Aging and its 11 hippocampal genes

Aging is being quite extensively studied these days and here is another advance in the field. Pardo et al. (2017) looked at what happens in the hippocampus of 2-months old (young) and 28-months old (old) female rats. Hippocampus is a seahorse shaped structure no more than 7 cm in length and 4 g in weight situated at the level of your temples, deep in the brain, and absolutely necessary for memory.

First the researchers tested the rats in a classical maze test (Barnes maze) designed to assess their spatial memory performance. Not surprisingly, the old performed worse than the young.

Then, they dissected the hippocampi and looked at neurogenesis and they saw that the young rats had more newborn neurons than the old. Also, the old rats had more reactive microglia, a sign of inflammation. Microglia are small cells in the brain that are not neurons but serve very important functions.

After that, the researchers looked at the hippocampal transcriptome, meaning they looked at what proteins are being expressed there (I know, transcription is not translation, but the general assumption of transcriptome studies is that the amount of protein X corresponds to the amount of the RNA X). They found 210 genes that were differentially expressed in the old, 81 were upregulated and 129 were downregulated. Most of these genes are to be found in human too, 170 to be exact.

But after looking at male versus female data, at human and mouse aging data, the authors came up with 11 genes that are de-regulated (7 up- and 4 down-) in the aging hippocampus, regardless of species or gender. These genes are involved in the immune response to inflammation. More detailed, immune system activates microglia, which stays activated and this “prolonged microglial activation leads to the release of pro-inflammatory cytokines that exacerbate neuroinflammation, contributing to neuronal loss and impairment of cognitive function” (p. 17). Moreover, these 11 genes have been associated with neurodegenerative diseases and brain cancers.

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These are the 11 genes: C3 (up), Cd74  (up), Cd4 (up), Gpr183 (up), Clec7a (up), Gpr34 (down), Gapt (down), Itgam (down), Itgb2 (up), Tyrobp (up), Pld4 (down).”Up” and “down” indicate the direction of deregulation: upregulation or downregulation.

I wish the above sentence was as explicitly stated in the paper as I wrote it so I don’t have to comb through their supplemental Excel files to figure it out. Other than that, good paper, good work. Gets us closer to unraveling and maybe undoing some of the burdens of aging, because, as the actress Bette Davis said, “growing old isn’t for the sissies”.

Reference: Pardo J, Abba MC, Lacunza E, Francelle L, Morel GR, Outeiro TF, Goya RG. (13 Jan 2017, Epub ahead of print). Identification of a conserved gene signature associated with an exacerbated inflammatory environment in the hippocampus of aging rats. Hippocampus, doi: 10.1002/hipo.22703. ARTICLE

By Neuronicus, 25 January 2017

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Amusia and stroke

Although a complete musical anti-talent myself, that doesn’t prohibit me from fully enjoying the works of the masters in the art. When my family is out of earshot, I even bellow – because it cannot be called music – from the top of my lungs alongside the most famous tenors ever recorded. A couple of days ago I loaded one of my most eclectic playlists. While remembering my younger days as an Iron Maiden concert goer (I never said I listen only to classical music :D) and screaming the “Fear of the Dark” chorus, I wondered what’s new on the front of music processing in the brain.

And I found an interesting recent paper about amusia. Amusia is, as those of you with ancient Greek proclivities might have surmised, a deficit in the perception of music, mainly the pitch but sometimes rhythm and other aspects of music. A small percentage of the population is born with it, but a whooping 35 to 69% of stroke survivors exhibit the disorder.

So Sihvonen et al. (2016) decided to take a closer look at this phenomenon with the help of 77 stroke patients. These patients had an MRI scan within the first 3 weeks following stroke and another one 6 months poststroke. They also completed a behavioral test for amusia within the first 3 weeks following stroke and again 3 months later. For reasons undisclosed, and thus raising my eyebrows, the behavioral assessment was not performed at 6 months poststroke, nor an MRI at the 3 months follow-up. It would be nice to have had behavioral assessment with brain images at the same time because a lot can happen in weeks, let alone months after a stroke.

Nevertheless, the authors used a novel way to look at the brain pictures, called voxel-based lesion-symptom mapping (VLSM). Well, is not really novel, it’s been around for 15 years or so. Basically, to ascertain the function of a brain region, researchers either get people with a specific brain lesion and then look for a behavioral deficit or get a symptom and then they look for a brain lesion. Both approaches have distinct advantages but also disadvantages (see Bates et al., 2003). To overcome the disadvantages of these methods, enter the scene VLSM, which is a mathematical/statistical gimmick that allows you to explore the relationship between brain and function without forming preconceived ideas, i.e. without forcing dichotomous categories. They also looked at voxel-based morphometry (VBM), which a fancy way of saying they looked to see if the grey and white matter differ over time in the brains of their subjects.

After much analyses, Sihvonen et al. (2016) conclude that the damage to the right hemisphere is more likely conducive to amusia, as opposed to aphasia which is due mainly to damage to the left hemisphere. More specifically,

“damage to the right temporal areas, insula, and putamen forms the crucial neural substrate for acquired amusia after stroke. Persistent amusia is associated with further [grey matter] atrophy in the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), locating more anteriorly for rhythm amusia and more posteriorly for pitch amusia.”

The more we know, the better chances we have to improve treatments for people.

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unless you’re left-handed, then things are reversed.

References:

1. Sihvonen AJ, Ripollés P, Leo V, Rodríguez-Fornells A, Soinila S, & Särkämö T. (24 Aug 2016). Neural Basis of Acquired Amusia and Its Recovery after Stroke. Journal of Neuroscience, 36(34):8872-8881. PMID: 27559169, DOI: 10.1523/JNEUROSCI.0709-16.2016. ARTICLE  | FULLTEXT PDF

2.Bates E, Wilson SM, Saygin AP, Dick F, Sereno MI, Knight RT, & Dronkers NF (May 2003). Voxel-based lesion-symptom mapping. Nature Neuroscience, 6(5):448-50. PMID: 12704393, DOI: 10.1038/nn1050. ARTICLE

By Neuronicus, 9 November 2016

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Transcranial direct current stimulation & cognitive enhancement

There’s so much research out there… So much that some time ago I learned that in science, as probably in other fields too, one has only to choose a side of an argument and then, provided that s/he has some good academic search engines skills and institutional access to journals, get the articles that support that side. Granted, that works for relatively small questions restricted to narrow domains, like “is that brain structure involved in x” or something like that; I doubt you would be able to find any paper that invalidates theories like gravity or central dogma of molecular biology (DNA to RNA to protein).

If you’re a scientist trying to answer a question, you’ll probably comb through some dozens papers and form an opinion of your own after weeding out the papers with small sample sizes, the ones with shoddy methodology or simply the bad ones (yes, they do exists, even scientists are people and hence prone to mistakes). And if you’re not a scientist or the question you’re trying to find an answer for is not from your field, then you’ll probably go for reviews or meta-analyses.

Meta-analyses are studies that look at several papers (dozens or hundreds), pool their data together and then apply some complicated statistics to see the overall results. One such meta-analysis concerns the benefits, if any, of transcranial direct current stimulation (tDCS) on working memory (WM) in healthy people.

tDCS is a method of applying electrical current through some electrodes to your neurons to change how they work and thus changing some brain functions. It is similar with repetitive transcranial magnetic stimulation (rTMs), only in the latter case the change in neuronal activity is due to the application of a magnetic field.

Some people look at these methods not only as possible treatment for a variety of disorders, but also as cognitive enhancement tools. And not only by researchers, but also by various companies who sell the relatively inexpensive equipment to gamers and others. But does tDCS work in the first place?

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Mancuso et al. (2016) say that there have been 3 recent meta-analyses done on this issue and they found that “the effects [of tDCS on working memory in healthy volunteers] are reliable though small (Hill et al., 2016), partial (Brunoni & Vanderhasselt, 2014), or nonexistent (Horvath et al., 2015)” (p. 2). But they say these studies are somewhat flawed and that’s why they conducted their own meta-analysis, which concludes that “the true enhancement potential of tDCS for WM remains somewhat uncertain” (p.19). Maybe it works a little bit if used during the training phase of a working memory task, like n-back, and even then that’s a maybe…

Boring, you may say. I’ll grant you that. So… all that work and it revealed virtually nothing new! I’ll grant you that too. But what this meta-analysis brings new, besides adding some interesting statistics, like controlling for publication bias, is a nice discussion as to why they didn’t find nothing much, exploring possible causes, like the small sample and effects sizes, which seem to plague many behavioral studies. Another explanation which, to tell you the truth, the authors do not seem to be too enamored with is that, maybe, just maybe, simply, tDCS doesn’t have any effect on working memory, period.

Besides, papers with seemingly boring findings do not catch the media eye, so I had to give it a little attention, didn’t I 😉 ?

Reference: Mancuso LE, Ilieva IP, Hamilton RH, & Farah MJ. (Epub 7 Apr 2016, Aug 2016) Does Transcranial Direct Current Stimulation Improve Healthy Working Memory?: A Meta-analytic Review. Journal of Cognitive Neuroscience, 28(8):1063-89. PMID: 27054400, DOI: 10.1162/jocn_a_00956. ARTICLE

 By Neuronicus, 2 August 2016

Mu suppression and the mirror neurons

A few decades ago, Italian researchers from the University of Parma discovered some neurons in monkey which were active not only when the monkey is performing an action, but also when watching the same action performed by someone else. This kind of neuron, or rather this particular neuronal behavior, had been subsequently identified in humans scattered mainly within the frontal and parietal cortices (front and top of your head) and called the mirror neuron system (MNS). Its role is to understand the intentions of others and thus facilitate learning. Mind you, there are, as it should be in any healthy vigorous scientific endeavor, those who challenge this role and even the existence of MNS.

Hobson & Bishop (2016) do not question the existence of the mirror neurons or their roles, but something else. You see, proper understanding of intentions, actions and emotions of others is severely impaired in autism or some schizophrenias. Correspondingly, there have been reports saying that the MNS function is abnormal in these disorders. So if we can manipulate the neurons that help us understanding others, then we may be able to study the neurons better, and – who knows? – maybe even ‘switch them on’ and ‘off’ when needed (Ha! That’s a scary thought!).

EEG WIKI
Human EEG waves (from Wikipedia, under CC BY-SA 3.0 license)

Anyway, previous work said that recording a weak Mu frequency in the brain regions with mirror neurons show that these neurons are active. This frequency (between 8-13 Hz) is recorded through electroencephalography (EEG). The assumption is as follows: when resting, neurons fire synchronously; when busy, they fire each to its own, so they desynchronize, which leads to a reduction in the Mu intensity.

All well and good, but there is a problem. There is another frequency that overlaps with the Mu frequency and that is the Alpha band. Alpha activity is highest when a person is awake with eyes closed, but diminishes when the person is drowsy or, importantly, when making a mental effort, like paying great attention to something. So, if I see a weak Mu/Alpha frequency when the subject is watching someone grabbing a pencil, is that because the mirror neurons are active or because he’s sleepy? There are a few gimmicks to disentangle between the two, from the setup of the experiment in such a way that it requires same attention demand over tasks to the careful localization of the origin of the two waves (Mu is said to arise from sensoriomotor regions, whereas Alpha comes from more posterior regions).

But Hobson & Bishop (2016) argue that this disentangling is more difficult than previously thought by carrying out a series of experiments where they varied the baseline, in such a way that some were more attentionally demanding than others. After carefully analyzing various EEG waves and electrodes positions in these conditions, they conclude that “mu suppression can be used to index the human MNS, but the effect is weak and unreliable and easily confounded with alpha suppression“.

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What makes this paper interesting to me, besides its empirical findings, is the way the experiment was conducted and published. This is a true hypothesis driven study, following the scientific method step by step, a credit to us all scientists. In other words, a rare gem.  A lot of other papers are trying to make a pretty story from crappy data or weave some story about the results as if that’s what they went for all along when in fact they did a bunch of stuff and chose what looked good on paper.

Let me explain. As a consequence of the incredible pressure put on researchers to publish or perish (which, believe me, is more than just a metaphor, your livelihood and career depend on it), there is an alarming increase in bad papers, which means

  • papers with inappropriate statistical analyses (p threshold curse, lack of multiple comparisons corrections, like the one brilliantly exposed here),
  • papers with huge databases in which some correlations are bound to appear by chance alone and are presented as meaningful (p-hacking or data fishing),
  • papers without enough data to make a meaningful conclusion (lack of statistical power),
  • papers that report only good-looking results (only positive results required by journals),
  • papers that seek only to provide data to reinforce previously held beliefs (confirmation bias)
  • and so on.

For these reasons (and more), there is a high rate of rejection of papers submitted to journals (about 90%), which means more than just a lack of publication in a good journal; it means wasted time, money and resources, shattered career prospects for the grad students who did the experiments and threatened job security for everybody involved, not to mention a promotion of distrust of science and a disservice to the scientific endeavor in general. So some journals, like Cortex, are moving toward a system called Registered Report, which asks for the rationale and the plan of the experiment before this is conducted, which should protect against many of the above-mentioned plagues. If the plan is approved, the chances to get the results published in that journal are 90%.

This is one of those Registered Report papers. Good for you, Hobson & Bishop!

Reference: Hobson HM & Bishop DVM (Epub April 2016). Mu suppression – A good measure of the human mirror neuron system?. Cortex, doi: 10.1016/j.cortex.2016.03.019 ARTICLE | FREE FULLTEXT PDF | RAW DATA

By Neuronicus, 14 July 2016

Not all children diagnosed with ADHD have attention deficits

ADHD

Given the alarming increase in the diagnosis of attention deficit/hyperactivity disorder (ADHD) over the last 20 years, I thought pertinent to feature today an older paper, from the year 2000.

Dopamine, one of the chemicals that the neurons use to communicate, has been heavily implicated in ADHD. So heavily in fact that Ritalin, the main drug used for the treatment of ADHD, has its main effects by boosting the amount of dopamine in the brain.

Swanson et al. (2000) reasoned that people with a particular genetic abnormality that makes their dopamine receptors work less optimally may have more chances to have ADHD. The specialist reader may want to know that the genetic abnormality in question refers to a 7-repeat allele of a 48-bp variable number of tandem repeats in exon 3 of the dopamine receptor number 4 located on chromosome 11, whose expression results in a weaker dopamine receptor. We’ll call it DRD4,7-present as opposed to DRD4,7-absent (i.e. people without this genetic abnormality).

They had access to 96 children diagnosed with ADHD after the diagnostic criteria of DSM-IV and 48 matched controls (children of the same gender, age, school affiliation, socio-economic status etc. but without ADHD). About half of the children diagnosed with ADHD had the DRD4,7-present.

The authors tested the children on 3 tasks:

(i) a color-word task to probe the executive function network linked to anterior cingulate brain regions and to conflict resolution;
(ii) a cued-detection task to probe the orienting and alerting networks linked to posterior parietal and frontal brain regions and to shifting and maintenance of attention; and
(iii) a go-change task to probe the alerting network (and the ability to initiate a series of rapid response in a choice reaction time task), as well as the executive network (and the ability to inhibit a response and re-engage to make another response) (p. 4756).

Invalidating the authors’ hypothesis, the results showed that the controls and the DRD4,7-present had similar performance at these tasks, in contrast to the DRD4,7-absent who showed “clear abnormalities in performance on these neuropsychological tests of attention” (p. 4757).

This means two things:
1) Half of the children diagnosed with ADHD did not have an attention deficit.
2) These same children had the DRD4,7-present genetic abnormality, which has been previously linked with novelty seeking and risky behaviors. So it may be just possible that these children do not suffer from ADHD, but “may be easily bored in the absence of highly stimulating conditions, may show delay aversion and choose to avoid waiting, may have a style difference that is adaptive in some situations, and may benefit from high activity levels during childhood” (p. 4758).

Great paper and highly influential. The last author of the article (meaning the chief of the laboratory) is none other that Michael I. Posner, whose attentional networks, models, and tests feature every psychology and neuroscience textbook. If he doesn’t know about attention, then I don’t know who is.

One of the reasons I chose this paper is because it seems to me that a lot of teachers, nurses, social workers, or even pediatricians feel qualified to scare the living life out of parents by suggesting that their unruly child may have ADHD. In deference to most form the above-mentioned professions, the majority of people recognize their limits and tell the concerned parents to have the child tested by a qualified psychologist. And, unfortunately, even that may result in dosing your child with Ritalin needlessly when the child’s propensity toward a sensation-seeking temperament and extravert personality, may instead require a different approach to learning with a higher level of stimulation (after all, the children form the above study had been diagnosed by qualified people using their latest diagnosis manual).

Bottom line: beware of any psychologist or psychiatrist who does not employ a battery of attention tests when diagnosing your child with ADHD.

Reference: Swanson J, Oosterlaan J, Murias M, Schuck S, Flodman P, Spence MA, Wasdell M, Ding Y, Chi HC, Smith M, Mann M, Carlson C, Kennedy JL, Sergeant JA, Leung P, Zhang YP, Sadeh A, Chen C, Whalen CK, Babb KA, Moyzis R, & Posner MI. (25 April 2000). Attention deficit/hyperactivity disorder children with a 7-repeat allele of the dopamine receptor D4 gene have extreme behavior but normal performance on critical neuropsychological tests of attention. Proceedings of the National Academy of Sciences of the United States of America, 97(9):4754-4759. doi: 10.1073/pnas.080070897. Article | FREE PDF

P.S. If you think that “weeell, this research happened 16 years ago, surely something came out of it” then think again. The newer DSM-V’s criteria for diagnosis are likely to cause an increase in the prevalence of diagnosis of ADHD.

By Neuronicus, 26 February 2016

“Stop” in the brain

Neural correlates of stopping. License: PD
Neural correlates of stopping. License: PD

A lot of the neuroscience focuses on “what happens in the brain when/before/after subject does/thinks/feels x“. A lot, but not all. So what happens in the brain when subject is specifically told to NOT do/think/feel x.

Jha et al. (2015) used magnetoencephalography, a non-invasive method to record the electrical activity of neurons, to see what the brain does during several variants of the Stop-Signal Task. The task is very simple: a right- or left- pointing arrow appears on a screen which tells the subject to press a button correspondingly to his left or his right. In 50% of the trials, immediately after the arrow, a vertical red line appears which tells the subject to stop, i.e. don’t press the button. The variants that the authors developed allowed them to modulate the context of the stopping signal, as well as assess the duration of the stopping process.

The main findings of the paper are the involvement of the right inferior frontal gyrus in the duration of stopping (meaning the time it takes to execute the Stop process) and the pre-supplementary motor area in context manipulation (meaning the more complex the context, the more activity in this region). Curiously, the right, but not left inferior frontal gyrus activation was irrespective to the hand used for stopping.

Reference: Jha A, Nachev P, Barnes G, Husain M, Brown P, & Litvak V (Nov 2015, Epub 9 Mar 2015). The Frontal Control of Stopping. Cerebral Cortex, 25: 4392–4406. doi: 10.1093/cercor/bhv027. Article | FREE PDF

By Neuronicus, 2 November 2015

It’s what I like or what you like? I don’t know anymore…

The plasticity in medial prefrontal cortex (mPFC) underlies the changes in self preferences to match another's through learning. Modified from Fig. 2B from Garvert et al. (2015)
The plasticity in medial prefrontal cortex (mPFC) underlies the changes in self preferences to match another’s, through learning. Modified from Fig. 2B from Garvert et al. (2015), which is an open access article under the CC BY license.

One obvious consequence of being a social mammal is that each individual wants to be accepted. Nobody likes rejection, be it from a family member, a friend or colleague, a job application, or even a stranger. So we try to mould our beliefs and behaviors to fit the social norms, a process called social conformity. But how does that happen?

Garvert et al. (2015) shed some light on the mechanism(s) underlying the malleability of personal preferences in response to information about other people preferences. Twenty-seven people had 48 chances to make a choice on whether gain a small amount of money now or more money later, with “later” meaning from 1 day to 3 months later. Then the subjects were taught another partner choices, no strings attached, just so they know. Then they were made to chose again. Then they got into the fMRI and there things got complicated, as the subjects had to choose as they themselves would choose, as their partner would choose, or as an unknown person would choose. I skipped a few steps, the procedure is complicated and the paper is full of cumbersome verbiage (e.g. “We designed a contrast that measured the change in repetition suppression between self and novel other from block 1 to block 3, controlled for by the change in repetition suppression between self and familiar other over the same blocks” p. 422).

Anyway, long story short, the behavioral results showed that the subjects tended to alter their preferences to match their partner’s (although not told to do so, it had no impact on their own money gain, there were not time constraints, and sometimes were told that the “partner” was a computer).

These behavioral changes were matched by the changes in the activation pattern of the medial prefrontal cortex (mPFC), in the sense that learning of the preferences of another, which you can imagine as a specific neural pattern in your brain, changes the way your own preferences are encoded in the same neural pattern.

Reference: Garvert MM, Moutoussis M, Kurth-Nelson Z, Behrens TE, & Dolan RJ (21 January 2015). Learning-induced plasticity in medial prefrontal cortex predicts preference malleability. Neuron, 85(2):418-28. doi: 10.1016/j.neuron.2014.12.033. Article + FREE PDF

By Neuronicus, 11 October 2015

The FIRSTS: brain active before conscious intent (1983)

Actor Jim Carry pretending to be attacked by its own hand in the movie Liar Liar. Dir. Tom Shadyac. Universal Pictures, 1997.
Actor Jim Carrey pretending to be attacked by its own hand in the movie Liar Liar. Dir. Tom Shadyac. Universal Pictures, 1997.

Free will. And with these two words I just opened a can of worms, didn’t I? Modern neuroscience poked its fingers at the eternal problem of whether humans have free will or not, usually with the help of the fMRI, and, more recently trying (and succeeding) to manipulate it with rTMS. But before these fancy techniques, there was the old-fashioned EEG.

In 1983, Libet et al. had 5 subjects sitting comfortably in a chair and watching a clock. Subjects were instructed to make a move of their right hand whenever they want AND to remember the position of the clock hand when they felt the urge to move. During the experiments, the subjects had electrodes on the scalp that measured their cortical activity and electrodes on their hand that measured muscle activity.

The brain activity began at least 1 second before the hand movement and Libet et al. called this activity the “readiness potential”. The muscle activity began 200 miliseconds before the person reported that s/he wanted to move their hand. In other words, brain tells the hand to move and very shortly after you are aware of the want to move your hand. “Brain activity therefore causes conscious intention rather than the other way around: there is no ‘ghost in the machine’.” (Haggard, 2008).

Reference: Libet B, Gleason CA, Wright EW, Pearl DK. (September 1983). Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). The unconscious initiation of a freely voluntary act. Brain, 106 (Pt 3):623-42. DOI: dx.doi.org/10.1093/brain/106.3.623. Article

By Neuronicus, 10 October 2015