neural correlates

Explanations of SZ: Biologial: Neural Correlates
  • Measurement of structure/function in brain that occurs in conjunction with an experience
  • Schizophrenia may be linked to structural abnormalities in the brain
  • Positive and negative symptoms both have correlates

Negative symptoms: 

  • Ventral striatum has been linked to avolition
    • Linked to anticipation of reward for actions
    • Abnormality therefore leads to lack of motivation
      • Kirschner et al (2016) found, using fMRIs, that the ventral striatum was involved in apathy and avolition (however, it was not linked to affective flattening). 
  • Abnormally large ventrcles: brains are lighter and there are less neurons present within their brains.
  • Smaller amygdala: less emotion: affective flattening

Positive symptoms:

  • Activity in the anterior cingulate gyrus and superior temporal gyrus being reduced has been linked to auditory hallucinations
    • Noga et al (1995) found that the cingulate gyrus in Sz patients was smaller in comparison to a control group (using MRI), and there was a negative correlation between size of cingulate gyrus and severity of hallucinations. 
      • However, these differences were not significant after statistical testing  
  • Basal Ganglia: motor dysfunction: catatonia
  • PFC: less activity: delusions
  • Visual and auditory cortex: same activity when hallucinating as when sane people have genuine experiences


  • Findings inconsistent, so inconclusive
  • MRIs: we can see these things happening and do not have to wait until after death
  • Causality? do the differences in the brain cause Sz or does the Sz cause the differences?
    • Antipsychotics could cause enlarged ventricles
  • Reductionist: what about other factors?
  • Deterministic: unavoidable
Balancing Time and Space in the Brain: A New Model Holds Promise for Predicting Brain Dynamics

For as long as scientists have been listening in on the activity of the brain, they have been trying to understand the source of its noisy, apparently random, activity. In the past 20 years, “balanced network theory” has emerged to explain this apparent randomness through a balance of excitation and inhibition in recurrently coupled networks of neurons. A team of scientists has extended the balanced model to provide deep and testable predictions linking brain circuits to brain activity.

Lead investigators at the University of Pittsburgh say the new model accurately explains experimental findings about the highly variable responses of neurons in the brains of living animals. On Oct. 31, their paper, “The spatial structure of correlated neuronal variability,” was published online by the journal Nature Neuroscience.

The new model provides a much richer understanding of how activity is coordinated between neurons in neural circuits. The model could be used in the future to discover neural “signatures” that predict brain activity associated with learning or disease, say the investigators.

“Normally, brain activity appears highly random and variable most of the time, which looks like a weird way to compute,” said Brent Doiron, associate professor of mathematics at Pitt, senior author on the paper, and a member of the University of Pittsburgh Brain Institute (UPBI). “To understand the mechanics of neural computation, you need to know how the dynamics of a neuronal network depends on the network’s architecture, and this latest research brings us significantly closer to achieving this goal.”

Earlier versions of the balanced network theory captured how the timing and frequency of inputs—excitatory and inhibitory—shaped the emergence of variability in neural behavior, but these models used shortcuts that were biologically unrealistic, according to Doiron.

“The original balanced model ignored the spatial dependence of wiring in the brain, but it has long been known that neuron pairs that are near one another have a higher likelihood of connecting than pairs that are separated by larger distances. Earlier models produced unrealistic behavior—either completely random activity that was unlike the brain or completely synchronized neural behavior, such as you would see in a deep seizure. You could produce nothing in between.”

In the context of this balance, neurons are in a constant state of tension. According to co-author Matthew Smith, assistant professor of ophthalmology at Pitt and a member of UPBI, “It’s like balancing on one foot on your toes. If there are small overcorrections, the result is big fluctuations in neural firing, or communication.”

The new model accounts for temporal and spatial characteristics of neural networks and the correlations in the activity between neurons—whether firing in one neuron is correlated with firing in another. The model is such a substantial improvement that the scientists could use it to predict the behavior of living neurons examined in the area of the brain that processes the visual world.

After developing the model, the scientists examined data from the living visual cortex and found that their model accurately predicted the behavior of neurons based on how far apart they were. The activity of nearby neuron pairs was strongly correlated. At an intermediate distance, pairs of neurons were anticorrelated (When one responded more, the other responded less.), and at greater distances still they were independent.

“This model will help us to better understand how the brain computes information because it’s a big step forward in describing how network structure determines network variability,” said Doiron. “Any serious theory of brain computation must take into account the noise in the code. A shift in neuronal variability accompanies important cognitive functions, such as attention and learning, as well as being a signature of devastating pathologies like Parkinson’s disease and epilepsy.”

While the scientists examined the visual cortex, they believe their model could be used to predict activity in other parts of the brain, such as areas that process auditory or olfactory cues, for example. And they believe that the model generalizes to the brains of all mammals. In fact, the team found that a neural signature predicted by their model appeared in the visual cortex of living mice studied by another team of investigators.

“A hallmark of the computational approach that Doiron and Smith are taking is that its goal is to infer general principles of brain function that can be broadly applied to many scenarios. Remarkably, we still don’t have things like the laws of gravity for understanding the brain, but this is an important step for providing good theories in neuroscience that will allow us to make sense of the explosion of new experimental data that can now be collected,” said Nathan Urban, associate director of UPBI.
Watch a Resting Brain Light Up With Activity
The blood rushing around in your brain is actually a good indicator of what your neurons are doing.

fMRI has been a staple of cognitive neuroscience for years, but it rests on the assumption that blood flow to different areas of the brain correlates with neural activity in those areas. Until recently, we didn’t have great ways to test that assumption.

But now we have a direct way of looking at neural activity– we can use genetic engineering to put a protein that glows when neurons fire into the mouse brain. So, scientists used this direct reporter and looked at blood flow in the mouse brain at the same time, and it turns out that they correlate pretty well! fMRI remains a powerful method for cognitive neuroscience, with just a little more data to back it up.

Plus, cool pictures!

anonymous asked:

What looks like the most probable "one true case of depression" when you collectively compare the most interesting stuff such as ketamine, sleep deprivation, NSI-189, psychedelics and BDNF, relating them to clusters of symptoms and neural/physiological correlates? Could treating depression and other common neuropsychiatric disorders be as "easy" as designing a technology which would promote general synaptic plasticity?

I assume you mean “one true cause”?

Right now my total wild guess is something like “level of neurogenesis mediated by BDNF, which for some reason AMPA excitation can short-term compensate for”, but I don’t know. I tried to do a “much more than you wanted to know”-style review on this, but I gave up in disgust at how little I could figure out.


The neural correlates explanation is different than the dopamine hypothesis

A lot of textbooks this year have missed this out

We NEED this for the exam, so you may need to find other sources to learn it


anonymous asked:

Is it true that chimpanzees and other primates throw poop?

Chimpanzees and some other nonhuman primates - like macaques and capuchins to name a few-  do indeed throw stones, food, toys (enrichment objects), and yes even feces.

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(From this youtube video.)
Warning: The chimpanzee in the video is in a very aggressive and agitated display. Frankly I wouldn’t click on the link because I hate giving this person views. There were signs posted by the staff indicating this female was in estrus (‘heat’) and the OP and friends “decided to mess around with the monkey.”  Just warning you since some of you may find this video upsetting. I certainly do.

Now we tend to focus on the poo aspect here, but just take a minute to recognize how astronomically astounding throwing behavior is. To be able to judge an object’s weight, shape, and other characteristics accurately enough to send said object hurtling through the air at a desired velocity towards a desired target… and then to accurately hit that target! IT’S AMAZING!!! 

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Frustration at unequal pay. Capuchin monkey throwing cucumber at researcher when another individual gets a grape for the same task. (de Waal, x)

Mind you, I’m not saying that chimps or monkeys are consciously determining the effect of drag, wind resistance, or other factors during these quick mental calculations when they throw something. But just take a moment to think about throwing a baseball with a friend. Or tossing your car keys to a buddy who is the D.D. for the evening. Or maybe even lobbing a paper airplane at a coworker. You don’t sit there and work out the calculations for precisely how much force is required and what the perfect release angle is for each object… well… maybe some of you do… but most of us consider all these factors very rapidly in the process of what we like to call aiming.

In fact, researchers at Emory University have looked into nonhuman primate throwing behavior and “found that chimps that both threw more and were more likely to hit their targets showed heightened development in the motor cortex, and more connections between it and the Broca’s area, which they say is an important part of speech in humans.” (x)

Long story short, yes, many primates do throw feces (and other objects). Throwing behavior can be a way to intimidate others, to express aggression / frustration, to flirt (gain the attention of potential mates), or as a part of play behavior. It’s an amazingly varied behavior that we in the Primatology community are still learning about every day.

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Figure 1. Stills from video recordings, showing moments of two throwing events. (a) Pedrita running with a stone just before throwing it at Beiçola;(b) Pedrita picking up a stone, (c, d) running, and (e) throwing the stone at Bochechudo. (Video S1. MP4 download) (x)

Journal Sources:

Falótico T, Ottoni EB (2013) Stone Throwing as a Sexual Display in Wild Female Bearded Capuchin Monkeys, Sapajus libidinosus. PLoS ONE 8(11): e79535. doi:10.1371/journal.pone.0079535 [full text]

Hopkins, W.D., Russel, J.L., Schaeffer, J.A. (2011). The neural and cognitive correlates of aimed throwing in chimpanzees: a magnetic resonance image and behavioural study on a unique form of social tool use, Phil. Trans. R. Soc. B 12, vol. 367 no. 1585 37-47, doi: 10.1098/rstb.2011.0195 
full text]

Huffman et al. (2008). Cultured Monkeys: Social Learning Cast in Stones. Current Directions in Psychological Science, 17 (6): 410 DOI: 10.1111/j.1467-8721.2008.00616.x

Westergaard, G.C., Liv, C., Haynie, M.K., & Soumi, S.J. (2000). A comparative study of aimed throwing by monkeys and humans. Neuropsychologia, 38, 1511-1517
Neural correlates of maintaining one’s political beliefs in the face of counterevidence
Jonas T. Kaplan, Sarah I. Gimbel & Sam Harris

People often discount evidence that contradicts their firmly held beliefs. However, little is known about the neural mechanisms that govern this behavior. We used neuroimaging to investigate the neural systems involved in maintaining belief in the face of counterevidence, presenting 40 liberals with arguments that contradicted their strongly held political and non-political views. Challenges to political beliefs produced increased activity in the default mode network—a set of interconnected structures associated with self-representation and disengagement from the external world. Trials with greater belief resistance showed increased response in the dorsomedial prefrontal cortex and decreased activity in the orbitofrontal cortex. We also found that participants who changed their minds more showed less BOLD signal in the insula and the amygdala when evaluating counterevidence. These results highlight the role of emotion in belief-change resistance and offer insight into the neural systems involved in belief maintenance, motivated reasoning, and related phenomena.

Neuroscientists find evidence for ‘visual stereotyping’

The stereotypes we hold can influence our brain’s visual system, prompting us to see others’ faces in ways that conform to these stereotypes, neuroscientists at New York University have found.

“Our findings provide evidence that the stereotypes we hold can systematically alter the brain’s visual representation of a face, distorting what we see to be more in line with our biased expectations,” explains Jonathan Freeman, an assistant professor in NYU’s Department of Psychology and the senior author of the paper, which appears in the journal Nature Neuroscience.

“For example, many individuals have ingrained stereotypes that associate men as being more aggressive, women as being more appeasing, or Black individuals as being more hostile—though they may not endorse these stereotypes personally,” Freeman observes. “Our results suggest that these sorts of stereotypical associations can shape the basic visual processing of other people, predictably warping how the brain ‘sees’ a person’s face.”

Prior research has shown that stereotypes seep into the ways we think about and interact with other people, shaping many aspects of our behavior—despite our better intentions. But the researchers’ findings show that stereotypes may also have a more insidious impact, shaping even our initial visual processing of a person in a way that conforms to our existing biases.

“Previous studies have shown that how we perceive a face may, in turn, influence our behavior,” notes Ryan Stolier, an NYU doctoral student and lead author of the research. “Our findings therefore shed light upon an important and perhaps unanticipated route through which unintended bias may influence interpersonal behavior.”

The research relies on an innovative mouse-tracking technique that uses an individual’s hand movements to reveal unconscious cognitive processes—and, specifically, the stereotypes they hold. Unlike surveys, in which individuals can consciously alter their responses, this technique requires subjects to make split-second decisions about others, thereby uncovering a less conscious preference through their hand-motion trajectory. Using this mouse-tracking software Freeman developed, the millimeters of movement of a test subject’s mouse cursor can be linked with brain-imaging data to discover otherwise hidden impacts on specific brain processes.

In the first of two studies, Freeman and Stolier monitored subjects’ brain activity—using functional magnetic resonance imaging (fMRI)—while these subjects viewed different faces: male and female as well as those of various races and depicting a range of emotions. Outside the brain scanner, the subjects were asked to quickly categorize the gender, race, and emotion of the faces using the mouse-tracking technique. Despite their conscious responses, the subjects’ hand movements revealed the presence of several stereotypical biases. Notably, men, and particularly Black men, were initially perceived “angry,” even when their faces were not objectively angry; and women were initially perceived “happy,” even when their faces were not objectively happy. In addition, Asian faces were initially perceived “female” and Black faces were initially perceived “male,” regardless of the faces’ actual gender. The researchers confirmed, using a separate group of subjects, that the specific pattern of visual biases observed matched prevalent stereotypical associations in the U.S. to a significant degree.

The researchers’ fMRI findings backed these assessments, demonstrating that such stereotypical biases may be entrenched in the brain’s visual system, specifically in the fusiform cortex, a region involved in the visual processing of faces. For instance, the neural-activation patterns elicited by Black male faces in this region were more similar to those elicited by objectively angry faces, even when such faces did not display any actual angry features (e.g., due to stereotypes of Black individuals as hostile). Moreover, the extent of this stereotypical similarity in neural-activation patterns was correlated with the extent of bias observed in a subject’s hand movements. For example, the extent to which a subject’s hand initially veered toward the “angry” response when categorizing a non-angry Black male face predicted the extent to which neural-activation patterns for Black male faces and angry faces were more strongly correlated in the subject’s fusiform cortex.

The numerous other biases described above were also observed in the brain-imaging results. As another example, the neural-activation patterns elicited by White female faces were more similar to those elicited by objectively happy faces, even when such faces did not display any actual happy features (e.g., due to stereotypes of women as appeasing). In addition, neural-activation patterns elicited by Asian faces were more similar to those elicited by female faces, regardless of the actual gender (due to stereotypes associating Asians with more feminine traits).

In the second study, the researchers replicated the overall findings in a larger group of subjects and ruled out alternative explanations, such as whether inherent physical resemblance or visual similarities in certain faces may explain the results. They also measured each subject’s own stereotypical associations using an additional task and demonstrated that it was a subject’s own unique associations that specifically predicted the visual biases and neural-activation patterns observed. These findings cemented the evidence that one’s own learned stereotypes can change the way that an individual sees another person’s face and also demonstrated that this form of visual stereotyping is not limited to particular associations. Rather, whatever associations an individual has learned over his or her lifetime are likely to be expressed in the form of this visual stereotyping, the findings suggest.

“If stereotypes we have learned can change how we visually process another person, this kind of visual stereotyping may only serve to reinforce and possibly exacerbate the biases that exist in the first place,” Freeman notes.

“Ultimately, this research could be used to develop better interventions to reduce or possibly eliminate unconscious biases,” he adds. “The findings highlight the need to address these biases at the visual level as well, which may be more entrenched and require specific forms of intervention. This visual bias occurs the moment we glimpse at another person, well before we have a chance to correct ourselves or regulate our behavior.”