Tuesday, November 30, 2021

Is Precision Psychiatry Realistic?

Fig. 1 (Fernandes et al., 2017). Domains related to ‘precision psychiatry’.


The right drug for the right patient” was a catch phrase in the early years of the personalized medicine movement (2000), represented by the emerging field of pharmacogenomics. No more “one size fits all” prescribing — the Human Genome Project will allow doctors to predict how you will respond to any given medication.

The last time I went to the drug store, I picked up my cheap generic prescription without the benefit of genomic testing.

The term “personalized medicine” was outdated by 2011. The National Research Council (PDF) preferred the new and improved “precision medicine” brand because...

...there was concern that the word "personalized" could be misinterpreted to imply that treatments and preventions are being developed uniquely for each individual; in precision medicine, the focus is on identifying which approaches will be effective for which patients based on genetic, environmental, and lifestyle factors.


What is Precision Psychiatry?

If you believe the hype (circa 2017), it's a paradigm shift! It's perfection! [yes, really]...
...a high level of exactness in measurement will be achieved such that, eventually, it will be personalised. It can be conceptualised as a highly sophisticated and intricate classification system, where infinitesimal categories will, ideally, attain perfection in a detailed multidimensional classification.


...It's quixotic (if taken to those lofty extremes). Precision psychiatry is an incredibly popular topic for review papers, but don't hold your breath awaiting its implementation. Especially if you're a patient. 


What is Stratified Psychiatry?

Fig. 1 (Arns et al., 2021). An infographic summarizing the more ‘diagnostic based one-size-fits-all psychiatry’ that is currently in use (left), to more ‘prognostic’ models such as Stratified Psychiatry (right-top) and Precision Psychiatry (right-bottom).


Perhaps Stratified Psychiatry is more realistic, where subgroups of patients with similar biomarker profiles are assigned to treatments predicted to show a higher probability of clinical response. But the infographic above may be conflating precision psychiatry and personalized psychiatry (it seems that way in the text as well). So is stratified psychiatry a more modest version of precision psychiatry, with only a few biomarkers (vs. 35 or so)? Without the grandiosity of anticipating infinitesimal categories with perfect diagnostic accuracy or the absolute faith in RDoC (NIMH Research Domain Criteria)? 


In the end, making promises you can't keep helps no one (see The Lie of Precision Medicine).


References

Arns M, van Dijk H, Luykx JJ, van Wingen G, Olbrich S. Stratified psychiatry: Tomorrow's precision psychiatry? European Neuropsychopharmacology. 2022 Feb 1;55:14-9.
 
Fernandes BS, Williams LM, Steiner J, Leboyer M, Carvalho AF, Berk M. The new field of ‘precision psychiatry’. BMC medicine. 2017 Dec;15(1):1-7.
 

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Sunday, October 31, 2021

Xylological Delusions of Being a Tree


The mythology surrounding reverse inter-metamorphosis, a delusional syndrome that involves transformation into a beast, has frightened and fascinated for hundreds of years. A special instance of reverse inter-metamorphosis is clinical lycanthropy, the delusion that one has been transformed into a wolf (or another animal). A recent review identified 43 cases in the literature between 1852 and today (Guessoum et al., 2021). Psychotic depression and schizophrenia were the most common co-existing psychiatric diagnoses in these individuals.



The article advocates a cultural and person-centered approach to treatment, as did many of the original authors. The wolf has different characteristics and symbolic meanings across various cultures. As in other realms, popular media and folklore shape the content of the delusion. Occasionally, there may be a literal event that triggers the belief of transmogrification.


Clinical Kynanthropy After a Dog Bite

This case report of kynanthropy delusional transformation into a dog was notable because the COVID-19 lockdown might have exacerbated the condition. The patient was a 28-year-old single male who started grinning, barking, and walking on four legs after being bitten by a dog... but not until two years later (Jain et al., 2021).

He was apparently alright until two months before the presentation, when, due to the COVID-19-related nationwide lockdown, he read excessively on the internet about dog bites. ... he developed a feeling that his tongue is moving like a dog’s and began having repetitive thoughts about converting into a dog. Gradually, his sleep reduced to 1–2 hours/day, and he expressed fear that if he sleeps, he might get up as a dog. He sought repeated reassurance from his family that he hasn’t transformed into a dog, to the extent that they got irritated and asked him to see a doctor. These repetitive thoughts would be present for the whole day, and he would chant God's name to get relief from them.


The man had no history of rabies, mood disorder, or substance use. Upon examination, he was anxious but did not present with depression or psychotic features. Instead, he had repetitive doubts about turning into a dog. The patient was diagnosed with moderate OCD (with poor insight) and given a prescription of 20 mg fluoxetine (Prozac). A month later, he reported improvement in his repetitive thoughts, and at three months showed an improvement in social and occupational function. He realized he had spent way too much time on the internet reading and watching videos about dog bites.

 

Botanical Inter-metamorphosis

A unique arboreal delusion was briefly described in a recent abstract (Bakhshi & Hirsch, 2021): a depressed young woman believed she had been transformed into a tree. The major manifestations were standing still for long periods of time and repeating, "I am a tree."

A 21-year-old right-handed cisgender female, two months prior to presentation, noted stiffness and difficulty with ambulation. One-month prior to admission, she experienced recurrent depression with myriad vegetative and nonvegetative symptoms of depression. On admission her chief complaint was "I am a tree", standing motionless and minimally responding to query. After treatment with quetiapine, mirtazapine and hydroxyzine for a one-week period, her perception of being a tree fully resolved.

 

The authors considered a range of delusional diagnoses for her condition:

  • Cotard’s syndrome – NO, she did not think she was dead
  • Ekbom syndrome (delusional parasitosis) – NO, she did not think her body was infested with bugs
  • Reverse Inanimate Capgras Syndrome – MAYBE? (but "instead of an imposter replacing a close friend, who then is inserted into the sufferer; a tree has replaced the sufferer.")
  • Intermetamorphosis – NO, misindentification of another, not self
  • Botanical Intermetamorphosis – NO, another person transformed into a plant, not self
  • Reverse Intermetamorphosis – MAYBE? ("However, in this situation, the objects are all human or animate animals not botanicals.") – but isn't this the point, to report on a botanical?
  • Fregoli syndrome – NO, altered physical identity of another, not self
  • Reverse Fregoli syndrome – NO, patient assumes the physical identity of another person, not a plant
  • Botanical Variant of Interparietal Syndrome – NO, she did not have parietal lobe damage. ("In this condition, parts of the body are perceived to be lifeless, due to lesions of the inferior parietal lobe...")


The groundbreaking conclusion was that doctors should check for delusions involving plant life in all of these syndromes, because botanical variants have not been described before.

 
Further Reading

Werewolves of London, Ontario

Haunting Delusions of Identity

Ophidianthropy: The Delusion of Being Transformed into a Snake

Psychopharmacology of Lycanthropy

The oldest remaining werewolf movie 


Keanu Reeves as Ortiz the Dog Boy in Freaked



References

Bakhshi HS, Hirsch AR. (2021). Xylological Variant of Reverse Fregoli Syndrome, Delusions of Being a Tree. CNS Spectrums 26(2):145.

Guessoum SB, Benoit L, Minassian S, Mallet J, Moro MR. (2021). Clinical Lycanthropy, Neurobiology, Culture: A Systematic Review. Frontiers in Psychiatry Oct. 13, 1693.

Jain VP, Gupta N, Kale VP. (2021). Clinical Kynanthropy: A Case Report of Psychological Manifestation of a Dog Bite. Indian Journal of Psychological Medicine. Oct 13:02537176211047132.

 


 

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Thursday, September 30, 2021

A Curious Case of Auditory-Gustatory Synesthesia... in someone who can't smell

 

A fascinating case study from 1907 describes the self-reported sensory “taste” experiences evoked by hearing specific words, names, or sounds (Pierce, 1907). The subject was a young woman about to graduate from college. As far as she could tell, she's always had these experiences, and for most of her life she didn't know they were unusual. This surprise upon discovering the uniqueness of one's one internal experience is similar to what is reported by many contemporary individuals with less typical phenomenology, such as aphantasia (the inability to generate visual images).

Pierce noted that the subject was anosmic (had a loss of smell), although this was not formally tested. Nonetheless, he observed:

Coffee burning upon a stove is not noticed, though she may be close by. Camphor placed in the nostrils gives only a stinging sensation. Ammonia can be sniffed without discomfort. And, as with all anosmics, foods are discriminated on the basis of the pure taste qualities, or by the characters of texture, astringency, and so on, which any food-complex may possess. These sensory defects are worth noting for their possible significance in connection with the theory of this special case of synæsthesia.

 

Some aspects of the report were rather quaint by modern standards (e.g., the first person narration), but others were quite prescient.

But what evidence have we that an actual case of synæsthesia is here being reported, and not a case of artificial association due to a lively dramatic fancy? This is a point that must be raised, for it is very easy to entertain a suspicion that these phenomena are essentially ungenuine. Now, of course, in matters of this kind general impressions and personal knowledge of the subject count heavily. And on both these grounds I have no hesitation in asserting my conviction that the above-cited equivalents are the expression of a genuine synæsthesia.



One key piece of evidence was the consistency of gustatory experiences associated with the same words at a later date:

...the equivalents possess a constancy which would hardly be possible apart from a true synæsthesia. After an interval of six months a number of words were given at random from the original list, with the result that the identical equivalents were described in almost precisely the same language.

 

Also, there was a somatotopic-like arrangement for some of the gustatory experiences:

Many of the experiences are given quite definite locations in the mouth. Thus the equivalents of Ethel and Hall (tactual) are felt at the tip of the tongue; of lox (irritation) at the back of the throat; of Judith (salt) at the sides of the tongue; of Sarah (cold) on the lips; of amethyst (bitter) "at the back of the mouth, on the roof, where the root of the tongue seems to hit it" ...

 

Pierce tried to discern whether similar sounds could be related to similar gustatory experiences, but that wasn't the case. Nor were they related to similarities in articulation. He still preferred a physiological explanation, yet...
In attempting to decide whether the above-cited experiences are to be explained by the physiological or by the psychological theory, we are, apparently, in no better and in no worse case than in respect to all varieties of synæsthesia. No decisive facts are at hand.

 

An interesting commentary in JAMA predated MRI connectivity studies of synesthesia by nearly a century in its speculation about alternative brain wiring:

There is little evidence one way or the other as to whether or not this phenomenon is to be explained on purely physiologic grounds, depending on a cross circuiting of the association fibers between different brain centers.



Pierce concludes his paper with a pithy aphorism that acknowledges the inconclusiveness of the study:
Still, here no less than in all known cases of synaesthesis, we can only regret that our theory is so lame while our facts are so secure.


References

Pierce AH. (1907). Gustatory audition; a hitherto undescribed variety of synæsthesia. The American Journal of Psychology. Jul 1:341-52.

Commentary (1907). GUSTATORY AUDITION. JAMA. XLIX(10):857-858.


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Tuesday, August 31, 2021

Reading Aloud without a Mask, Olfactory Bulbs, Omega Variant

Here's the latest alarming COVID news to distract you from fires and hurricanes.
{I'm very sorry if you are experiencing either of these disasters personally. Donations ideas: El Dorado Community Foundation and The Mutual Aid Response Network.}


Caldor Fire: Karl Mondon/Bay Area News Group. Hurricane Ida: Edmund D. Fountain/New York Times.

 

Masklessness

Outbreak Associated with SARS-CoV-2 B.1.617.2 (Delta) Variant in an Elementary School — Marin County, California, May–June 2021

An unvaccinated elementary school teacher was experiencing nasal congestion and fatigue but continued working. The teacher read aloud to the students while maskless and two days later received a positive test for the Delta variant. Half of the class was infected, with the greatest risk among those sitting in front. Transmission to siblings and parents ensued, for a total of 27 cases.

"Among the five infected adults, one parent and the teacher were unvaccinated; the others were fully vaccinated. The vaccinated adults and one unvaccinated adult were symptomatic with fever, chills, cough, headache, and loss of smell."

 

The CDC created a figure showing the seating chart, transmission pattern, and whether the individual was symptomatic. The desks were six feet apart, an air filter in front, and the door and windows were open. All five children in the front row were infected.

 

from the CDC: Classroom layout and seating chart for 24 students in index patient’s class, by SARS-CoV-2 testing date, result or status, and symptoms—Marin County, California, May–June 2021

Conclusion and recommendation:

"Ineligibility because of age and lack of vaccination contribute to persistent elevated risk for outbreaks in schools, especially as new SARS-CoV-2 variants emerge. However, implementation of multiple prevention strategies within schools can mitigate this risk."



Olfactory Bulbs 

Speaking of smell, over 20 papers show MRI signal abnormalities in the olfactory bulbs of COVID-19 patients with anosmia (loss of smell). This isn't new, but anosmia has been reported in breakthrough cases as well. The images below show some resolution in a patient from time 1 to time 2.

Magnetic Resonance Imaging Alteration of the Brain in a Patient With Coronavirus Disease 2019 (COVID-19) and Anosmia

"...we can speculate that SARS-CoV-2 might invade the brain through the olfactory pathway and cause an olfactory dysfunction of sensorineural origin."

 

modified from Fig 2 (Politi et al., 2020). A coronal 2-dimensional FLAIR image shows a slight reduction of the hyperintensity and the thickness of the olfactory bulbs [right side], suggesting a postinfection olfactory loss.


modified from Fig 2 (Politi et al., 2020). FLAIR image showing complete resolution of the previously seen signal alteration within the cortex of the right gyrus rectus [bottom image].



Omega Variant 

OK, I made that up. However (alpha, beta, delta, epsilon, iota, gamma, OTHER):



Predominance of antibody-resistant SARS-CoV-2 variants in vaccine breakthrough cases from the San Francisco Bay Area, California (preprint):

"These findings suggest that vaccine breakthrough cases are preferentially caused by circulating antibody-resistant SARS-CoV-2 variants, and that symptomatic breakthrough infections may potentially transmit COVID-19 as efficiently as unvaccinated infections, regardless of the infecting lineage."

 

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Saturday, July 31, 2021

Why would nasally-transferred coronavirus only affect the left side of the brain?


WE GET QUESTIONS!

Q – “I survived a mild case of COVID. Should I be worried about the volume of gray matter in olfactory-related structures in the left hemisphere of my brain?”

A – Most of what you've read on social media may be overstated.

One of the scariest things about SARS-CoV-2 (other than possible death) is that it affects multiple organs, including the brain. The vast majority of studies have compared measures in COVID survivors to those obtained from participants without COVID. These cross-sectional studies cannot determine whether pre-existing differences can account for disease-related 'changes'.

An important new preprint by Douaud and colleagues reported results from a longitudinal study that obtained MRIs from participants before and during the pandemic. Carefully matched cohorts of COVID+ (n=394) and COVID- (n=388) people were given a second scan 3 years after their initial entry into the study. The majority of patients were not hospitalized. The authors hypothesized that brain areas related to smell and taste, senses which show notable decrements in infected patients, would be altered in the COVID-19 population.

 


from Vaughan & Jackson (2014). The “piriform axis” is an unusual oblique angle that shows primary olfactory cortex (Pir, piriform cortex) in the orbitofrontal region of the frontal lobe and nearby medial temporal lobe structures related to emotion and memory (Am, amygdala and Hip, hippocampus).


The frightening entrée of the virus into the brain may be through the nose. In mice, the S1 subunit of the coronavirus spike protein crosses the protective blood brain barrier and ends up in the olfactory bulb within 10-30 min (Rhea et al., 2021). After that, we don't really know what happens. So it's a stretch to suggest that neurotropism (viral infection of brain cells) causes alterations at a macro level in humans that can be detected by structural MRI. Anyway, that's the hypothesis.

What was unclear to me, however, was the number of other analyses conducted as part of the study. Other than the smell/taste regions of interest (ROIs), there were 2,360 distinct measures of brain structure or function (including resting-state fMRI and task-related fMRI, which we assume were not significantly different in the patients).

Sticking to the ROIs, the authors ran permutation tests that corrected for multiple comparisons and found that only the left hemisphere was affected. WHY?? Despite the Proustian speculation...

“...where the left hemisphere seem [sic] to be more involved in the emotional aspect of olfactory memory (the famous madeleine de Proust association that seemed particularly targeted in COVID-19).”

...it's mysterious why the virus would have deleterious effects on the brain by only invading the left nostril.

 

Fig. 1. (Douaud et al., 2021). The three main regions showing significant loss of grey matter (thickness, volume) between the two time-points specifically for the COVID patients are the parahippocampal gyrus, the lateral orbitofrontal cortex, and the superior insula. All results were localised to the left hemisphere.


The other notable aspect of the results was the massive overlap in distribution between the COVID+ and COVID- participants (see snarky commentary in the first figure). The authors helpfully showed the full spread of values for the time 1 vs. time 2 difference (clustered around zero). But did gray matter volume and thickness actually increase in some people? Although the group differences were statistically significant, were there any functional consequences? Severity of smell/taste loss? Effects on memory or emotion? We don't know.

COVID-19 can have persistent, disabling effects in some people, including young and previously healthy individuals (the “long haulers”, see Davis et al., 2021). There is no doubt about the reality of Long Covid.

On the other hand, alarmist coverage of preliminary neuroimaging findings is not helpful. The differences in Fig. 1 of Douaud et al. do not depict a shrinkage of 4 standard deviations, despite what some widely circulated tweets may claim. As one of the authors explains:


 

In fact, the exploratory analysis showed the largest loss of brain volume was non-specific (and not discussed). And perhaps not due to neurotropic invasion?

 


Longitudinal studies are extremely valuable, and the authors are to be commended for this. We'll wait for future papers to verify these findings, but for now I'm not totally convinced.


References

Davis HE, Assaf GS, McCorkell L, Wei H, Low RJ, Re'em Y, Redfield S, Austin JP, Akrami A. (2021). Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 2021 Jul 15. Epub ahead of print. PMID: 34308300.
 
Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, Lange F, Andersson JL, Griffanti L, Duff E, Jbabdi S, Taschler B. (preprint). Brain imaging before and after COVID-19 in UK Biobank. medRxiv. June 20, 2021.

Rhea EM, Logsdon AF, Hansen KM, Williams LM, Reed MJ, Baumann KK, Holden SJ, Raber J, Banks WA, Erickson MA. (2021). The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice. Nature Neuroscience 24(3):368-78.

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Tuesday, June 29, 2021

The rs-FC fMRI Law of Attraction (i.e., Resting-State Functional Connectivity of Speed Dating Choice)


Feeling starved for affection after 15 months of pandemic-mandated social distancing? Ready to look for a suitable romantic partner by attending an in-person speed dating event? Just recline inside this noisy tube for 10 minutes, think about anything you like, and our algorithm will Predict [the] Compatibility of a Female-Male Relationship!

This new study by Kajimura and colleagues garnered a lot of attention on Twitter, where it was publicized by @INM7_ISN (Simon Eickhoff) and @Neuro_Skeptic. The prevailing sentiment was not favorable (check the replies)...

 

 

Full disclosure: I was immediately biased against the claims made in this study...

This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.

...and have covered earlier attempts at linking speed dating choice to a proxy of neural activity. But I wanted to be fair and see what the authors did, since their results reflect an enormous amount of work.

Here I will argue that a 10 minute brain scan cannot predict who you will choose at a speed dating event. The resultant measures are even further away from identifying a compatible mate for you, since only 5% of speed dating interactions result in a relationship of any sort (6% for sexual relationships and 4% for romantic relationships, according to one study).

I was flabbergasted that anyone would think a “resting” state MRI scan (looking at “+” for 10 min) and its resulting pattern of correlated BOLD signal fluctuations would reflect a level of superficial desirability that can be detected by a potential mate at greater than chance level. Another disclosure: this is far from my field of expertise. So I searched the literature. Apparently, “patterns of functional brain activity during rest encode latent similarities (e.g., in terms of how people think and behave) that are associated with friendship” (Hyon et al., 2020). However, that study was conducted in a small town in South Korea (total population 860), allowing a detailed social network analysis. Plus, people knew each other well and experienced many of the same day to day events, which could shape their functional connectomes. Not exactly relevant for predicting strangers' speed dating choices, eh?

Another paper identified a “global personality network” based on data from 984 participants in the Human Connectome Project (Liu et al., 2019). The sample was large enough to support a training set of n=801 and a “hold-out” dataset (n=183) for validation purposes. The results supported the authors' “similar brain, similar personality” hypothesis. But in the dating world, how much do “similars” attract (compared to the popular saying, “opposites attract”)? Well why not construct (dis)similarity profiles between potential pairs by taking the absolute value of differences in functional connectivity (FC), and combine those with values of similarities in FC? Does that make sense?? And in order to arrive at this metric, there's a whole lot of machine learning (but with much smaller training sets)...

Identity Classification 

A separate sample of 44 individuals from the Human Connectome Project was used to construct the Similarity of Connectivity Pattern between pairs (Kajimura et al., 2021). These 44 participants had each been scanned twice, allowing 44 self-self pairs (Jessica at time 1 vs. Jessica at time 2), which were compared to 44 self-other pairs (Jessica at time 1 vs. Jennifer at time 2). Self-self “feature values” always show a positive correlation, and these were used to define “individual-specific information.”

26,680 feature values?

To start, 116 regions of interest (ROIs) were defined by Automated Anatomical Labeling (AAL). Pairwise comparisons of these for Self scan #1 vs. Self scan #2 (or vs. Other scan #2) resulted in a vector of 6,670 functional connectivities for each data point [(116 × 115)/2]. Then multiply this by four (!!) and you get 26,680 values fed into a machine learning classifier. Why four? Because the slow fluctuating BOLD signals were decomposed into four frequency bands for the classification procedure. Was this necessary? Does it add robustness, or merely more opportunities for false positive results?


Fig. 3 (Kajimura et al., 2021). Top 100 feature values, i.e. absolute values of differences between functional connectivity that contributed to identity classification for three frequency bands [the fourth was eliminated because the classifier could not distinguish between self-self and self-other pairs].

The machine learning algorithm was sparse logistic regression with elastic-net regularization (SLR-EN), which usually prevents overfitting, but I don't know if the algorithm can overcome 26,680 feature values with only 44 subjects. Maybe I'm misunderstanding (and others can correct me if I'm wrong), but the number of participants is rather low for SLR-EN given the number of input parameters? Then...
The classification accuracy was evaluated using a stratified k-fold cross-validation procedure. ... The ratio of the number of correctly classified labels was then obtained as the classification accuracy.


The regional results are below, showing a 7 x 7 brain network matrix with similarity in red (positive coefficients) and dissimilarity in blue (negative coefficients). We're still in the realm of correctly classifying self-self, so dissimilarites were considered artifacts of overfitting [but similarities were not?]. If the contribution from similar > dissimilar with binomial tests, this was considered an indicator of self. This was true of F1 (53 out of 67, p<.001) and F2 (52 out of 67, p<.001), but not F3, which was at chance (33 out of 67).


Fig. 4 (modified from Kajimura et al., 2021). Ratio of self-self classification connectivity in terms of brain networks. Red and blue matrices display the results of similarity- and dissimilarity-based contributions [at three frequency bands]. ... Vis, visual network; Som, somatosensory-motor network; Sal, salience network; Lim, limbic system; Con, executive control network; Def, default mode network; Cer, cerebellum.


Separate Statistical Analysis — a bevy of Pearsons 

Before we turn to speed dating, two more analyses are shown below for the identity classification study. The first involved a boatload of FDR-corrected Pearson’s correlations of the functional connectivity vectors for self–self pairs vs. self–other pairs (Fig 2A). The next shows the effectiveness of the machine learning (ML) algorithm in classifying these pairs (Fig 2B).

 

Fig. 2 (modified from Kajimura et al., 2021). Identity classification. (A) Similarities in overall functional connectivity profile was significantly higher for the self–self pair (dark-colored distribution) than the self–other pair (light-colored distribution) for all frequency bands. [I've included arrows to point out where they start to diverge] (B) Distribution of differences between ML classification accuracy.

As the authors predicted, self-self comparisons yielded more similar connectivites than self-other pairs. The ML algorithm identified self for three of the frequency bands (F1-F3) at greater than chance levels (12.4%. 14.8%, and 16.3% better than chance, respectively). However, the algorithm is still wrong a lot of the time. This is especially important for the matchmaking study...


Speed Dating

The authors provided a nice self-explanatory graphic presenting an overview of the Speed Dating study (click on image for a larger view). Data collection and analysis followed the flow of the Identity experiment.



Participants and Social Event

The participants were 42 heterosexual young adults (20-23 yrs), with 20 females and 22 males. Why these numbers were not perfectly matched, I do not know. The resting state fMRI scan was several days before the first speed dating session. [I'm assuming it was the first, because the Methods say there were three speed dating events. There was also a post-dating scan, which was described in another paper]. The three hour event was held in a large room where pairs of participants had 3 min long conversations with every member of the opposite sex. After each conversation, all the men moved to the next table. When all the speed dates were over, each person was asked to identify at least half of the opposite sex individuals they'd like to chat with again.

Well, there's a problem here — a requirement to select half the dates could result in less-than-optimal choices in some individuals. This requirement was necessary for sampling purposes, but it makes you wonder about the quality of the matches. Also, there was a strong possibility of unilateral matches — one individual thinks they've found their dream partner but that feeling was not reciprocated. When both members of a pair said "yes" they were considered compatible. Out of a total of 440 possible pairs, 158 were compatible and 282 incompatible.

The Compatible vs. Incompatible comparisons are the key findings of the study (Fig. 5, with A and B panels as above). Unlike the Identity comparison, compatible male-female pairs did not show more similar functional connnectivity patterns than incompatible pairs (Fig. 5A).

Well then...

“This indicates that the compatibility of female–male relationships is not necessarily represented by the similarity of functional connectivity patterns.”

Yes.

“Unlike identity classification, compatibility classification was supported by the considerable negative coefficients of the features” (shown in Fig. 6 of the paper). We shall not interpret this as opposites attract

 


 
Fig. 5 (Kajimura et al., 2021). Compatibility classification. (A) Similarity of overall functional connectivity profile. There was no significant difference between compatible (dark-colored distribution) and incompatible (light-colored distribution) pairs. (B) Distribution of differences between the classification accuracy with true labels of pairs and that with a randomized label for each frequency band. Vertical lines indicate chance levels.


Fig. 5B shows classification accuracy for compatible pairs, which was above chance for F1 and F2. Before investing in a commercial venture,  however, you should know that the benefit beyond guessing is only 5.47% and 4.95%, respectively. Thus, I disagree with the claim that...
...the current results indicate that resting-state functional connectivity has information about behavioral tendencies that two individuals actually exhibit during a dyadic interaction, which cannot be measured by self-report methods and thus may remain hidden unless we use neuroimaging methods concurrently.

To review the potential limitations of the study, we can't assess the quality of matches (meh vs. enthusiastic), we don't know what the participants were thinking about during their rsfMRI scan (see Gonzalez-Castillo et al., 2021), and we don't know their mental state during the scan. Although rs-FC fMRI is often considered a stable trait”, state factors and motion artifacts can affect the results on a given day (Geerligs et al., 2015). Indeed, ~35% of the time, the present paper was unsuccessful in classifying the same person run on two different days (and that's excluding one of four frequencies that was not above chance).

Is there something intrinsic encoded in BOLD signal fluctuations that can predict who we will find appealing (and a potential “match) after a three minute interaction?  Decisions at speed dating events are mostly based on physical attractiveness, so it seems very implausible to me.


Further Reading (the Speed Dating Collection)

The Neuroscience of Speed Dating Choice

The Electroencephalogram Cocktail Party

EEG Speed Dating

The Journal of Speed Dating Studies

Winner of Best Title

How I Meditated with Your Mother: Speed Dating at Temples and Shrines in Contemporary Japan

 

References

Geerligs L, Rubinov M, Henson RN. (2015). State and trait components of functional connectivity: individual differences vary with mental state. Journal of Neuroscience 35(41):13949-61.

Gonzalez-Castillo J, Kam JW, Hoy CW, Bandettini PA. (2021). How to Interpret Resting-State fMRI: Ask Your Participants. Journal of Neuroscience 41(6):1130-41.
 
Hyon R, Youm Y, Kim J, Chey J, Kwak S, Parkinson C. (2020). Similarity in functional brain connectivity at rest predicts interpersonal closeness in the social network of an entire village. Proceedings of the National Academy of Sciences 117(52):33149-60.

 
 
 

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Monday, May 31, 2021

Did dreams evolve to transcend overfitting?


A fascinating new paper proposes that dreams evolved to help the brain generalize, which improves its performance on day to day tasks. Incorporating a concept from deep learning, Erik Hoel (2021):

“...outlines the idea that the brains of animals are constantly in danger of overfitting, which is the lack of generalizability that occurs in a deep neural network when its learning is based too much on one particular dataset, and that dreams help mitigate this ubiquitous issue. This is the overfitted brian [sic] hypothesis.”

 

The Overfitted Brain Hypothesis (OHB) proposes that the bizarre phenomenology of dreams is critical to their functional role. This view differs from most other neuroscientific theories, which treat dream content as epiphenomenal — a byproduct of brain activity involved in memory consolidation, replay, forgetting, synaptic pruning, etc.  

In contrast, Hoel suggests that “it is the very strangeness of dreams in their divergence from waking experience that gives them their biological function.”

The hallucinogenic, category-breaking, and fabulist quality of dreams means they are extremely different from the “training set” of the animal (i.e., their daily experiences).
. . .

To sum up: the OBH conceptualizes dreams as a form of purposefully corrupted input, likely derived from noise injected into the hierarchical structure of the brain, causing feedback to generate warped or “corrupted” sensory input. The overall evolved purpose of this stochastic activity is to prevent overfitting. This overfitting may be within a particular module or task such a specific brain region or network, and may also involve generalization to out-of-distribution (unseen) novel stimuli.


Speaking of overfitting, I was reminded of Google's foray into artificial neural networks for image classification, which was all the rage in July 2015. The DeepDream program is a visualization tool that shows what the layers of the neural network have learned:

One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation.


The image above is characteristic of the hallucinogenic output from the DeepDream web interface, and it illustrates that the original training set was filled with dogs, birds, and pagodas.  DeepDream images inspired blog posts with titles like, Do neural nets dream of electric sheep? and Do Androids Dream of Electric Bananas? and my favorite, Scary Brains and the Garden of Earthly Deep Dreams.


Reference

Hoel E. (2021). The overfitted brain: Dreams evolved to assist generalization. Patterns 2(5):100244.

 


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