NFT as a superplacebo:
Whether real or sham, neurofeedback demands high engagement and immerses patients in a seemingly cutting-edge technological environment over many recurring sessions. ... In this regard, neurofeedback may represent an especially powerful form of placebo intervention—a kind of superplacebo.

Amusingly, they define superplacebo as “A treatment that is actually a placebo although neither the prescribing practitioner nor the receiving patient is aware of the absence of evidence to recommend it therapeutically.” If everyone thinks neurofeedback treatment works, it is more likely to do so, even though it bears no relation to self-regulation of selective neural activity. Future studies with refined NFT protocols may yet “tune” the brain in a desired direction, but for now... buyer beware.


Further Reading

Brain training: The future of psychiatric treatment?

DeVos-Associated Company Alleges Brain-Training Autism 'Fix'


Footnotes

1 The paper also reviewed neurofeedback studies that use hemodynamic measures. NFT based on fMRI is a newer (and more expensive) development that won't be covered here.

2 Neurocore claims: “Our ADHD Outcomes*  90% report fewer or less frequent ADHD symptoms.  85% experience a clinically important reduction of ADHD symptoms.  76% achieve non-clinical status.  53% no longer meet symptomatic thresholds for ADHD. ”


References

Ros T, Frewen P, Théberge J, Michela A, Kluetsch R, Mueller A, Candrian G, Jetly R, Vuilleumier P, Lanius RA. (2016). Neurofeedback tunes scale-free dynamics in spontaneous brain activity. Cerebral Cortex. DOI: 10.1093/cercor/bhw285

Manuel Schabus, Hermann Griessenberger, Maria-Teresa Gnjezda, Dominik P.J. Heib, Malgorzata Wislowska, Kerstin Hoedlmoser (2017). Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia. Brain: 10.1093/brain/awx011

Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M., Rana, M., Oblak, E., Birbaumer, N., & Sulzer, J. (2016). Closed-loop brain training: the science of neurofeedback. Nature Reviews Neuroscience, 18 (2), 86-100. DOI: 10.1038/nrn.2016.164

Thibault RT, Lifshitz M, Raz A. (2017). Neurofeedback or Neuroplacebo? Brain, in press. PDF

Thibault RT, Raz A. (2017). The psychology of neurofeedback: Clinical intervention even if applied placebo. American Psychologist, in press. PDF

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Saturday, February 18, 2017

Using Discourse Analysis to Assess Cognitive Decline

Figure from Gauthier et al. (2005).


Alzheimer's Disease (AD) and other dementias are progressive neurodegenerative conditions that unfold over time. Subtle symptoms such as forgetfulness and word finding problems may progress to mild cognitive impairment (MCI), and then escalate to full-blown dementia. Recent efforts to classify prodromal states have included automated analysis of spontaneous speech, which loses complexity as the disease progresses.

In one study, Frazier Fraser et al. (2015) applied machine learning methods to speech transcripts and audio files from the DementiaBank database. The participants were 167 patients with probable AD and 97 controls. The authors considered a total of 370 linguistic features, and found that a subset of 35 was able to classify patients vs. controls with 82% accuracy.1 While an advance over previous studies, this is not yet useful for diagnostic purposes. Another limitation was the relatively short length of the speech samples.2

Using factor analysis, the researchers found that four dimensions of speech3 were most indicative of dementia:
  • Semantic impairmentusing overly simple words
  • Acoustic impairment e.g., speaking more slowly
  • Syntactic impairment  using less complex grammar
  • Information impairment not clearly identifying the main aspects of a picture they were told to describe

Public figures who give repeated interviews leave a searchable record of spontaneous speech that can be analyzed for changes over time. Presidential press conferences provide another rich source of data for linguistic analysis.

Berisha et al. (2015) examined transcripts from the press conferences given by Ronald Reagan (1981-1989) and George H.W. Bush (1989-1993). We know that President Reagan received a formal diagnosis of Alzheimer's disease in 1994, five years after leaving office. And as far as we know, the elder Bush is still cognitively intact for his age (he's 92 now).

The quantified linguistic features included:
  • Number of unique words
  • Non-specific nouns – e.g., thing, something, anything
  • Filler words – well, so, basically, actually, literally, um, ah
  • Low-imageability, high frequency verbs – e.g., get, give, go, have, do

Reagan showed a significant decline in the number of unique words over the course of his presidency, but Bush did not.



Likewise, Reagan showed a significant increase in the use of non-specific nouns and fillers, but Bush did not.



There are several caveats here. Reagan was 69 when he was elected, while Bush was 64. Reagan was president for eight years and Bush for only four years; yet Bush held over twice as many press conferences as Reagan. Nonetheless, the results are consistent with a decline in cognitive function (which is not uncommon when aging from 69 to 77). Can we can classify Reagan as having MCI on the basis of these results? I don't think so. We'd really need comparable data from a population of demographically matched elderly participants.


President Donald Trump

After his Feb. 16 press conference, the public debate over whether President Trump is mentally unbalanced has intensified. Much of the current and past discussion has centered on the possibility of Narcissistic Personality Disorder (NPD), as speculated in The Atlantic and Vanity Fair and The Guardian. Sure, Trump has many of these qualities (that predate his actual grandiose status as POTUS):
  1. Grandiosity with expectations of superior treatment from others
  2. Fixated on fantasies of power, success, intelligence, attractiveness, etc.
  3. Self-perception of being unique, superior and associated with high-status people and institutions
  4. Needing constant admiration from others
  5. Sense of entitlement to special treatment and to obedience from others
  6. Exploitative of others to achieve personal gain
  7. Unwilling to empathize with others' feelings, wishes, or needs
  8. Intensely envious of others and the belief that others are equally envious of them
  9. Pompous and arrogant demeanor
And we can call him narcissistic in the generic sense of the word. But do we need to diagnose him with a quasi-psychiatric disorder, as in this NY Times letter signed by 35 mental health professionals?4
Mr. Trump’s speech and actions demonstrate an inability to tolerate views different from his own, leading to rage reactions. His words and behavior suggest a profound inability to empathize. Individuals with these traits distort reality to suit their psychological state, attacking facts and those who convey them (journalists, scientists).

In a powerful leader, these attacks are likely to increase, as his personal myth of greatness appears to be confirmed. We believe that the grave emotional instability indicated by Mr. Trump’s speech and actions makes him incapable of serving safely as president.

Dr. Allen Frances, chair of the DSM-IV task force, has forcefully argued that Trump does not meet criteria for NPD, because he is not distressed by his behavior:
Mr. Trump causes severe distress rather than experiencing it and has been richly rewarded, rather than punished, for his grandiosity, self-absorption and lack of empathy. It is a stigmatizing insult to the mentally ill (who are mostly well behaved and well meaning) to be lumped with Mr. Trump (who is neither).

Discourse Analysis

Here I'll suggest a different approach: can we quantify age-related neurological change using spontaneous speech?



“You know what uranium is, right?  It's this thing called nuclear weapons and other things.  Like, lots of things are done with uranium, including some bad things.  Nobody talks about that.  I didn't do anything for Russia.  I've done nothing for Russia.”

This is the most egregious example in the one hour, 17 minute train wreck. But there are other signs. He used the construction “very, very” 20 times. The word “thing” (and its variants) was uttered 102 times.

Am I going to diagnose him with anything? Of course not. That's unethical! But I will say that since Mr. Trump has been a public figure for nearly 40 years, we can objectively analyze his spontaneous speech and quantify any changes over time. I must emphasize that there is no magical scale to use for classification or comparison purposes (at least not yet). We don't know what's normal age-related decline and what's pathological.

I suggest that the best corpus of spontaneous speech data is the collection of Trump interviews/conversations with David Letterman. I believe they're unscripted, and there are many of them on YouTube (I've linked to eight below). Letterman has aged too, so you might as well analyze his speech as well.





Footnotes

1 The authors performed...
...a 10-fold cross-validation procedure in which a unique 10% of the data (i.e., the ‘test set’) are used in each iteration for evaluation, and the remaining 90% (i.e., the ‘training set’) are used to select the most useful features (of the 370 available as described in “Features” above) and construct our models. The reported accuracy is an average across the 10 folds. In a given fold, data from any individual speaker can occur in the test set or the training set, but not both.
2 A show-stopping limitation is that the two groups were not matched for age or education. The mean age was 71.8 for AD vs. 65.2 for controls, and years of education 12.5 vs. 14.1 yrs.

3 See also Alzheimer’s Disease Markers Found in Speech Patterns (link via @aholdenj).

4 BTW, they're not supposed to diagnose non-patients, that's unethical.


References

Berisha V, Wang S, LaCross A, & Liss J (2015). Tracking discourse complexity preceding Alzheimer's disease diagnosis: a case study comparing the press conferences of Presidents Ronald Reagan and George Herbert Walker Bush. Journal of Alzheimer's Disease, 45 (3), 959-63 PMID: 25633673

Fraser, K., Meltzer, J., & Rudzicz, F. (2015). Linguistic Features Identify Alzheimer’s Disease in Narrative Speech. Journal of Alzheimer's Disease, 49 (2), 407-422 DOI: 10.3233/JAD-150520

Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, Belleville S, Brodaty H, Bennett D, Chertkow H, Cummings JL. (2006). Mild cognitive impairment. The Lancet  367:1262-70.

Thomas, C., Keselj, V., Cercone, N., Rockwood, K., & Asp, E. (2005). Automatic detection and rating of dementia of Alzheimer type through lexical analysis of spontaneous speech. IEEE International Conference, 3, 1569-1574. doi: 10.1109/ICMA.2005.1626789


Donald Trump on the David Letterman Show

11-10-1988 Letterman Donald Trump

Donald Trump Interview on Letterman Show (1997)

Donald Trump Interview on David Letterman Show (1998)

Donald Trump on David Letterman Show (2008-08-08)

Donald Trump talks business and banks on David Letterman Show (2009-02-18)

Donald Trump on David Letterman Show (2010)

Donald Trump on David Letterman 17 October, 2013 Full Interview

Donald Trump on David Letterman January 8th 2015 Full Interview

You can find them all here.


other Trump

The Trump Archive - over 900 televised speeches, interviews, debates, and other news broadcasts related to President Donald Trump. See post at Internet Archive.

Donald Trump 1980 Interview (Brokaw)


Ronald Reagan videos

The President's News Conference - 1/29/81

The President's News Conference - 8/12/86

Iran/Contra Excerpt from 11/19/86

The President's News Conference - late Oct 1987

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