Sunday, May 14, 2017

Looking for Empathy in All the Wrong Places: Bizarre Cases of Factitious Disorder




Factitious disorder is a rare psychiatric condition where an individual deliberately induces or fabricates an ailment because of a desire to fulfill the role of a sick person. This differs from garden variety malingering, where an individual feigns illness for secondary gain (drug seeking, financial gain, avoidance of work, etc.). The primary goal in factitious disorder is to garner attention and sympathy from caregivers and medical staff.

The psychiatric handbook DSM-5 identifies two types of factitious disorder:
  • Factitious Disorder Imposed on Self (formerly known as Munchausen syndrome when the feigned symptoms were physical, rather than psychological).
  •  
  • Factitious Disorder Imposed on Another: When an individual falsifies illness in another, whether that be a child, pet or older adult (formerly known as Munchausen syndrome by proxy).

Since the desire to elicit empathy is one of the main objectives in this disorder, it is odd indeed when the “patient” feigns a frightening or repellent condition. A recent report by Fischer et al. (2016) discussed a particularly flagrant example: the case of a middle-aged man who falsely claimed to be a sexually sadistic serial killer to impress his psychotherapist. Not surprisingly, his ruse was a complete failure.

The case report noted that Mr. S had been a loner his entire life:
 ... He described having anxiety growing up, mainly in social situations. ... Mr. S had a history of alcohol abuse starting in his mid-twenties and continuing into his early forties. He denied any significant medical history. He denied legal difficulties, psychiatric hospitalizations, and suicide attempts. He was single, had never been married, had no children, and reported having only one close friend for most of his life. He never had a close long-term romantic relationship and stated a clear preference for living a solitary life. 

Mr. S had served in the military but did not see combat, and afterwards worked the graveyard shift as a security guard (all the better to avoid people).
One year prior to his admission to the psychiatric hospital, Mr. S sought outpatient therapy for depression and engaged in weekly supportive psychotherapy with a young female psychology intern. His psychiatrist started an SSRI antidepressant and a low dose of antipsychotic medication for “depression with psychotic features.” Mr. S's alleged psychosis consisted of “voices” of crowds of people saying things that he could not make out, which he experienced while working the night shift. He consistently attended his therapy sessions and was noted to be making progress. However, several months into his therapy, Mr. S told his therapist that he had been involved in of military combat and described himself as a decorated war hero. After several therapy sessions in which he [falsely] recounted his combat experiences, Mr. S was queried as to whether he ever killed anyone, to which Mr. S replied, “During the military or after the military?” He then told his therapist that he had followed, raped, and killed numerous women during the 20 years since leaving the military.

He recounted his imaginary crimes to the young female intern:
Mr. S reported that he would follow a potential female victim for several months before raping and strangling her to death with a rope. Although he claimed to rape and kill the women, he did not describe any sexual arousal from the subjugation, torture, or killing of his alleged victims. He refused to disclose how many women he had killed, where he had killed them, or how he had disposed of their bodies. He described having purchased various supplies to aid in abduction, which he kept in the back of his van while cruising for victims. These supplies included rope and two identical sets of clothes and shoes to help evade detection by the police. He described using various techniques to track his victims, as well as evade surveillance of his activities. He informed his therapist that he was actively following a woman he had encountered in a local public library several days earlier. Mr. S acknowledged that he studied the modus operandi of famous sexually sadistic serial killers by reading books. The patient's therapist, feeling frightened and threatened by these disclosures, transferred his case to her supervisor, who then saw the patient for a few therapy sessions. Mr. S reported worsening depression, hearing more “voices,” and attempting to self-amputate his leg using a tourniquet. Consequently, Mr. S was involuntarily detained as a “danger to self” and “danger to others” for evaluation in the local psychiatric hospital.

He was diagnosed with major depressive disorder, single episode, unspecified severity, with psychotic features. His routine physical, neurological exam, and lab work all yielded normal results.
...The inpatient treatment team contacted the District Attorney's office in order to file for continued involuntary hospitalization due to the patient's homicidal ideation and history of violence. Subsequent police investigation and review of records could not substantiate any of the patient's claims of committing multiple homicides in the Pacific Northwest.
. . .

After the District Attorney accepted the application for the prolonged involuntary civil commitment (180-day hold), Mr. S was confronted with the inconsistencies between his self-reported symptoms and objective findings and the failure to corroborate his claims of prior homicides. In response, Mr. S then confessed that he “had made the whole thing up…about the killings…all of it” because he “wanted attention.” He said that he had never followed, raped, or killed anyone and never had an intention to do so. He said that he did not know why he claimed this, other than an “impulse came over me and I acted on it.”

His false identity as a serial killer backfired, and he couldn't understand why his therapist had discontinued their sessions:
He had believed that his feigned history and symptomatology would make him a “more interesting” patient to his therapist. He reported feeling rejected when his therapist transferred his care to her supervisor. He had little insight into why his therapist may have been frightened by his behavior. Mr. S revealed that following his initial fabrications, and despite his initial involuntary hospitalization, he had felt too embarrassed to admit the truth.

His original diagnosis was revised to “factitious disorder with psychological symptoms, and cluster A traits (particularly schizoid and schizotypal traits) without meeting criteria for any one specific personality disorder.” Because of these personality traits, he had no insight into why his therapist might feel threatened by his terrifying stories.

There are at least two other papers describing cases of factitious disorder with repugnant feigned symptoms: one reported a case of factitious pedophilia, and the other reported a case of factitious homicidal ideation.


Thanks to Dr. Tannahill Glen for the link.


References

Fischer, C., Beckson, M., & Dietz, P. (2017). Factitious Disorder in a Patient Claiming to be a Sexually Sadistic Serial Killer. Journal of Forensic Sciences, 62 (3), 822-826 DOI: 10.1111/1556-4029.13340

Porter, T., & Feldman, M. (2011). A Case of Factitious Pedophilia. Journal of Forensic Sciences, 56 (5), 1380-1382 DOI: 10.1111/j.1556-4029.2011.01804.x

Thompson CR, & Beckson M (2004). A case of factitious homicidal ideation. The journal of the American Academy of Psychiatry and the Law, 32 (3), 277-81. PMID: 15515916



Appendix

What are the symptoms of Factitious Disorder?

  • Dramatic but inconsistent medical history
  • Unclear symptoms that are not controllable, become more severe, or change once treatment has begun
  • Predictable relapses following improvement in the condition
  • Extensive knowledge of hospitals and/or medical terminology, as well as the textbook descriptions of illness
  • Presence of many surgical scars
  • Appearance of new or additional symptoms following negative test results
  • Presence of symptoms only when the patient is alone or not being observed
  • Willingness or eagerness to have medical tests, operations, or other procedures
  • History of seeking treatment at many hospitals, clinics, and doctors’ offices, possibly even in different cities
  • Reluctance by the patient to allow health care professionals to meet with or talk to family members, friends, and prior health care providers

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Tuesday, April 18, 2017

The Big Ideas in Cognitive Neuroscience, Explained



Are emergent properties really for losers? Why are architectures important? What are “mirror neuron ensembles” anyway? My last post presented an idiosyncratic distillation of the Big Ideas in Cognitive Neuroscience symposium, presented by six speakers at the 2017 CNS meeting. Here I’ll briefly explain what I meant in the bullet points. In some cases I didn't quite understand what the speaker meant so I used outside sources. At the end is a bonus reading list.

The first two speakers made an especially fun pair on the topic of memory: they held opposing views on the “engram”, the physical manifestation of a memory in the brain.1 They also disagreed on most everything else.


1. Charles Randy Gallistel (Rutgers University) What Memory Must Look Like

Gallistel is convinced that Most Neuroscientists Are Wrong About the Brain. This subtly bizarre essay in Nautilus (which was widely scorned on Twitter) succinctly summarized the major points of his talk. You and I may think the brain-as-computer metaphor has outlived its usefulness, but Gallistel says that “Computation in the brain must resemble computation in a computer.” 

Shannon information is a set of possible messages encoded as bit patterns and sent over a noisy channel to a recipient that will hopefully decode the message with minimal error. In this purely mathematical theory, the semantic content (meaning) of a message is irrelevant. The brain stores numbers and that's that.

  • Memories (“engrams”) are not stored at synapses.
Instead, engrams reside in molecules inside cells. The brain “encodes information into molecules inside neurons and reads out that information for use in computational operations.” A 2014 paper on conditioned responses in cerebellar Purkinje cells was instrumental in overturning synaptic plasticity (strengthening or weakening of synaptic connections) as the central mechanism for learning and memory, according to Gallistel.2 Most other scientists do not share this view.3

  • The engram is inter-spike interval.
Spike train solutions based on rate coding are wrong. Meaning, the code is not conveyed by the firing rate of neurons. Instead, numbers are conveyed to engrams via a combinatorial interspike interval code. Engrams then reside in cell-intrinsic molecular structures. In the end, memory must look like the DNA code.

  • Emergent properties are for losers.
“Emergent property” is a code word for “we don't know.”



2. Tomás Ryan (@TJRyan_77) Information Storage in Memory Engrams

Ryan began by acknowledging that he had tremendous respect for Gallistal's speech which was in turn powerful, illuminating, very categorical, polarizing, and rigid. But wrong. Oh so very wrong. Memory is not essentially molecular, we should not approach memory and the brain from a design perspective, and information storage need not mimic a computer.

  • The brain does not use Shannon information.
More precisely, “the kind of information the brain uses may be very different from Shannon information.” Why is that? Brains evolved, in kludgy ways that don't resemble a computer. The information used by the brain may be encoded without having to reduce it to Shannon form, and may not be quantifiable as units.

  • Memories (“engrams”) are not stored at synapses.
Memory is not stored by changes in synaptic weights, Ryan and Gallistel agree on this. The dominant view has been falsified by a number of studies including one by Ryan and colleagues that used engram labeling. Specific “engram cells” can be labeled during learning using optogenetic techniques, and later stimulated to induce the recall of specific memories. These memories can be reactivated even after protein synthesis inhibitors have (1) induced amnesia, and (2) prevented the usual memory consolidation-related changes in synaptic strength.

  • We learn entirely through spike trains.
Spike trains are necessary but not sufficient to explain how information is coded in the brain. On the other hand, instincts are transmitted genetically and are not learned via spike trains.

  • The engram is an emergent property.
And fitting with all of the above, “the engram is an emergent property mediated through synaptic connections” (not through synaptic weights). Stable connectivity is what stores information, not molecules.


Angela Friederici (Max Planck Institute for Human Cognitive and Brain Sciences) Structure and Dynamics of the Language Network

Following on the heels of the rodent engram crowd, Friederici pointed out the obvious limitations of studying language as a human trait.

  • Language is genetically predetermined.
The human ability to acquire language is based on a genetically predetermined structural neural network. Although the degree of innateness has been disputed, a bias or propensity of brain development towards particular modes of information processing is less controversial. According to Friederici, language capacity is rooted in “merge”, a specific computation that binds words together to form phrases and sentences.

  • The “merge” computation is localized in BA 44.
This wasn't one of my original bullet points, but I found this statement rather surprising and unbelievable. It implies that our capacity for language is located in the anterior ventral portion of Brodmann's area 44 in the left hemisphere (the tiny red area in the PHRASE > LIST panel below).



The problem is that acute stroke patients with dysfunctional tissue in left BA 44 do not have impaired syntax. Instead, they have difficulty with phonological short-term memory (keeping strings of digits in mind, like remembering a phone number).

  • There is something called mirror neural ensembles.
    I'll just have to leave this slide here, since I really didn't understand it, even on the second viewing.



    “This is a poor hypothesis,” she said.


    Jean-Rémi King (@jrking0) Parsing Human Minds

    King's expertise is in visual processing (not language), but his talk drew parallels between vision and speech comprehension. A key goal in both domains is to identify the algorithm (sequence of operations) that translates input into meaning.

    • Recursion is big. 
    Despite these commonalities, the structure of language presents the unique challenge of nesting (or recursion): each constituent in a sentence can be made of subconstituents of the same nature, which can result in ambiguity.


    • Architectures are important. 
    Decoding aspects of a sensory stimulus using MEG and machine learning is lovely, but it doesn't tell you the algorithm. What is the computational architecture? Is it sustained or feedforward or recurrent?

      Each architecture could be compatible with a pattern of brain activity at different time points. But do the classifiers at different time points generalize to other time points? This can be determined by a temporal generalization analysis, which “reveals a repertoire of canonical brain dynamics.”


      Danielle Bassett (@DaniSBassett A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

      Bassett previewed an arc of exciting ideas where we've shown progress, followed by frustrations and failures, which may ultimately provide an opening for the really Big Ideas. Her focus is on learning from a network perspective, which means patterns of connectivity in the whole brain. What is the underlying network architecture that facilitates the spatial distributed effects?



      What is the relationship between these two notions of modularity?
      [I ask this as an honest question.]

      Major challenges remain, of course.

      • Build a bridge from networks to models of behavior.
      Incorporate well-specified behavioral models such as reinforcement learning and the drift diffusion model of decision making. These models are fit to the data to derive parameters such as the alpha parameter from reinforcement learning rate. Models of behavior can help generate hypotheses about how the system actually works.

      • Use generative models to construct theories. 
      Network models are extremely useful, but they're not theories. They're descriptors. They don't generate new frameworks for understanding what the data should look like. Theory-building is obviously critical for moving forward.


      John Krakauer (@blamlab Big Ideas in Cognitive Neuroscience: Action

      Krakauer mentioned the Big Questions in Neuroscience symposium at the 2016 SFN meeting, which motivated the CNS symposium as well as a splashy critical paper in Neuron. He raised an interesting point about how the term “connectivity” has different meanings, i.e. the type of embedded connectivity that stores information (engrams) vs. the type of correlational connectivity when modules combine with each other to produce behavior. [BTW, is everyone here using “modules” in the same way?]

      • Machine learning will save us. 
      Krakauer discussed work on motor learning using adaptation paradigms and simple execution tasks. But there's a dirty secret: there is no computational model, no algorithmic theory of how practice makes you better on those tasks. Can the computational view get an upgrade from machine learning? Go out and read the manifesto by Marblestone, Wayne, and Kording: Toward an Integration of Deep Learning and Neuroscience. And you better learn about cost functions, because they're very important.4



      • Go back to behavioral neuroscience.
      This is the only way to work out the right cost functions. Bottom line: Networks represent weighting modules into the cost function.4 


      OVERALL, there was an emphasis on computational approaches with nods to the three levels of David Marr:

      computation – algorithm – implementation



      We know from from Krakauer et al. 2017 (and from CNS meetings past and present) that co-organizer David Poeppel is a big fan of Marr. The end goal of a Marr-ian research program is to find explanations, to reach an understanding of brain-behavior relations. This requires a detailed specification of the computational problem (i.e., behavior) to uncover the algorithms. The correlational approach of cognitive neuroscience and even the causal-mechanistic circuit manipulations of optogenetic neuroscience just don't cut it anymore.



      Footnotes

      1 Although neither speaker explicitly defined the term, it is most definitely not the engram as envisioned by Scientology: “a detailed mental image or memory of a traumatic event from the past that occurred when an individual was partially or fully unconscious.” The term was first coined by Richard Semon in 1904.

      2 This paper (by Johansson et al, 2014) appeared in PNAS, and Gallistel was the prearranged editor.

      3 For instance, here's Mu-ming Poo: “There is now general consensus that persistent modification of the synaptic strength via LTP and LTD of pre-existing connections represents a primary mechanism for the formation of memory engrams.”

      4 If you don't understand all this, you're not alone. From Machine Learning: the Basics.
      This idea of minimizing some function (in this case, the sum of squared residuals) is a building block of supervised learning algorithms, and in the field of machine learning this function - whatever it may be for the algorithm in question - is referred to as the cost function. 


      Reading List

      Everyone is Wrong

      Here's Why Most Neuroscientists Are Wrong About the Brain. Gallistel in Nautilus, Oct. 2015.

      Time to rethink the neural mechanisms of learning and memory. Gallistel CR, Balsam PD. Neurobiol Learn Mem. 2014 Feb;108:136-44.

      Engrams are Cool

      What is memory? The present state of the engram. Poo MM, Pignatelli M, Ryan TJ, Tonegawa S, Bonhoeffer T, Martin KC, Rudenko A, Tsai LH, Tsien RW, Fishell G, Mullins C, Gonçalves JT, Shtrahman M, Johnston ST,  Gage FH, Dan Y, Long J, Buzsáki G, Stevens C. BMC Biol. 2016 May 19;14:40.

      Engram cells retain memory under retrograde amnesia. Ryan TJ, Roy DS, Pignatelli M, Arons A, Tonegawa S. Science. 2015 May 29;348(6238):1007-13.

      Engrams are Overrated

      For good measure, some contrarian thoughts floating around Twitter...


      “Can We Localize Merge in the Brain? Yes We Can”

      Merge in the Human Brain: A Sub-Region Based Functional Investigation in the Left Pars Opercularis. Zaccarella E, Friederici AD. Front Psychol. 2015 Nov 27;6:1818.

      The neurobiological nature of syntactic hierarchies. Zaccarella E, Friederici AD. Neurosci Biobehav Rev. 2016 Jul 29. doi: 10.1016/j.neubiorev.2016.07.038.

      Really?

      Asyntactic comprehension, working memory, and acute ischemia in Broca's area versus angular gyrus. Newhart M, Trupe LA, Gomez Y, Cloutman L, Molitoris JJ, Davis C, Leigh R, Gottesman RF, Race D, Hillis AE.  Cortex. 2012 Nov-Dec;48(10):1288-97.

      Patients with acute strokes in left BA 44 (part of Broca's area) do not have impaired syntax.


      Dynamics of Mental Representations

      Characterizing the dynamics of mental representations: the temporal generalization method. King JR, Dehaene S. Trends Cogn Sci. 2014 Apr;18(4):203-10.

      King JR, Pescetelli N, Dehaene S. Brain Mechanisms Underlying the Brief Maintenance of Seen and Unseen Sensory InformationNeuron. 2016; 92(5):1122-1134.


      A Spate of New Network Articles by Bassett

      A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior. Bassett DS, Mattar MG. Trends Cogn Sci. 2017 Apr;21(4):250-264.

      This one is most relevant to Dr. Bassett's talk, as it is the title of her talk.

      Network neuroscience. Bassett DS, Sporns O. Nat Neurosci. 2017 Feb 23;20(3):353-364.

      Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity. Bassett DS, Khambhati AN, Grafton ST. Annu Rev Biomed Eng. 2017 Mar 27. doi: 10.1146/annurev-bioeng-071516-044511.

      Modelling And Interpreting Network Dynamics [bioRxiv preprint]. Ankit N Khambhati, Ann E Sizemore, Richard F Betzel, Danielle S Bassett. doi: https://doi.org/10.1101/124016


      Behavior is Underrated

      Neuroscience Needs Behavior: Correcting a Reductionist Bias. Krakauer JW, Ghazanfar AA, Gomez-Marin A, MacIver MA, Poeppel D. Neuron. 2017 Feb 8;93(3):480-490.

      The first author was a presenter and the last author an organizer of the symposium.



      Thanks to @jakublimanowski for the tip on Goldstein (1999).

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      Tuesday, April 04, 2017

      What are the Big Ideas in Cognitive Neuroscience?


      This year, the Cognitive Neuroscience Institute (CNI) and the Max-Planck-Society organized a symposium on Big Ideas in Cognitive Neuroscience. I enjoyed this fun forum organized by David Poeppel and Mike Gazzaniga. The format included three pairs of speakers on the topics of memory, language, and action/motor who “consider[ed] some major challenges and cutting-edge advances, from molecular mechanisms to decoding approaches to network computations.”

      Co-host Marcus Raichle recalled his inspiration for the symposium: a similar Big Ideas session at the Society for Neuroscience meeting. But human neuroscience was absent from all SFN Big Ideas, so Dr. Raichle contacted Dr. Gazzaniga, who “made it happen” (along with Dr. Poeppel). The popular event was standing room only, and many couldn't even get into the Bayview Room (which was too small a venue). More context:
      “Recent discussions in the neurosciences have been relentlessly reductionist. The guiding principle of this symposium is that there is no privileged level of analysis that can yield special explanatory insight into the mind/brain on its own, so ideas and techniques across levels will be necessary.”

      The two hour symposium was a welcome addition to hundreds of posters and talks on highly specific empirical findings. Sometimes we must take a step back and look at the big picture. But since I'm The Neurocritic, I'll start out with some modest suggestions for next time.

      • There was no time for questions or discussion.
      • There were too many talks.
      • It would be nice for all speakers to try to bridge different levels of analysis.
      • This is a small point, but ironically the first two speakers (Gallistel, Ryan) did not talk about human neuroscience.

      So my idea is to have four speakers on one topic (memory, let's say) with two at the level of Gallistel and Ryan1, and two who approach human neuroscience using different techniques. Talks are strictly limited to 20 minutes. Then there is a 20 minute panel discussion where everyone tries to consider the implications of the other levels for their own work. Then (ideally) there is time for 20 minutes of questions from the audience. However, since I'm not an expert in organizing such events, allotting 20 minutes for the audience could be excessive. So the timing could be restructured to 25 min for talks, 10-15 min panel, 5-10 min audience. Or combine the round table with audience participation.

      Last year, Symposium Session 7 on Human Intracranial Electrophysiology (which included the incendiary tDCS challenge by György Buzsáki) had a round table discussion as Talk 5, which I thought was very successful.

      Video of the Big Ideas symposium is now available on YouTube, but in case you don't want to watch the entire two hours, I'll present a brief summary below.


      Big Box Neuroscience

      Here's an idiosyncratic distillation of some major points from the symposium.

      • The brain is an information processing device in the sense of Shannon information theory.
      • The brain does not use Shannon information.
      • Memories (”engrams”) are not stored at synapses.
      • We learn entirely through spike trains.
      • The engram is inter-spike interval.
      • The engram is an emergent property.
      • Emergent properties are for losers.
      • Language is genetically predetermined.
      • There is something called mirror neural ensembles.
      • Recursion is big.
      • Architectures are important.
      • Build a bridge from networks to models of behavior.
      • Use generative models to construct theories.
      • Machine learning will save us.
      • Go back to behavioral neuroscience.

      Maybe I'll explain what this all means in the next post. You can also check out the official @CogNeuroNews coverage.


      ADDENDUM (April 18 2017): The sequel is finally up: The Big Ideas in Cognitive Neuroscience, Explained


      Footnote

      1 Controversy is always entertaining, and these two had diametrically opposed views.




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      Friday, March 24, 2017

      What's Popular at #CNS2017?

      Memory wins again!



      Word cloud for 835 poster titles at CNS 2017.


      The 2017 Cognitive Neuroscience Society annual meeting will start tomorrow, March 25. To no one's surprise, memory is the most popular topic in the bottom-up abstract submission sweepstakes.

      In contrast, the top-down selections of the Cognosenti are light on memory, with a greater emphasis on attention, speech, mind-wandering, and reward.



      Word cloud for 16 titles/abstracts in four Invited Symposia.


      The member-generated Symposium Sessions are once again memory-centric, but with the key additions of speech, learning, information, and oscillations.



      Word cloud for 43 titles/abstracts in nine Uninvited Symposia.


      The hot area of the brain this year is OFC, the orbitofrontal cortex.

      Kicking off the meeting is a new addition to the program, a symposium on Big Ideas in Cognitive Neuroscience, which will focus on language, motor control/action, and (you guessed it) memory:

      Six speakers, in three pairs, will consider some major challenges and cutting-edge advances, from molecular mechanisms to decoding approaches to network computations. The presentations and debate aim to provide a tentative outline of what might be a productive and ambitious agenda for our fields.

      Speakers:
      • Charles R. Gallistel (Rutgers University) and Tomás Ryan (Trinity College Dublin & MIT) on memory.
      • Angela Friederici (Max-Planck-Institute) and Jean-Rémi King (NYU) on language.
      • John Krakauer (Johns Hopkins University) and Danielle Bassett (University of Pennsylvania) on action/motor.

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      Sunday, March 12, 2017

      A brain-enhancement amusement park mockumentary


      “There was a level of undefined brain activity, about 30% higher, than the kids who stayed on the ground.”


      The Centrifuge Brain Project is an awesome short film by Till Nowak, featuring a deadpan performance by Leslie Barany.





      The fictitious website of the Institute for Centrifugal Research (ICR) is one of the best since LACUNA Inc. (which lives on at archive.org):

      Welcome to the homepage of ICR - the world's leading research laboratory in the highly specialized field of spinning people around.

      We are proud of our history - a chronicle of passion and pioneering achievements in the realms of brain manipulation, excessive G-Force and prenatal simulations. Established in 1976 by Dr. Matthew Brenswick and Dr. Nick Laslowicz, the institute has never stopped doubting the generally accepted laws of physics.


      WEDDING CAKE CENTRIFUGE
      established 1985.
      Number of seats: 96
      G-Force: 2.3
      Model no. 810XN-96922


      “Some of the test results that year were a little too extreme to be published.”


      STEAM PRESSURE CATAPULT
      established 2003.
      Number of seats: 172
      G-Force: 9
      Model no. 01758X-KAZT


      “Unpredictability was an important aspect of our work.”



      Coming soon: Derealization during utricular stimulation.

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      Monday, March 06, 2017

      Patent for Stimulation of Brodmann Areas 1-48 and all other structures


      Fig. 1 (Roskams-Edriset al., 2017). The number of patents implicating specific brain regions has risen from 1976 to the mid 2010s. Results were obtained by searching The Lens patent database (http://lens.org/).


      “What is the ethical value of awarding patent rights that implicate regions of the brain?”

      Do the applicants intend to patent the function of specific brain areas? This absurd scenario was the first thing that came to mind. The murky waters of neurotech patent law were explored by a group of neuroethics and intellectual property experts (Roskams-Edriset al., 2017) who noted several problems:
      The first practical challenge to patents that relate to brain regions is well known to patent law: the danger of overbroad, vague, or obvious claims.

      One egregious example is US 9327069 B2, Methods and systems for treating a medical condition by promoting neural remodeling within the brain. The language is extremely vague (and redundant):
      Methods of treating a medical condition include applying at least one stimulus to a stimulation site within the brain of a patient with an implanted stimulator in accordance with one or more stimulation parameters. The [sic] at least one stimulus is configured to promote neural remodeling within the brain of the patient. Systems for treating a medical condition include an implantable stimulator configured to apply at least one stimulus to a stimulation site within the brain of a patient in accordance with one or more stimulation parameters...

      What kind of stimulus will “promote neural remodeling?” All of them. As stated in the Detailed Description:
      The stimulus may include an electrical stimulation current, one or more drugs, gene infusion, chemical stimulation, thermal stimulation, electromagnetic stimulation, mechanical stimulation, and/or any other suitable stimulus.

      What brain conditions can be treated? All of them.
      Many medical conditions have been linked to faulty neural connections and/or abnormal developmental pruning of axons, dendrites, and synapses within the brain. Such medical conditions include, but are not limited to, autism, psychological disorders (e.g., schizophrenia, compulsive behaviors, and depression), neurodegenerative diseases (e.g., Huntington's disease, Alzheimer's disease, and amyotrophic lateral sclerosis), and chromosomal abnormalities (e.g., Down syndrome and Klinefelter syndrome).

      Finally, what are the implicated brain regions? You guessed it.
      Nearly every brain area has been implicated in the disorders listed above. In particular, it is believed that faulty neural connections and/or abnormal developmental pruning of neural structures within the temporal lobe, limbic system, pituitary gland, brainstem, cerebral cortex, and/or any other midbrain structure are at least in part responsible for the deficits of one or more of the disorders listed above.

      The Claims that apply in this patent (e.g., stimulation sites and medical conditions) are slightly more specific, but still outlandish:
      4. ... said stimulation site comprises at least one or more of a temporal lobe, cerebral ventricle, structure within a limbic system, pituitary gland, brainstem, and cerebral cortex.

      5. ...said medical condition comprises at least one or more of autism, a psychological disorder, a neurodegenerative disease, a chromosomal abnormality, a bad habit, and an injury to said brain.
      A bad habit??


      100,000,000 Hz

      Even better is Patent US 9,050,463 (Systems and methods for stimulating cellular function in tissue), which touts the application of electrical fields with frequencies of 100,000,000 Hz and above to the entire nervous system...
      ....various structures within the brain or nervous system including but not limited to dorsal lateral prefrontal cortex, any component of the basal ganglia, nucleus accumbens, gastric nuclei, brainstem, thalamus, inferior colliculus, superior colliculus, periaqueductal gray, primary motor cortex, supplementary motor cortex, occipital lobe, Brodmann areas 1-48, primary sensory cortex, primary visual cortex, primary auditory cortex, amygdala, hippocampus, cochlea, cranial nerves, cerebellum, frontal lobe, occipital lobe, temporal lobe, parietal lobe, sub-cortical structures, spinal cord, nerve roots, sensory organs, and peripheral nerves.

       ...to treat all known diseases:
      Such pathologies that may be treated include but are not limited to Multiple Sclerosis, Amyotrophic Lateral Sclerosis, Alzheimer's Disease, Dystonia, Tics, Spinal Cord Injury, Traumatic Brain Injury, Drug Craving, Food Craving, Alcohol Craving, Nicotine Craving, Stuttering, Tinnitus, Spasticity, Parkinson's Disease, Parkinsonianism, Obsessions, Depression, Schizophrenia, Bipolar Disorder, Acute Mania, Catonia, Post-Traumatic Stress Disorder, Autism, Chronic Pain Syndrome, Phantom Limb Pain, Epilepsy, Stroke, Auditory Hallucinations, Movement Disorders, Neurodegenerative Disorders, Pain Disorders, Metabolic Disorders, Addictive Disorders, Psychiatric Disorders, Traumatic Nerve Injury, and Sensory Disorders.




      Oddly, the specific Claims in this patent include an indication limited to Parkinson's disease. But the list of targeted brain regions (see above) is irrelevant in this disorder.


      Roskams-Edriset al. (2017) conclude with a warning drawn from previous efforts to patent human genes: “brain biomaterial and brain processes cannot be invented and, like genes, they similarly ought not to be owned.” There should be no legal rights to brain regions, or else we risk losing autonomy over our own thoughts and actions.


      Reference

      Roskams-Edris, D., Anderson-Redick, S., Kiss, Z., & Illes, J. (2017). Situating brain regions among patent rights and moral risks. Nature Biotechnology, 35 (2), 119-121. DOI: 10.1038/nbt.3782

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      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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